Education Longitudinal 

Study of 2002: BaseYear to First Follow-up 

Data File 

Documentation 

U.S. Department of Education 

Institute of Education Sciences 

NCES 2006–344 

U.S. Department of Education 

Institute of Education Sciences 

NCES 2006–344 

Education Longitudinal 

Study of 2002: BaseYear to First Follow-up 

Data File 

Documentation 

October 2005 

Steven J. Ingels 

Daniel J. Pratt 

James E. Rogers 

Peter H. Siegel 

Ellen S. Stutts 

RTI International 

Jeffrey A. Owings 

Project Officer 

National Center for 

Education Statistics 
U.S. Department of Education 

Margaret Spellings 

Secretary 

Institute of Education Sciences 

Grover J. Whitehurst 

Director 

National Center for Education Statistics 

Mark Schneider 

Commissioner 

The National Center for Education Statistics (NCES) is the primary federal entity for collecting, analyzing, 

and reporting data related to education in the United States and other nations. It fulfills a congressional 

mandate to collect, collate, analyze, and report full and complete statistics on the condition of 

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NCES activities are designed to address high-priority education data needs; provide consistent, reliable, 

complete, and accurate indicators of education status and trends; and report timely, useful, and highquality data to the U.S. Department of Education, the Congress, the states, other education 

policymakers, practitioners, data users, and the general public. Unless specifically noted, all information 

contained herein is in the public domain. 

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Washington, DC 20006-5651 

October 2005 

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The NCES World Wide Web Electronic Catalog is http://nces.ed.gov/pubsearch 

Suggested Citation 

Ingels, S.J., Pratt, D.J., Rogers, J.E., Siegel, P.H., and Stutts, E.S. (2005). Education Longitudinal Study of 

2002: Base-Year to First Follow-up Data File Documentation (NCES 2006–344). U.S. Department of 

Education. Washington, DC: National Center for Education Statistics. 

For ordering information on this report, write to 

U.S. Department of Education 

 ED Pubs 

P.O. Box 1398 

Jessup, MD 20794-1398 

or call toll free 1-877-4ED-Pubs or order online at http://www.edpubs.org. 

Content Contact: 

Jeffrey A. Owings 

(202) 502-7423 

Jeffrey.Owings@ed.gov 
Foreword 

This manual has been produced to familiarize data users with the procedures followed for 

data collection and processing for the base year and first follow-up of the Education Longitudinal 

Study of 2002 (ELS:2002). It also provides the necessary documentation for use of the publicuse data files, as they appear on the ELS:2002 base-year to first follow-up electronic codebook 

(ECB) (NCES 2006-346). 

Analysts do not need to be sophisticated statisticians or computer programmers to use the 

ELS:2002 ECB. Most social scientists and policy analysts should find the dataset organized and 

equipped in a manner that facilitates straightforward production of statistical summaries and 

analyses. This manual provides extensive documentation of the content of the data files and how 

to access and manipulate them. 

Chapter 1 serves as an introduction to ELS:2002. It includes an overview and history of 

the National Center for Education Statistics (NCES) program of longitudinal high school 

cohorts, summarizes the ELS:2002 objectives, and supplies an overview of the base-year and 

longitudinal study design. 

Chapter 2 describes the first follow-up data collection instruments, including both the 

development and content of the in-school student, transfer, dropout, early graduate, 

homeschooled, and school administrator questionnaires, as well as the student assessment in 

mathematics. 

The sample design and weighting procedures used both in the base-year and first followup studies are documented in chapter 3, as are weights, imputation, and the calculation of design 

effects. 

Data collection schedules, training, procedures, and results are presented in chapter 4. 

Chapter 5 describes data preparation and processing, including the receipt control system, optical 

scanning, machine editing, and data file preparation. Chapter 6 describes the contents of the data 

files, including the data structure and analysis populations. 

The appendixes include, among other topics, an introduction to the public-use ECB 

(appendix A), bse-year and first follow-up questionnaires (appendix B), documentation for 

imputed variables (appendix C), information on variables not included in the public-use files but 

available in restricted files for licensed users (appendix D), a glossary of terms (appendix E), 

student questionnaire critical items (appendix F), cross-cohort comparisons (appendix H), and a 

synopsis of the ELS:2002 first follow-up field test (appendix J). 

Jeffrey A. Owings 

Associate Commissioner 

Elementary/Secondary & Libraries Studies 

iii 

Acknowledgments 

Daniel J. Pratt of RTI served as the ELS:2002 base-year and first follow-up project 

director. Steven J. Ingels of RTI was principal investigator. Jeffrey A. Owings served as the 

NCES project officer. Key RTI task leaders were Ellen Stutts (associate project director), 

Debbie Herget (first follow-up in-school data collection), Doug Currivan (first follow-up out-ofschool data collection), James Rogers (data processing), and Peter Siegel (sampling and 

statistics). Other RTI staff who played major roles in ELS:2002 were Christopher Alexander, 

Kimberly Ault, Stephen Black, Laura J. Burns, Debbie Capps, James Chromy, Elizabeth 

Copello, Marianne Daye, D. Wesley Dukes, Brian Evans, Catherine Forstner, Sherry HubbardBednasz, Ruby Johnson, Tiffany Lytle, Mani Medarametla, Greg Mosorjak, Melanie Pressley, 

Denise Rhatigan, Helen Smith, Milorad Stojanovic, David Wilson, and Donghui Wang. 

Assessment development, scaling, and equating were conducted by Judith M. Pollack, Donald A. 

Rock, and Michelle Najarian, under a subcontract with Educational Testing Service (ETS). ETS 

staff contributed assessment documentation to this manual. 

The authors of this report would like to thank the many individuals who assisted in the 

planning of ELS:2002. We are particularly indebted to the ELS:2002 Technical Review Panel, 

whose members reviewed plans for the study, helped refine them, and provided important 

suggestions to help guide development of the instrumentation. The following individuals serve 

as members of the ELS:2002 Technical Review Panel: Marco Clark, Richard Duran, Jeremy 

Finn, Thomas B. Hoffer, Thomas Kane, Sally Kilgore, Richard Lawrence, Samuel R. Lucas, 

Aaron Pallas, and Andy Rogers. 

Special thanks are in order to Jeffrey A. Owings, Associate Commissioner for 

Elementary/Secondary & Libraries Studies at NCES, in whose division ELS:2002 is housed. 

Other NCES staff who have provided help, support, and assistance include Lisa Hudson, Steve 

Kaufman, Mariann Lemke, Andrew G. Malizio, Edith McArthur, and Marilyn M. Seastrom. 

Staff in other offices of the U.S. Department of Education who have contributed to the study 

include Clifford Adelman, Sharon Belli, Jeffery Rodamar, and Marsha Silverberg. 

Special thanks are also due to Leslie A. Scott of the Education Statistics Services Institute 

and to Denise M. Davis, formerly of the National Commission on Libraries and Information 

Science. 

Many others, far too many to name individually, contributed to ELS:2002, including 

survey administrators and other contractor and subcontractor staff, and of course the literally 

thousands of students, parents, and school personnel who generously gave of their time and 

approval to provide the data that the study reports. We extend our thanks to all. 

Lastly, we want to acknowledge the help of several other individuals at RTI in the 

preparation of this document: Michael Planty, who assisted in the review process; Wallace 

Campbell and Sallie Fiore, who edited the document; Lawanda King and Sharon Powell, who 

provided assistance in document production; and Diane Caudill, who provided graphics support. 

v 

Contents 

Page 

Foreword.....................................................................................................................................iii 

Acknowledgments ......................................................................................................................v 

List of Tables..............................................................................................................................xi 

List of Figures ..........................................................................................................................xxi 

Chapter 1 Introduction ...............................................................................................................1 

1.1 Overview of the Data File Documentation ..................................................................................1 

1.2 Historical Background .................................................................................................................2 

1.2.1 NCES Education High School Longitudinal Studies Program ..............................................2 

1.2.2 National Longitudinal Study of the High School Class of 1972 (NLS-72) ...........................2 

1.2.3 High School and Beyond (HS&B).........................................................................................4 

1.2.4 National Education Longitudinal Study of 1988 (NELS:88).................................................5 

1.3 Education Longitudinal Study of 2002 (ELS:2002) ....................................................................7 

1.3.1 ELS:2002 Study Objectives ...................................................................................................8 

1.3.2 ELS:2002 Research and Policy Issues .................................................................................10 

1.3.3 Overview of the Base-Year Study Design ...........................................................................13 

1.3.4 Overview of the First Follow-up Study Design ...................................................................14 

Chapter 2 Instrumentation .......................................................................................................17 

2.1 Introduction................................................................................................................................17 

2.1.1 Instrument Development Process and Procedures ...............................................................17 

2.1.2 Instrument Development Goals and Constraints..................................................................18 

2.2 Base-Year and First Follow-up Questionnaires.........................................................................21 

2.2.1 Base-Year Questionnaires....................................................................................................21 

2.2.2 First Follow-up Questionnaires............................................................................................22 

2.3 Base-Year to First Follow-up Cognitive Test Battery ...............................................................33 

2.3.1 Base-Year Reading and Mathematics Assessments.............................................................33 

2.3.2 First Follow-up Assessment.................................................................................................33 

Chapter 3 Sample Design, Weighting, Design Effects, and Data Quality............................43 

3.1 Introduction................................................................................................................................43 

3.1.1 Base-Year Sample Design....................................................................................................43 

3.1.2 First Follow-up Sample Design ...........................................................................................43 

3.1.3 Weighting.............................................................................................................................43 

3.1.4 Standard Errors and Design Effects.....................................................................................44 

3.1.5 Imputation ............................................................................................................................44 

3.1.6 Disclosure Risk Analysis and Protection .............................................................................45 

3.1.7 Data Quality: Student and Item Nonresponse Bias Analyses ..............................................45 

3.2 Base-Year Sample Design .........................................................................................................45 

vii 
Contents 

Page 

3.3 First Follow-up Sample Design .................................................................................................47 

3.3.1 Eligibility .............................................................................................................................48 

3.3.2 Subsampling.........................................................................................................................51 

3.3.3 Student Sample Freshening..................................................................................................52 

3.4 Calculation of Weights and Results of Weighting.....................................................................55 

3.4.1 Analysis Populations............................................................................................................55 

3.4.2 Uses of Student-Level Data; Student Weights.....................................................................57 

3.4.3 Uses of School-Level Data; School-Level Weights.............................................................59 

3.4.4 Weights ................................................................................................................................59 

3.5 Standard Errors and Design Effects...........................................................................................90 

3.5.1 Standard Errors ....................................................................................................................90 

3.5.2 Design Effects ......................................................................................................................92 

3.6 Imputation................................................................................................................................101 

3.6.1 Imputation Variables..........................................................................................................101 

3.6.2 Imputation Methodologies.................................................................................................101 

3.6.3 Definition of Eligibility for Imputation..............................................................................103 

3.6.4 Imputation Results .............................................................................................................103 

3.6.5 Imputation Evaluation........................................................................................................104 

3.7 Disclosure Risk Analysis and Protections ...............................................................................104 

3.8 Student Nonresponse Bias Analysis ........................................................................................105 

Chapter 4 Data Collection Methodology and Results..........................................................143 

4.1 Data Collection Overview........................................................................................................143 

4.2 First Follow-up Pre-Data-Collection Activities.......................................................................148 

4.2.1 School Recruitment............................................................................................................149 

4.2.2 Presurvey Contacts With Schools ......................................................................................150 

4.2.3 Tracing the Student Sample ...............................................................................................150 

4.2.4 Training..............................................................................................................................152 

4.3 Data Collection Procedures—In-School..................................................................................154 

4.4 Data Collection Procedures—School Administrator Survey...................................................161 

4.5 Data Collection Procedures—Out-of-School...........................................................................162 

4.6 First Follow-up Yield...............................................................................................................163 

Chapter 5 Data Preparation and Processing........................................................................165 

5.1 Overview of Systems Design, Development, and Testing....................................................... 165 

5.2 Data Receipt.............................................................................................................................166 

5.3 Coding for Hardcopy Instruments............................................................................................167 

5.4 Data Capture for Optically Scanned Instruments.....................................................................167 

5.5 Data Cleaning and Editing .......................................................................................................168 

5.6 Data Capture and Editing for CATI.........................................................................................169 

5.7 Data Processing and File Preparation ......................................................................................169 

Chapter 6 Data File Contents.................................................................................................171 

6.1 Data Structure ..........................................................................................................................171 

6.2 First Follow-up Analysis Populations......................................................................................172 

6.3 First Follow-up Weights and Flags..........................................................................................172 

6.4 Composite and Classification Variables ..................................................................................172 

viii 
Contents 

Page 

6.5 Naming Conventions ...............................................................................................................173 

6.6 Guide to the Hardcopy Codebooks ..........................................................................................173 

References...............................................................................................................................175 

Appendixes 

A Introduction to the Electronic Codebook..................................................................................A-1 

B Base-Year and First Follow-up Questionnaires........................................................................B-1 

C Documentation for Imputed Variables .....................................................................................C-1 

D Public-Use Masked/Suppressed Variables Available on Restricted Files for 

Licensed Users..........................................................................................................................D-1 

E Glossary of Terms .................................................................................................................... E-1 

F Student Questionnaire Critical Items........................................................................................ F-1 

G Base-Year to First Follow-up Electronic Codebook.................................................................G-1 

H Cross-Cohort Comparisons...................................................................................................... H-1 

I Standard Errors and Design Effects...........................................................................................I-1 

J Synopsis of the ELS:2002 First Follow-up Field Test (2003)...................................................J-1 

ix 

List of Tables 

Table Page 

1 Assessment availability status, by sample group: 2004.............................................................23 

2 Crosswalk: First follow-up questionnaire type, by shared and nonshared items: 2004............24 

3 Base-year key variables and test data available, by type of first follow-up new 

participants: 2004 ......................................................................................................................32 

4 Number of items in each ELS:2002 base-year test form for assessing achievement in 

mathematics and reading, by test form: 2002............................................................................33 

5 Item Response Theory (IRT)-estimated number-right mathematics scores in 85-item 

metric: 2004...............................................................................................................................36 

6 ELS:2002 and Program for International Student Assessment: Spring 2003 

(PISA:spring 2003), by sample characteristics: 2002 and 2003................................................38 

7 ELS:2002 and Program for International Student Assessment: Spring 2003 

(PISA:spring 2003) equating sample: 2002 and 2003...............................................................38 

8 Linking methods for implementing Program for International Student Assessment: 

Spring 2003 (PISA:spring 2003) math scales in ELS:2002: 2002 and 2003..............................39 

9 ELS:2002 Item Response Theory (IRT) National Education Longitudinal Study of 

1988 (NELS:88)-equated estimated number-right score and proficiency probability 

scores: 2004 ................................................................................................................................40 

10 Number of students excluded and accommodated: 2004 ...........................................................50 

11 Change in questionnaire eligibility status between base year and first follow-up: 2004............50 

12 Base-year nonrespondent subsample, by school sector and student type: 2004.........................51 

13 Number of 12th-grade student lists provided by schools, by type: 2004....................................53 

14 Types of problems encountered with student lists: 2004............................................................53 

15 Number of freshened sample members, by eligibility: 2004......................................................54 

16 Relationship among weights, populations, respondents, and universe flags: 2004 ....................61 

17 Average weight adjustment factors used to adjust cross-sectional weights for refusal, by 

selected characteristics: 2004 ....................................................................................................65 

18 Average weight adjustment factors used to adjust cross-sectional weights for other 

nonresponse, by selected characteristics: 2004..........................................................................71 

19 Average weight adjustment factors for poststratifying cross-sectional weights to control 

totals, by selected characteristics: 2004.....................................................................................76 

20 Statistical properties of cross-sectional weights: 2004 ..............................................................77 

21 Average weight adjustment factors used to adjust panel weights for refusal, by selected 

characteristics: 2004 ...................................................................................................................78 

22 Average weight adjustment factors used to adjust panel weights for other nonresponse, 

by selected characteristics: 2004 ................................................................................................84 

xi 
List of Tables 

Table Page 

23 Average weight adjustment factors for poststratifying panel weights to control totals, 

by selected characteristics: 2004 ................................................................................................89 

24 Statistical properties of panel weights: 2004 .............................................................................90 

25 Mean design effects (DEFFs) and root design effects (DEFTs) for the first follow-up 

full sample, by selected characteristics: 2004.............................................................................94 

26 Mean design effects (DEFFs) and root design effects (DEFTs) for the first follow-up 

panel sample, by selected characteristics: 2004..........................................................................95 

27 Mean design effects (DEFFs) and root design effects (DEFTs) for base-year student 

questionnaire data, by selected characteristics: 2002 ................................................................96 

28 First follow-up imputation variables, by number and weighted proportion imputed: 

2004 ..........................................................................................................................................101 

29 Nonresponse bias before and after nonresponse adjustment for base-year sophomores 

using the cross-sectional weight, by selected categorical variables: 2004 ..............................111 

30 Nonresponse bias before and after nonresponse adjustment for base-year sophomores 

using the panel weight, by selected categorical variables: 2004 .............................................115 

31 Nonresponse bias before and after nonresponse adjustment for transfer students, by 

selected categorical variables: 2004 ........................................................................................119 

32 Nonresponse bias before and after nonresponse adjustment for dropouts, by selected 

categorical variables: 2004 ......................................................................................................123 

33 Nonresponse bias before and after nonresponse adjustment for early graduates, by 

selected categorical variables: 2004 ........................................................................................127 

34 Nonresponse bias before and after nonresponse adjustment for homeschooled students, 

by selected categorical variables: 2004 ....................................................................................132 

35 Summary of ELS:2002 base-year completion and coverage rates, by instrument: 2002 ........144 

36 Summary of ELS:2002 first follow-up completion and coverage rates, by instrument: 

2004 ..........................................................................................................................................144 

37 Summary of ELS:2002 first follow-up completion and coverage rates, overall results by 

student questionnaire, math assessment, and school questionnaire, by selected 

characteristics: 2004 ................................................................................................................145 

38 Summary of ELS:2002 first follow-up completion and coverage rates, overall results by 

transfer, dropout, early graduate, and homeschool questionnaire, by selected 

characteristics: 2004 ................................................................................................................146 

39 Questionnaire completion rate for ELS:2002 senior cohort, by selected characteristics: 

2004 ..........................................................................................................................................148 

40 Survey administration training agenda: 2004 ..........................................................................153 

41 Telephone interviewer training agenda: 2004 .........................................................................155 

42 Proportion of student questionnaire cases completed in-school versus out-of-school, by 

selected characteristics: 2004 ..................................................................................................158 

xii 
 

List of Tables 

Table Page 

43 Student questionnaire completion rates at base-year schools that allowed in-school data 

collection in the first follow-up, by selected characteristics: 2004..........................................159 

44 Math test completion—all eligible students (students still associated with a base-year 

school at time of data collection, regardless of whether the school permitted an inschool survey session), by selected characteristics: 2004........................................................160 

45 Math test completion—only base-year schools allowing survey days in the first followup, as a percentage of questionnaire completers, by selected characteristics: 2004................161 

46 Overall yield, by method of data collection (unweighted percents): 2004 ..............................163 

47 Overall unweighted response rates, by base-year status: 2004................................................164 

List of Appendix Tables 

Table Page 

C-1 ELS:2002 imputation variables, by respondent status: 2004...................................................C-4 

C-2 ELS:2002 imputation variables, by imputation class and sort variables: 2004 .......................C-5 

C-3 Variables included in multiple imputation model for student ability estimates for 

reading and mathematics: 2002 and 2004 ...............................................................................C-7 

C-4 ELS:2002 imputation variable distributions before and after imputation: 2004 .....................C-8 

C-5 Summary of differences between imputed and unimputed data, by topic: 2002...................C-16 

C-6A Percentage of high school sophomores, by sex: 2002 ............................................................C-17 

C-6B Standard errors for table C-6A estimates (percentage of high school sophomores, by 

sex): 2002 ..............................................................................................................................C-17 

C-7A Percentage of high school sophomores, by family living arrangement: 2002.......................C-18 

C-7B Standard errors for table C-7A estimates (percentage of high school sophomores, by 

family living arrangement): 2002..........................................................................................C-18 

C-8A Percentage of high school sophomores, by mother’s highest level of education: 2002 ........C-19 

C-8B Standard errors for table C-8A estimates (percentage of high school sophomores, by 

mother’s highest level of education): 2002 ...........................................................................C-19 

C-9A Percentage of high school sophomores, by father’s highest level of education: 2002 ..........C-20 

C-9B Standard errors for table C-9A estimates (percentage of high school sophomores, by 

father’s highest level of education): 2002 .............................................................................C-20 

C-10A Percentage of high school sophomores whose native language is English, by 

race/ethnicity: 2002 ...............................................................................................................C-21 

C-10B Standard errors for table C-10A estimates (percentage of high school sophomores 

whose native language is English, by race/ethnicity): 2002..................................................C-21 

C-11A Percentage of high school sophomores, by socioeconomic status and race/ethnicity: 

2002 ........................................................................................................................................C-22 

xiii 
List of Tables 

Table Page 

C-11B Standard errors for table C-11A estimates (percentage of high school sophomores, by 

socioeconomic status and race/ethnicity): 2002 .................................................................... C-22 

C-12A Percentage of high school sophomores, by school sector and socioeconomic status: 

2002........................................................................................................................................ C-23 

C-12B Standard errors for table C-12A estimates (percentage of high school sophomores, by 

school sector and socioeconomic status): 2002.....................................................................C-23 

C-13A Percentage of high school sophomores, by high school program and selected student 

characteristics: 2002 .............................................................................................................. C-24 

C-13B Standard errors for table C-13A estimates (percentage of high school sophomores, by 

high school program and selected student characteristics): 2002.......................................... C-25 

C-14A Percentage of high school sophomores who report having been in various kinds of 

courses or programs in high school, by selected student characteristics: 2002.....................C-26 

C-14B Standard errors for table C-14A estimates (percentage of high school sophomores who 

report having been in various kinds of courses or programs in high school, by selected 

student characteristics): 2002 ................................................................................................C-27 

C-15A Percentage of high school sophomores saying they usually or often come to school 

unprepared, by selected student characteristics: 2002...........................................................C-28 

C-15B Standard errors for table C-15A estimates (percentage of high school sophomores 

saying they usually or often come to school unprepared, by selected student 

characteristics): 2002.............................................................................................................C-29 

C-16A Percentage of high school sophomores who agreed or strongly agreed with various 

statements about the school’s climate and teaching, by selected student characteristics: 

2002........................................................................................................................................ C-30 

C-16B Standard errors for table C-16A estimates (percentage of high school sophomores who 

agreed or strongly agreed with various statements about the school’s climate and 

teaching, by selected student characteristics): 2002...............................................................C-32 

C-17A Percentage of high school sophomores’ use of calculators and computers, by selected 

student characteristics: 2002.................................................................................................. C-33 

C-17B Standard errors for table C-17A estimates (percentage of high school sophomores’ use 

of calculators and computers, by selected student characteristics): 2002..............................C-34 

C-18A Item Response Theory (IRT)-estimated number-right scores for mathematics, by 

selected student characteristics: 2002....................................................................................C-35 

C-18B Standard errors for table C-18A estimates (Item Response Theory [IRT]-estimated 

number-right scores for mathematics, by selected student characteristics): 2002.................C-36 

C-19A High school sophomore probability of proficiency at reading level 1, by selected 

student characteristics: 2002.................................................................................................. C-37 

C-19B Standard errors for table C-19A estimates (high school sophomore probability of 

proficiency at reading level 1, by selected student characteristics): 2002............................. C-38 

xiv 
List of Tables 

Table Page 

C-20A High school sophomore probability of proficiency at reading level 2, by selected 

student characteristics: 2002.................................................................................................. C-39 

C-20B Standard errors for table C-20A estimates (high school sophomore probability of 

proficiency at reading level 2, by selected student characteristics): 2002............................. C-40 

C-21A High school sophomore probability of proficiency at reading level 3, by selected 

student characteristics: 2002.................................................................................................. C-41 

C-21B Standard errors for table C-21A estimates (high school sophomore probability of 

proficiency at reading level 3, by selected student characteristics): 2002............................. C-42 

C-22A High school sophomore probability of proficiency at math level 1, by selected student 

characteristics: 2002 .............................................................................................................. C-43 

C-22B Standard errors for table C-22A estimates (high school sophomore probability of 

proficiency at math level 1, by selected student characteristics): 2002................................. C-44 

C-23A High school sophomore probability of proficiency at math level 2, by selected student 

characteristics: 2002 .............................................................................................................. C-45 

C-23B Standard errors for table C-23A estimates (high school sophomore probability of 

proficiency at math level 2, by selected student characteristics): 2002................................. C-46 

C-24A High school sophomore probability of proficiency at math level 3, by selected student 

characteristics: 2002 .............................................................................................................. C-47 

C-24B Standard errors for table C-24A estimates (high school sophomore probability of 

proficiency at math level 3, by selected student characteristics): 2002................................. C-48 

C-25A High school sophomore probability of proficiency at math level 4, by selected student 

characteristics: 2002 .............................................................................................................. C-49 

C-25B Standard errors for table C-25A estimates (high school sophomore probability of 

proficiency at math level 4, by selected student characteristics): 2002................................. C-50 

C-26A High school sophomore probability of proficiency at math level 5, by selected student 

characteristics: 2002 .............................................................................................................. C-51 

C-26B Standard errors for table C-26A estimates (high school sophomore probability of 

proficiency at math level 5, by selected student characteristics): 2002................................. C-52 

C-27A Percentage of high school sophomores who participate in academic clubs, athletics, and 

cheerleading/drill team, by selected student characteristics: 2002........................................ C-53 

C-27B Standard errors for table C-27A estimates (percentage of high school sophomores who 

participate in academic clubs, athletics, and cheerleading/drill team, by selected student 

characteristics): 2002............................................................................................................. C-54 

C-28A Percentage of high school sophomores who participate in hobby clubs, music, and 

vocational clubs, by selected student characteristics: 2002................................................... C-55 

C-28B Standard errors for table C-28A estimates (percentage of high school sophomores who 

participate in hobby clubs, music, and vocational clubs, by selected student 

characteristics): 2002............................................................................................................. C-56 

C-29A Percentage of high school sophomores, by employment status and selected student 

characteristics: 2002 .............................................................................................................. C-57 

xv 
List of Tables 

Table Page 

C-29B Standard errors for table C-29A estimates (percentage of high school sophomores, by 

employment status and selected student characteristics): 2002.............................................C-58 

C-30A Percentage of high school sophomores who report that they engage in various activities 

at least once or twice a week, by selected student characteristics: 2002...............................C-59 

C-30B Standard errors for table C-30A estimates (percentage of high school sophomores who 

report that they engage in various activities at least once or twice a week, by selected 

student characteristics): 2002 ................................................................................................C-60 

C-31A Percentage of high school sophomores who report that various life values related to 

work are very important to them, by selected student characteristics: 2002 ......................... C-61 

C-31B Standard errors for table C-31A estimates (percentage of high school sophomores who 

report that various life values related to work are very important to them, by selected 

student characteristics): 2002 ................................................................................................C-62 

C-32A Percentage of high school sophomores who report that various life values related to 

family are very important to them, by selected student characteristics: 2002 .......................C-63 

C-32B Standard errors for table C-32A estimates (percentage of high school sophomores who 

report that various life values related to family are very important to them, by selected 

student characteristics): 2002 ................................................................................................C-64 

C-33A Percentage of high school sophomores who report that various life values related to 

friendships and leisure time are very important to them, by selected student 

characteristics: 2002 .............................................................................................................. C-65 

C-33B Standard errors for table C-33A estimates (percentage of high school sophomores who 

report that various life values related to friendships and leisure time are very important 

to them, by selected student characteristics): 2002................................................................ C-66 

C-34A Percentage of high school sophomores who report that various life values related to 

community are very important to them, by selected student characteristics: 2002 ............... C-67 

C-34B Standard errors for table C-34A estimates (percentage of high school sophomores who 

report that various life values related to community are very important to them, by 

selected student characteristics): 2002 ..................................................................................C-68 

C-35A Percentage of high school sophomores who expect to attain various levels of education, 

by selected student characteristics: 2002...............................................................................C-69 

C-35B Standard errors for table C-35A estimates (percentage of high school sophomores who 

expect to attain various levels of education, by selected student characteristics): 2002 ....... C-70 

C-36A Percentage of high school sophomores who report various intentions with regard to 

entering college after high school graduation, by selected student characteristics: 2002 .....C-71 

C-36B Standard errors for table C-36A estimates (percentage of high school sophomores who 

report various intentions with regard to entering college after high school graduation, 

by selected student characteristics): 2002 .............................................................................C-72 

xvi 
List of Tables 

Table Page 

C-37A Percentage of high school sophomores who report that fathers, mothers, school 

counselors, and teachers think college is the most important thing for them to do right 

after high school, by selected student characteristics: 2002 ...................................................C-73 

C-37B Standard errors for table C-37A estimates (percentage of high school sophomores who 

report that fathers, mothers, school counselors, and teachers think college is the most 

important thing for them to do right after high school, by selected student 

characteristics): 2002..............................................................................................................C-74 

C-38A Percentage of high school sophomores’ expected occupation at age 30, by sex: 2002.........C-75 

C-38B Standard errors for table C-38A estimates (percentage of high school sophomores’ 

expected occupation at age 30, by sex): 2002 .......................................................................C-76 

C-39A Comparison of estimates between ELS:2002 imputed and unimputed data, NELS:88 

data, and HS&B data, by selected student characteristics: 1980, 1990, and 2002 ................C-77 

C-39B Standard errors for table C-39A estimates (comparison of estimates between ELS:2002 

imputed and unimputed data, NELS:88 data, and HS&B data, by selected student 

characteristics): 1980, 1990, and 2002 .................................................................................. C-79 

D-1 Restricted-use unique variables in base-year to first follow-up student-level and 

school-level megafiles: 2004 .................................................................................................. D-3 

F-1 ELS:2002 first follow-up student questionnaire critical items: 2004 ......................................F-3 

F-2 ELS:2002 first follow-up new participant student questionnaire additional critical items 

(base-year classification variables): 2004................................................................................F-4 

H-1 Elements of the socioeconomic composite, by study: Selected years, 1972–2002 ................ H-6 

H-2 Elements of socioeconomic composite, by source: 2002 ...................................................... H-6 

H-3 Cross-cohort item crosswalk for longitudinal studies, by item: Selected years, 

1972–2002 .............................................................................................................................H-11 

I-1 Student design effects, by item using first follow-up questionnaire weight—All: 2004..........I-3 

I-2 Student design effects, by item using first follow-up questionnaire weight—Male: 2004.......I-4 

I-3 Student design effects, by item using first follow-up questionnaire weight—Female: 

2004 ..........................................................................................................................................I-5 

I-4 Student design effects, by item using first follow-up questionnaire weight—American 

Indian or Alaska Native: 2004..................................................................................................I-6 

I-5 Student design effects, by item using first follow-up questionnaire weight—Asian: 

2004 ..........................................................................................................................................I-7 

xvii 
List of Tables 

Table Page 

I-6 Student design effects, by item using first follow-up questionnaire weight—Black or 

African American: 2004 ...........................................................................................................I-8 

I-7 Student design effects, by item using first follow-up questionnaire weight—Hispanic 

or Latino: 2004 .........................................................................................................................I-9 

I-8 Student design effects, by item using first follow-up questionnaire weight—More 

than one race: 2004.................................................................................................................I-10 

I-9 Student design effects, by item using first follow-up questionnaire weight—White: 

2004 .........................................................................................................................................I-11 

I-10 Student design effects, by item using first follow-up questionnaire weight—Public: 

2004 .........................................................................................................................................I-12 

I-11 Student design effects, by item using first follow-up questionnaire weight—Catholic: 

2004 .........................................................................................................................................I-13 

I-12 Student design effects, by item using first follow-up questionnaire weight—Other 

private: 2004...........................................................................................................................I-14 

I-13 Student design effects, by item using first follow-up questionnaire weight—Low 

socioeconomic status (SES): 2004..........................................................................................I-15 

I-14 Student design effects, by item using first follow-up questionnaire weight—Middle 

socioeconomic status (SES): 2004..........................................................................................I-16 

I-15 Student design effects, by item using first follow-up questionnaire weight—High 

socioeconomic status (SES): 2004..........................................................................................I-17 

I-16 Student design effects, by item using first follow-up questionnaire weight—Urban: 

2004 .........................................................................................................................................I-18 

I-17 Student design effects, by item using first follow-up questionnaire weight— 

Suburban: 2004.......................................................................................................................I-19 

I-18 Student design effects, by item using first follow-up questionnaire weight—Rural: 

2004 .........................................................................................................................................I-20 

I-19 Student design effects, by item using base-year to first follow-up panel weight— 

All: 2004.................................................................................................................................I-21 

I-20 Student design effects, by item using base-year to first follow-up panel weight— 

Male: 2004..............................................................................................................................I-22 

I-21 Student design effects, by item using base-year to first follow-up panel weight— 

Female: 2004 ..........................................................................................................................I-23 

I-22 Student design effects, by item using base-year to first follow-up panel weight— 

American Indian or Alaska Native: 2004 ...............................................................................I-24 

I-23 Student design effects, by item using base-year to first follow-up panel weight— 

Asian: 2004.............................................................................................................................I-25 

I-24 Student design effects, by item using base-year to first follow-up panel weight— 

Black or African American: 2004...........................................................................................I-26 

xviii 
List of Tables 

Table Page 

I-25 Student design effects, by item using base-year to first follow-up panel weight— 

Hispanic or Latino: 2004 ........................................................................................................I-27 

I-26 Student design effects, by item using base-year to first follow-up panel weight— 

More than one race: 2004 .......................................................................................................I-28 

I-27 Student design effects, by item using base-year to first follow-up panel weight— 

White: 2004 ............................................................................................................................I-29 

I-28 Student design effects, by item using base-year to first follow-up panel weight— 

Public: 2004............................................................................................................................I-30 

I-29 Student design effects, by item using base-year to first follow-up panel weight— 

Catholic: 2004.........................................................................................................................I-31 

I-30 Student design effects, by item using base-year to first follow-up panel weight— 

Other private: 2004.................................................................................................................I-32 

I-31 Student design effects, by item using base-year to first follow-up panel weight—Low 

socioeconomic status (SES): 2004..........................................................................................I-33 

I-32 Student design effects, by item using base-year to first follow-up panel weight— 

Middle socioeconomic status (SES): 2004 .............................................................................I-34 

I-33 Student design effects, by item using base-year to first follow-up panel weight—High 

socioeconomic status (SES): 2004..........................................................................................I-35 

I-34 Student design effects, by item using base-year to first follow-up panel weight— 

Urban: 2004 ............................................................................................................................I-36 

I-35 Student design effects, by item using base-year to first follow-up panel weight— 

Suburban: 2004.......................................................................................................................I-37 

I-36 Student design effects, by item using base-year to first follow-up panel weight— 

Rural: 2004 .............................................................................................................................I-38 

I-37 Dropout design effects, by item using first follow-up questionnaire weight—All: 

2004 .........................................................................................................................................I-39 

I-38 Dropout design effects, by item using base-year to first follow-up panel weight—All: 

2004 .........................................................................................................................................I-40 

J-1 Response rate comparisons, by school consent type and incentive type: 2003 ....................... J-7 

J-2 ELS:2002 in-school unweighted completion rate, by school consent type and incentive 

type: Spring term 2004 ............................................................................................................ J-9 

J-3 Field test items, form A, “Yellow Form,” by usage: 2003 .................................................... J-11 

J-4 Field test items, form B, “Blue Form,” by usage: 2003.........................................................J-12 

J-5 Field test sample counts, selected characteristics: 2003 ........................................................ J-13 

J-6 Test form, by timing, number of items, and completion rates: 2003.....................................J-13 

J-7 Percentage of omitted responses for reformatted items, by study stage: 2003......................J-14 

J-8 Summary of classical item analysis statistics, by test form: 2003.........................................J-16 

xix 
List of Tables 

Table Page 

J-9 Summary of Item Response Theory (IRT) estimates: 2003 ...................................................J-17 

J-10 Reliabilities, by test form: 2003..............................................................................................J-18 

J-11 Summary statistics for reformatted items, by item type: 2003 ...............................................J-19 

J-12 Summary statistics for difficult items: 2003...........................................................................J-20 

xx 
List of Figures 

Figure Page 

1 Longitudinal design for the NCES high school cohorts: 2004 ....................................................3 

2 Student analysis populations, by year: 2004...............................................................................56 

3 Student analysis population respondent counts, by year: 2004 ..................................................57 

4 Full sample mean design effects and root design effects, by longitudinal study: Selected 

years, 1972–2004........................................................................................................................97 

5 Mean design effects and root design effects, by NELS:88 and ELS:2002 panel sample 

(sophomore cohort): Selected years, 1988–2004........................................................................98 

6 Before versus after nonresponse adjustment estimates for relative bias for base-year 

sophomores using the cross-sectional weight: 2004.................................................................137 

7 Before versus after nonresponse adjustment estimates for relative bias for base-year 

sophomores using the panel weight: 2004................................................................................137 

8 Before versus after nonresponse adjustment estimates for relative bias for transfer 

students using the cross-sectional weight: 2004.......................................................................138 

9 Before versus after nonresponse adjustment estimates for relative bias for dropouts 

using the cross-sectional weight: 2004 .....................................................................................138 

10 Before versus after nonresponse-adjustment estimates for relative bias for early 

graduates using the cross-sectional weight: 2004.....................................................................139 

11 Before versus after nonresponse adjustment estimates for relative bias for 

homeschooled students using the cross-sectional weight: 2004...............................................139 

12 Minimum bias ratio by Type I error rate for base-year sophomores using the crosssectional weight: 2004 ..............................................................................................................140 

13 Minimum bias ratio by Type I error rate for base-year sophomores using the panel 

weight: 2004 .............................................................................................................................140 

14 Minimum bias ratio by Type I error rate for transfer students using the cross-sectional 

weight: 2004 .............................................................................................................................141 

15 Minimum bias ratio by Type I error rate for dropouts using the cross-sectional weight: 

2004 ..........................................................................................................................................141 

16 Minimum bias ratio by Type I error rate for early graduates using the cross-sectional 

weight: 2004 .............................................................................................................................142 

17 Minimum bias ratio by Type I error rate for homeschooled students using the crosssectional weight: 2004 ..............................................................................................................142 

xxi 

 

Chapter 1 

Introduction 

1.1 Overview of the Data File Documentation 

This report provides guidance and documentation for users of the public release for the 

combined base-year and first follow-up data of the Education Longitudinal Study of 2002 

(ELS:2002). ELS:2002 is sponsored by the National Center for Education Statistics (NCES) of 

the Institute of Education Sciences, U.S. Department of Education. The base-year and first 

follow-up studies were conducted through a contract to RTI International (RTI),1

 a universityaffiliated, nonprofit research organization based in North Carolina, in collaboration with its 

subcontractors, the Educational Testing Service of Princeton, New Jersey, and MPR Associates 

of Berkeley, California. This manual contains information about the purposes of ELS:2002, the 

base-year and first follow-up data collection instruments, the sample design, and data collection 

and data processing procedures. The manual provides guidance for understanding and using data 

from all components of the base year and first follow-up. 

The ELS:2002 base-year to first follow-up dataset has been produced in both public-use 

and restricted-use versions (see appendix D for a summary of differences between the public and 

restricted electronic codebooks [ECBs]). The released data files reflect alteration or suppression 

of some of the original data. Such edits were imposed to minimize the risk of disclosing the 

identity of responding schools and individuals. Although the primary focus of this manual is the 

public-release version of the data as issued in ECB format, much of the information supplied is 

also applicable to the restricted-use ECB. 

Chapter 1 addresses three main topics. First, it supplies an overview of the NCES 

education longitudinal studies program, thus situating ELS:2002 in the context of the earlier 

NCES high school cohorts studied in the 1970s, 1980s, and 1990s. Second, it introduces 

ELS:2002 by delineating its principal objectives. Third, it provides an overview of the base-year 

and first follow-up study designs. In subsequent chapters, additional topics are addressed: 

instrumentation (chapter 2), sample design and weighting (chapter 3), data collection methods 

and results (chapter 4), data preparation and processing (chapter 5), and data file contents 

(chapter 6). Appendixes provide additional information, including an introduction to the publicuse ECB (appendix A), base-year and first follow-up questionnaires (appendix B), 

documentation for imputed variables (appendix C), information on variables not included in 

public-use files but available in restricted-use files for licensed users (appendix D), a glossary of 

terms (appendix E), student questionnaire critical items (appendix F), base-year and first followup ECBs (appendix G), cross-cohort comparisons (appendix H), standard errors and design 

effects (appendix I), and a synopsis of the ELS:2002 first follow-up field test (appendix J). 

1

 RTI International is a trade name of Research Triangle Institute. 

1 
 

Chapter 1: Introduction 

1.2 Historical Background 

1.2.1 NCES Education High School Longitudinal Studies Program 

In response to its mandate to “collect and disseminate statistics and other data related to 

education in the United States” and the need for policy-relevant, nationally representative 

longitudinal samples of elementary and secondary students, NCES instituted the National 

Education Longitudinal Studies program. The aim of this continuing program is to study the 

educational, vocational, and personal development of students at various stages in their 

educational careers and the personal, familial, social, institutional, and cultural factors that may 

affect that development. 

NCES (and ELS:2002) is authorized by section 406(b) of the General Education 

Provision Act (20 U.S.C. 1221e) as amended by the Education Sciences Reform Act of 2002. 

The Education Sciences Reform Act of 2002 replaced the former Office of Educational Research 

and Improvement (OERI) with the Institute of Education Sciences (IES), in which NCES is now 

housed. 

The high school longitudinal studies program consists of three completed studies: the 

National Longitudinal Study of the High School Class of 1972 (NLS-72), the High School and 

Beyond (HS&B) longitudinal study of 1980, and the National Education Longitudinal Study of 

1988 (NELS:88). In addition, base-year and first follow-up data for ELS:2002, the fourth 

longitudinal study in the series, are now available. Taken together, these studies describe (or will 

describe) the educational experiences of students from four decades—the 1970s, 1980s, 1990s, 

and 2000s—and also provide bases for further understanding of the correlates of educational 

success in the United States. Figure 1 includes a temporal presentation of these four longitudinal 

education studies and highlights their component and comparison points. Figure 1 does not 

identify all future follow-up points for ELS:2002; final decisions have yet to be made concerning 

them. However, the general expectation is that ELS:2002 sophomores will be followed until 

about age 30. 

1.2.2 National Longitudinal Study of the High School Class of 1972 (NLS-72) 

The National Education Longitudinal Studies program began over 30 years ago with the 

implementation of NLS-72.2

 NLS-72 was designed to provide longitudinal data for education 

policymakers and researchers who link educational experiences in high school with important 

downstream outcomes such as labor market experiences and postsecondary education enrollment 

and attainment. With a national probability sample of 19,001 high school seniors from 1,061 

public and religious and other private schools, the NLS-72 sample was representative of 

approximately 3 million high school seniors enrolled in 17,000 U.S. high schools during the 

spring of the 1971–72 school year. Each member of this cohort was asked to complete a student 

questionnaire and a cognitive test battery. In addition, administrators at the sample members’ 

2

 For documentation on NLS-72, see Riccobono et al. (1981) and Tourangeau et al. (1987). While recent NCES 

reports and user documentation may be found on the NCES website (http://nces.ed.gov), some older documentation 

may be unavailable. NLS-72 and older HS&B manuals may be downloaded from the International Archive of 

Education Data (IAED) at the Inter-university Consortium for Political and Social Research (ICPSR) at the University 

of Michigan (http://www.icpsr.umich.edu). Materials may also be obtained in microfiche or photocopy format from the 

Education Resources Information Center (ERIC) database (http://www.eric.ed.gov). 

2 
Figure 1. Longitudinal design for the NCES high school cohorts: 2004 

3
 

Chapter 1: Introduction 

schools were asked to supply information about the schools’ programs, resources, and grading 

systems, as well as survey data on each student. No parent survey was conducted. However, 

postsecondary education transcripts were collected from the institutions attended by students. 

Five follow-up surveys were completed with this student cohort, with the final data collection 

taking place in 1986, when the sample members were 14 years removed from scheduled high 

school graduation and approximately 32 years old. 

A wide variety of data was collected in the NLS-72 surveys. For example, in addition to 

background information about the students and their families, the base-year and follow-up 

surveys collected data on each respondent’s educational activities (e.g., schools attended, grades 

received, and degree of satisfaction with educational institutions). Participants were also asked 

about their work experiences, periods of unemployment, job satisfaction, military service, 

marital status, and children. Attitudinal information on self-concept, goals, community 

involvement, and personal evaluations of educational activities were also included in the study. 

1.2.3 High School and Beyond (HS&B) 

The second in the series of NCES longitudinal studies was launched in 1980. HS&B 

included one cohort of high school seniors comparable to the NLS-72 sample; however, the 

study also extended the age span and analytical range of NCES longitudinal studies by surveying 

a sample of high school sophomores. Base-year data collection took place in the spring term of 

the 1979–80 academic year with a two-stage probability sample. More than 1,000 schools 

served as the first-stage units, and 58,000 students within these schools were the second-stage 

units. Both cohorts of HS&B participants were resurveyed in 1982, 1984, and 1986; the 

sophomore group also was surveyed in 1992.3 In addition, to better understand the school and 

home contexts for the sample members, data were collected from teachers (a teacher comment 

form in the base year asked for teacher perceptions of HS&B sample members), principals, and a 

subsample of parents. High school transcripts were collected for a subsample of sophomore 

cohort members. As in NLS-72, postsecondary transcripts were collected for both HS&B 

cohorts; however, the sophomore cohort transcripts cover a much longer time span (to 1993). 

With the study design expanded to include a sophomore cohort, HS&B provided critical 

data on the relationships between early high school experiences and students’ subsequent 

educational experiences in high school. For the first time, national data were available that 

showed students’ academic growth over time and how family, community, school, and 

classroom factors promoted or inhibited student learning. Researchers were able to use data 

from the extensive battery of achievement tests within the longitudinal study to assess growth in 

knowledge and cognitive skills over time. Moreover, data were then available to analyze the 

school experiences of students who later dropped out of high school and, eventually, to 

investigate their later educational and occupational outcomes. These data became a rich resource 

3

 For a summation of the HS&B sophomore cohort study, see Zahs et al. (1995). For further information on HS&B, 

see the NCES website: http://nces.ed.gov/surveys/hsb/. 

4 
 

Chapter 1: Introduction 

for policymakers and researchers over the next decade and provided an empirical base to inform 

the debates of the education reform movement that began in the early 1980s.4 

1.2.4 National Education Longitudinal Study of 1988 (NELS:88) 

Much as NLS-72 captured a high school cohort of the 1970s and HS&B captured high 

school cohorts of the 1980s, NELS:88 was designed to study high school students of the 1990s— 

but with a premeasure of their achievement and status, prior to their entry into high school. 

NELS:88 represents an integrated system of data that tracked students from junior high or middle 

school through secondary and postsecondary education, labor market experiences, and marriage 

and family formation. Because ELS:2002 repeats so many of its innovations and design features, 

it will be useful to provide a detailed, round-by-round picture of NELS:88. 

Data collection for NELS:88 was initiated with the 8th-grade class of 1988 in the spring 

term of the 1987–88 school year. Along with a student survey, NELS:88 included surveys of 

parents (base year and second follow-up), teachers (base year, first follow-up, and second 

follow-ups), and school administrators (base year, first follow-up, and second follow-up). The 

sample was also surveyed after scheduled high school graduation, in 1994 and 2000.5 

1.2.4.1 NELS:88 Base Year 

The NELS:88 base year (1988) successfully surveyed 24,599 students, out of some 

26,432 selected 8th-graders, across 1,052 public, Catholic, and other private schools. In addition 

to filling out a questionnaire, students also completed assessments in four subjects (mathematics, 

science, reading, and social studies). The base year also surveyed one parent, two teachers, and 

the principal of each selected student. The base-year research instruments collected information 

about home, school, and individual factors that could serve as predictors for later outcomes (such 

as, viewed in terms of positive outcomes, graduating from high school, making a smooth 

transition into the workforce, or completing postsecondary education). Information collected in 

the base year included family income, parental education, and occupation; parental aspirations 

for their 8th-grader; the 8th-grader’s educational and occupational aspirations and plans, school 

experiences, extracurricular activities, jobs and chores, television viewing, and reading; teacher 

perceptions of the 8th-grader’s classroom performance and personal characteristics; curricular 

and instructional information about the classes in which teachers taught the 8th-grader; the 

teacher’s own background and activities; and the principal’s reports on the educational setting 

and environment of the school. 

4

 For a summary of reforms instituted between the time the HS&B cohort was in high school and the NELS:88 cohort 

was in middle/junior high and high school, see Rasinski et al. (1993). For a summary of state education reforms 

instituted during the earlier school years of the ELS:2002 cohort, see Hurst et al. (2003). 5 The entire compass of NELS:88, from its baseline through its final follow-up in 2000, is described in Curtin et al. 

(2002). More detailed information about the high school surveys of NELS:88 can be found in Ingels et al. (1994). 

Final outcomes for NELS:88 (in 2000) are reported in Ingels et al. (2002). The most extensive documentation of the 

NELS:88 assessment battery is found in Rock and Pollack (1995a). The quality of NELS:88 data in the in-school 

rounds is examined in Kaufman and Rasinski (1991) and McLaughlin and Cohen (1997). The sample design is 

documented in Spencer et al. (1990). Eligibility and exclusion issues are addressed in Ingels (1996). NCES keeps 

an updated version of the NELS:88 bibliography on its website. The bibliography encompasses both project 

documentation and research articles, monographs, dissertations, and paper presentations employing NELS:88 data 

(see http://nces.ed.gov/surveys/nels88/Bibliography.asp). 

5 
 

Chapter 1: Introduction 

1.2.4.2 NELS:88 First Follow-up 

A first follow-up took place in 1990. In the NELS:88 first follow-up (initial data 

release), there are 19,260 participants (18,220 students and 1,040 dropouts) from a sample of 

20,700. (There were some changes to the file in the second follow-up re-release of the 1990 

data, which shows a revised sample size of 20,840). At that time, student cohort members, their 

teachers, and their principals were resurveyed. The first follow-up presented three major new 

analytic opportunities: (1) longitudinal analysis of gains in tested achievement and the correlates 

of achievement gains, (2) identification of high school dropouts and investigation of why some 

students drop out of school and others persist, and (3) cross-cohort comparison (1990 high 

school sophomores could be compared to sophomores in 1980). 

Achievement gain. One major goal of NELS:88 was to measure students’ academic 

growth over time and to identify the specific school (and nonschool) processes that may foster 

academic achievement. The first follow-up tests were tailored to students’ ability as measured in 

the base year; more difficult test forms were assigned to students with a higher ability estimate. 

The first follow-up, by retesting the 8th-grade NELS:88 cohort, was able to measure cognitive 

gains between 8th and 10th grades in mathematics, science, reading, and social studies. In turn, 

these gains could be related to the data collected on home and school correlates of achievement, 

starting in 1988. Because NELS:88 developed hierarchical criterion-referenced proficiency 

scores (in reading, science, and mathematics), gain can be looked at in more than just 

quantitative terms—one can use the proficiency levels to locate the place on the growth 

continuum where the gain took place (e.g., at a lower or at a higher skill area) and, in turn, better 

relate gains to specific school processes and curricular sequences.6 

Dynamics of school disengagement and dropping out. Another major goal of the first 

follow-up was to study the educational trajectory of those who drop out of high school and to 

better understand the factors that help some at-risk students persist in their education. By 

beginning with the 8th grade, NELS:88 was able to capture the population of early dropouts— 

those who left school prior to spring term of 10th grade—as well as (in the second follow-up) 

later dropouts (who left after spring of 10th grade) as had been studied in HS&B. 

Cross-cohort comparison. A third goal of the 1990 wave was to compare NELS:88 

sophomores with the earlier cohort of high school sophomores studied in HS&B. To ensure 

comparability of the two samples, NELS:88 “freshened” the sophomore sample by giving a 

chance of selection to 1990 sophomores who had not been 8th-graders in 1988 (or had not been 

in the United States). Thus, a nationally representative sophomore cohort was included in 

NELS:88 in the first follow-up (1990). 

1.2.4.3 NELS:88 Second Follow-up 

The second follow-up took place in the spring term of the 1991–92 school year, when 

most sample members were in their final semester of high school. There were 21,188 student 

and dropout participants. This follow-up provided a culminating measurement of learning in the 

course of secondary school and also collected information to facilitate investigation of the 

6

 Further information about NELS:88 proficiency scores can be found in Rock and Pollack (1995a). For examples of 

their use in achievement gain analysis, see Rock and Pollack (1995b) and Scott et al. (1995). 

6 
Chapter 1: Introduction 

transition into the labor force and postsecondary education after high school. As in the first 

follow-up, the sample was freshened, this time to represent the high school senior class of 1992. 

Trend comparisons can be made to the high school classes of 1972 and 1980 that were studied in 

NLS-72 and HS&B. The NELS:88 second follow-up also surveyed students who were identified 

as dropouts in 1990 and identified and surveyed additional students who had left school since the 

prior wave. In late 1992 and early 1993, high school transcripts were collected for sample 

members. 

1.2.4.4 NELS:88 Third Follow-up 

The third follow-up took place in 1994, when most sample members had completed high 

school. The primary goals of the 1994 round were (1) to provide data for trend comparisons with 

NLS-72 and HS&B, (2) to address issues of employment, (3) to address issues of postsecondary 

access and choice, and (4) to ascertain how many dropouts had returned to school and by what 

route. There were 14,915 participants. 

1.2.4.5 NELS:88 Fourth Follow-up 

The fourth follow-up took place in 2000, when most sample members who attended 

college and technical schools had completed their postsecondary education. The study data 

address issues of employment, family formation, and postsecondary persistence and attainment. 

There were 12,144 participants in the questionnaire phase of the study. In fall 2000 and early 

2001, postsecondary transcripts were collected, further increasing the analytic potential of the 

data and the possibility of examining trends over time. 

1.3 Education Longitudinal Study of 2002 (ELS:2002) 

ELS:2002 represents a major longitudinal effort designed to provide trend data about 

critical transitions experienced by students as they proceed through high school and into 

postsecondary education or their careers. The 2002 sophomore cohort is being followed, initially 

at 2-year intervals, to collect policy-relevant data about educational processes and outcomes, 

especially as such data pertain to student learning, predictors of dropping out, and high school 

effects on students’ access to, and success in, postsecondary education and the workforce. 

In the spring term 2002 base year of the study, high school sophomores were surveyed 

and assessed in a national sample of high schools with 10th grades. Their parents, teachers, 

principals, and librarians were surveyed as well. 

In the first of the follow-ups, base-year students who remained in their base-year schools 

were resurveyed and tested (in mathematics) 2 years later, along with a freshening sample that 

makes the study representative of spring 2004 high school seniors nationwide. Students who had 

transferred to a different school, had switched to a homeschool environment, graduated early, or 

who had dropped out were administered a questionnaire. 

This section introduces ELS:2002, lists some of the major research and policy issues that 

the study addresses, and explains the four levels of analysis—cross-sectional, longitudinal, crosscohort, and international comparison—that can be conducted with ELS:2002 data. 

7 
 

Chapter 1: Introduction 

1.3.1 ELS:2002 Study Objectives 

ELS:2002 is designed to monitor the transition of a national sample of young people as 

they progress from 10th grade through high school and on to postsecondary education or the 

world of work, or both. 

ELS:2002 has two distinctive features. First, it is a longitudinal study, in which the same 

units (schools and students) are surveyed repeatedly over time. Individual students will be 

followed through high school and for a number of years thereafter. The base-year schools were 

surveyed twice, in 2002 and in 2004. Second, in the high school years, ELS:2002 is an 

integrated, multilevel study that involves multiple respondent populations. The respondents 

include students, their parents, their teachers, and their schools (from which data are collected at 

four levels: from the principal, the librarian, a facilities checklist, and school course catalogues 

and records, which will support a course offerings component in the first follow-up transcript 

study). Each of the two distinctive features—the longitudinal nature of the ELS:2002 design and 

its multilevel focus—will be explained in greater detail below. 

The transition through high school and beyond into postsecondary institutions and the 

labor market is both complex (youth may follow many different paths) and prolonged (it takes 

place over a period of years). The complexity and time frame for this transition make 

longitudinal approaches especially appropriate. By surveying the same young people over time, 

it is possible to record the changes taking place in their lives. It is also possible to gather 

information about the ways that earlier achievements, aspirations, and experience predict what 

happens to the respondents later. In the baseline data collection (spring 2002), ELS:2002 

measured students’ tested achievement in reading and mathematics. ELS:2002 also obtained 

information from students about their attitudes and experiences. 

These same students have been resurveyed 2 years later (in 2004), in the ELS:2002 first 

follow-up, to measure changes such as achievement gains in mathematics and changes in 

enrollment status (e.g., the situation of students who drop out of school compared with those who 

persist in their education). 

Cohort members will be followed for a number of years thereafter so that later outcomes 

(e.g., their access to and persistence in higher education or their success in the labor market) can 

be understood in terms of their earlier aspirations, achievement, and high school situation. 

ELS:2002 gathers information at multiple levels. It obtains information not only from 

students and their school records, but also from students’ parents, teachers, and the 

administrators (principal and library media center director) of their schools. Data from their 

teachers, for example, provide information both about the student’s and the teacher’s 

backgrounds and activities. This multilevel focus supplies researchers with a comprehensive 

picture of the home, community, and school environments and their influences on the student. 

8 
 

Chapter 1: Introduction 

This multiple-respondent perspective is unified by the fact that, for most purposes, the student is 

the basic unit of analysis.7 

Using this multilevel and longitudinal information, the base year (2002) and first followup (2004) of ELS:2002 will help researchers and policymakers explore and better understand 

such issues as the importance of home background and parental aspirations for a child’s success; 

the influence of different curriculum paths and special programs; the effectiveness of different 

high schools; and whether a school’s effectiveness varies with its size, organization, climate or 

ethos, curriculum, academic press, or other characteristics. These data will facilitate 

understanding of the impact of various instructional methods and curriculum content and 

exposure in bringing about educational growth and achievement. 

After the high school years, ELS:2002 will continue to follow its sample of students into 

postsecondary education or the labor market, or both. For students who continue on to higher 

education, researchers can use ELS:2002 to measure the effects of their high school careers on 

subsequent access to postsecondary institutions, their choices of institutions and programs, and 

as time goes on, their postsecondary persistence, attainment, and eventual entry into the labor 

force and adult roles. For students who go directly into the workforce (whether as dropouts or 

high school graduates), ELS:2002 will be able to determine how well high schools have prepared 

these students for the labor market and how they fare within it. 

Key elements in the ELS:2002 longitudinal design are summarized by wave below. 

Base Year (2002) 

• Completed baseline survey of high school sophomores in spring term 2002. 

• Completed cognitive tests in reading and mathematics. 

• Completed survey of parents, English teachers, and mathematics teachers. Collected 

school administrator questionnaires. 

• Included additional components for this study—a school facilities checklist and a 

media center (library) questionnaire. 

• Established sample sizes of approximately 750 schools and over 17,000 students. 

Schools are the first-stage unit of selection, with sophomores randomly selected 

within schools. 

• Oversampled Asian8

 and Hispanic students and private schools. 

• Designed linkages with the Program for International Student Assessment (PISA); 

scored reporting linkages to the prior longitudinal studies. 

7

 Base-year school administrator, library media center, and facilities data can be used to report on the nation’s 

schools with 10th grades in the 2001–02 school year. A first follow-up course offerings file will further enrich the 

information available about high schools with 10th grades in 2002. However, if history is a guide, most analysts will 

employ the school-level data to provide further contextual information on the student. 

8

 Except where indicated otherwise, the race/ethnicity variable for this report includes six categories: (1) American 

Indian or Alaska Native; (2) Asian or Pacific Islander, including Native Hawaiian; (3) Black, including African 

American; (4) Hispanic or Latino; (5) More than one race; and (6) White. All race categories exclude individuals of 

Hispanic or Latino origin. 

9 
Chapter 1: Introduction 

First Follow-up (2004) 

• Most sample members were seniors, but some were dropouts or in other grades (early 

graduates or retained in an earlier grade). 

• Student questionnaire (different versions for students who remained in the base-year 

school, transferred to a new school, completed high school early, or were 

homeschooled), dropout questionnaire, assessment in mathematics, and school 

administrator questionnaire were administered. 

• Returned to the same schools but separately followed transfer students and surveyed 

them outside of school. 

• Freshened for a senior cohort. 

• High school transcript component in 2004 (coursetaking records at the student level 

for grades 9–12) and course offerings component at the school level. 

Second Follow-up (2006) 

• Post-high-school follow-up with web-based instrument for self-administration, 

computer-assisted telephone interview (CATI), or computer-assisted personal 

interview (CAPI). 

• Survey 2 years after scheduled high school graduation. 

Further Follow-ups 

• Number of (and dates for) further web/CATI/CAPI follow-ups to be determined. 

1.3.2 ELS:2002 Research and Policy Issues 

Apart from helping to describe the status of high school students and their schools, 

ELS:2002 will provide information to help address a number of key policy and research 

questions. The study is intended to produce a comprehensive dataset for the development and 

evaluation of education policy at all government levels. Part of its aim is to inform 

decisionmakers, education practitioners, and parents about the changes in the operation of the 

educational system over time and the effects of various elements of the system on the lives of the 

individuals who pass through it. Issues that can be addressed with data collected in the high 

school years include the following: 

• students’ academic growth in mathematics; 

• the process of dropping out of high school; 

• the role of family background and the home education support system in fostering 

students’ educational success; 

• the features of effective schools; 

• the relationship between coursetaking choices and success in the high school years 

(and thereafter); 

10 
Chapter 1: Introduction 

• the distribution of educational opportunities as registered in the distinctive school 

experiences and performance of students from various subgroups. Such subgroups 

include the following: 

− students in public and private high schools; 

− language minority students; 

− students with disabilities; 

− students in urban, suburban, and rural settings; 

− students in different regions of the country; 

− students from upper, middle, and lower socioeconomic status levels; 

− male and female high school students; and 

− students from different racial or ethnic groups. 

• steps taken to facilitate the transition from high school to postsecondary education or 

the world of work. 

After ELS:2002 students have completed high school, a new set of issues can be examined. 

These issues include the following: 

• the later educational and labor market activities of high school dropouts; 

• the transition of those who do not go directly on to postsecondary education or to the 

world of work; 

• access to and choice of undergraduate and graduate educational institutions; 

• persistence in attaining postsecondary educational goals; 

• rate of progress through the postsecondary curriculum; 

• degree attainment; 

• barriers to persistence and attainment; 

• entry of new postsecondary graduates into the workforce; 

• social and economic rate of return on education to both the individual and society; 

and 

• adult roles, such as family formation and civic participation. 

These research and policy issues can be investigated at several distinct levels of analysis. The 

overall scope and design of the study provide for the four following analytical levels: 

• cross-sectional profiles of the nation’s high school sophomores (2002), seniors 

(2004), and post-sophomore-year dropouts (2004); 

• longitudinal analysis (including examination of life course changes); 

• intercohort comparisons with American high school students of earlier decades; and 

11 
 

 

Chapter 1: Introduction 

• international comparisons: U.S. 15-year-olds to 15-year-olds in other nations, 

including longitudinal outcomes for the United States that can be related to scale 

scores in mathematics and reading from PISA. 

1.3.2.1 Cross-Sectional Profiles 

Cross-sectional data permit characterization of the nation’s high school sophomores in 

the spring term of the 2001–02 school year. Initial cross-sectional findings from the base year 

are available in an NCES report, A Profile of the American High School Sophomore in 2002.

9 

Because of sample freshening, the results 2 years later provide a basis for profiling the nation’s 

high school seniors in the spring term of the 2003–04 school year. A report on seniors is now 

being prepared for release. 

1.3.2.2 Longitudinal Analysis 

Longitudinal analysis has become possible with the release of data from the 2004 first 

follow-up. The primary research objectives of ELS:2002 are longitudinal in nature. The study 

provides the basis for within-cohort comparison by following the same individuals over time to 

measure achievement growth in mathematics and monitor enrollment status over the high school 

years. With future follow-ups, the study can also record such key outcomes as postsecondary 

entry and attainment, labor market experiences, and family formation. In turn, these outcomes 

can be related to antecedents identified in earlier rounds, including individual, home, school, and 

community factors. 

1.3.2.3 Intercohort Comparisons 

As part of an important historical series of studies that repeats a core of key items each 

decade, ELS:2002 offers the opportunity for the analysis of trends in areas of fundamental 

importance, such as patterns of coursetaking, rates of participation in extracurricular activities, 

academic performance, and changes in goals and aspirations. With completion of the first 

follow-up in 2004, researchers can now compare ELS:2002 high school seniors’ experiences, 

attitudes, and achievement with that of NELS:88 seniors in 1992, HS&B seniors in 1980, and 

NLS-72 seniors in 1972. They will also be able to compare ELS:2002 dropouts in 2004 with the 

high school dropouts studied by HS&B in 1982 and by NELS:88 in 1992. 

Upon release of ELS:2002 academic transcript data, trend comparisons can also be made 

with academic transcript data containing students’ high school course histories and sequences, 

since comparable transcript studies have been conducted, starting with HS&B (1982) and 

including NELS:88 (1992) and the National Assessment of Educational Progress (NAEP) (1987, 

1990, 1994, 1998, and 2000). 

1.3.2.4 International Comparisons 

A feature of ELS:2002 that expands the study’s power beyond that of the predecessor 

studies is that it will be used to support international comparisons. Items have been included on 

9

 See Ingels et al. (2005). A small, but growing, ELS:2002 bibliography can be found at 

http://nces.ed.gov/surveys/els2002/Bibliography.asp. 

12 
 

 

Chapter 1: Introduction 

the ELS:2002 achievement tests from PISA. The Organization for Economic Cooperation and 

Development’s (OECD’s) PISA (Lemke et al. 2001) is an internationally standardized 

assessment, jointly developed by the 32 participating countries (including the United States) and 

administered to 15-year-olds in groups in their schools. PISA covers three domains: reading 

literacy, numeracy, and scientific literacy; ELS:2002 test results have been linked to PISA 

reading and mathematics scores so that the PISA scale can be used in ELS:2002 analyses. PISA 

aims to define each domain not merely in terms of mastery of the school curriculum, but also in 

terms of important knowledge and skills needed in adult life. Emphasis is placed on the mastery 

of processes, the understanding of concepts, and the ability to function in various situations 

within each domain. 

1.3.3 Overview of the Base-Year Study Design10 

The ELS:2002 base-year study was carried out in a national probability sample of 752 

public, Catholic, and other private schools in the spring term of the 2001–02 school year. Of 

17,591 eligible selected sophomores, 15,362 completed a base-year questionnaire, as did 13,488 

parents, 7,135 teachers, 743 principals, and 718 librarians. 

Seven study components comprise the base-year design: assessments of students 

(achievement tests in mathematics and reading); a survey of students; surveys of parents, 

teachers, school administrators, and librarians; and a facilities checklist (completed by survey 

administrators, based on their observations at the school). The student assessments measured 

achievement in mathematics and reading; the baseline scores can serve as a covariate or control 

variable for later analyses. Mathematics achievement was reassessed 2 years hence, so that 

achievement gain over the last 2 years of high school can be measured and related to school 

processes and mathematics coursetaking. The student questionnaire gathered information about 

the student’s background, school experiences and activities, plans and goals for the future, 

employment and out-of-school experiences, language background, and psychological orientation 

toward learning. 

One parent of each participating sophomore was asked to respond to a parent survey. 

The parent questionnaire was designed to gauge parental aspirations for their child, home 

background and the home education support system, the child’s educational history prior to 10th 

grade, and parental interactions with and opinions about the student’s school. For each student 

enrolled in English or mathematics, a teacher was also selected to participate in a teacher survey. 

The teacher questionnaire collected the teacher’s evaluations of the student and provided 

information about the teacher’s background and activities. The head librarian or media center 

director at each school was asked to complete a library media center questionnaire, which 

inquired into the school’s library media center facility, its staffing, its technological resources, 

collection and expenditures, and scheduling and transactions. Finally, the facilities checklist was 

a brief observational form completed for each school. The form collected information about the 

condition of school buildings and facilities. 

10 Although this manual covers the base year as well as first follow-up of ELS:2002, much more detailed information 

about the base year can be found in Ingels et al. (2004). 

13 
Chapter 1: Introduction 

1.3.4 Overview of the First Follow-up Study Design 

The basis for the sampling frame for the first follow-up was the sample of schools and 

students studied in the ELS:2002 base year. There were two overlapping but conceptually 

different target student populations, or populations of inferential interest, for the first follow-up. 

One population (the ELS:2002 sophomore cohort) consists of those students who were enrolled 

in the 10th grade in the spring term of 2002. The other population (the ELS:2002 senior cohort) 

comprises those students who were enrolled in the 12th grade in the spring term of 2004. The 

former population includes students who dropped out of school between 10th and 12th grades, 

students who graduated early, students who went from a school setting to a homeschooling 

setting, and students who fell behind the modal grade progression of their peers (e.g., students 

who repeated a grade and were 11th-graders in spring 2004). Because of these two target 

populations and the major analytical subgroups, the full-scale sample encompasses the following 

types of students in the spring of 2004: 

• ELS:2002 base-year student sample members enrolled (in either the 12th grade or 

some other grade) in the school in which they were originally sampled; 

• ELS:2002 base-year sophomores who dropped out of school prior to first follow-up 

(2004) data collection; 

• ELS:2002 base-year student respondents who finished high school early, including 

those who graduated from high school early, as well as those who did not graduate 

because they achieved alternative certification (e.g., exam-certified equivalency such 

as a GED); 

• ELS:2002 base-year student respondents who transferred out of the school in which 

they were originally sampled (including homeschooled students); 

• ELS:2002 base-year sample students who were deemed unable to participate directly 

during the base year owing to severe disability or insufficient command of the 

English language such that they could not complete a questionnaire; and 

• students at the base-year sample school who were enrolled in the 12th grade in spring 

of 2004 but who were not in 10th grade in the United States during the 2001–02 

school year. In spring term 2002, such students may have been out of the country, 

been enrolled in school in the United States in a grade other than 10th, had an 

extended illness or injury, been homeschooled, been institutionalized, or temporarily 

dropped out of school. These students comprised the first follow-up “freshening” 

sample. 

While all groups in the sample as categorized above were eligible to complete a 

questionnaire, different instruments were tailored to different study populations. The guiding 

intuition was to provide a core of items that all sample members would respond to, supplemented 

by items specific to the circumstances of a particular group (such as dropouts, for example, for 

whom questions about their current school situation would not be relevant). In chapter 2, the 

various questionnaires—student, abbreviated student, transfer student, early graduate, 

homeschool, out-of-school (dropout), and new student supplement—are described at length. 

14 
Chapter 1: Introduction 

For some classifications of the sample, a first follow-up test score in mathematics has 

either been collected (students still in the base-year school) or imputed (students who have 

transferred to a new school). For other categories of sample members, such as dropouts, early 

graduates, and the homeschooled, a test score has neither been collected nor imputed. (It should 

also be noted that missing base-year test score data have been imputed for base-year 

nonrespondents who became respondents in the first follow-up.) 

For all classifications of sample members, information about student coursetaking 

(covering all years of high school and including the sequence in which courses were taken and 

grades earned) will be collected late in 2004 and early 2005 through the high school transcript 

component of the ELS:2002 first follow-up study. 

At the school level, the first follow-up has extended information about base-year schools 

through administration of a school administrator questionnaire. In addition, information about 

school course offerings will be collected in the first follow-up transcript study. Finally, further 

information about participating schools at the time of the first follow-up survey can be obtained 

(on the restricted file only) by linking (via the NCES identification code [NCESID]) to the 

Common Core of Data (CCD) or Private School Study (PSS), and, via zip codes, to 2000 Census 

data. The NCES school district database and its Census data also are accessible on the restricteduse file by means of the standard NCES school ID. 

15 

 

 

 

Chapter 2 

Instrumentation 

2.1 Introduction 

The base-year (2002) data collection instruments for the Education Longitudinal Study of 

2002 (ELS:2002) consisted of five separate questionnaires (student, parent, teacher, school 

administrator, and library media center), two achievement tests (assessments in reading and 

mathematics), and a school observation form (facilities checklist). 

The first follow-up (2004) data collection instruments comprised seven questionnaires 

and an achievement test in mathematics. The first follow-up questionnaires included a student 

questionnaire, a transfer student questionnaire, a new participant student questionnaire (NPSQ), a 

homeschool student questionnaire, an early graduate questionnaire, a dropout (not currently in 

school) questionnaire, and a school administrator questionnaire. A new participant supplement 

(NPS) (repeating questions from the base year) and an abbreviated version of the student 

questionnaire were also offered.11 The base-year and first follow-up questionnaires can be found 

as portable document format (PDF) files on the NCES ELS:2002 website 

(http://nces.ed.gov/surveys/els2002/). 

2.1.1 Instrument Development Process and Procedures 

In general, the development and review process for each questionnaire consisted of the 

following steps: 

1. Sharing of draft data elements. Draft elements of the questionnaires were shared with 

other government agencies, policy groups, and interested parties. 

2. Technical review panel (TRP) review. The ELS:2002 TRP, a specially appointed, 

independent group of substantive, methodological, and technical experts, reviewed 

the questionnaires. 

3. National Center for Education Statistics (NCES) review. The questionnaires 

underwent interdivisional review at NCES. 

4. Questionnaire revision. The survey instruments were revised based on reviewer 

comments. 

5. Writing of justification. A justification was written for the data elements, noting issue 

areas, constructs to be measured within each, and items that would be used to 

measure each construct. 

6. Office of Management and Budget (OMB) review. The federal OMB reviewed the 

instruments. 

7. Questionnaire revision. The questionnaires were revised based on OMB comments. 

11 In fact, the new participant student questionnaire is simply the new participant supplement and abbreviated first 

follow-up student questionnaire, joined together to create one booklet, for convenience of administration. 

17 
Chapter 2: Instrumentation 

8. Field testing and revision. The instruments were field tested and revised based on 

field test results. 

Specific assessment items for the base-year mathematics and reading tests and first 

follow-up mathematics test were typically not subject to these reviews, but the larger assessment 

framework and goals and the results (as seen in overall item statistics from the field test) were an 

integral element within the review process and, in particular, the deliberations of the TRP. 

The field testing of school enlistment and data collection and processing procedures, 

questionnaires, and assessments was an especially important step in the development of the fullscale base-year and first follow-up studies. Field test instruments were evaluated in a number of 

ways. For the questionnaires, field test analyses included evaluation of item nonresponse, 

examination of test-retest reliabilities, calculation of scale reliabilities, and examination of 

correlations between theoretically related measures. For the achievement tests in mathematics 

and reading, item parameters were estimated for both 10th and 12th grade in the base-year field 

test. Both classical and Item Response Theory (IRT) techniques were employed to determine the 

most appropriate items for inclusion in the final (base-year main study) forms of the two tests. 

Psychometric analyses included various measures of item difficulty and discrimination, 

investigation of reliability and factor structure, and analysis of differential item functioning. In 

the first follow-up field test, similar classical and IRT psychometric analyses were conducted but 

with a slightly different end in terms of final format: adaptiveness was ensured through a twostage test in the base year, whereas the test designed for the first follow-up main study based 

assignment of form on the prior round ability estimate (as had been done in the National 

Education Longitudinal Study of 1988 [NELS:88]). The base-year field test report is available 

from NCES (Burns et al. 2003). Findings of the first follow-up field test are summarized in 

appendix J of this report. 

2.1.2 Instrument Development Goals and Constraints 

Since the primary research objectives of ELS:2002 are longitudinal in nature, the first 

priority was to select the items that would prove most useful in predicting future outcomes as 

measured in future survey waves, or that would represent near-term (2004) outcomes predicted 

by base-year (2002) variables. 

The second priority was to obtain needed cross-sectional data, whenever consistent with 

the longitudinal objectives, particularly data that could be used for intercohort comparison with 

past studies or linkage to certain current data collection efforts. Wherever possible, all ELS:2002 

instruments were designed to provide continuity and consistency with the earlier education 

longitudinal studies of high school cohorts. Where appropriate, ELS:2002 drew from the 

National Longitudinal Study of the High School Class of 1972 (NLS-72), the High School and 

Beyond (HS&B) longitudinal study, and, most particularly, NELS:88. In addition, questionnaire 

and test items were in some cases drawn from other NCES programs, such as the National 

Assessment of Educational Progress (NAEP) (especially for the assessments), the Program for 

International Student Assessment (PISA) (for both assessments and psychological scales related 

to orientation toward learning), the Schools and Staffing Survey (SASS) (particularly but not 

exclusively for items related to the library media center questionnaire), or the Early Childhood 

Longitudinal Study Kindergarten Cohort (ECLS-K) (from which was borrowed the concept of a 

18 
 

 

Chapter 2: Instrumentation 

facilities checklist). Continuity with ELS:2002’s historical predecessors and with other NCES 

survey and assessment programs was pursued to ensure a common standard of measurement that 

would permit comparisons and increase the usefulness of the ELS:2002 data. Apart from the 

intercohort or cross-study comparisons that can be sustained through use of the questionnaire and 

transcript data, ELS:2002 provides score linkages with the testing programs of PISA, HS&B, and 

NELS:88. 

Although maintaining trend items to support intercohort comparisons was a major aim of 

instrument development, there was also a need to provide new items to address new areas of 

policy concern and to reflect recent advances in theory. For example, in the base year in 

particular, educational technology items were developed to reflect the fact that computers have 

become a major factor in learning in recent years. Psychological scales that reflect recent work 

in self-efficacy theory and related areas were also added. 

Another consideration in the development of the ELS:2002 instruments was the need to 

obtain factual information from the best source among the various respondent populations. (This 

was especially an issue for the base year, in which both parents and students were surveyed.) In 

some cases, the decision to use the best source resulted in a longer wait to secure the information 

(e.g., the 2002 student questionnaire was not used to collect information on courses taken or 

grades, and the 2004 questionnaire was used for this purpose only minimally; academic 

transcripts are a more reliable source of this information, and they were collected in 2005, after 

most students had completed high school). In most cases, information has been collected from 

one source only. However, in a few instances, a particular datum for which there was more than 

one acceptable source in terms of data quality was deemed to be of such importance that some 

redundancy between instruments seemed an acceptable price to pay. For example, whereas 

parents are the best source of information about highest parental educational attainment, the 

importance of this item was such that it was asked on both the base-year student and parent 

questionnaires to increase the number of sample members for whom this information would be 

available (and was asked again, of new participants, in the first follow-up).12 

Finally, some changes in the law regarding questionnaire content that could be asked of 

students in a school setting under conditions of implied consent had to be taken into account. 

Specifically, the Protection of Pupil Rights Amendment (PPRA) proscribes collection of 

information in the following seven areas when minor students are required to participate in a 

survey, unless prior written parental consent has been obtained: 

1. political affiliations or beliefs of the student or the student’s parent; 

2. mental and psychological problems of the student or the student’s family; 

3. sexual behavior or attitudes; 

12 In their analysis of NELS:88 base-year (8th-grade) student and parent reports of parental education, Kaufman and 

Rasinski (1991) found that, although the number of precise matches between student and parent report was only 

moderate, validity coefficients were relatively high (father = 0.82, mother = 0.76). McLaughlin and Cohen (1997), in a 

reanalysis of NELS:88 parent and student data, found the percent matching to be only 55.8 percent on father’s 

education and 56.5 percent on mother’s education. Nevertheless, they report polychoric correlations of 0.87 for 

father’s education and 0.84 for mother’s, indicating a high degree of convergence between student and parent 

reports. Student reports increase in quality with age. For high school seniors in HS&B, Fetters, Stowe, and Owings 

(1984) show validity coefficients of 0.89 for father’s education (compared to the 0.82 recorded for 8th-graders by 

Kaufman and Rasinski [1991]) and 0.85 for mother’s education (versus 0.76 for 8th-graders in NELS:88). 

19 
 

 

Chapter 2: Instrumentation 

4. illegal, antisocial, self-incriminating, or demeaning behavior; 

5. critical appraisals of other individuals with whom respondents have close family 

relationships; 

6. legally recognized privileged or analogous relationships, such as those of lawyers, 

physicians, and ministers; and 

7. income. 

In addition, when the PPRA was amended in the No Child Left Behind Act of 2001, an 

eighth area was added to the list: 

8. religious practices, affiliations, or beliefs of the student or student’s parent. 

A number of topic areas covered in prior studies, such as HS&B and NELS:88, were 

therefore excluded from the ELS:2002 base-year and first follow-up student questionnaires, 

including all items on use of tobacco, alcohol, and drugs and past and present illegal, sexual, or 

antisocial behaviors, as well as psychological problems and appraisals of family members. A 

few additional items retained on the base-year student questionnaire that later raised PPRA 

concerns were suppressed from the final dataset (this fact accounts for the several gaps in the 

questionnaire and variable name number sequences for the base-year student survey). 

Basic elements that are or will be encompassed in the ELS:2002 research instruments can 

be classified in three broad categories: 

• background information (normally collected in the base year only, except for 

respondents first entering the sample in a later round); 

• process information (information about possible influences on the student in the 

home, school, and community environment, as he or she moves through secondary 

school and beyond into the world of postsecondary education and the adult 

workforce); and 

• outcome information (the eventual outcomes of the transition process, including later 

educational attainment and labor market status). The base-year questionnaires are 

rich in background and process items. The first follow-up questionnaires inquire both 

into process and outcomes, while also establishing a new baseline for examining the 

transition out of high school.13 The focus of the final waves of the study will be 

outcome data. 

13 An example of a first follow-up item that did not appear on the base-year student questionnaire and was intended 

to help chart the transition from high school to postsecondary enrollment is F1S52, which asks about the importance 

of various factors in choosing a postsecondary institution. An example of a new item designed to measure an 

outcome would be F1S14, which inquires into academic progress as judged by whether the student has remained in 

modal sequence and is now in 12th grade. An example of a repeated measure would be the educational expectation 

and life values questions. Ultimately, these plans and expectations can be related to future educational, 

occupational, and social outcomes. Since these items were asked in the base year, are re-asked in the first followup, and will be re-asked again in future follow-ups, they help provide a basis for examining the stability of values and 

goals over time. Finally, because most of these items have been used with the prior NCES longitudinal high school 

cohorts, they provide a basis for comparing the goals and values of sophomores in 2002 and seniors in 2004 with 

earlier cohorts, including high school seniors in 1972, 1980, 1982, and 1992. 

20 
 

Chapter 2: Instrumentation 

2.2 Base-Year and First Follow-up Questionnaires 

2.2.1 Base-Year Questionnaires 

2.2.1.1 Student Questionnaire 

The ELS:2002 base-year student questionnaire was typically self-administered. 

Sophomore sample members normally completed the questionnaire in a group administration in 

their schools. A small number of students were surveyed outside of school, with a shortened 

version of the questionnaire in a computer-assisted telephone interview (CATI). Assessments in 

reading and mathematics were given at the same time (i.e., during the group administration), in a 

two-stage process in which the first stage was a routing test. The full questionnaire was 

available only in English, although a shortened Spanish version was also produced. 

The student questionnaire was divided into seven sections: (1) locating information, 

(2) school experiences and activities, (3) plans for the future, (4) non-English language use, 

(5) money and work, (6) family, and (7) beliefs and opinions about self. 

2.2.1.2 Base-Year Parent Questionnaire 

The parent questionnaire was to be completed by the parent or guardian most familiar 

with the sophomore’s school situation and experience. Guided by this definition of the preferred 

respondent, the parent survey respondent was self-selected. 

The parent questionnaire was available in both English and Spanish. Both a hardcopy 

version and an electronic CATI version14 were produced. The parent questionnaire addressed the 

following five topic areas: (1) family background, (2) their child’s school life, (3) their child’s 

family life, (4) their opinions about their child’s school, and (5) their aspirations and plans for 

their child’s future. 

2.2.1.3 Base-Year Teacher Questionnaire 

The teacher questionnaire was to be completed by the English teacher and the 

mathematics teacher of each ELS:2002 sophomore. The teacher questionnaire was designed to 

illuminate questions of the quality, equality, and diversity of educational opportunity by 

obtaining information in two content areas: 

• Teacher evaluations of students. The teacher’s assessment of the student’s schoolrelated behavior and academic performance and educational and career plans and 

goals. Respondents completed this section with respect to the sample members they 

instructed in a particular subject. 

14 The approach to parent telephone interviews in the ELS:2002 base year differed from that followed in NELS:88. In 

NELS:88, to minimize the possibility of mode of administration effects, the parent was asked to read along in the 

hardcopy questionnaire as the questions were read over the telephone. The interview was not computer assisted. In 

ELS:2002, the decision was made to take advantage of the logical consistency editing and other features of CATI, 

and considerable effort was made to constrain the hardcopy questionnaire to items and formats compatible with a 

CATI administration. ELS:2002 parents were not interviewed over the telephone with the hardcopy questionnaire in 

hand. This difference accounts for some variations between the NELS:88 and ELS:2002 parent survey instruments. 

21 
Chapter 2: Instrumentation 

• Teacher background. Information about the teacher’s background and activities (e.g., 

academic training, subject areas of instruction, years of teaching experience, and 

participation in professional growth activities). 

2.2.1.4 Base-Year School Administrator Questionnaire 

The base-year school administrator questionnaire collected information on the school in 

six areas: (1) school characteristics, (2) student characteristics, (3) teaching staff characteristics, 

(4) school policies and programs, (5) technology, and (6) school governance and climate. The 

school administrator data can be used contextually, as an extension of the student data, when the 

student is the fundamental unit of analysis. At the same time, the ELS:2002 base-year school 

sample is nationally representative and can stand alone as a basis for generalizing to the nation’s 

regular high schools with sophomores in the 2001–02 school year. 

2.2.1.5 Library Media Center Questionnaire 

For the school library media center component, the school librarian, media center 

director, or school administrator supplied information about library media center size, 

organization, and staffing; technology resources and electronic services; extent of library and 

media holdings, including both collections and expenditures; and levels of facility utilization, 

including scheduling for use by students and teachers. Finally, the questionnaire also supplied 

information about the library media center’s use in supporting the school’s curriculum, that is, 

how library media center staff collaborate with and support teachers to help them plan and 

deliver instruction. Information in the library media center questionnaire can be used as 

contextual data with the student as the unit of analysis or to generalize to libraries within all 

regular high schools with 10th grades in the United States in the 2001–02 school year. 

2.2.1.6 School Facilities Checklist 

Instrumentation for the facilities component comprised a checklist to be completed by the 

survey administrator. The survey administrator was asked to observe a number of conditions at 

the school, including the condition of the hallways, main entrance, lavatories, classrooms, 

parking lots, and surrounding neighborhood. Of special interest were indicators of security 

(metal detectors, fire alarms, exterior lights, fencing, security cameras, etc.) and maintenance and 

order (trash, graffiti, clean walls and floors, noise level, degree of loitering, etc.). Information 

gathered in the facilities checklist can be used as contextual data with the student as the unit of 

analysis, or data can be used at the school level to generalize to all regular high schools with 10th 

grades in the United States in the 2001–02 school year. 

2.2.2 First Follow-up Questionnaires 

2.2.2.1 Introduction 

Although assessments will be discussed separately in section 2.3, it is useful, as a point of 

entry into the first follow-up instrumentation, to consider the fact of test availability in 

conjunction with the main sample populations for which questionnaires were designed. As 

table 1 makes clear, not all groups were tested in the first follow-up, nor were test scores imputed 

for all groups. 

22 
Chapter 2: Instrumentation 

Table 1. Assessment availability status, by sample group: 2004 

Group (status in 2004) Base year First follow-up 

2002 sophomores in core (base-year) schools in 2004 

2002 sophomores in transfer schools in 2004 

2004 freshened seniors 

Tested1

Tested1

Unavailable 

Tested2 

Imputed 

Tested2 

2002 sophomores: 2004 dropouts 

2002 sophomores: 2004 early graduates 

2002 sophomores: homeschooled in 2004 

Tested1

Tested1

Tested1

 Unavailable 

 Unavailable 

Imputed 

1

 Imputed for base-year nonrespondents. 

2

 Imputed for first follow-up participant test noncompleters. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

The following questionnaires were employed in the ELS:2002 first follow-up: student 

questionnaire, dropout questionnaire, early graduate questionnaire, transfer student 

questionnaire, homeschool student questionnaire, and NPSQ. A school administrator 

questionnaire was also offered. For the ELS:2002 data user, it is necessary to specify which 

items are common to various questionnaires and which are unique, and how each questionnaire 

group relates to the analytic populations of interest. 

For example, one important analysis population is high school seniors in 2004. These 

individuals will have completed student, new participant, or transfer student questionnaires, but 

not dropout or early graduate questionnaires. Even within any of these three questionnaire 

groupings, only a subset of students will in fact be members of the senior cohort. Another 

possible population for analysis (particularly for the examination of school effects) consists of 

students who remained in their base-year school between 2002 and 2004. (In examining this 

group, one may also wish to take into account the movers who transferred to a new school.) 

A further important analytic population is the broader sophomore cohort panel. The 

base-year to first follow-up combined data file provides a basis for examining the 2002 high 

school sophomore cohort 2 years later. In determining 2004 outcomes for 2002 sophomores, a 

comprehensive picture of the cohort’s situation can be obtained only by looking at the range of 

situations represented in the first follow-up questionnaires. Some members of the cohort will 

have remained in the base-year school and will have completed the student questionnaire. 

Others will have transferred to a new school and will have completed a transfer student 

questionnaire. Some will be in a homeschool situation, whereas others may be dropouts or early 

graduates. One particular subset of the completers of the NPSQ, 12th-grade freshened students, 

would need to be excluded from investigations of sophomore cohort outcomes in 2004. 

Although analysis populations are properly selected through flags or universe variables, it is also 

important for the analyst to know which data elements are shared in common across various 

questionnaire completion groups. Table 2 provides a crosswalk that shows shared and unique 

items across the first follow-up questionnaires. (Note that the NPSQ is not included on the 

crosswalk because its coverage is equivalent to that of the NPS plus the abbreviated student 

questionnaire.) 

23 
15 

20 

25 

30 

35 

Table 2. Crosswalk: First follow-up questionnaire type, by shared and nonshared items: 2004 

24 

Abbreviated Ne

Student student Home- Early Drop- participa

Question questionnaire questionnaire Transfer school graduate out suppleme

What grade are you in 14 14 18 14 

What diploma/certificate 15 19 15 19 Science coursework 16 16 20 16 29 27 Math coursework 17 17 21 17 30 28 Confidence in math 18 Calculators/computers in math 19 Computer use in math classes College entrance tests 21 18 22 18 How studied for college tests 22 Talent Search 23 Years participated in Talent Search, etc. 24 Victimization Extracurricular activities 26 19 23 20 31 Time spent on extracurriculars 27 20 24 21 32 Does school have library 28 21 25 How often uses school library 29 22 26 

How often uses public library 23 27 22 33 49 Hours on homework 31 24 28 23 

Hours on math homework 32 

Additional reading 33 25 29 24 34 50 Hours watching TV 34 26 30 25 35 51 Hours playing video games 27 31 26 36 52 Computer use for schoolwork/other 36 28 32 27 37 53 

Computer use at various locations 37 29 33 28 38 54 Computer use for fun, school, learn 38 things 

See notes at end of table. 
40 

45 

50 

55 

60 

Table 2. Crosswalk: First follow-up questionnaire type, by shared and nonshared items: 2004—Continued 

25 

Abbreviated Ne

Student student Home- Early Drop- participa

Question questionnaire questionnaire Transfer school graduate out suppleme

Activities outside of school 39 30 34 29 39 55 

Life values 31 35 30 40 56 How will spend summer 41 How far in school thinks will get 42 32 36 31 41 57 How far mother and father wants to go 43 33 37 32 42 58 

Most important thing right after high 

school 44 34 38 33 

Plan to go to school right after high 

school 35 39 34 

Reasons decided not to go right after 

high school 46 36 40 35 

Plan to continue education in future 47 37 41 36 44 

Where went for info on college entrance 48 Type of school will most likely attend 49 38 42 37 45 

To how many schools applied 39 43 38 46 

Two most likely schools 51 40 44 39 47 

Importance of school characteristics 52 41 45 40 48 

Plan to work right after high school 53 42 46 41 Full-time job lined up 54 43 47 42 Who helped select jobs Job expects after high school 56 44 48 43 Job expects at age 30 57 45 49 44 56 66 

How much education for job at age 30 58 46 50 45 57 67 

Ever worked for pay 59 47 51 46 

Hours per week 48 52 47 

Hours on weekend 61 

Volunteered in past 2 years 62 49 53 48 58 68 

See notes at end of table. 
Table 2. Crosswalk: First follow-up questionnaire type, by shared and nonshared items: 2004—Continued 

26 

Abbreviated Ne

Student student Home- Early Drop- participa

Question questionnaire questionnaire Transfer school graduate out supplemeTypes of volunteer organizations 63 How often discuss with parents 64 50 54 49 Friends’ plans for after high school 65 51 55 50 59 69 When began going to this school 15 Reasons for transferring 16 Agreement w/ statements re 17 school/teachers Participated in any school-sponsored 191 activities? 

Name of school last attended 18 18 

When last attended high school 20 19 What grade were you in then 21 20 How earned GED 24 42 Why decided to complete GED 25 43 Earned GED in what state 26 44 When did you graduate/receive 27 45 equivalency 

Why decided to graduate/complete 28 early Enrolled in postsecondary education 43 since leaving high school How many jobs since high school 49 59 

Current/most recent job 50 60 When started this job 51 61 Still have job 52 62 When did you leave job 53 63 How much do you earn 54 64 Hours per week 55 65 

See notes at end of table. 
Table 2. Crosswalk: First follow-up questionnaire type, by shared and nonshared items: 2004—Continued 

27 

Abbreviated Ne

Student student Home- Early Drop- participa

Question questionnaire questionnaire Transfer school graduate out supplemeDid you pass that grade 21 Before last left, ever leave 22 When left for first time 23 When returned to school 24 Attended high school in 2002–03 25 How many school days missed 26 Reasons for leaving school 22 29 Good decision 23 30 What people at school did 31 What parents did 32 Things that happened in past 2 years 33 Ever in alternative program 34 When entered most recent alternative 35 

program 

Still enrolled 36 When left 37 Who referred to alternative program 38 Services received from alternative 39 

program 

How many alternative programs 40 participated in Plan to get a GED? 41 Currently taking a GED class 46 Plan to go back to high school/take 47 GED class When expects to get GED 48 Date of birth Sex 

See notes at end of table. 
Table 2. Crosswalk: First follow-up questionnaire type, by shared and nonshared items: 2004—Continued 

28 

Question 

Student 

questionnaire 

Abbreviated 

student 

questionnaire Transfer 

Homeschool Early graduate Drop- out 

Ne

participa

suppleme

Student is Hispanic Student’s Hispanic subdivision Race 

Student’s Asian subdivision English is student’s native language Student’s native language English skills Enrolled in any U.S. school in spring 

term of 2002 

1

Ever held back a grade Grades repeated Lives in household at least half of time 

1

1

1

Mother/female guardian’s work 

Father/male guardian’s work 

Parents’ education 

1

1

1

Family has items in home 1

1 The sole purpose of this item was to reduce the length of the questionnaire for certain respondents by routing them around the set of de

Therefore, this item is not included in the data file. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “BFollow-up, 2004.” 
Chapter 2: Instrumentation 

2.2.2.2 Questionnaire Assignment and Content 

First follow-up student questionnaire assignment and content. The student 

questionnaire was administered to sophomore cohort members who had remained in their baseyear school as well as to a freshening sample of 12th-graders in those same schools. Students 

who completed the student questionnaire also were normally eligible for the first follow-up 

mathematics assessment. Some students were administered an abbreviated version of the 

questionnaire (these cases are flagged on the data file). The questionnaire was primarily selfadministered in in-school survey sessions, and secondarily, for some students, out of school 

through CATI or occasionally through mail or field interviews. 

Some alterations were required to adapt the paper-and-pencil questionnaire to CATI. 

Generally, the wording of the paper-and-pencil questions was made more conversational for the 

telephone interview to facilitate the interviewer-respondent interaction. On occasion, adaptations 

were made to account for the fact that those interviewed by telephone did not have the benefit of 

seeing the entire question with all of its elements at once. For example, students were asked to 

report how much coursework they had taken in various subject areas. Respondents who 

completed the paper-and-pencil form were able to see the full range of mathematics courses 

listed more or less in the sequence in which they are taught. In this context, it was clear to 

respondents that “general math” referred to a basic math course as opposed to a catchall 

category. However, without the visual cues, telephone respondents may have misinterpreted 

general math to include all math courses. Therefore, for the telephone interview, general math 

was moved to the end of the list of math courses. Similar adaptations were required for the other 

telephone-administered questionnaires as well (transfer student, dropout, and so on). Generally, 

CATI telephone data collection took place subsequent to in-school data collection. Also, there 

was more ambiguity about the status (dropout, early graduate, transfer, homeschooled, and so 

on) of sample members interviewed outside the school setting. For this reason, the CATI 

interview included a series of screening questions to ensure that the proper questionnaire was 

administered. Such a screener was also used for field cases subject to in-person interview. 

The student questionnaire comprised eight content modules. Part I of the questionnaire 

requested contact information in support of the longitudinal design. 

Part II covered the student’s school experiences and activities. Data generated from this 

section provide information about extracurricular participation, computer use in English and 

math, the transition process from sophomore year to upper-level secondary school, and the 

relationship of curricular programs and coursetaking to educational achievement and persistence. 

Some of these data may be viewed as outcomes, influenced by factors studied in the base year, 

and others as predictors of outcomes in future rounds. 

Part III, “How You Spend Your Time,” inquired about time usage on homework, TV 

viewing, video and computer games, computers, nonschool reading, library utilization, and other 

activities. Part IV focused on plans and expectations for the future. It included questions that 

elicited information about students’ educational and life goals and values. Part V, on education 

after high school, contained items on postsecondary planning steps and choice criteria. Part VI 

dealt with plans for work after high school. Part VII inquired about working for pay, including 

hours worked per week. Finally, Part VIII consisted of items on community, family, and friends. 

29 
Chapter 2: Instrumentation 

First follow-up dropout questionnaire assignment and content. Dropouts were 

defined as sophomore cohort members who were out of school in the spring term of 2004, who 

had not received a high school diploma or General Educational Development (GED) credentials, 

and who had missed 4 or more consecutive weeks not due to accident or illness. Students who 

had a dropout episode but who had been in school for at least 2 weeks at the time of their 

school’s survey day were administered the student questionnaire. The dropout questionnaire was 

administered in multiple modalities—self-administration, in-person interviewer administration, 

and over the telephone by means of CATI. 

There was considerable overlap between the student and dropout questionnaires. Part I 

collected locating information for longitudinal follow-up. Part II contained items on school 

experiences and activities. Dropouts were asked questions about the school they last attended 

and their participation in alternative educational programs. In addition, they were asked to 

supply their specific reasons for leaving school prior to graduation. They were asked as well 

about plans to get a GED or return to high school. Part III covered time use (reading, library 

patronage, television, videogames, computer use, and so on). Part IV asked about plans and 

expectations for the future. Part V provided information to identify the type and amount of work 

that dropouts were engaged in. It gathered information about students’ work status and history, 

how much they earned, and how many hours they worked. Part VI asked about volunteer work 

or community college and the educational behaviors of friends. 

Early graduate questionnaire assignment and content. Early graduates were 

interviewed outside the school setting, in multiple data collection modalities but most commonly 

by telephone. Early graduates were defined as sophomore cohort members who had graduated 

from high school or received a GED on or before March 15, 2004. The approach to early 

graduates differs somewhat across the several NCES high school cohort studies. In HS&B, the 

group that was captured was high school completers who finished early (i.e., prior to 

March 1, 1982). In NELS:88 and ELS:2002, an additional group is included, those who 

completed by alternative means (e.g., GED) prior to their classmates who were in the modal 

graduation sequence. In both HS&B and NELS:88, early graduates completed supplementary 

questions in addition to the full student questionnaire (answering from the vantage point of their 

recent high school experience). In ELS:2002, early graduates completed only a subset of the 

items on the student questionnaire, complemented by additional items pertaining to their 

situation. More specifically, early graduates were asked with whom they consulted when 

deciding to graduate early, the basis for that decision, and the means by which they did so. They 

also provided a history of their work and educational experiences since leaving high school. 

Transfer student questionnaire assignment and content. Sophomore cohort members 

who had transferred out of their base-year school to a new school received the transfer student 

questionnaire. Transfer students were asked a subset of items from the student questionnaire, 

covering the following topics: school experiences and activities; time use; plans and 

expectations for the future; education after high school; work after high school; and community, 

family, and friends. In addition, transfer students were asked when they transferred and their 

reasons for doing so. Transfer students did not complete a cognitive test, but their test scores 

have been imputed. Thus, test scores are available for all classes of senior cohort members— 

sophomore cohort members who were seniors 2 years later regardless of whether they were 

“movers” or “stayers” and freshened seniors. 

30 
Chapter 2: Instrumentation 

Homeschool student questionnaire assignment and content. ELS:2002 does not 

provide a representative sample of homeschooled high school students. (In the base year, all 

study sophomores were selected from regular U.S. high schools.) Instead, homeschooled 

students in ELS:2002 generalize only to sophomores in regular high schools in spring term 2002 

who were in a homeschool situation 2 years later. The primary motive for administering a 

separate questionnaire to this subset of the sophomore cohort was that neither the transfer student 

questionnaire items nor the dropout items fully fit their situation. 

Homeschooled students were asked about their schooling activities and status, including 

their grade, coursework completed in sciences and math, and steps taken toward college; how 

they spend their time; their plans and expectations for the future, including education and work 

after high school; work experiences; and community, family, and friends. 

New participant supplement questionnaire (NPSQ) assignment and content; NPS. 

There are essentially three categories of students who are ELS:2002 new participants in the first 

follow-up. One class is high school seniors who entered the study through the freshening 

sample. A second class of new participants is that of base-year nonrespondents who completed a 

questionnaire in the first follow-up. The third and final class is that of sophomore cohort 

members who were ineligible in 2002 because of inability to complete a questionnaire, but who 

were reclassified as capable of completing a questionnaire in 2004. (An example might be an 

English language learner who was not proficient in English in 2002 but, with 2 additional years 

of instruction, had reached a level of English proficiency sufficient to deal with the ELS:2002 

first follow-up questionnaire.) While the first of these three classes is by definition a student, the 

second and third groups include both students and out-of-school members of the sophomore 

cohort (such as dropouts and early graduates). 

Any student new to the study at any of the core (base-year) schools was administered the 

NPSQ. However, transfer students and out-of-school cohort members were administered the 

relevant questionnaire and a NPS containing the key base-year items. For example, any student 

new to the study who had transferred to a new school was administered the transfer student 

questionnaire and a NPS. Any new respondent who was out of school, however, such as a 

dropout or early graduate, was administered the appropriate out-of-school questionnaire, as well 

as a NPS. Table 3 summarizes, for all new participants, use of the NPS and NPSQ, as well as 

base-year and first follow-up assessment status. 

The NPSQ gathered information that had been collected (for other students) in the base 

year on new participants’ demographic characteristics, parental education and occupation, and 

language use. These items are identical to those on the NPS. In addition, a subset of items 

included on the student questionnaire was also posed to new participants. These items (which 

are identical in content to the abbreviated student questionnaire) relate to topics such as school 

experiences and activities; time use; plans and expectations for the future; education and work 

after high school; and work, community, family, and friendship experiences. In contrast, the 

NPS gathered the key base-year variables that also were included on the NPSQ. 

31 
 

Chapter 2: Instrumentation 

Table 3. Base-year key variables and test data available, by type of first follow-up new 

participants: 2004 

Source of base- Availability of 

year standard base-year Availability of 

classification reading and first follow-up 

Group of first follow-up new participants variables math scores math scores 

Sophomore cohort members in core (base-year) 

schools in 2004 

Sophomore cohort members in new schools in 

2004 

Sophomore cohort members out of school in 2004: 

dropouts 

Sophomore cohort members out of school in 2004: 

early graduates 

Freshened 2004 seniors 

Sophomore cohort members homeschooled in 

2004 

NPSQ 

NPS 

NPS 

NPS 

NPSQ 

NPS 

Imputed Tested 

Imputed Imputed 

Imputed Unavailable 

Imputed Unavailable 

Unavailable Tested 

Imputed Unavailable 

NOTE: NPSQ = New Participant Supplement Questionnaire. NPS = New Participant Supplement; this instrument 

contains only the key base-year items. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

School administrator questionnaire content and content linkages. The school 

administrator questionnaire collected information on the school in four areas: school 

characteristics, structure, and policies; student characteristics and programs; teacher and library 

staff characteristics; and principal reports on the school environment. It should be noted that 

many school-level variables of analytic interest also pose high risk of disclosure of school 

identities. For this reason, a number of analysis variables have been limited to the restricted-use 

file or may be accessed through a link provided only on the restricted-use file.15 

It should also be noted that school-level data are not nationally representative of 

American high schools in 2004, since the first follow-up sample did not factor in “births” of new 

schools and “deaths” of existing schools between 2002 and 2004. First follow-up school data, 

however, do provide a statistical portrait of a nationally representative sample of American high 

schools with 10th grades in 2002 (2 years later). 

15 An example of the latter is the link to the NCES Common Core of Data (CCD) and Private School Survey (PSS) 

provided via the NCES identification code (NCESID). An analyst with a restricted-use license could import into the 

analysis such variables as, for example, grade span (highest grade and lowest grade of school for any of the relevant 

academic years); percent minority; proportion free lunch qualifiers; enrollment; grade 9 enrollment (2000–01), grade 

10 enrollment (2001–02), grade 11 enrollment (2002–03), grade 12 enrollment (2003–04); metropolitan status 

(urbanicity): locale code; student/teacher ratio; FTEs: total number full-time classroom teachers; student enrollment: 

overall; school type (regular, vocational, special education, other), and so on. A further example of such a restricteduse link is to school zip code, which permits locale variables to be imported from the 2000 decennial Census. 

32 
 

 

 

 

Chapter 2: Instrumentation 

2.3 Base-Year to First Follow-up Cognitive Test Battery 

2.3.1 Base-Year Reading and Mathematics Assessments 

The purpose of the ELS:2002 assessment battery is to provide measures of student 

achievement in reading and mathematics that can be related to student background variables and 

educational processes, for individuals and for population subgroups. The reading and 

mathematics tests must provide accurate measurement of the status of individuals at a given 

point in time. The mathematics test must provide accurate measurement of their cognitive 

growth over time. Assessment data in ELS:2002 will be used to study factors that contribute to 

individual and subgroup differences in achievement. 

2.3.1.1 Test Design and Format 

Test specifications for ELS:2002 were adapted from frameworks used for NELS:88. 

Math tests contained items in arithmetic, algebra, geometry, data/probability, and advanced 

topics and were divided into process categories of skill/knowledge, understanding/ 

comprehension, and problem solving. Through inclusion of PISA items, the ELS:2002 math 

tests placed a somewhat greater emphasis on practical applications and problem solving than did 

the NELS:88 test forms. Reading tests consisted of reading passages of one paragraph to one 

page in length, followed by three to six questions based on each passage. The reading passages 

included literary material as well as topics in the natural and social sciences. Several passages 

required interpretation of graphs. Questions were categorized as reproduction of detail, 

comprehension, or inference/evaluation. The test questions were selected from previous 

assessments: NELS:88, NAEP, and PISA. Most, but not all, were multiple choice. The number 

of items in each stage of the test is indicated in table 4. 

Table 4. Number of items in each ELS:2002 base-year test form for assessing achievement in 

mathematics and reading, by test form: 2002 

Form Mathematics Reading 

Routing test 15 14 

Second stage tests 

 Form X (low difficulty) 25 16 

Form Y (middle difficulty) 27 17 

Form Z (high difficulty) 27 15 

Form V (single stage: limited time, mathematics only) 23 † 

† Not applicable. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

2.3.2 First Follow-up Assessment 

This section describes the development and format of the ELS:2002 first follow-up 

mathematics assessment, scoring procedures, score descriptions, and summary statistics. It 

includes a discussion of links (through equating or concordance) with other studies (NELS:88 

and PISA:2000). 

33 
 

Chapter 2: Instrumentation 

The purpose of the ELS:2002 assessments is to provide measures of student achievement 

in reading and mathematics that can be related to student background variables and educational 

processes, for individuals and for population subgroups. Reading16 and mathematics 

assessments were conducted in the sophomore base year; in the first follow-up, only a 

mathematics test was administered. Assessment data in ELS:2002 will be used to study factors 

associated with individual and subgroup differences in achievement. The reading and 

mathematics tests must provide accurate measurement of the status of individuals at a given 

point in time and, for mathematics, must provide accurate measurement of their cognitive growth 

over time. 

2.3.2.1 Test Design and Format 

As with the base-year test design, the specifications for the ELS:2002 first follow-up 

math test were adapted from frameworks used for NELS:88. Mathematics tests contained items 

in arithmetic, algebra, geometry, data/probability, and advanced topics and were divided into 

process categories of skill/knowledge, understanding/comprehension, and problem solving. 

However, like the base-year test, the ELS:2002 mathematics tests placed a greater emphasis on 

practical applications and problem solving than did the NELS:88 tests. The test questions were 

selected from previous assessments: NELS:88, NAEP, and PISA. Items were field tested17 

one year prior to the 10th- and 12th-grade surveys, and some items were modified based on field 

test results. Final forms were assembled based on psychometric characteristics and coverage of 

framework categories. In the base year, about 10 percent of math questions were open ended; all 

of the 12th-grade mathematics questions were presented in multiple-choice format. 

The ELS:2002 assessments were designed to maximize the accuracy of measurement that 

could be achieved in a limited amount of testing time, while minimizing floor and ceiling effects, 

by matching sets of test questions to initial estimates of students’ achievement. (For definitions 

of floor effects, ceiling effects, and other technical terms, see the glossary in appendix E.) In the 

base year, this was accomplished by means of a two-stage test. In 10th grade, all students 

received a short multiple-choice routing test, scored immediately by survey administrators who 

then assigned each student to a low, middle, or high difficulty second-stage form, depending on 

the student’s number of correct answers in the routing test. In the 12th-grade administration, 

students were assigned to an appropriate test form based on their performance in 10th grade. Cut 

points for the 12th-grade low, middle, and high forms were calculated by pooling information 

from the field tests for 10th and 12th grades in 2001, the 12th-grade field test in 2003, and the 

10th-grade national sample. Item and ability parameters were estimated on a common scale. 

Growth trajectories for longitudinal participants in the 2001 and 2003 field tests were calculated, 

and the resulting regression parameters were applied to the 10th-grade national sample. Test 

forms were designed to match the projected achievement levels of the lowest and highest 25 

percent, and the middle 50 percent, of the base-year sample 2 years later. An additional test form 

with a broad range of item difficulty was assembled for administration to follow-up participants 

who were new to the sample or who had not received a math score in 10th grade. Additions to 

and deletions from the base-year sample resulted in 23 percent, 42 percent, and 26 percent of the 

follow-up sample taking the low, middle, and high difficulty forms, respectively, with the 

16 Please refer to base-year documentation (Ingels et al. 2004, NCES 2004–405) for additional information on the 

10th-grade reading test. 

17 For more details about the field tests, see Burns et al. (2003) (NCES 2003–03) and appendix J of this manual. 

34 
 

Chapter 2: Instrumentation 

remaining 10 percent taking the broad-band form. Each of the four test forms contained 32 

multiple-choice items. 

2.3.2.2 Scoring Procedures 

Eleven test records were deleted because tests were incomplete (fewer than 10 items 

answered) and six more because regular response patterns (e.g., all answers were “A,” or 

“ABCABCABC...,” etc.) indicated lack of motivation to answer questions to the best of the 

student’s ability. 

The scores used to describe students’ performance on the direct cognitive assessment are 

broad-based measures that report performance as a whole. The scores are based on IRT, which 

uses patterns of correct, incorrect, and omitted answers to obtain ability estimates that are 

comparable across different test forms.18 In estimating a student’s ability, IRT also accounts for 

each test question’s difficulty, discriminating ability, and a guessing factor. 

IRT has several advantages over raw number-right scoring. By using the overall pattern 

of right and wrong responses to estimate ability, IRT can compensate for the possibility of a lowability student guessing several difficult items correctly. If answers on several easy items are 

wrong, a correct difficult item is assumed, in effect, to have been guessed. Omitted items are 

also less likely to cause distortion of scores, as long as enough items have been answered right 

and wrong to establish a consistent pattern. Unlike raw number-right scoring, which necessarily 

treats omitted items as if they had been answered incorrectly, IRT procedures use the pattern of 

responses to estimate the probability of correct responses for all test questions. Finally, IRT 

scoring makes it possible to compare scores obtained from test forms of different difficulty. The 

common items present in overlapping forms and in overlapping administrations (10th grade and 

12th grade) allow test scores to be placed on the same scale. 

In the first follow-up survey, IRT procedures were used to estimate longitudinal gains in 

achievement over time by using common items present in both the 10th- and 12th-grade forms. 

Items were pooled from both the 10th- and 12th-grade administrations and anchored to the IRT 

scale of the NELS:88 survey of 1988–92. Item parameters were fixed at NELS:88 values for the 

items that had been taken from the NELS:88 test battery and to base-year values for nonNELS:88 items. In each case, the fit of the follow-up item response data to the fixed parameters 

was evaluated, and parameters for common items whose current performance did not fit previous 

patterns were reestimated, along with non-NELS:88 items new to the follow-up tests. 

2.3.2.3 Score Descriptions and Summary Statistics 

Several different types of scores that can be used to describe students’ performance on 

the cognitive assessment are described in detail below. IRT-estimated number-right scores 

measure students’ performance on the whole item pool. NELS:88-equated number-right scores 

estimate how a student would have performed on the 1992 mathematics scale of NELS:88. 

Standardized scores (T-scores) report students’ performance relative to their peers. Quartile 

scores divide the estimated population distributions for convenience in analyzing relationships of 

cognitive skills with other variables. NELS:88-equated proficiency probabilities estimate the 

18 For an account of Item Response Theory, see Hambleton (1989) or Hambleton, Swaminathan, and Rogers (1991). 

35 
 

Chapter 2: Instrumentation 

probability that a given student would have demonstrated proficiency for each of the five 

mathematics levels defined for the NELS:88 survey in 1992.19 

IRT-estimated number right. These scores are estimates of the number of items 

students would have answered correctly if they had responded to all of the 85 questions in the 

mathematics item pool (i.e., all items that appeared on any of the mathematics forms) in the 

10th- and 12th-grade administrations combined. The ability estimates and item parameters 

derived from the IRT calibration can be used to calculate each student’s probability of a correct 

answer for each of the items in the pool. These probabilities are summed to produce the IRTestimated number-right scores. These scores are not integers because they are sums of 

probabilities, not counts of right and wrong answers. 

It is important to note that the item pool for base-year and first follow-up mathematics 

forms combined differs from the sophomore-only pool used to report scale scores in the base 

year. The combined sophomore forms contained a total of 72 items, with 13 additional, harder 

items added in 12th grade to extend the range of the scale. To place base-year and first followup scores on the same scale so that gains over time can be measured, the base-year IRTestimated number-right scores have been replaced with scores on the new 85-item combined 

scale. Table 5 shows variable names, descriptions, and summary statistics for the IRT-estimated 

number-right scores in the new metric that applies to both rounds of the survey. First follow-up 

statistics are reported both for all first follow-up participants and for the subset of students who 

were in 12th grade at the time of the survey. The samples include all students with test scores, as 

well as imputed scores for students who were not tested. (For a discussion of imputation in 

ELS:2002, see chapter 3 of this manual). The reliability of the test scores is a function of the 

variance of repeated estimates of the IRT ability parameter (within-variance), compared with the 

variability of the whole sample. For the combined base-year and first follow-up mathematics 

tests, the reliability was 0.92. This applies to all scores derived from the IRT estimation, 

including the standardized and quartile scores. 

Table 5. Item Response Theory (IRT)-estimated number-right mathematics scores in 85-item 

metric: 2004 

Variable 

name Description Range 

Weighted 

mean 

Weighted 

standard 

deviation 

BYTXMIR2 Mathematics IRT-estimated number right, base year, 

reestimated on longitudinal scale 

0-85 42.2 14.0 

F1TXMIR2 Mathematics IRT-estimated number right, longitudinal scale, 

all first follow-up participants 

0-85 48.3 15.1 

F1TXMIR2 Mathematics IRT-estimated number right, longitudinal scale, 

first follow-up participants who were in 12th grade 

0-85 48.6 15.1 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

19 For further information on the NELS:88 proficiency levels, see Rock and Pollack (1995a), Psychometric Report for 

the NELS:88 Base Year Through Second Follow-up (NCES 95–382). For examples of the use of the NELS:88-

equated probability proficiency scores in the context of cross-sectional estimation of status in ELS:2002, see 

chapter 5 of Ingels et al. (2005), A Profile of the American High School Sophomore in 2002 (NCES 2005–338). For 

examples of longitudinal use of the probability of proficiency scores (in NELS:88), see chapter 4 of Scott et al. (1995), 

Two Years Later: Cognitive Gains and School Transitions of NELS:88 Eighth Graders (NCES 95–436). 

36 
Chapter 2: Instrumentation 

Standardized scores (T-scores). T-scores provide norm-referenced measurements of 

achievement; that is, estimates of achievement level relative to the population as a whole. A 

high mean T-score for a particular subgroup indicates that the group’s performance is high in 

comparison with other groups. It does not represent mastery of a particular set of skills, only that 

the subgroup’s mastery level is greater than a comparison group. In other words, T-scores 

provide information on status compared to students’ peers, whereas the IRT-estimated numberright scores represent status with respect to achievement on a particular criterion set of test items. 

The T-scores can only provide an indicator of the extent to which an individual or a subgroup 

ranks higher or lower than the national average. The standardized score reported in the database 

(F1TXMSTD) is a transformation of the IRT theta (ability) estimate, rescaled to a mean of 50 

and standard deviation of 10. 

Quartile scores. Quartile scores (BYTXMQU) divide the sample into four equal groups, 

based on the weighted distribution of mathematics scores. Quartile 1 corresponds to the lowest 

achieving quarter of the population, quartile 4 to the highest. 

2.3.2.4 Links to Other Surveys 

Scores for ELS:2002 first follow-up are reported on scales that permit comparisons with 

mathematics data for NELS:88 12th-graders in 1992. In addition, ELS:2002 base-year 

mathematics scores were linked to the 2003 PISA mathematics scale. (In the base year, 

ELS:2002 reading scores were put on the PISA [2000] literacy scale; for details see Ingels et al. 

[2004], NCES 2004–405.) The link to the NELS:88 scales represents a “true” equating. This 

means that the tests may be considered interchangeable or, in other words, a score on one exam 

should be equivalent to a score on the other exam. Several conditions must be met for equating 

two tests. Most important, the tests must measure the same content. Similarity of format, length, 

reliability, and subgroup performance also supports the interpretation of interchangeable scores. 

PISA concordance. The ELS:2002 and PISA mathematics tests did not share enough 

items to permit common-item equating, so score scales were linked by means of equipercentile 

equating (see Ingels et al. [2004] for a description of the reading equating with PISA). If two 

exams measuring the same construct are given to two samples from the same population, the 

score corresponding to a certain percentile on one exam may be considered to be equivalent to 

the score on the other exam that represents the same percentile of the population. ELS:2002 and 

PISA test instruments, scoring methods, and populations differed in several respects that affect 

the equating procedures and interpretation of linked scores. 

The most important difference between PISA and ELS:2002 is the definition of the 

population sampled in each case. Equipercentile equating assumes that the two samples being 

equated come from the same population. However, important differences exist between PISA 

and ELS:2002 (see table 6). The PISA population was based on age (students born in 1987), 

whereas ELS:2002’s population was based on grade (high school sophomores). Although the 

spring term administration dates for PISA and ELS:2002 overlapped, the range of PISA dates 

was later in the school year, suggesting the possibility of higher scores due to additional weeks 

or months of schooling. 

37 
Chapter 2: Instrumentation 

Table 6. ELS:2002 and Program for International Student Assessment: Spring 2003 (PISA:spring 

2003), by sample characteristics: 2002 and 2003 

ELS:2002 sample PISA:spring 2003 sample 

10th-graders only Different grades 

Different ages; modal age = 15 Ages 15.25 to 16.25 years 

Testing began in January 2002 Testing began in April 2003 

14,543 tested 3,983 tested 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002); and Program for International Student Assessment (PISA). 

Because of these differences, subsamples of each group were used to compute equivalent 

percentiles (see table 7). Transformations were computed based on the 10th-graders from each 

survey who were within a specified range of ages and testing dates. The resulting transformation 

was then applied to all ELS:2002 students. To make the PISA sample more nearly equivalent to 

the ELS:2002 sample, only PISA 10th-graders were used in the equating subsample. To make 

the ELS:2002 sample more nearly equivalent to the PISA sample, only ELS:2002 students 

between the ages of 15.25 years and 16.25 years (the approximate age range for PISA 

examinees) were used. ELS:2002 students who were tested before March 15 or after May 31 

were deleted from the equating sample. The restricted samples were intended to be 

representative of 10th-graders between the ages of 15.25 and 16.25 years. 

Table 7. ELS:2002 and Program for International Student Assessment: Spring 2003 (PISA:spring 

2003) equating sample: 2002 and 2003 

ELS:2002 equating sample PISA:spring 2003 equating sample 

10th-graders only 10th-graders only 

15.25- to 16.25-year-olds 15.25- to 16.25-year-olds 

Exams offered from March 15 to May 31 Exams offered from April 1 to May 31 

Equating sample N = 2,743 Equating sample N = 2,400 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002); and Program for International Student Assessment (PISA). 

The equipercentile equating was carried out using five-moment smoothing of the 

weighted frequency distributions. Plots of the equipercentile-equated scores showed extreme 

deviations in the tails of the distribution from a trend line based on linear approximation. These 

deviations are probably due to the methodology employed in PISA scoring: the PISA scores are 

transformations of normally distributed IRT ability estimates, which, if no shrinkage is imposed, 

tend to have long tails. The ELS:2002 scores, which are sums of probabilities, do not. As a 

result, the equipercentile conversion becomes distorted in the tails of the distributions. 

Throughout most of the score range, a quarter point difference in ELS:2002 math scale 

corresponds to a difference of 2 to 3 points in the PISA metric. But, in the extreme tails of the 

distribution, a quarter point difference in ELS:2002 math score corresponds to a difference of 5 

to 10 points or more in the PISA metric. For this reason, the equipercentile equating was carried 

out without the data in the top and bottom tails of each distribution. Then the equipercentile 

transformation was used to link the scores for the middle 90 percent of the students, and the 

remaining scores were linked based on the linear approximation of the equating transformation. 

The cutoff points for using equipercentile versus linear transformation were selected such that 

38 
Chapter 2: Instrumentation 

the ELS:2002 to PISA link would be monotonic. Table 8 shows the linking methods for 

implementing PISA:spring 2003 math scales in ELS:2002. 

Table 8. Linking methods for implementing Program for International Student Assessment: 

Spring 2003 (PISA:spring 2003) math scales in ELS:2002: 2002 and 2003 

ELS:2002 scale score range Equating method Weighted percent of data 

12.60–22.05 Linear approximation 10.0 

22.06–51.81 Equipercentile transformation 76.0 

51.82–68.90 Linear approximation 14.0 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002); and Program for International Student Assessment (PISA). 

Data users should keep in mind that the differences between the ELS:2002 and 

PISA:spring 2003 tests, scoring methods, and populations mean that the link reported here 

cannot be considered to be a true equating. Although procedures were carried out to compensate 

for population differences and scoring methods, no claim is made that the scores may be treated 

as equivalent. It is more appropriate to refer to this link as a concordance: the PISA-scale score 

represents the score level achieved by students of the same percentile rank in two populations 

that were matched as closely as possible given the differences described above. 

NELS:88-equated scores. Equating the ELS:2002 scale scores to the NELS:88 scale 

scores was completed through common-item or anchor equating. The ELS:2002 and NELS:88 

mathematics tests shared 44 math items. These common items provided the link that made it 

possible to obtain ELS:2002 student ability estimates on the NELS:88 ability scale. (The 

ELS:2002 data for 12 additional math items did not fit the NELS:88 IRT parameters, so these 

items were not treated as common items for the purpose of equating.) Parameters for the 

common items were fixed at their NELS:88 values, resulting in ability estimates consistent with 

the NELS:88 metric. 

The NELS:88-equated IRT-estimated number-right scores for mathematics are estimates 

of the number of items students would have answered correctly had they taken the NELS:88 

exam and responded to all items in the mathematics items pool. The NELS:88 item pool 

contained 81 mathematics items in all test forms administered in grades 8, 10, and 12. Table 9 

shows mathematics scores for ELS:2002 students, reported on the NELS:88 score scale. 

Proficiency probability scores. The criterion-referenced NELS:88-equated proficiency 

probability scores are based on clusters of items that mark different levels on the mathematics 

scale. Clusters of four items were identified in the NELS:88 tests that marked five hierarchical 

levels in mathematics: 

1. simple arithmetical operations on whole numbers; 

2. simple operations with decimals, fractions, powers, and roots; 

3. simple problem solving requiring the understanding of low-level mathematical 

concepts; 

4. understanding of intermediate-level mathematical concepts and/or multistep solutions 

to word problems; and 

39 
Chapter 2: Instrumentation 

5. complex multistep word problems and/or advanced mathematical material. 

The proficiency levels are hierarchical in the sense that mastery of a higher level typically 

implies proficiency at lower levels. In NELS:88, students were judged to be proficient if three of 

the four items in a cluster were answered correctly. The NELS:88-equated proficiency 

probabilities were computed using IRT-estimated item parameters calibrated in NELS:88. Each 

proficiency probability represents the probability that a student would pass a given proficiency 

level defined as above in the NELS:88 sample. 

Table 9 shows variable names, descriptions, and summary statistics for the NELS:88-

equated proficiency probability scores. 

Table 9. ELS:2002 Item Response Theory (IRT) National Education Longitudinal Study of 1988 

(NELS:88)-equated estimated number-right score and proficiency probability scores: 

2004 

Weighted Weighted standard 

Variable name Description Range mean deviation 

F1NELS2M Mathematics—NELS-equated estimated 0–81 50.10 14.20 

number right (1992 scale) 

F1TX1MPP Mathematics—level 1 0–1 .96 .12 

F1TX2MPP Mathematics—level 2 0–1 .78 .37 

F1TX3MPP Mathematics—level 3 0–1 .62 .45 

F1TX4MPP Mathematics—level 4 0–1 .35 .41 

F1TX5MPP Mathematics—level 5 0–1 .04 .14 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Choosing the appropriate score for analysis. The IRT-estimated number-right, 

standardized scores (T-scores), proficiency, and quartile scores are all derived from the IRT 

model and are based on all of the student’s responses to the mathematics assessment. That is, the 

pattern of right and wrong answers, as well as the characteristics of the assessment items 

themselves, are used to estimate a point on an ability continuum, and this ability estimate, theta, 

then provides the basis for criterion-referenced and norm-referenced scores. The choice of the 

most appropriate score for analysis purposes should be driven by the context in which it is to be 

used. 

The IRT-estimated number-right scores are overall, criterion-referenced measures of 

status at a point in time. The criterion is the set of skills defined by the framework and 

represented by the assessment item pool. These scores are useful in identifying cross-sectional 

differences among subgroups in overall achievement level. They provide a summary measure of 

achievement useful for correlational analysis with status variables, such as demographics, school 

type, or behavioral measures, and may be used in multivariate models as well. These scores may 

also be used as longitudinal measures of overall growth. However, gains made at different points 

on the score scale have qualitatively different interpretations. For example, students who made 5-

point gains by mastering arithmetical operations are learning very different lessons from those 

gaining 5 points at the high end of the scale by learning more advanced mathematics. Although 

the gains in number of scale score points may be the same, the interpretation, and the relationship 

40 
Chapter 2: Instrumentation 

with other factors such as coursework, can be expected to be quite different. Comparison of gain 

in scale score points is most meaningful for groups that started with similar initial status. 

The standardized scores (T-scores) are also overall measures of status at a point in time, 

but they are norm-referenced rather than criterion-referenced. They do not answer the question, 

“What skills do students have?” but rather, “How do they compare with their peers?” The 

transformation to a familiar metric with a mean of 50 and standard deviation of 10 facilitates 

comparisons in standard deviation units. For example, an individual with a T-score of 65 (or a 

subgroup with a mean of 65) has demonstrated achievement one and one-half standard 

deviations above the national average for 12th-graders, whereas a score of 45 would correspond 

to half a standard deviation below the norm. These numbers do not indicate whether students 

have mastered a particular body of material, but rather what their standing is relative to others. 

Quartile scores are convenient normative scores for the user who wishes to focus on 

analysis of background or process variables separately for students at different achievement 

levels. For example, one might want to compare the school experiences or educational 

aspirations of students in the lowest mathematics quartile with those of students in the highest 

quartile group. 

NELS:88-equated estimated number-right and proficiency probability scores may be 

used in a number of ways. Because they are calibrated on the NELS:88 scale, they may be used 

for cross-sectional comparisons of students’ mathematics achievement in 2004 compared with 

their counterparts in 1992. The NELS:88-equated number-right scores reflect performance on 

the whole pool of 81 NELS:88 mathematics items, whereas the proficiency probability scores are 

criterion-referenced scores that target a specific set of skills. The mean of a proficiency 

probability score aggregated over a subgroup of students is analogous to an estimate of the 

percentage of students in the subgroup who have displayed mastery of the particular skill. The 

proficiency probability scores are particularly useful as measures of gain, because they can be 

used to relate specific treatments (such as selected coursework) to changes that occur at different 

points along the score scale. For example, two groups may have similar gains in total scale score 

points, but for one group, gain may take place at an upper skill level, and for another, at a lower 

skill level. One would expect to see a relationship between gains in probability of proficiency at 

a particular level and curriculum exposure, such as taking mathematics courses relevant to the 

skills being mastered. 

41 

 

Chapter 3

Sample Design, Weighting, 

Design Effects, and Data Quality 

3.1 Introduction 

This chapter describes the Education Longitudinal Study of 2002 (ELS:2002) base-year 

and first follow-up sample designs, weighting, standard errors and design effects, imputation, 

disclosure analysis and protections, and unit and item nonresponse bias analyses. This section 

provides an overview of each of these subjects, and the details are provided in later sections of 

the chapter. 

3.1.1 Base-Year Sample Design 

The ELS:2002 base-year sample design comprises two primary target populations— 

schools with 10th grades and sophomores in those schools—in the spring term of the 2001–02 

school year. ELS:2002 used a two-stage sample selection process. First, schools were selected. 

These schools were then asked to provide sophomore enrollment lists. A full discussion of the 

sample design and response rates is presented in this chapter and in chapter 4. 

Schools and students are the study’s basic units of analysis. School-level data reflect a 

school administrator questionnaire, a library media center questionnaire, a facilities checklist, 

and the aggregation of student data to the school level. Student-level data consist of student 

questionnaire and assessment data and reports from students’ teachers and parents. (School-level 

data, however, can also be reported at the student level and serve as contextual data for students.) 

3.1.2 First Follow-up Sample Design 

The basis for the sampling frame for the first follow-up was the sample of schools and 

students used in the ELS:2002 base-year sample. There are two slightly different target 

populations for the follow-up. One population consists of those students who were enrolled in 

the 10th grade in 2002. The other population consists of those students who were enrolled in the 

12th grade in 2004. The former population includes students who dropped out of school between 

10th and 12th grades, and such students are a major analytical subgroup. Note that in the first 

follow-up, a student is defined as a member of the student sample, that is, an ELS:2002 spring 

2002 sophomore or a freshened first follow-up spring 2004 12th-grader.20 

3.1.3 Weighting 

The general purpose of the weighting scheme was to compensate for unequal 

probabilities of selection of students into the base-year sample and freshened students into the 

first follow-up sample and to adjust for the fact that not all students selected into the sample 

20 In spring term 2002, such students may have been out of the country, been enrolled in school in the United States 

in a grade other than 10th, had an extended illness or injury, been homeschooled, been institutionalized, or 

temporarily dropped out of school. These students comprised the first follow-up “freshening sample.” Freshening 

ensures that a nationally representative sample of high school seniors was selected. 

43 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

actually participated. Four sets of weights were computed subsequent to first follow-up data 

collection: 

• A cross-sectional weight for the expanded sample that includes the students who 

completed a questionnaire in the first follow-up or were incapable of completing the 

questionnaire. (This weight is on the restricted-use file only.) 

• A cross-sectional first follow-up weight for sample members who completed a 

questionnaire in the first follow-up. 

• A first follow-up panel weight (longitudinal weight) for the expanded sample that 

includes sample members who completed a questionnaire in both the base year and 

first follow-up, including those with base-year imputed data, or who were 

questionnaire incapable. (This weight is on the restricted-use file only.) 

• A first follow-up panel weight for sample members who completed a questionnaire in 

both the base year and first follow-up, including those with base-year imputed data. 

Student weights were adjusted for nonresponse, and these adjustments were designed to 

significantly reduce or eliminate nonresponse bias for data elements known for most respondents 

and nonrespondents. In addition, student weights were poststratified to base-year weighted 

totals. Weighting is discussed in detail in section 3.4. 

3.1.4 Standard Errors and Design Effects 

The variance estimation procedure had to take into account the complex sample design, 

including stratification and clustering. One common procedure for estimating variances of 

survey statistics is the Taylor series linearization procedure. This procedure takes the first-order 

Taylor series approximation of the nonlinear statistic and then substitutes the linear 

representation into the appropriate variance formula based on the sample design. For stratified 

multistage surveys, the Taylor series procedure requires analysis strata and analysis primary 

sampling units (PSUs). Therefore, analysis strata and analysis PSUs were created in the base 

year and used again in the first follow-up. The impact of the departures of the ELS:2002 

complex sample design from a simple random sample design on the precision of sample 

estimates can be measured by the design effect. Appendix I presents standard errors and design 

effects for 30 means and proportions based on the ELS:2002 student data for the sample (as a 

whole and for selected subgroups). 

3.1.5 Imputation 

The imputation procedures used for the first follow-up study include logical imputation, 

weighted sequential hot deck procedure, and a multiple imputation procedure. Eighteen 

variables were selected for imputation. Fourteen of the variables were key demographic and 

family background variables that were also chosen for imputation in the base year. These key 

variables were imputed (when not provided by respondents in the new participant supplement 

data) for first follow-up respondents who were one of the following: base-year nonrespondents, 

12th-grade freshened sample members, or base-year questionnaire eligible students (who were 

part of the base-year expanded sample only but became first follow-up eligible respondents). 

Additionally, the 10th-grade student ability estimates for mathematics and reading were imputed 

44 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

for the base-year nonrespondents who became first follow-up respondents since they were 

included in the spring 2002 sophomore cohort. These ability estimates had been imputed, if 

missing, in the base year for base-year respondents. 

Two first follow-up variables were imputed, as applicable, when the data were missing. 

Student enrollment status as of spring 2004 was imputed for the first follow-up respondents if 

enrollment status was not provided by the sample school. The first follow-up mathematics 

ability estimate was imputed, if missing, for first follow-up respondents who were considered inschool students: students at the base-year school or at another (transfer) school as of spring 

2004. (Sample members who dropped out, finished high school early, or were being 

homeschooled as of spring 2004 were not defined as in-school students, so no ability estimates 

were determined for them.) Only students at the base-year schools were tested—ability 

estimates were imputed for all transfer student respondents. 

With the exception of the ability estimates, all variables chosen for imputation had less 

than 15 percent missing data. Imputation is discussed in detail in section 3.6. 

3.1.6 Disclosure Risk Analysis and Protection 

Because of the paramount importance of protecting the confidentiality of NCES data 

containing information about specific individuals, ELS:2002 first follow-up data were subject to 

various procedures to minimize disclosure. As a first step, all ELS:2002 data files (school and 

student) were reviewed to identify high-risk variables. As a second step, a technique called “data 

swapping” was carried out, both for school-level data and for student-level data. The swapping 

was conducted independently from the base-year swapping. As a final step, the ELS:2002 data 

underwent a disclosure risk analysis. In this analysis, school characteristics information 

available on the data files was compared with information on publicly available universe files of 

schools. Disclosure avoidance procedures are discussed in detail in section 3.7. 

3.1.7 Data Quality: Student and Item Nonresponse Bias Analyses 

The overall weighted student response rate was 88.7 percent, although the response rate 

for certain domains was below 85 percent. Student unit nonresponse bias analyses were 

performed. The bias due to nonresponse prior to computing weights and after computing 

weights was estimated based on the data collected from both respondents and nonrespondents, as 

well as frame data. An item nonresponse bias analysis was also performed for all questionnaire 

variables in which response fell below 85 percent. Details of the bias analyses are given in 

section 3.8. 

3.2 Base-Year Sample Design 

The sample design for ELS:2002 is similar in many respects to the designs used in the 

three prior studies of the National Education Longitudinal Studies Program: the National 

Longitudinal Study of the High School Class of 1972 (NLS-72), the High School and Beyond 

(HS&B) longitudinal study, and the National Education Longitudinal Study of 1988 (NELS:88). 

ELS:2002 is different from NELS:88 in that the ELS:2002 base-year sample students are 10thgraders rather than 8th-graders. As in NELS:88, Hispanics and Asians were oversampled in 

45 
 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

ELS:2002. However, for ELS:2002, counts of Hispanics and Asians were obtained from the 

Common Core of Data (CCD) and the Private School Survey (PSS) to set the initial 

oversampling rates. 

ELS:2002 used a two-stage sample selection process. First, schools were selected with 

probability proportional to size (PPS), and school contacting resulted in 1,221 eligible public, 

Catholic, and other private schools from a population of approximately 27,000 schools 

containing sophomores. Of the eligible schools, 752 participated in the study. These schools 

were then asked to provide sophomore enrollment lists. In the second stage of sample selection, 

approximately 26 students per school were selected from these lists. Additional information on 

the base-year sample design can be found in the base-year data file user’s manual (Ingels et al. 

2004), chapter 3 and appendix J. 

The target population of schools for the ELS:2002 base year consisted of regular public 

schools, including state Department of Education schools and charter schools, and Catholic and 

other private schools that contained 10th grades and were in the United States (the 50 states and 

the District of Columbia). 

The sampling frame of schools was constructed with the intent to match the target 

population. However, selected schools were determined to be ineligible if they did not meet the 

definition of the target population. Responding schools were those schools that had a survey day 

(i.e., data collection occurred for students in the school).21 Of the 1,268 sampled schools, there 

were 1,221 eligible schools and 752 responding schools (67.8 percent weighted response rate). 

A subset of most but not all responding schools also completed a school administrator 

questionnaire and a library or media center questionnaire (98.5 percent and 95.9 percent 

weighted response rates, respectively). Most nonresponding schools or their districts provided 

some basic information about school characteristics, so that the differences between responding 

and nonresponding schools could be better understood, analyzed, and adjusted. Additionally, the 

RTI field staff completed a facilities checklist for each responding school (100 percent response 

rate). 

The target population of students for the full-scale ELS:2002 consisted of spring-term 

sophomores in 2002 (excluding foreign exchange students) enrolled in schools in the school 

target population. The sampling frames of students within schools were constructed with the 

intent to match the target population. However, selected students were determined to be 

ineligible if they did not meet the definition of the target population. Of the 19,218 sampled 

schools, there were 17,591 eligible students and 15,362 participants (87.3 percent weighted 

response rate). 

The ELS:2002 survey instruments comprised two assessments (reading and mathematics) 

and a student questionnaire. Participation in ELS:2002 was defined by questionnaire 

completion. Although most students were asked to complete the assessment battery in addition 

to the questionnaire, there were some cases in which a student completed the questionnaire but 

21 One eligible school had no eligible students selected in the sample. This school was considered a responding 

school. 

46 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

did not complete the assessments. Guidelines were provided to schools to assist them in 

determining whether students would be able to complete the ELS:2002 survey instruments. 

Students who could not (by virtue of limited English proficiency or physical or mental 

disability) complete the ELS:2002 survey instruments (including the questionnaire and the tests) 

were part of the expanded sample of 2002 sophomores who will be followed in the study and 

whose eligibility status was reassessed 2 years hence. There were 163 such students. To obtain 

additional information about their home background and school experiences, contextual data 

were collected from the base-year parent, teacher, and school administrator surveys. 

The student sample was selected, when possible, in the fall or early winter so that sample 

teachers could be identified and materials could be prepared well in advance of Survey Day. 

However, selecting the sample in advance meant that some students transferred into the sample 

schools and others left between the time of sample selection and Survey Day. To address this 

issue, sample updating was conducted closer to the time of data collection. Complete enrollment 

lists were collected at both the time of initial sampling and the time of the sample update. 

One parent of the sample student and English and mathematics teachers of the sample 

student were also included in the base-year sample. A full discussion of the sample design and 

response rates is presented in the ELS:2002 base-year data file user’s manual (Ingels et al. 2004). 

3.3 First Follow-up Sample Design 

As described in section 3.1.2, there are two target populations for the ELS:2002 first 

follow-up. Because of these two target populations and the major analytical subgroups, the 

sample included the following types of students: 

• ELS:2002 base-year student respondents who were currently enrolled in either the 

12th grade or some other grade in the school in which they were originally sampled. 

All such students were included in the follow-up sample. 

• ELS:2002 base-year student respondents who finished high school early, including 

those who graduated from high school early, as well as those who did not graduate 

because they had alternative certification (e.g., exam-certified equivalency such as the 

General Educational Development [GED] credential). All such students were 

included in the follow-up sample. 

• ELS:2002 base-year sample students who were deemed unable to participate during 

the base year owing to disability or insufficient command of the English language. 

All such students were included in the follow-up sample. 

• ELS:2002 base-year student respondents who dropped out of school prior to data 

collection in the 12th grade. All such students were included in the follow-up sample. 

• ELS:2002 base-year student respondents who transferred out of the school in which 

they were originally sampled. All such students were included in the follow-up 

sample. 

47 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

• Nonrespondents (including those who did not have parental consent) of the ELS:2002 

base-year full-scale sample who were at the base-year school, finished high school 

early, or transferred. Such students are discussed in section 3.3.2. 

• Students at the base-year sample school who were currently enrolled in the 12th grade 

but who were not in 10th grade in the United States during the 2002 school year. 

During 2002 such students may have been out of the country, been enrolled in school 

in the United States in a grade other than 10th, had an extended illness or injury, been 

institutionalized, been homeschooled, or temporarily dropped out of school. Such 

students are discussed in section 3.3.3. 

If a base-year school split into two or more schools, many of the ELS base-year sample 

members moved en masse to a new school, and they were followed to the destination school. 

These schools can be thought of as additional base-year schools in a new form. Specifically, a 

necessary condition of adding a new school in the first follow-up was that it arose from a 

situation such as the splitting of an original base-year school, thus resulting in a large transfer of 

base-year sample members (usually to one school, but potentially to more). Four base-year 

schools split, and five new schools were spawned from these four schools. At these new schools, 

as well as at the original base-year schools, students were tested and interviewed. Additionally, 

student freshening was done, and the administrator questionnaire was administered. 

3.3.1 Eligibility 

All spring-term 2002 sophomores in eligible schools, except for foreign exchange 

students, were eligible for the base-year study and were assumed eligible again in the first 

follow-up. Additionally, all spring-term 2004 seniors in eligible schools, except for foreign 

exchange students, were eligible for the first follow-up. Some base-year students were out of 

scope for this round, but they may be eligible again in future rounds. Reasons for being out of 

scope included being institutionalized or out of the country. Also, some base-year students died 

between the base year and the first follow-up. 

Several categories of students who were ineligible for HS&B and NELS:88 were eligible 

for ELS:2002 (though it did not mean that such students were necessarily tested or that they 

completed questionnaires). In NELS:88, the following categories of students were deemed 

ineligible: 

• students with disabilities (including students with physical or mental disabilities, or 

serious emotional disturbance, and who normally had an assigned Individual 

Education Program [IEP]) whose degree of disability was deemed by school officials 

to make it impractical or inadvisable to assess them; and 

• students whose command of the English language was insufficient, in the judgment of 

school officials, for understanding the survey materials and who therefore could not 

validly be assessed in English. 

In ELS:2002, the treatment of these categories of students was addressed as discussed 

below. 

48 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

3.3.1.1 Schools Given Clear Criteria for Including/Excluding Students 

Students were not excluded categorically (e.g., just because they received special 

education services, had IEPs, or received bilingual education or English as a second language 

services), but rather on a case-by-case (individual) basis. The guiding assumption was that many 

students with IEPs or limited English proficiency (LEP) would be able to participate, and schools 

were asked, if unsure, to include the student. Although both questionnaire and assessment data 

were sought, the minimum case of participation was completion of the student questionnaire. 

Hence, some students who could not be assessed could nevertheless participate (i.e., complete 

the questionnaire). 

In addition, the ELS:2002 assessments were more accessible to many students who 

formerly (as in NELS:88) might have been excluded, because unlike NELS:88, ELS:2002 

offered various testing accommodations. Schools and parents were urged to permit the study to 

survey and test students under these special conditions. 

The suggested criterion for exclusion of students from survey instrument completion on 

language grounds followed the current practice for the National Assessment of Educational 

Progress (NAEP) students. Students were regarded as capable of taking part in the survey 

session (test and questionnaire administration) if they had received academic instruction 

primarily in English for at least 3 years or had received academic instruction in English for less 

than 3 years, but school staff judged or determined that they were capable of participating. In 

terms of exclusion from taking the instruments on disability grounds, it was suggested that only 

if the student’s IEP specifically recommended against their participation in assessment programs 

should they be excluded, and then only from the tests if questionnaire-level participation were 

possible. Moreover, if their IEP stated that they could be assessed if accommodations were 

provided, then their participation became a question of whether the school could supply the 

particular accommodation. The specific accommodations offered by schools are explained 

below. 

3.3.1.2 Accommodations Offered to Increase Participation 

To the extent possible, given practical and monetary constraints, accommodations were 

offered to increase the number of participants. All tests taken under conditions of special 

accommodations were flagged on the data file (F1TXACC is the accommodation indicator), and 

the nature of the accommodation was noted. 

In theory, many kinds of accommodations were possible. There were accommodations of 

test presentation, response, setting, and allotted testing time. In addition to accommodations for 

the assessments, special measures were employed to facilitate questionnaire completion (e.g., in 

some instances, ELS:2002 students were administered the student questionnaire by survey staff, 

if self-administration was not possible for them). 

One type of accommodation offered is alternative test presentation (e.g., on mathematics 

tests, one might read problems aloud, have someone sign the directions using American Sign 

Language, use a taped version of the test, provide a braille or large-print edition of the test, or 

supply magnifying equipment). Although the study could not, for example, provide braille 

translations, when a school could assist in providing a presentational accommodation (as with 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

magnifying equipment or an aide who translated directions into American Sign Language), this 

alternative was deemed an acceptable accommodation. 

A second type of accommodation sometimes offered is alternative means of test 

responses (e.g., responses made in braille or American Sign Language or produced using a 

keyboard or specially designed writing tool). However, ELS:2002 was not able to provide 

special accommodations for responding. 

A third type of accommodation sometimes offered is providing an alternative setting. For 

example, an emotionally disturbed student might not be a good candidate for a group 

administration but might be able to be assessed alone. ELS:2002 made this type of 

accommodation available where possible or permissible by the school. 

A fourth possible kind of accommodation is in timing or length of administration. There 

were two options for proceeding: (1) give extra time or (2) keep testing time constant in minutes 

tested but give more breaks. Table 10 lists the counts for students excluded from survey 

instrument completion and students accommodated. 

Table 10. Number of students excluded and accommodated: 2004 

Excluded or accommodated Number 

Number of students excluded 100 

Mental or physical disability 90 

 Language barrier (LEP/NEP)1 10 

Number of students accommodated 48 

1

 LEP = limited English proficient; NEP = non-English proficient. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

3.3.1.3 Questionnaire Eligibility Status Changes in the First Follow-up 

The questionnaire eligibility status for some students changed between the base year and 

first follow-up. As shown in table 11, there were 16 students eligible for the questionnaire in the 

base year who were questionnaire ineligible in the first follow-up. Of these 16 students, 14 were 

base-year respondents, and 2 were base-year nonrespondents. Also shown in table 11 is that, of 

the 163 base-year questionnaire ineligible students, 105 were questionnaire eligible in the first 

follow-up, 57 were still questionnaire ineligible in the first follow-up, and 1 was deceased. 

Table 11. Change in questionnaire eligibility status between base year and first follow-up: 2004 

Base-year eligibility status First follow-up questionnaire eligibility Count 

Questionnaire eligible Questionnaire ineligible 16 

 Respondent Questionnaire ineligible 14 

 Nonrespondent Questionnaire ineligible 2 

Questionnaire ineligible Questionnaire eligible 105 

Questionnaire ineligible Questionnaire ineligible 57 

Questionnaire ineligible Deceased 1 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

3.3.1.4 Records and Contextual Data Gathered for Students Unable to be Surveyed or 

Validly Assessed 

High school transcripts have been collected for students unable to be surveyed or validly 

assessed. School-level data, such as school administrator survey responses in the base year and 

first follow-up, have been linked to these students. Contextual or expanded sample crosssectional and panel weights—as contrasted to the student questionnaire completion weights— 

have been created and are included on the restricted-use data file. See section 3.4 for a 

description of these weights and their uses. 

3.3.2 Subsampling 

A base-year nonrespondent student was defined as a student that was selected in the base 

year and did not complete a student questionnaire or portion of the questionnaire. Many of these 

students were enrolled in the same school during the follow-up. For the first follow-up, a 

subsample of 1,000 nonrespondent students was selected from the 2,229 base-year 

nonrespondents. Initially, a subsample of 1,620 nonrespondents was selected. All 

nonresponding students were included with certainty (i.e., probability equal to one), except for 

White students in public schools who were randomly subsampled. Then, to help the response 

rate and to conserve resources, the subsample of 1,620 was randomly subsampled across all 

student types to 1,000 nonrespondents. See table 12 for a summary of the nonrespondent 

subsample. 

Table 12. Base-year nonrespondent subsample, by school sector and student type: 2004 

Base-year 

School sector and student type nonrespondents Initial subsample Final subsample 

Public 1,843 1,234 764 1 All other races 1,006 397 246 

 Asian 289 289 179 

Black or African American 286 286 177 

 Hispanic or Latino 262 262 162 

Catholic 193 193 119 1 All other races 169 169 105 

 Asian 5 5 3 

Black or African American 4 4 2 

 Hispanic or Latino 15 15 9 

Other private 193 193 117 1 All other races 161 161 98 

 Asian 18 18 11 

Black or African American 14 14 8 

 Hispanic or Latino # # # 

#

 Rounds to zero. 

1 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and 

Multiracial. All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

3.3.3 Student Sample Freshening 

Because part of the target population consists of those students who were enrolled in the 

12th grade in the spring of 2004, the first follow-up included students at the base-year sample 

school who were enrolled in the 12th grade in the spring of 2004 but who were not in the 10th 

grade in the United States during the spring of 2002. During this time, such students may have 

been out of the country or may have been enrolled in school in the United States in a grade other 

than 10th (either at the sampled school or at some other school). In addition, some students may 

have reenrolled, although in spring 2002 they were temporarily out of school, owing to illness, 

injury, institutionalization, homeschooling, or school dropout. 

Student freshening was limited to the base-year sample schools and the five new schools 

added due to school splits because all sample students were identified at these schools regardless 

of their status 2 years later, and they could be linked to potential freshened students. Freshened 

lists were not obtained from transfer schools. Therefore, a small number of freshening eligible 

students from “new” schools that were not on the 2002 school sampling frame did not have a 

chance of selection. 

In October 2003, each sample school was asked to provide an electronic or hard copy 

listing of all their 12th-grade students enrolled in the 2003–04 school year. This requested listing 

was similar to the listing requested in the base year. The information requested for each eligible 

student included the following: 

• student ID number; 

• Social Security number; 

• full name; 

• sex; and 

• race/ethnicity. 

The race/ethnicity variable was used to stratify the students. 

The sample school was given instructions for submitting the electronic and hardcopy 

lists. The electronic lists were requested to be a column formatted or comma delimited ASCII 

file or an Excel file. Schools were able to provide the electronic lists by sending them in an 

e-mail, providing a diskette or CD-ROM containing the file, or uploading the file to the 

ELS:2002 website. If the school could not provide an electronic list, then it was requested that 

the hardcopy lists were sorted in alphabetical order within race/ethnicity strata to facilitate 

stratified sampling. As shown in table 13, of the 615 enrollment lists received, 46.7 percent sent 

in electronic lists, 49.1 percent sent in hardcopy lists, and 4.2 percent sent in both types. The 

students from these 615 schools were selected such that the sample would be representative (i.e., 

linked to a representative sample of students in a representative sample of schools), as described 

in the following paragraphs. However, estimates based on respondents could potentially be 

biased due to nonresponse or excluding “new” schools. Nonresponse bias analysis was not 

conducted for the freshening nonresponse. However, nonresponse adjustment factors were 

computed to account for potential bias due to the school-level freshening nonresponse (see 

weighting section). Any bias due to excluding “new” schools is likely to be small due to the 

small number of freshening-eligible students. Approximately 130 schools did not send a 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

freshened list, either because they refused to provide the list or because they indicated they had 

no freshening eligible students. Also, about 20 schools either sent in lists too late or sent lists 

that were incomplete and could not be used. 

Table 13. Number of 12th-grade student lists provided by schools, by type: 2004 

Type of list received Frequency1 Percent 

 Total 615 100.00 

Both electronic and hardcopy 26 4.23 

Electronic copy 287 46.67 

Hardcopy 302 49.11 

1 The counts include all schools that sent in a 12th-grade student list, but three of these schools sent in a list that was 

not sufficient to use for freshening. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

Quality assurance (QA) checks were performed on all lists received. Any list that was 

unreadable immediately failed the QA checks. Additionally, any list that did not allow the 

students to be stratified failed the QA checks, unless the original sophomore list also did not 

contain race/ethnicity. To verify that the school provided a complete list of eligible students, the 

school’s count of 12th-grade students from the most recent CCD (for public schools) and PSS 

(for private/Catholic schools) databases were compared with the counts (overall and within 

strata) of 12th-graders from the list provided. If any of the counts of 12th-graders for total 

students or by the race/ethnicity strata on the provided list were more than 25 percent lower or 

higher than the counts from the CCD data, then the list failed the QA checks, unless the provided 

count was greater than zero and the absolute difference was less than 50. However, if the 

provided count of Hispanics, Asians, or Blacks was zero and the original list count was less than 

five, the count did not fail the QA checks. 

Table 14 shows that of the lists received, 512 passed all QA checks, 16 lists failed the QA 

check regarding student counts, 74 failed the QA check regarding identification of race stratum, 

2 lists were unreadable, 4 lists had insufficient documentation, and 4 lists had multiple or other 

problems. 

Table 14. Types of problems encountered with student lists: 2004 

Type of problem Frequency Percent 

 Total 612 100.00 

None 512 83.66 

Unreadable file or list 2 0.33 

Count out of bounds 16 2.61 

Cannot identify strata 74 12.09 

Insufficient documentation 4 0.65 

Multiple problems 1 0.16 

Other problems 3 0.49 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Schools that failed the QA checks were contacted to resolve the discrepancy. When it 

was determined that the initial list provided by the school was not satisfactory, a replacement list 

was requested. If the school confirmed that the provided list was correct or if the school sent a 

replacement list, then the freshening process was initiated. If the school refused to send a 

replacement list, then the freshening process was initiated, when possible. 

If both the original and new enrollment lists were electronic, they were sorted 

alphabetically within stratum (as the original list was sorted for sample selection) to facilitate the 

comparison of the original and new lists. If one of the lists was electronic and one was hard 

copy, then the electronic list was sorted alphabetically within stratum and printed for the 

freshening process. If both of the lists were hard copy, then the lists were used as is in the 

freshening process. 

The freshening process began by identifying the base-year sample students on the new 

list. If the student immediately following each sampled base-year student within the 

race/ethnicity strata on the new list was not on the original list, then that student was selected as 

a potential addition to the sample. Whenever a potential new sample student was identified, the 

next student on the list was examined to determine whether that student was on the original list. 

If this next student was not on the original list, then that student was a potential addition to the 

sample. This process was continued until reaching a student who was on the original list. Then, 

this process was repeated with the next base-year sample student on the list.22 

Next, the school was contacted to determine the eligibility of the freshened students. Any 

student identified as eligible by the school was selected into the sample. 

Table 15 shows that 2,712 freshened students were included in the first follow-up sample. 

Of these 2,712 students, 238 (8.8 percent) were found to be eligible for inclusion in the study, 

and 2,474 students (91.2 percent) were found to be ineligible. Of the 238 eligible freshened 

students, 31 were questionnaire ineligible. Eligibility was determined for all freshened students. 

The high ineligibility rate was expected because the freshening procedure selected 12th-grade 

students who were not on the sophomore list without information on their status in the 10th 

grade. Many of these sampled students were sophomores at other regular U.S. schools in the 

spring of 2002 who transferred to a sample school, which contributed to the high ineligibility 

rate. The number of freshened students was approximately 0.39 students per school (238 

students out of 612 schools that sent usable 12th-grade enrollment lists). 

Table 15. Number of freshened sample members, by eligibility: 2004 

Freshened eligibility status Count Percent 

 Total 2,712 100.00 

Eligible 207 7.63 

 Questionnaire ineligible 31 1.14 

Ineligible 2,474 91.22 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

22 This process is also known as the half-open interval rule. 

54 
 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

3.4 Calculation of Weights and Results of Weighting 

3.4.1 Analysis Populations 

The sample design for ELS:2002 supports a number of analyses, which in turn permit 

accurate inferences to be made to three major groups or target populations. Within these 

populations are important analytical domains. 

Population A: Spring 2002 sophomores 

Domains: 

• spring 2002 sophomores capable of completing the student questionnaire 

• all spring 2002 sophomores including those capable and not capable of completing 

the questionnaire 

• spring 2002 sophomores in base-year school in spring 2004 

• spring 2002 sophomores in a different school in spring 2004 (transfers) 

• spring 2002 sophomores who were dropouts in spring 2004 

• spring 2002 sophomores who graduated or achieved equivalency early, that is, prior 

to March 15, 2004 

• spring 2002 sophomores who were homeschooled in spring 200423 

• spring 2002 White sophomores 

• spring 2002 Black sophomores 

• spring 2002 Hispanic sophomores 

• spring 2002 Asian sophomores 

• spring 2002 public school sophomores 

• spring 2002 private school sophomores 

Population B: Spring 2004 12th-grade students 

Domains: 

• spring 2004 12th-grade students capable of completing the student questionnaire 

• all spring 2004 12th-grade students including those capable and not capable of 

completing the questionnaire 

• spring 2004 12th-grade students who were graduating high school seniors in spring 

2004 

23 Although conceptually spring 2002 sophomores who were homeschooled in 2004 may be thought of as an analysis 

population, they were not designed to be so and were therefore not subject to minimum sample size requirements. 

The group is of limited analytic utility owing both to the low sample size and to the narrowness of the population 

definition. The compelling practical reason for distinguishing this group was so that they could be administered only 

those items consonant with their unique situation as out-of-school students. 

55 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

• spring 2004 White 12th-grade students 

• spring 2004 Black 12th-grade students 

• spring 2004 Hispanic 12th-grade students 

• spring 2004 Asian 12th-grade students 

• spring 2004 public school 12th-grade students 

• spring 2004 private school 12th-grade students 

Figure 2 helps illustrate that, whereas some students are in only population A or 

population B, many students are in both populations—that is, both a spring 2002 sophomore and 

a spring 2004 12th-grade student. Figure 3 further illustrates the overlap between the two 

populations. 

Figure 2. Student analysis populations, by year: 2004 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

56 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Figure 3. Student analysis population respondent counts, by year: 2004 

Number of 

respondents 

14,000 

12,000 

10,000 

8,000 

6,000 

4,000 

2,000 

0 

A: Spring 2002 10th-grade students 

B: Spring 2004 12th-grade students 

1,579 

202 

13,308 

A, Not B B, Not A Both A and B 

Population 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

Population C: Spring 2002 10th-grade schools 

Domains: 

• control 

• urbanicity 

• region 

Analytic uses of these three populations, and the weighting required to support the 

analyses, are discussed in sections 3.4.2 (student level) and 3.4.3 (school level). 

3.4.2 Uses of Student-Level Data; Student Weights 

3.4.2.1 Population A: Spring 2002 Sophomores 

This population can be employed in both cross-sectional and longitudinal analyses. 

(Note to the user: The expanded weights [BYEXPWT and F1XPNLWT] are only available on 

the restricted-use file.) Weights for cross-sectional analyses were created in the base year. 

BYSTUWT can be used for cross-cohort comparisons of students capable of completing the 

questionnaire (on an intercohort time-lag basis employing the sophomore classes of 1980 and 

1990). BYEXPWT generalizes to the entire population, including both students capable and 

incapable of completing the questionnaire. 

57 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

The weight F1PNLWT was created for all persons who completed a questionnaire or a 

sufficient portion of a questionnaire, both in the base year and the first follow-up. Also, baseyear data were imputed when not available from the new participant supplement (NPS) for first 

follow-up respondents, and these cases also have F1PNLWT. The panel weight can be used for 

both intracohort (across rounds of ELS:2002) and intercohort (longitudinal comparative analysis) 

purposes. An example of using a panel weight for intracohort analysis is to take a cohort of 

sophomores, look at their enrollment 2 years later, and determine what proportion have dropped 

out. An example of using a panel weight for intercohort analysis is to compute math gains 

between sophomore and senior years using the ELS:2002 panel weight and also for the NELS:88 

panel weight and then comparing the gain between sophomore and senior year for the two 

cohorts. Missing test data were imputed, so a version of the panel weight adjusted for test 

nonresponse was unnecessary. The weight F1XPNLWT was created for the expanded sample of 

students capable and not capable of completing the questionnaire. See section 3.4.4 for more 

details. 

Base-year nonrespondents who responded in the first follow-up are considered to be part 

of this population, but there is no base-year weight (BYSTUWT or BYEXPWT) for them. The 

NPS ensured that the standard classification variables collected in the base year were also 

available for this group. However, key variables were imputed for base-year nonrespondents 

who were first follow-up respondents (see section 3.6), so that these students could be analyzed 

as part of the sophomore panel using F1PNLWT and/or F1XPNLWT. BYSTUWT and 

BYEXPWT were not recomputed. 

Transcripts will provide continuous data covering grades 9 through 12 for students who 

remained in school and were in the modal grade sequence (or a lesser range of data for students 

who dropped out or fell behind the modal progression). A cross-sectional 2004 transcript weight 

(F1TRSCWT) will be produced, encompassing cases that meet the following conditions, for 

sample members for whom a transcript has been obtained: (a) member of the 10th-grade or the 

12th-grade cohort who was a student questionnaire completer in the base year, first follow-up, or 

both; or (b) member of the questionnaire-incapable expanded sample. This weight will 

generalize to the analysis population of spring 2002 sophomores by subsetting the sample 

through the use of a flag (G10COHRT). In addition, a transcript panel weight (F1TRPWT) will 

be produced for all individuals who have a transcript in 2004 and who are regular or expanded 

sample participants in both 2002 and 2004, including base-year nonrespondents with imputed 

data. See section 3.4.4 for more details. 

3.4.2.2 Population B: Spring 2004 12th-Grade Students 

This population can also be employed in both cross-sectional and longitudinal analyses. 

(Note to the user: The expanded weight [F1EXPWT] is only available on the restricted-use file.) 

The longitudinal analyses will be conducted after further rounds of the study. Weights for crosssectional (including cross-cohort) analyses (F1QWT) were created for students capable of 

completing the questionnaire. This weight should be used in conjunction with a flag 

(G12COHRT) that identifies the sample member as part of the senior cohort. F1EXPWT will 

generalize to the entire population, including students capable and incapable of completing the 

questionnaire. See section 3.4.4 for more details. 

58 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Note that generalizations about the mathematics achievement of the 2004 senior class 

involve imputation for the transfer students and other seniors who were not tested (see section 

3.6). 

The cross-sectional transcript weight described above will also generalize to the analysis 

population of spring 2004 12th-graders by subsetting the sample through the use of a flag 

(G12COHRT). See section 3.4.4 for more details. 

3.4.3 Uses of School-Level Data; School-Level Weights 

This population of spring 2002 10th-grade schools can be employed in cross-sectional 

analyses and potentially in longitudinal analyses. Weights for cross-sectional analyses were 

created in the base year. BYSCHWT can be used for spring 2002 10th-grade schools. 

The first follow-up school data can be analyzed using the student weight. That is, the 

school data can be analyzed in relation to student characteristics (i.e., the administrator data are 

linked to student data, with the student as the fundamental unit of analysis). 

Although it is not possible to produce a cross-sectional 2004 school weight because the 

first follow-up school sample is not nationally representative of American high schools in 2004, 

the base-year school weight can be used for longitudinal analyses treating the base-year schools 

as a panel. Although there are multiple data points for analysis, the weight maintains 

generalizability only to schools in 2002. 

3.4.4 Weights 

Four sets of weights were computed: 

• A cross-sectional weight for the expanded sample that includes sample numbers who 

completed all or a sufficient portion of the questionnaire in the first follow-up, the 

base-year students who were still incapable of completing the questionnaire 2 years 

later, base-year students who were newly incapable of completing the questionnaire, 

and freshened students who were incapable of completing the questionnaire 

(F1EXPWT). This weight is only available on the restricted-use file. 

• A cross-sectional first follow-up weight for sample members who completed all or a 

sufficient portion of the questionnaire in the first follow-up (F1QWT). 

• A first follow-up panel weight (longitudinal weight) for the expanded sample that 

includes students who fully or partially completed a questionnaire in both the base 

year and first follow-up, students who fully or partially completed a questionnaire in 

the first follow-up and had base-year data imputed if not on the NPS (see section 3.6), 

and students who were questionnaire incapable in the base year and/or the first 

follow-up (F1XPNLWT). This expanded sample panel weight is only available on 

the restricted-use file. 

• A first follow-up panel weight for sample members who fully or partially completed a 

questionnaire in both the base year and first follow-up or who fully or partially 

59 
 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

completed a questionnaire in the first follow-up and had base-year data imputed if not 

on the NPS (F1PNLWT). 

Also, two weights (only available on the restricted-use file) will be computed and 

documented later: 

• a cross-sectional transcript weight for sample members for whom transcript data have 

been collected and who either fully or partially completed a questionnaire in the first 

follow-up or were members of the expanded sample (F1TRSCWT); and 

• a panel transcript weight for sample members for whom transcript data have been 

collected and who either fully or partially completed a questionnaire in both the base 

year and first follow-up, fully or partially completed a questionnaire in the first 

follow-up and had base-year data imputed if not on the NPS, or were members of the 

expanded sample (F1TRPWT). 

Additionally, there are two flags that can be used in analyses to identify members of the 

sophomore and senior cohorts: 

• a flag indicating a member of the sophomore cohort, that is, spring 2002 sophomore 

(G10COHRT); and 

• a flag indicating a member of the senior cohort, that is, spring 2004 12th-grader 

(G12COHRT). 

Table 16 illustrates the relationship among the first four weights listed above plus the 

base-year weights, universe flags, populations described in section 3.4.1, and respondents. 

Below, the weighting procedures are described for the first four of these weights. The 

procedures for calculating F1QWT differ somewhat for base-year sample students and first 

follow-up freshened sample students. 

3.4.4.1 F1EXPWT for Base-Year Sample Students 

The expanded sample cross-sectional weight was computed for the expanded sample that 

includes students who fully or partially completed the questionnaire and students incapable of 

completing the questionnaire.24 In addition to the expanded sample students identified in the 

base year, such students could be those who were base-year nonrespondents, became disabled 

between the base year and first follow-up, or were misclassified in the base year. 

With a few exceptions, base-year eligible sample students remained eligible for the first 

follow-up sample. Students who died were out of scope for the first follow-up. Students who 

left the country, were unavailable for the duration of the study (e.g., in military boot camp), or 

were institutionalized were temporarily out of scope for the first follow-up, although they may be 

eligible in future rounds. 

24 The expanded sample weights and the full expanded sample are available on the restricted-use file but not on the 

public-use file. 

60 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 16. Relationship among weights, populations, respondents, and universe flags: 2004 

Weight1 Universe flag Population Respondent 

BYSTUWT G10COHRT Spring 2002 

sophomore 

Fully or partially completed questionnaire in 2002 

BYEXPWT G10COHRT Spring 2002 

sophomore 

Fully or partially completed questionnaire in 2002 or 

incapable of completing a questionnaire 

F1PNLWT G10COHRT Spring 2002 

sophomore 

Fully or partially completed questionnaire in 2002 and 2004 

(base-year data may be imputed) 

F1XPNLWT G10COHRT Spring 2002 

sophomore 

Fully or partially completed questionnaire in 2002 and 2004 

(base-year data may be imputed) or incapable of 

completing a questionnaire in 2002 or 2004 

F1QWT G10COHRT Spring 2002 

sophomore 

Fully or partially completed questionnaire in 2004 

G12COHRT Spring 2004 12thgrader 

F1EXPWT G10COHRT Spring 2002 

sophomore 

Fully or partially completed questionnaire in 2004 or 

incapable of completing a questionnaire in 2004 

G12COHRT Spring 2004 12thgrader 

1 The expanded sample weights and the full expanded sample are available on the restricted-use file but not on the 

public-use file. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

First, the student-level design weight (F1DWT) was calculated as equal to the base-year 

design weight multiplied by the reciprocal of the student’s probability to be included in the first 

follow-up. All base-year eligible sample students have a base-year design weight (BYDWT) that 

accounts for the base-year school probability of selection (adjusted for nonresponse) and for the 

base-year student probability of selection within the sample school. This base-year design 

weight is not adjusted for base-year student nonresponse. The student’s probability of selection 

in the first follow-up is 1.0 for base-year respondents and base-year questionnaire-incapable 

students and less than 1.0 for base-year nonrespondents. This weight is used because all baseyear respondents are in the first follow-up sample, and 1,000 out of 2,229 base-year 

nonrespondents were subsampled to be included in the first follow-up sample. Different 

subsampling rates were used for the various school types and student types. Note that hostile 

refusals—those who requested to be removed from the study for all rounds—had a positive 

probability of selection but were always treated as first follow-up nonrespondents. The formula 

for F1DWT for student i is 

F1DWTi = BYDWTi * (1 / P1i), 

where P1i is the probability of selection for student i for the first follow-up sample. 

In the base year, all nonresponding students were assumed to be eligible. Adjusting the 

weights of base-year nonrespondents to compensate for the small portion of students who were 

actually ineligible was considered. However, in CATI, only nine ineligible students were 

identified, so it was assumed that all of the nonrespondents were eligible. If the assumption was 

made that some nonrespondents were ineligible, the adjustment would be negligible. In the first 

61 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

follow-up, some of these nonrespondents still had unknown eligibility, including some for whom 

the name was unknown. Again, they were assumed to be eligible, as they were in the base year. 

Next, generalized exponential models (GEM) (Folsom and Singh 2000) were used. The 

GEM approach is a general version of weighting adjustments and was based on a generalization 

of Deville and Särndal’s logit model (Deville and Särndal 1992). GEM is not a competing 

method to weighting classes or logistic regression; rather, it is a method employed to do weight 

adjustments with a choice of optional features to employ. It is a formalization of weighting 

procedures such as nonresponse adjustment, poststratification, and weight trimming. GEM 

controls at the margins as opposed to controlling at the cell level, as weighting class adjustments. 

This approach allows more variables to be considered. GEM is designed so that the sum of the 

unadjusted weights for all eligible units equals the sum of the adjusted weights for respondents. 

GEM also constrains the nonresponse adjustment factors to be greater than or equal to one. 

The questionnaire-incapable students are generally included as part of the expanded set of 

cases, but a small number of hostile refusals were treated as nonrespondents. Therefore, a simple 

weighting class nonresponse adjustment was performed. The classes were formed by school 

type, given the small number of questionnaire-ineligible students. This nonresponse adjustment 

factor is WTADJ1, and these students have a second nonresponse adjustment factor (WTADJ2) 

equal to one (see below). For questionnaire-capable students, a first follow-up respondent is 

defined as a student who completed the questionnaire or a significant portion of the 

questionnaire. The variables used in the nonresponse weight adjustment were those available for 

most respondents and nonrespondents that are described in section 3.8. 

The student nonresponse was performed in two stages—refusal and other nonresponse— 

because the predictors of response propensity were potentially different at each stage. The 

nonresponse models reduce the bias due to nonresponse for the model predictor variables and 

related variables. Therefore, using these two stages of nonresponse adjustment achieved greater 

reduction in nonresponse bias to the extent that different variables were significant predictors of 

response propensity at each stage. 

For data known for most but not all students, data collected from responding students and 

weighted hot deck imputation were used so that data are available for all eligible sample 

students. These variables were main effects in the models. They were also used in Automatic 

Interaction Detection (AID) analyses (with response as the dependent variable) to determine 

important interactions for the nonresponse adjustment models. The outcomes of these first 

models were nonresponse adjustment factors (WTADJ1 and WTADJ2). The unequal weighting 

effects (UWEs) and maximum adjustment factors were monitored to ensure reasonable values. 

Next, the GEM approach was used to poststratify the nonresponse adjusted weights— 

that is, F1DWT * WTADJ1 * WTADJ2—to meet overall and marginal totals of the base-year 

expanded sample weights (BYEXPWT). The full expanded sample was included in this 

adjustment, and the control totals were the base-year expanded weight sums, because students 

can potentially move in and out of being questionnaire incapable (i.e., being questionnaire 

capable or questionnaire incapable is not static). The variables used in poststratification were 

school type and student race/ethnicity. This adjustment ensures that the first follow-up weight 

62 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

sums are equal to the base-year weight sums for these variables. GEM generated a 

poststratification adjustment factor (WTADJ3). 

Extreme weights occur in the ELS:2002 data due to small probabilities of sample 

selection or due to weight adjustments. These extreme weights (either very small or very large) 

can significantly increase the variance of estimates. One way to account for this and decrease 

the variance is to trim and smooth extreme weights within prespecified domains. Note that 

trimming weights has the potential to increase bias. However, the increase in bias is often offset 

by the decrease in variance due to weight trimming. As a result, this reduces the mean square 

error (MSE) of an estimate, defined as variance plus bias squared. 

The innovation introduced in GEM is the ability to incorporate specific lower and upper 

bounds. An important application of this feature is to identify at each adjustment step an initial 

set of cases with extreme weights and to use specific bounds to exercise control over the final 

adjusted weights. Thus, there is built-in control for extreme weights in GEM. 

GEM uses the median +/– X * IQR, where X is any number, typically between 2 and 3, 

and IQR is the interquartile range. There are also different points in the weight adjustment 

process during which weight trimming can occur. GEM has options to make adjustments for 

extreme weights as part of the nonresponse and as part of the poststratification. GEM adjusted 

for ELS:2002 extreme weights during both nonresponse adjustments, as well as during the 

poststratification. For GEM, a variable or set of variables is identified to be used to identify 

extreme weights within each level of the variable(s), and the variables race and school type were 

chosen. Prior to running GEM, the unweighted and weighted percentage of extreme weights was 

examined for all four levels of race crossed with the three levels of school type using various 

values to multiply by the IQR (2.0, 2.1, 2.2,...4.0), and the value of 2.5 was chosen. 

The final student weight for the expanded sample student i is the product of the first 

follow-up design weight, the nonresponse adjustment factors, and the poststratification factor, 

such that 

F1EXPWTi = F1DWTi * WTADJ1i * WTADJ2i * WTADJ3i. 

3.4.4.2 F1EXPWT for First Follow-up Freshened Sample Students 

The expanded sample cross-sectional weight was computed for eligible freshened sample 

students who fully or partially completed the questionnaire or who were incapable of completing 

the questionnaire. These sample students were not in the base-year population (i.e., not in 10th 

grade in the United States in spring 2002). During 2002, such students may have been out of the 

country, been enrolled in school in the United States in a grade other than 10th, had an extended 

illness or injury, been institutionalized, been homeschooled, or temporarily dropped out of 

school. A 12th-grade enrollment list was requested from each base-year school or from the new 

school if the base-year school was closed, split, or did not enroll 12th-graders. Students were 

identified who were on the 12th-grade enrollment list but not on the sophomore list. Each of 

these students was linked to a student on the sophomore enrollment list, and they were selected 

for the freshened sample if the linked sophomore had been selected for the base-year sample. 

63 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

The first follow-up design weight (F1DWT) for each freshened sample student is therefore equal 

to the base-year design weight of the linked sophomore. 

After the freshened sample students were selected, the schools were asked to identify 

those that were eligible for freshening (i.e., those that were not in the base-year population). Of 

2,702 sampled freshened students, 425 (16 percent) were determined by the school to be eligible. 

Freshened eligibility was determined by the school for all freshened students. However, more 

than 150 of these freshened students determined by the school to be eligible were later 

determined during the student interview to be ineligible. There were no nonresponding 

freshened students with undetermined eligibility. 

In the first follow-up, 612 schools sent a 12th-grade enrollment list that was sufficient for 

selecting freshened students. This number includes new schools that were added as a result of 

base-year schools that split. Another 13 schools did not send a 12th-grade enrollment list 

because they either did not have any 12th-graders that were new to the school since spring 2002 

or they did not enroll 12th-graders. Therefore, 127 of the 752 base-year participating schools did 

not provide a freshened list. 

The freshened student weights were adjusted upward to account for the school 

nonresponse to freshening. Weighting classes were formed from the variables school type and 

school metropolitan status. Each class had a minimum of 30 eligible freshened students. First, 

the average number of eligible freshened students per school that sent in a 12th-grade list was 

calculated. Next, this average was multiplied by the number of schools that did not send in a list. 

Then, this number was added to the eligible freshened students, and this sum was divided by the 

number of eligible freshened students. The result is the weight adjustment factor WTADJ1j for 

weighting class j: 

WTADJ1j = ((Avgj * NRj) + FEj) / FEj, 

where: 

Avgj is the average number of eligible freshened students per school that sent in a 12thgrade list in weighting class j; 

NRj is the number of schools in weighting class j that did not respond to the request to 

send in a 12th-grade list; and 

FEj is the number of eligible freshened students in weighting class j. 

The nonresponse adjustment for the freshened sample students was done together with 

the nonresponse adjustment for the base-year sample students because of the small number of 

eligible freshened students. A flag for freshened students was included in the nonresponse 

models. The outcomes of the nonresponse models were nonresponse adjustment factors 

(WTADJ2 and WTADJ3). 

64 
 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 17 presents the final predictor variables used in the first-stage student nonresponse 

adjustment model, which includes both base-year and freshened sample students. This table also 

includes the average weight adjustment factors resulting from these variables: 3.73 percent 

unweighted and 14.30 percent weighted of the students were identified as having extreme 

weights. The first stage of nonresponse adjustment factors met the following constraints: 

• minimum: 0.10 

• median: 1.08 

• maximum: 2.12 

Table 18 presents the final predictor variables used in the second-stage student 

nonresponse adjustment model, which includes both base-year and freshened sample students. 

This table includes the average weight adjustment factors resulting from these variables: 3.13 

percent unweighted and 8.93 percent weighted of the students were identified as having extreme 

weights. The second stage of nonresponse adjustment factors met the following constraints: 

• minimum: 0.09 

• median: 1.05 

• maximum: 2.35 

Table 17. Average weight adjustment factors used to adjust cross-sectional weights for refusal, 

by selected characteristics: 2004 

Model predictor variables1 

 Total 

Number of responding 

students and “other” 

nonresponding 

students2 

15,608 

Weighted 

response 

rate 

94.97 

Average 

weight 

adjustment 

factor 

1.11 

School sector 

 Public 

 Catholic 

 Other private 

12,262 

1,929 

1,417 

95.07 

94.63 

92.89 

1.11 

1.07 

1.20 

School urbanicity

 Urban 

 Suburban 

 Rural 

5,325 

7,449 

2,834 

94.56 

94.79 

96.05 

1.13 

1.10 

1.09 

10th-grade enrollment 

 0–99 

 100–249 

 250–499 

≥ 500 

See notes at end of table. 

3,033 

3,971 

4,992 

3,612 

96.26 

95.71 

94.69 

94.22 

1.11 

1.08 

1.12 

1.12 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 17. Average weight adjustment factors used to adjust cross-sectional weights for refusal, 

by selected characteristics: 2004—Continued 

Number of 

responding students Average 

and “other” Weighted weight 

Model predictor variables1 nonresponding 

students2 response rate 

adjustment 

factor 

Type of grades within school 

 K–12, PreK–10th, 1st–12th, PreK/1st–9th/12th and PreK–12 

 schools 1,021 95.97 1.21 

Middle grades but no elementary 1,638 95.14 1.08 

 Only high school 12,949 94.90 1.10 

Number of grades within the school 

4 11,906 95.03 1.10 

> or < 4 3,702 94.73 1.13 

Number of days in school year 

Less than 180 days 4,055 95.49 1.10 

 180 days 8,642 95.10 1.11 

More than 180 days 2,911 93.88 1.13 

Minutes per class period 

≤ 45 3,733 94.65 1.11 

 46–50 3,346 94.59 1.11 

 51–80 4,168 94.85 1.13 

≥ 81 4,361 95.56 1.09 

Class periods per day

 1–4 4,504 95.60 1.09 

 5–6 3,849 94.33 1.12 

7 4,215 94.63 1.11 

 8–9 3,040 95.33 1.11 

IEP3 percentage 

≤ 5 percent 6,042 94.77 1.11 

 6–10 percent 4,023 94.88 1.10 

 11–15 percent 3,450 95.29 1.10 

> 15 percent 2,093 94.93 1.14 

LEP4

 percentage 

 0 percent 6,722 95.73 1.10 

 1 percent 3,053 94.24 1.11 

 2–5 percent 2,631 94.44 1.11 

≥ 6 percent 3,202 95.01 1.13 

See notes at end of table. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 17. Average weight adjustment factors used to adjust cross-sectional weights for refusal, 

by selected characteristics: 2004—Continued 

Number of 

responding students Average 

and “other” Weighted weight 

Model predictor variables1 nonresponding 

students2 response rate 

adjustment 

factor 

Free or reduced-price lunch 

0 percent 2,753 92.89 1.11 

1–10 percent 3,484 93.72 1.12 

11–30 percent 4,693 95.45 1.11 

≥ 31 percent 4,678 95.95 1.09 

Number of full-time teachers 

 1–40 4,033 96.00 1.09 

 41–70 3,938 95.13 1.09 

 71–100 4,038 94.70 1.13 

 > 100 3,599 94.48 1.12 

Number of part-time teachers

 0–1 4,545 95.17 1.10 

 2–3 4,467 95.48 1.11 

 4–6 3,768 94.11 1.12 

≥ 7 2,828 94.85 1.11 

Full-time teachers certified 

 0–90 percent 4,016 95.63 1.11 

 91–99 percent 2,755 94.46 1.11 

 100 percent 8,837 94.97 1.11 

School coeducational status 

 Coeducational school 14,814 95.00 1.11 

 All-female school 366 91.82 1.08 

 All-male school 428 95.08 1.06 

Total enrollment 

 0–600 students 3,672 96.45 1.09 

 601–1,200 students 4,652 94.68 1.11 

 1,201–1,800 students 3,563 94.70 1.10 

 > 1,800 students 3,721 94.59 1.13 

See notes at end of table. 

67 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 17. Average weight adjustment factors used to adjust cross-sectional weights for refusal, 

by selected characteristics: 2004—Continued 

Number of 

responding students Average 

and “other” Weighted weight 

Model predictor variables1 nonresponding 

students2 response rate 

adjustment 

factor 

Census region 

Northeast 2,881 94.65 1.12 

Midwest 3,903 95.04 1.10 

South 5,629 95.79 1.08 

West 3,195 93.94 1.16 

All other races 10th-grade enrollment 

≤ 80 percent 7,821 95.09 1.11 

> 80 percent 7,787 94.84 1.11 

Asian 10th-grade enrollment 

≤ 2 percent 6,034 95.25 1.09 

> 2 percent 9,574 94.80 1.12 

Black or African American 10th-grade enrollment 

≤ 4 percent 5,279 94.50 1.11 

> 4 percent 10,329 95.21 1.11 

Hispanic or Latino 10th-grade enrollment 

≤ 3 percent 5,993 94.63 1.10 

> 3 percent 9,615 95.17 1.11 

CHAID5

 segments 

CHAID segment 1 = 1–40 full-time teachers; public school; 

≤ 2 percent Asian 10th-grade enrollment 1,323 94.41 1.12 

CHAID segment 2 = 1–40 full-time teachers; public school; 

> 2 percent Asian 10th-grade enrollment 405 87.90 1.15 

CHAID segment 3 = 1–40 full-time teachers; Catholic and 

other private schools; race = Hispanic or other 751 96.00 1.09 

CHAID segment 4 = 1–40 full-time teachers; Catholic and 

other private schools; race = Asian or Black 1,119 94.26 1.10 

CHAID segment 5 = 41–70 full-time teachers; 0–6 part-time 

 teachers; 1–6 class periods 599 90.59 1.16 

CHAID segment 6 = 41–70 full-time teachers; 0–6 part-time 

 teachers; 7–9 class periods 1,055 94.61 1.11 

CHAID segment 7 = 41–70 full-time teachers; ≥ 7 part-time 

 teachers; ≤ 180 school days 985 92.90 1.15 

CHAID segment 8 = 41–70 full-time teachers; ≥ 7 part-time 

 teachers; > 180 school days 1,052 98.62 1.07 

See notes at end of table. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 17. Average weight adjustment factors used to adjust cross-sectional weights for refusal, 

by selected characteristics: 2004—Continued 

Number of 

responding students Average 

and “other” Weighted weight 

Model predictor variables1 nonresponding 

students2 response rate 

adjustment 

factor 

CHAID5

 segments—Continued 

CHAID segment 9 = > 70 full-time teachers; 0–1 part-time

 teachers; ≤ 80 percent other 10th-grade enrollment 1,747 97.40 1.05 

CHAID segment 10 = > 70 full-time teachers; 0–1 part-time 

 teachers; > 80 percent other 10th-grade enrollment 2,546 96.47 1.10 

CHAID segment 11 = > 70 full-time teachers; ≥ 2 part-time 

 teachers; ≤ 45 minutes per class 1,966 95.18 1.11 

CHAID segment 12 = > 70 full-time teachers; ≥ 2 part-time 

 teachers; 46–80 minutes per class 197 98.37 1.15 

CHAID segment 13 = > 70 full-time teachers; ≥ 2 part-time 

 teachers; ≥ 81 minutes per class 645 91.04 1.16 

CHAID segment 14 = 11+ percent free or reduced-price

 lunch; in-school out-of-grade enrollment status; 1,801+

 total enrollment 526 95.86 1.14 

CHAID segment 15 = 11+ percent free or reduced-price 

lunch; out-of-school enrollment status; race = Asian, 

 White, or other 325 86.98 1.21 

CHAID segment 16 = 11+ percent free or reduced-price 

lunch; out-of-school enrollment status; race = Black, 

 Hispanic, Indian, or Pacific Islander 367 94.06 1.10 

Sex

 Male 7,811 95.16 1.11 

 Female 7,797 94.77 1.10 

Race/ethnicity6

 All other races 9,517 94.56 1.13 

 Asian 1,744 94.80 1.09 

 Black or African American 2,345 95.88 1.08 

 Hispanic or Latino 2,002 95.90 1.06 

Freshened status 

 Freshened 186 88.75 1.16 

Enrollment status 

In school, in grade (in grade 12) 12,842 95.67 1.10 

In school, out of grade (in grade 10 or 11, ungraded, or 

graduated early) 1,892 93.33 1.15 

Out of school (dropout or homeschooled) 874 90.20 1.17 

See notes at end of table. 

69 
 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 17. Average weight adjustment factors used to adjust cross-sectional weights for refusal, 

by selected characteristics: 2004—Continued 

Number of 

responding students Average 

and “other” Weighted weight 

Model predictor variables1 nonresponding 

students2 response rate 

adjustment 

factor 

School sector and race/ethnicity 

Public schools, All other races 6,882 94.68 1.13 

 Public schools, Asian 1,589 94.59 1.10 

Public schools, Black or African American 2,076 95.96 1.07 

 Public schools, Hispanic or Latino 1,715 95.89 1.07 

 Catholic schools, All other races 1,464 94.77 1.07 

 Catholic schools, Asian 77 98.06 1.02 

Catholic schools, Black or African American 175 92.37 1.09 

 Catholic schools, Hispanic or Latino 213 93.85 1.02 

Other private school, All other races 1,171 91.86 1.21 

 Other private schools, Asian 78 96.49 1.04 

Other private schools, Black or African American 94 94.35 1.30 

Other private schools, Hispanic or Latino 74 98.37 1.01 

1 Model predictor variables had a value of 0 or 1. Some of the listed model predictor variables were not actually in the model 

because they served as reference groups. For each group of variables, one of the categories (predictor variable) was used as a 

reference group. 

2 “Other” nonresponding students are students who were nonrespondents but did not explicitly refuse. Responding students are 

grouped with the “other” nonrespondents for the first nonresponse adjustment that adjusts for refusals. 

3 IEP = Individualized Education Program. 

4 LEP = limited English proficient. 

5 CHAID = chi-squared automatic interaction detection. 

6

 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and Multiracial. All race 

categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 

(ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 18. Average weight adjustment factors used to adjust cross-sectional weights for other 

nonresponse, by selected characteristics: 2004 

Average 

Number of Weighted weight 

Model predictor variables1 responding 

students 

response 

rate 

adjustment 

factor 

 Total 14,884 94.83 1.06 

School sector 

 Public 11,604 94.63 1.07 

 Catholic 1,899 98.69 1.02 

 Other private 1,381 95.38 1.06 

School urbanicity

 Urban 5,020 93.12 1.08 

 Suburban 7,140 95.42 1.06 

 Rural 2,724 95.98 1.05 

10th-grade enrollment 

 0–99 2,922 95.77 1.06 

 100–249 3,847 96.45 1.04 

 250–499 4,760 95.01 1.07 

≥ 500 3,355 93.00 1.09 

Type of grades within school 

K–12, PreK–10th, 1st–12th, PreK/1st–9th/12th and PreK–12 schools 995 96.05 1.06 

Middle grades but no elementary 1,570 95.29 1.05 

 Only high school 12,319 94.72 1.07 

Number of grades within the school 

4 11,330 94.72 1.07 

> or < 4 3,554 95.28 1.06 

Number of days in school year 

Less than 180 days 3,897 95.24 1.05 

 180 days 8,228 94.74 1.07 

More than 180 days 2,759 94.58 1.07 

Minutes per class period 

≤ 45 3,574 95.12 1.06 

 46–50 3,203 95.52 1.06 

 51–80 3,970 94.65 1.07 

≥ 81 4,137 94.33 1.07 

See notes at end of table. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 18. Average weight adjustment factors used to adjust cross-sectional weights for other 

nonresponse, by selected characteristics: 2004—Continued 

Average 

Number of Weighted weight 

Model predictor variables1 responding 

students 

response 

rate 

adjustment 

factor 

Class periods per day

 1–4 4,277 94.59 1.07 

 5–6 3,654 94.66 1.07 

7 4,029 94.65 1.06 

 8–9 2,924 95.80 1.06 

IEP2

 percentage 

≤ 5 percent 5,848 95.76 1.05 

 6–10 percent 3,811 94.64 1.07 

 11–15 percent 3,260 94.52 1.08 

> 15 percent 1,965 94.12 1.09 

LEP3

 percentage 

 0 percent 6,501 96.08 1.05 

 1 percent 2,932 95.96 1.05 

 2–5 percent 2,476 93.57 1.08 

≥ 6 percent 2,975 92.99 1.09 

Free or reduced-price lunch 

 0 percent 2,691 97.07 1.04 

 1–10 percent 3,372 96.46 1.05 

 11–30 percent 4,447 94.61 1.07 

≥ 31 percent 4,374 93.27 1.09 

Number of full-time teachers 

 1–40 3,886 96.16 1.05 

 41–70 3,812 96.56 1.05 

 71–100 3,810 94.16 1.08 

 > 100 3,376 93.29 1.08 

Number of part-time teachers

 0–1 4,273 93.69 1.07 

 2–3 4,287 95.35 1.06 

 4–6 3,608 95.22 1.06 

≥ 7 2,716 95.54 1.06 

See notes at end of table. 

72 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 18. Average weight adjustment factors used to adjust cross-sectional weights for other 

nonresponse, by selected characteristics: 2004—Continued 

Average 

Number of Weighted weight 

Model predictor variables1 responding 

students 

response 

rate 

adjustment 

factor 

Full-time teachers certified 

 0–90 percent 3,846 94.32 1.06 

 91–99 percent 2,606 93.92 1.08 

 100 percent 8,432 95.26 1.06 

School coeducational status 

 Coeducational school 14,100 94.74 1.07 

 All-female school 362 98.80 1.01 

 All-male school 422 99.16 1.01 

Total enrollment 

 0–600 students 3,546 96.14 1.05 

 601–1,200 students 4,490 96.13 1.05 

 1,201–1,800 students 3,371 94.14 1.07 

 > 1,800 students 3,477 93.44 1.09 

Census region

 Northeast 2,751 95.33 1.06 

 Midwest 3,723 95.03 1.06 

 South 5,375 94.62 1.06 

West 3,035 94.56 1.08 

All other races 10th-grade enrollment 

≤ 80 percent 7,349 93.41 1.08 

> 80 percent 7,535 96.36 1.05 

Asian 10th-grade enrollment 

≤ 2 percent 5,747 94.48 1.06 

> 2 percent 9,137 95.05 1.06 

Black or African American 10th-grade enrollment 

≤ 4 percent 5,100 96.09 1.05 

> 4 percent 9,784 94.18 1.07 

Hispanic or Latino 10th-grade enrollment 

≤ 3 percent 5,773 96.01 1.05 

> 3 percent 9,111 94.13 1.07 

See notes at end of table. 

73 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 18. Average weight adjustment factors used to adjust cross-sectional weights for other 

nonresponse, by selected characteristics: 2004—Continued 

Average 

Number of Weighted weight 

Model predictor variables1 responding 

students 

response 

rate 

adjustment 

factor 

CHAID4 segments 

CHAID segment 1 = ≤ 80 percent Other 10th-grade enrollment; race = 

American Indian or Alaska Native, Hispanic, race specified, or 

 White; in school, in grade 3,193 97.58 1.05 

CHAID segment 2 = ≤ 80 percent Other 10th-grade enrollment; race = 

American Indian or Alaska Native, Hispanic, race specified, or 

 White; in school, out of grade 512 91.90 1.13 

CHAID segment 3 = ≤ 80 percent Other 10th-grade enrollment; race = 

American Indian or Alaska Native, Hispanic, race specified, or 

 White; out of school 158 80.14 1.23 

CHAID segment 4 = ≤ 80 percent Other 10th-grade enrollment; race =

 Asian, Black or African American, Hispanic, no race specified, 

Multiracial, or Native Hawaiian/Pacific Islander; in school, in grade 2,550 94.74 1.06 

CHAID segment 5 = ≤ 80 percent Other 10th-grade enrollment; race =

 Asian, Black or African American, Hispanic, no race specified, 

 Multiracial, or Native Hawaiian/Pacific Islander; in school, out of 

grade 641 85.99 1.17 

CHAID segment 7 = > 80 percent Other 10th-grade enrollment; in 

 school, in grade; ≤ 2 percent Asian 10th-grade enrollment 2,803 96.99 1.04 

CHAID segment 10 = > 80 percent Other 10th-grade enrollment; in 

school, out of grade; 5 or more class periods per day 389 94.08 1.08 

CHAID segment 11 = > 80 percent Other 10th-grade enrollment; out of 

 school; 0 percent LEP students 135 78.16 1.29 

CHAID segment 12 = > 80 percent Other 10th-grade enrollment; out of 

 school; 1 percent LEP students 60 93.14 1.08 

Sex

 Male 7,432 94.76 1.07 

 Female 7,452 94.91 1.06 

Race/ethnicity5

 All other races 9,196 96.28 1.05 

 Asian 1,658 94.51 1.07 

 Black or African American 2,182 92.39 1.09 

 Hispanic or Latino 1,848 90.92 1.10 

Freshened status 

 Freshened 171 90.15 1.13 

See notes at end of table. 

74 
 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 18. Average weight adjustment factors used to adjust cross-sectional weights for other 

nonresponse, by selected characteristics: 2004—Continued 

Average 

Number of Weighted weight 

Model predictor variables1 responding 

students 

response 

rate 

adjustment 

factor 

Enrollment status 

In school, in grade (in grade 12) 12,476 97.02 1.04 

In school, out of grade (in grade 10 or 11, ungraded, or graduated 

early) 1,697 89.14 1.14 

Out of school (dropout or homeschooled) 711 79.91 1.25 

School sector and race/ethnicity 

Public schools, All other races 6,598 96.03 1.06 

 Public schools, Asian 1,510 94.43 1.07 

Public schools, Black or African American 1,924 92.30 1.09 

 Public schools, Hispanic or Latino 1,572 91.12 1.10 

 Catholic schools, All other races 1,448 99.05 1.01 

 Catholic schools, Asian 73 96.00 1.04 

Catholic schools, Black or African American 171 97.66 1.03 

 Catholic schools, Hispanic or Latino 207 97.07 1.03 

Other private school, All other races 1,150 98.08 1.04 

 Other private schools, Asian 75 94.99 1.07 

Other private schools, Black or African American 87 92.20 1.12 

Other private schools, Hispanic or Latino 69 77.53 1.31 

1

 Model predictor variables had a value of 0 or 1. Some of the listed model predictor variables were not actually in 

the model because they served as reference groups. For each group of variables, one of the categories (predictor 

variable) was used as a reference group. 

2

 IEP = Individualized Education Program. 

3

 LEP = limited English proficient. 

4

 CHAID = chi-squared automatic interaction detection. 

5

 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and 

Multiracial. All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

The freshened students were not included with the base-year sample students in the 

poststratification because, as in the base year, there are no reliable external counts of 12thgraders to use for control totals. Table 19 presents the poststratification control totals and the 

average weight adjustment factors for base-year students needed to achieve these totals: 2.00 

percent unweighted and 6.00 percent weighted of the students were identified as having extreme 

weights. The base-year student poststratification met the following constraints: 

• minimum: 0.07 

• median: 1.01 

• maximum: 1.04 

75 
 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 19. Average weight adjustment factors for poststratifying cross-sectional weights to control 

totals, by selected characteristics: 2004 

Average weight 

Model variable1

 Control total2 adjustment factor 

 Total 3,474,053 1.00 

Census region

 Northeast 641,468 1.00 

 Midwest 841,308 1.00 

 South 1,193,807 1.00 

 West 797,471 1.00 

School sector 

 Public 3,210,979 1.00 

 Catholic 146,214 1.00 

 Other private 116,860 1.01 

Sex

 Male 1,760,242 1.01 

 Female 1,713,810 1.00 

Race/ethnicity3

 All other races 2,311,679 1.00 

 Asian 134,793 1.00 

Black or African American 557,835 1.00 

 Hispanic or Latino 469,746 1.00 

1

 Model variables had a value of 0 or 1. 

2 The control totals were the base-year expanded weight sums (i.e., 10th-graders in spring 2002); 12th-grade 

freshened students were not included in the poststratification. 3

 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and 

Multiracial. All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

The final student weight for freshened student i is the product of the first follow-up 

design weight, the school nonresponse to freshening factor, and the nonresponse adjustment 

factors, such that 

F1EXPWTi = F1DWTi * WTADJ1i * WTADJ2i * WTADJ3i. 

Table 20 shows the statistical properties of F1EXPWT. 

3.4.4.3 F1QWT 

For sample students who fully or partially completed the first follow-up questionnaire, 

F1QWT = F1EXPWT. F1QWT is equal to F1EXPWT for sample students who fully or partially 

completed the questionnaire because such students are a subset of the expanded sample that 

76 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

includes students who fully or partially completed the questionnaire and students incapable of 

completing the questionnaire. Table 20 also shows the statistical properties of F1QWT. 

Table 20. Statistical properties of cross-sectional weights: 2004 

Weight F1QWT F1EXPWT 

Mean 232.29 232.36 

Variance 26,283.59 26,249.80 

Standard deviation 162.12 162.02 

Coefficient of variation (x 100) 69.79 69.73 

Minimum 1.77 1.77 

Maximum 1,427.47 1,427.47 

Skewness 1.21 1.21 

Kurtosis 2.41 2.41 

Sum 3,481,853.86 3,506,024.17 

Number of cases 14,989 15,089 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

3.4.4.4 F1XPNLWT 

The panel weight was computed for the expanded sample students who have base-year 

and first follow-up data. Such students include questionnaire-capable students who completed 

full or partial questionnaires in both the base year and first follow-up, questionnaire-capable 

students who completed full or partial questionnaires in the first follow-up and had base-year 

data imputed if not on the NPS (see section 3.6), and students who were questionnaire incapable 

in the base year and/or the first follow-up. The same procedures were used that were used in 

developing the first follow-up weight for base-year sample students. That is, GEM was used to 

perform nonresponse adjustment, extreme value adjustment, and poststratification. The same 

variables were input for GEM as for F1QWT, and the control totals from the base year for 

poststratification were the same as for F1QWT. 

Table 21 presents the final predictor variables used in the first-stage student nonresponse 

adjustment model and the average weight adjustment factors resulting from these variables: 

3.66 percent unweighted and 14.20 percent weighted of the students were identified as having 

extreme weights. The first stage of nonresponse adjustment factors met the following 

constraints: 

• minimum: 0.10 

• median: 1.08 

• maximum: 2.12 

Table 22 presents the final predictor variables used in the second-stage student 

nonresponse adjustment model and the average weight adjustment factors resulting from these 

variables: 2.99 percent unweighted and 8.54 percent weighted of the students were identified as 

having extreme weights. The second stage of nonresponse adjustment factors met the following 

constraints: 

77 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

• minimum: 0.09 

• median: 1.05 

• maximum: 1.94 

Table 21. Average weight adjustment factors used to adjust panel weights for refusal, by selected 

characteristics: 2004 

Number of 

responding sample Average 

members and Weighted weight 

Model predictor variables1 “other” 

nonrespondents2 response rate 

adjustment 

factor 

 Total 15,422 94.02 1.11 

School sector 

 Public 12,103 94.10 1.10 

 Catholic 1,928 93.67 1.07 

 Other private 1,391 92.21 1.20 

School urbanicity

 Urban 5,246 93.94 1.13 

 Suburban 7,366 93.48 1.10 

 Rural 2,810 95.49 1.09 

10th-grade enrollment 

 0–99 2,999 96.19 1.11 

 100–249 3,944 95.09 1.08 

 250–499 4,933 93.28 1.12 

≥ 500 3,546 93.22 1.12 

Type of grades within school 

 K–12, PreK–10th, 1st–12th, PreK/1st–9th/12th and PreK–12 

 schools 1,006 95.84 1.21 

Middle grades but no elementary 1,621 94.58 1.08 

Only high school 12,795 93.87 1.10 

Number of grades within the school 

4 11,769 94.06 1.10 

> or < 4 3,653 93.85 1.13 

Number of days in school year 

Less than 180 days 4,010 94.53 1.10 

 180 days 8,539 94.19 1.11 

More than 180 days 2,873 92.84 1.12 

See notes at end of table. 

78 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 21. Average weight adjustment factors used to adjust panel weights for refusal, by selected 

characteristics: 2004—Continued 

Model predictor variables1 

Minutes per class period 

≤ 45 

 46–50 

 51–80 

≥ 81 

Number of 

responding sample 

members and 

“other” 

nonrespondents2 

3,696 

3,303 

4,118 

4,305 

Weighted 

response 

rate 

93.07 

93.82 

93.84 

94.93 

Average 

weight 

adjustment 

factor 

1.11 

1.11 

1.12 

1.09 

Class periods per day

 1–4 

 5–6 

7 

 8–9 

4,444 

3,804 

4,163 

3,011 

95.05 

93.19 

93.63 

94.03 

1.09 

1.12 

1.11 

1.11 

IEP3

 percentage 

≤ 5 percent 

 6–10 percent 

 11–15 percent 

> 15 percent 

5,995 

3,968 

3,398 

2,061 

93.53 

93.84 

94.64 

94.18 

1.11 

1.09 

1.10 

1.13 

LEP4

 percentage 

 0 percent 

 1 percent 

 2–5 percent 

≥ 6 percent 

6,674 

3,017 

2,601 

3,130 

94.52 

93.30 

93.48 

94.42 

1.10 

1.11 

1.11 

1.13 

Free or reduced-price lunch 

 0 percent 

 1–10 percent 

 11–30 percent 

≥ 31 percent 

2,729 

3,458 

4,623 

4,612 

91.90 

91.89 

94.88 

95.30 

1.12 

1.12 

1.11 

1.09 

Number of full-time teachers 

 1–40 

 41–70 

 71–100 

 > 100 

4,004 

3,902 

3,981 

3,535 

95.62 

94.45 

93.62 

93.09 

1.09 

1.09 

1.13 

1.12 

See notes at end of table. 

79 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 21. Average weight adjustment factors used to adjust panel weights for refusal, by selected 

characteristics: 2004—Continued 

Number of 

responding sample Average 

members and Weighted weight 

Model predictor variables1 “other” 

nonrespondents2 response rate 

adjustment 

factor 

Number of part-time teachers

 0–1 4,470 94.58 1.10 

 2–3 4,414 94.50 1.11 

 4–6 3,728 93.25 1.12 

≥ 7 2,810 93.26 1.11 

Full-time teachers certified 

 0–90 percent 3,964 95.68 1.11 

 91–99 percent 2,721 93.20 1.11 

 100 percent 8,737 93.86 1.11 

School coeducational status 

 Coeducational school 14,636 94.04 1.11 

 All-female school 365 91.02 1.09 

 All-male school 421 94.23 1.06 

Total enrollment 

 0–600 students 3,637 96.23 1.09 

 601–1,200 students 4,614 93.68 1.11 

 1,201–1,800 students 3,508 93.31 1.10 

 > 1,800 students 3,663 93.65 1.13 

Census region

 Northeast 2,841 92.88 1.12 

 Midwest 3,877 94.41 1.09 

 South 5,558 94.84 1.09 

West 3,146 93.29 1.15 

All other races 10th-grade enrollment 

≤ 80 percent 7,708 94.55 1.11 

> 80 percent 7,714 93.46 1.11 

Asian 10th-grade enrollment 

≤ 2 percent 5,962 94.46 1.09 

> 2 percent 9,460 93.74 1.12 

See notes at end of table. 

80 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 21. Average weight adjustment factors used to adjust panel weights for refusal, by selected 

characteristics: 2004—Continued 

Number of 

responding sample Average 

members and Weighted weight 

Model predictor variables1 “other” 

nonrespondents2 response rate 

adjustment 

factor 

Black or African American 10th-grade enrollment 

≤ 4 percent 5,219 93.66 1.11 

> 4 percent 10,203 94.20 1.11 

Hispanic or Latino 10th-grade enrollment 

≤ 3 percent 5,933 93.18 1.10 

> 3 percent 9,489 94.53 1.11 

CHAID5

 segments 

CHAID segment 1 = 0–10 percent free or reduced-price lunch; 

0–99 sophomores; 1–40 full-time teachers 1,306 93.99 1.11 

CHAID segment 2 = 0–10 percent free or reduced-price lunch; 

0–99 sophomores; > 40 full-time teachers 395 87.38 1.15 

CHAID segment 3 = 0–10 percent free or reduced-price lunch; 

100–249 sophomores; 4 grade levels within the school 1,416 95.02 1.08 

CHAID segment 4 = 0–10 percent free or reduced-price lunch; 

100–249 sophomores; > or < 4 grade levels within the 

 school 451 91.25 1.16 

CHAID segment 5 = 0–10 percent free or reduced-price lunch; 

 > 249 sophomores; ≤ 10 percent IEP 1,654 89.94 1.13 

CHAID segment 6 = 0–10 percent free or reduced-price lunch; 

 > 249 sophomores; 11–15 percent IEP 712 93.90 1.11 

CHAID segment 7 = 0–10 percent free or reduced-price lunch; 

> 249 sophomores; > 15 percent IEP 253 87.93 1.21 

CHAID segment 8 = > 10 percent free or reduced-price lunch; 

in school, in grade (in grade 12); 0–99 sophomores 1,045 98.74 1.07 

CHAID segment 9 = > 10 percent free or reduced-price lunch; 

in school, in grade (in grade 12); 100–249 sophomores 1,724 97.37 1.05 

CHAID segment 10 = > 10 percent free or reduced-price 

lunch; in school, in grade (in grade 12); 250–499 

 sophomores 2,499 95.75 1.10 

CHAID segment 11 = > 10 percent free or reduced-price 

lunch; in school, in grade (in grade 12); ≥ 500 sophomores 1,910 94.48 1.11 

CHAID segment 12 = > 10 percent free or reduced-price

 lunch; in school, out of grade (in grade 10 or 11, ungraded, 

or graduated early); total enrollment 0–600 students 197 98.55 1.15 

CHAID segment 13 = > 10 percent free or reduced-price

 lunch; in school, out of grade (in grade 10 or 11, ungraded, 

or graduated early); total enrollment 601–1,800 students 645 88.90 1.16 

CHAID segment 14 = > 10 percent free or reduced-price

 lunch; in school, out of grade (in grade 10 or 11, ungraded, 

or graduated early); total enrollment > 1,800 students 526 95.61 1.13 

See notes at end of table. 

81 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 21. Average weight adjustment factors used to adjust panel weights for refusal, by selected 

characteristics: 2004—Continued 

Number of 

responding sample Average 

members and Weighted weight 

Model predictor variables1 “other” 

nonrespondents2 response rate 

adjustment 

factor 

CHAID segments—Continued 

CHAID segment 15 = > 10 percent free or reduced-price lunch; 

out of school (dropout or homeschooled); race = Asian, 

 White, or Other 325 85.08 1.21 

CHAID segment 16 = > 10 percent free or reduced-price lunch; 

out of school (dropout or homeschooled); race = Black, 

 Hispanic, Indian, or Pacific Islander 364 94.55 1.10 

Sex

 Male 7,703 94.00 1.11 

 Female 7,719 94.03 1.10 

Race/ethnicity6 

All other races 9,436 93.47 1.13 

 Asian 1,704 94.69 1.10 

 Black or African American 2,329 95.01 1.08 

 Hispanic or Latino 1,953 95.36 1.06 

Enrollment status 

In school, in grade (in grade 12) 12,659 94.71 1.10 

In school, out of grade (in grade 10 or 11, ungraded, or 

graduated early) 1,892 92.49 1.15 

Out of school (dropout or homeschooled) 871 89.37 1.17 

School sector and race/ethnicity 

Public schools, All other races 6,817 93.56 1.12 

 Public schools, Asian 1,556 94.53 1.10 

Public schools, Black or African American 2,062 95.09 1.07 

 Public schools, Hispanic or Latino 1,668 95.35 1.07 

 Catholic schools, All other races 1,464 93.71 1.07 

 Catholic schools, Asian 76 98.10 1.02 

Catholic schools, Black or African American 175 91.67 1.09 

 Catholic schools, Hispanic or Latino 213 93.19 1.02 

Other private school, All other races 1,155 91.15 1.21 

See notes at end of table. 

82 
 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 21. Average weight adjustment factors used to adjust panel weights for refusal, by selected 

characteristics: 2004—Continued 

Number of 

responding sample Average 

members and Weighted weight 

Model predictor variables1 “other” 

nonrespondents2 response rate 

adjustment 

factor 

School sector and race/ethnicity—Continued 

 Other private schools, Asian 72 95.52 1.04 

Other private schools, Black or African American 92 93.47 1.28 

Other private schools, Hispanic or Latino 72 98.35 1.00 

1

 Model predictor variables had a value of 0 or 1. Some of the listed model predictor variables were not actually in 

the model because they served as reference groups. For each group of variables, one of the categories (predictor 

variable) was used as a reference group. 

2

 “Other” nonresponding students are students who were nonrespondents but did not explicitly refuse. Responding 

students are grouped with the “other” nonrespondents for the first nonresponse adjustment that adjusts for refusals. 3

 IEP = Individualized Education Program. 

4

 LEP = limited English proficient. 

5

 CHAID = chi-squared automatic interaction detection. 

6

 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and 

Multiracial. All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

83 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 22. Average weight adjustment factors used to adjust panel weights for other nonresponse, 

by selected characteristics: 2004 

Number of Average 

responding Weighted weight 

Model predictor variables1 sample 

members 

response 

rate 

adjustment 

factor 

Total 14,713 94.53 1.06 

School sector 

Public 11,460 94.25 1.07 

Catholic 1,898 98.63 1.02 

Other private 1,355 96.81 1.06 

School urbanicity 

Urban 4,950 92.60 1.08 

Suburban 7,063 95.15 1.06 

Rural 2,700 95.89 1.05 

10th-grade enrollment 

0–99 2,888 95.69 1.06 

100–249 3,823 96.27 1.04 

250–499 4,704 94.64 1.07 

≥ 500 3,298 92.61 1.09 

Type of grades within school 

K–12, PreK–10th, 1st–12th, PreK/1st–9th/12th and PreK–12 schools 980 96.74 1.06 

Middle grades but no elementary 1,555 95.23 1.05 

Only high school 12,178 94.34 1.07 

Number of grades within the school 

4 11,206 94.42 1.06 

> or < 4 3,507 94.92 1.06 

Number of days in school year 

Less than 180 days 3,856 95.13 1.05 

180 days 8,135 94.41 1.07 

More than 180 days 2,722 94.11 1.07 

Minutes per class period 

≤ 45 3,540 94.21 1.06 

46–50 3,166 95.44 1.05 

51–80 3,925 94.43 1.07 

≥ 81 4,082 94.16 1.07 

See notes at end of table. 

84 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 22. Average weight adjustment factors used to adjust panel weights for other nonresponse, 

by selected characteristics: 2004—Continued 

Number of Average 

responding Weighted weight 

Model predictor variables1 sample 

members 

response 

rate 

adjustment 

factor 

Class periods per day

 1–4 4,219 94.35 1.07 

 5–6 3,612 94.14 1.07 

7 3,984 94.80 1.06 

 8–9 2,898 95.06 1.06 

IEP2 percentage 

≤ 5 percent 5,801 95.54 1.05 

 6–10 percent 3,760 94.39 1.07 

 11–15 percent 3,213 94.16 1.07 

> 15 percent 1,939 93.64 1.09 

LEP3 percentage 

 0 percent 6,457 95.74 1.05 

 1 percent 2,897 95.65 1.05 

 2–5 percent 2,447 93.09 1.08 

≥ 6 percent 2,912 92.87 1.09 

Free or reduced-price lunch 

 0 percent 2,667 96.81 1.04 

 1–10 percent 3,347 96.11 1.05 

 11–30 percent 4,386 94.40 1.07 

≥ 31 percent 4,313 92.79 1.09 

Number of full-time teachers 

 1–40 3,857 95.68 1.05 

 41–70 3,776 96.12 1.05 

 71–100 3,759 93.80 1.08 

 > 100 3,321 93.29 1.08 

Number of part-time teachers

 0–1 4,205 93.17 1.07 

 2–3 4,241 95.58 1.06 

 4–6 3,569 94.63 1.06 

≥ 7 2,698 95.03 1.06 

See notes at end of table. 

85 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 22. Average weight adjustment factors used to adjust panel weights for other nonresponse, 

by selected characteristics: 2004—Continued 

Number of Average 

responding Weighted weight 

Model predictor variables1 sample 

members 

response 

rate 

adjustment 

factor 

Full-time teachers certified 

 0–90 percent 3,797 93.90 1.06 

 91–99 percent 2,577 93.63 1.07 

 100 percent 8,339 94.97 1.06 

School coeducational status 

 Coeducational school 13,937 94.43 1.07 

 All-female school 361 98.76 1.01 

 All-male school 415 99.15 1.01 

Total enrollment 

 0–600 students 3,511 96.02 1.05 

 601–1,200 students 4,453 95.78 1.05 

 1,201–1,800 students 3,321 93.95 1.07 

 > 1,800 students 3,428 92.99 1.09 

Census region

 Northeast 2,715 94.53 1.06 

 Midwest 3,699 94.71 1.06 

 South 5,311 94.56 1.06 

West 2,988 94.27 1.08 

All other races 10th-grade enrollment 

≤ 80 percent 7,245 92.99 1.08 

> 80 percent 7,468 96.14 1.05 

Asian 10th-grade enrollment 

≤ 2 percent 5,684 94.04 1.06 

> 2 percent 9,029 94.83 1.06 

Black or African American 10th-grade enrollment 

≤ 4 percent 5,043 95.75 1.05 

> 4 percent 9,670 93.88 1.07 

Hispanic or Latino 10th-grade enrollment 

≤ 3 percent 5,716 95.86 1.05 

> 3 percent 8,997 93.71 1.07 

See notes at end of table. 

86 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 22. Average weight adjustment factors used to adjust panel weights for other nonresponse, 

by selected characteristics: 2004—Continued 

Number of Average 

responding Weighted weight 

Model predictor variables1 sample 

members 

response 

rate 

adjustment 

factor 

CHAID4 segments 

CHAID segment 1 = ≤ 80 percent Other 10th-grade enrollment; out of 

 school (dropout or homeschooled) 453 79.85 1.25 

CHAID segment 2 = ≤ 80 percent Other 10th-grade enrollment; in 

school, in grade (in grade 12); 0–499 sophomores 3,751 97.30 1.04 

CHAID segment 3 = ≤ 80 percent Other 10th-grade enrollment; in 

school, in grade (in grade 12); ≥ 500 sophomores 1,888 94.13 1.08 

CHAID segment 4 = ≤ 80 percent Other 10th-grade enrollment; in 

school, out of grade (in grade 10 or 11, ungraded, or graduated 

early); region = Northeast, Midwest, or South 884 85.79 1.17 

CHAID segment 5 = ≤ 80 percent Other 10th-grade enrollment; in 

school, out of grade (in grade 10 or 11, ungraded, or graduated 

early); region = West 269 93.18 1.11 

CHAID segment 6 = > 80 percent Other 10th-grade enrollment; in 

school, in grade (in grade 12); ≤ 2 percent Asian 10th-grade 

enrollment 2,773 96.92 1.04 

CHAID segment 7 = > 80 percent Other 10th-grade enrollment; in 

school, in grade (in grade 12); > 2 percent Asian 10th-grade 

enrollment 3,893 98.31 1.03 

CHAID segment 8 = > 80 percent Other 10th-grade enrollment; in 

school, out of grade (in grade 10 or 11, ungraded, or graduated 

early); ≤ 80 minutes per class period 397 93.12 1.08 

CHAID segment 9 = > 80 percent Other 10th-grade enrollment; in 

school, out of grade (in grade 10 or 11, ungraded, or graduated 

early); ≥ 81 minutes per class period 147 84.31 1.19 

CHAID segment 10 = > 80 percent Other 10th-grade enrollment; out of 

school (dropout or homeschooled); 1–70 full-time teachers 168 84.85 1.18 

CHAID segment 11 = > 80 percent Other 10th-grade enrollment; out of 

school (dropout or homeschooled); > 71 full-time teachers 90 74.14 1.34 

Sex

 Male 7,335 94.36 1.07 

 Female 7,378 94.69 1.06 

Race/ethnicity5

 All other races 9,120 95.87 1.05 

 Asian 1,619 94.19 1.07 

 Black or African American 2,169 91.78 1.09 

 Hispanic or Latino 1,805 91.15 1.10 

See notes at end of table. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 22. Average weight adjustment factors used to adjust panel weights for other nonresponse, 

by selected characteristics: 2004—Continued 

Number of Average 

responding Weighted weight 

Model predictor variables1 sample 

members 

response 

rate 

adjustment 

factor 

Enrollment status 

In school, in grade (in grade 12) 12,305 96.92 1.04 

In school, out of grade (in grade 10 or 11, ungraded, or graduated 

early) 1,697 88.46 1.14 

Out of school (dropout or homeschooled) 711 79.97 1.25 

School sector and race/ethnicity 

Public schools, All other races 6,538 95.60 1.06 

 Public schools, Asian 1,478 94.14 1.07 

Public schools, Black or African American 1,913 91.69 1.09 

 Public schools, Hispanic or Latino 1,531 91.01 1.09 

 Catholic schools, All other races 1,448 98.95 1.01 

 Catholic schools, Asian 72 96.10 1.04 

Catholic schools, Black or African American 171 97.64 1.03 

 Catholic schools, Hispanic or Latino 207 97.12 1.03 

Other private school, All other races 1,134 97.80 1.03 

 Other private schools, Asian 69 93.82 1.07 

Other private schools, Black or African American 85 91.20 1.12 

Other private schools, Hispanic or Latino 67 87.84 1.35 

1

 Model predictor variables had a value of 0 or 1. Some of the listed model predictor variables were not actually in 

the model because they served as reference groups. For each group of variables, one of the categories (predictor 

variable) was used as a reference group. 

2

 IEP = Individualized Education Program. 

3

 LEP = limited English proficient. 

4

 CHAID = chi-squared automatic interaction detection. 

5

 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and 

Multiracial. All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

Table 23 presents the poststratification control totals and the average weight adjustment 

factors needed to achieve these totals: 1.85 percent unweighted and 5.50 percent weighted of the 

students were identified as having extreme weights. The poststratification met the following 

constraints: 

• minimum: 0.07 

• median: 1.01 

• maximum: 1.04 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 23. Average weight adjustment factors for poststratifying panel weights to control totals, by 

selected characteristics: 2004 

Model variable1 Control total2 Average weight 

adjustment factor 

 Total 3,474,053 1.00 

Census region

 Northeast 641,468 1.00 

 Midwest 841,308 1.00 

 South 1,193,807 1.00 

 West 797,471 1.00 

School sector 

 Public 3,210,979 1.00 

 Catholic 146,214 1.00 

 Other private 116,860 1.01 

Sex

 Male 1,760,242 1.01 

 Female 1,713,810 1.00 

Race/ethnicity3

 All other races 2,311,679 1.00 

 Asian 134,793 1.00 

Black or African American 557,835 1.00 

 Hispanic or Latino 469,746 1.00 

1

 Model variables had a value of 0 or 1. 

2 The control totals were the base-year expanded weight sums (i.e., 10th-graders in spring 2002); 12th-grade 

freshened students were not included in the poststratification. 3

 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and 

Multiracial. All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

Table 24 shows the statistical properties of F1XPNLWT. 

3.4.4.5 F1PNLWT 

For questionnaire-capable students who have base-year and first follow-up data, 

F1PNLWT = F1XPNLWT. F1QWT is equal to F1EXPWT for questionnaire-capable students 

who have base-year and first follow-up data because such students are a subset of the expanded 

sample that includes questionnaire-capable and questionnaire-incapable students. Table 24 also 

shows the statistical properties of F1PNLWT. 

89 
 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 24. Statistical properties of panel weights: 2004 

Weight F1PNLWT F1XPNLWT 

Mean 231.31 231.20 

Variance 25,985.12 25,883.66 

Standard deviation 161.20 160.88 

Coefficient of variation (x 100) 69.69 69.59 

Minimum 1.75 1.75 

Maximum 1,445.49 1,445.49 

Skewness 1.21 1.21 

Kurtosis 2.48 2.49 

Sum 3,403,321.11 3,441,475.79 

Number of cases 14,713 14,885 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

3.4.4.6 Quality Control 

Quality control was emphasized on all activities, including weighting. Because of the 

central importance of the analysis weights to population estimation, a senior statistician 

thoroughly checked each set of weights. The most fundamental type of check was the 

verification of totals that are algebraically equivalent (e.g., marginal totals of the weights of 

eligible students prior to nonresponse adjustment and of respondents after nonresponse 

adjustment). In addition, various analytic properties of the initial weights, the weight adjustment 

factors, and the final weights were examined, both overall and within sampling strata, including 

• distribution of the weights; 

• ratio of the maximum weight divided by the minimum weight; and 

• unequal weighting design effect, or variance inflation effect (1 + CV2

). 

Additionally, two-dimensional tables before and after weight adjustments were reviewed 

to ensure that the weight distribution was not distorted. 

3.5 Standard Errors and Design Effects 

3.5.1 Standard Errors 

For probability-based sample surveys, most estimates are nonlinear statistics. For 

example, a mean or proportion, which is expressed as Σwy/Σw,

25 is nonlinear because the 

denominator is a survey estimate of the (unknown) population total. In this situation, the 

variances of the estimates cannot be expressed in closed form. One common procedure for 

estimating variances of survey statistics is the Taylor series linearization procedure. This 

procedure takes the first-order Taylor series approximation of the nonlinear statistic and then 

substitutes the linear representation into the appropriate variance formula based on the sample 

design. Woodruff presented the mathematical formulation of this procedure (Woodruff 1971). 

25 w is the estimated population, and y is a 0/1 variable indicating whether a certain characteristic is present for the 

sample member. 

90 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

The variance estimation must also take into account stratification and clustering. There are other 

variance estimation procedures, such as jackknife and balanced repeated replication (BRR). 

However, Taylor series estimation was used for the base year and is sufficient again for the first 

follow-up. Therefore, replicate weights were not produced. However, ELS:2002/04 will be 

available at a later date as a Data Analysis System (DAS). For the DAS, BRR replicate weights 

will be used. 

Variance estimation procedures assumed a with-replacement design at the first stage of 

sampling. Because school sampling rates were moderately low, this assumption yields estimates 

that are only slightly biased in the positive direction. For stratified multistage surveys and a 

with-replacement sample design, the Taylor series procedure requires the specification of 

analysis strata and analysis primary sampling units (PSUs). In the base year, 361 analysis strata 

were formed from the sampling strata used in the first stage of sampling, and the analysis PSUs 

were the individual schools. Given that the school sample was selected using probability with 

minimum replacement (pmr), for variance estimation in the base year, variance estimation strata 

were formed consisting of two PSUs per stratum (Chromy 1981). However, when there was an 

odd number of schools in a sampling stratum, one of the analysis strata formed had three PSUs. 

The same analysis strata and PSUs as in the base year were used in the first follow-up. Each 

PSU still has at least two responding students. Students from new schools created by school 

splits (see section 3.3) are in the same strata and PSUs as they were for the base-year school. 

Also, freshened students are in the same strata and PSUs as the base-year students to whom they 

are linked. 

As described in section 3.2, the ELS:2002 base-year sampling design was a stratified 

two-stage design. A stratified sample of schools was selected with probabilities proportional to a 

composite measure of size at the first stage, and a stratified systematic sample of students was 

selected from sample schools at the second stage. At the first stage, the school sampling rates 

varied considerably by school sampling strata. At the second stage, Asian and Hispanic students 

were sampled at higher rates than other students. Because of this complex sampling design, 

statistical analyses should be conducted using software that properly accounts for the complex 

survey design. 

Many commonly used statistical computing packages assume that the data were obtained 

from a simple random sample; that is, they assume that the observations are independent and 

identically distributed. When the data have been collected using a complex sampling design, the 

simple random sampling assumption usually leads to an underestimate of the sampling variance, 

which would lead to artificially small confidence intervals and liberal hypothesis test results (i.e., 

rejecting the null hypothesis when it is in fact true more often than indicated by the nominal 

Type I error level) (Carlson, Johnson, and Cohen 1993). 

Statistical strategies that have been developed to address this issue include first-order 

Taylor series expansion of the variance equation, balanced repeated replication, and the jackknife 

approach (Wolter 1985). Special-purpose software packages that have been developed for 

analysis of complex sample survey data include SUDAAN, WesVar, and Stata. Evaluations of 

the relative performances of these packages are reported by Cohen (1997). 

91 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

• SUDAAN is a commercial product developed by RTI; information regarding the 

features of this package and its lease terms is available from the website 

http://www.rti.org/sudaan. 

• WesVar is a product of Westat, Inc.; information regarding the features of this 

package and its lease terms is available from the website 

http://www.westat.com/wesvar. 

• Information regarding the features of Stata and its lease terms is available from the 

website http://www.stata.com. 

• In addition to the variance estimation packages noted above, the American Institutes 

for Research (AIR) has developed the AM Statistical Software. AM software can be 

downloaded for free from the following website: http://am.air.org/. 

Following is an example of generic SUDAAN code to produce estimates and standard 

errors using Taylor series. The symbols /* and */ in the code indicate the beginning and end of a 

comment. Note that the dataset must be sorted by analysis strata and analysis PSUs. 

proc descript data=/* insert filename*/ design=wr; 

nest analstr analpsu; /* these variables are the analysis strata and analysis PSUs, 

respectively */ 

weight F1QWT; 

var /*insert variables*/; 

subpopn /* insert domain of interest if domain is a subset of students*/; 

print nsum mean semean / style=nchs; 

run; 

3.5.2 Design Effects 

The impact of the departures of the ELS:2002 complex sample design from a simple 

random sample design on the precision of sample estimates can be measured by the design 

effect. The design effect is the ratio of the actual variance of the statistic to the variance that 

would have been obtained had the sample been a simple random sample. The design standard 

errors will be different from the standard errors that are based on the assumption that the data are 

from a simple random sample. The ELS:2002 sample departs from the assumption of simple 

random sampling in three major respects: student samples were stratified by student 

characteristics, students were selected with unequal probabilities of selection, and the sample of 

students was clustered by school. A simple random sample is, by contrast, unclustered and not 

stratified. Additionally, in a simple random sample, all members of the population have the 

same probability of selection. Generally, clustering and unequal probabilities of selection 

92 
 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

increase the variance of sample estimates relative to a simple random sample, and stratification 

decreases the variance of estimates. 

Standard errors and design effects were computed for all respondents and separately for 

only dropouts. Due to the lack of perfect overlap between questions on the student and dropout 

questionnaires and because dropouts were not administered tests, it was necessary to select two 

sets of 30 items. One set represents questions asked of all respondents, and the other set 

represents questions asked of all dropouts. 

Standard errors and design effects were computed for 30 means and proportions overall 

for all respondents and dropouts and for subgroups of all respondents. They were not computed 

for subgroups of dropouts due to small cell sizes. The subgroups are similar to those used in 

NELS:88 and the ELS:2002 base year: 

● sex (male and female); 

● race/ethnicity (American Indian or Alaska Native, Asian or Pacific Islander, Black or 

African American, Hispanic or Latino, More than one race, White and all other 

races);26 

● school sector (public, Catholic, and other private); 

● socioeconomic status (SES) (lowest quarter, middle two quarters, and highest 

quarter); and 

● urbanicity (urban, suburban, and rural). 

Tables 3.3.1–9 and 3.3.1–15 from the NELS:88 second follow-up data file user’s manual 

(Ingels et al. 1994) were used to guide the items picked. For all respondents, it was often 

difficult to find an ELS:2002 item that matched exactly with the NELS:88 item. For dropouts, it 

was a little easier to find matching variables. The items chosen are a good representation of the 

different items on the ELS:2002 questionnaires. Therefore, these items should provide a range 

of data that give a reasonable average, as well as a reading on design effects for subgroups. 

However, because item matching with NELS:88 was difficult, the ELS:2002 design effects may 

not be comparable with the NELS:88 repeated design effects. Ideally, one would like to 

compare exact items between survey systems. Appendix K design effect tables from the 

ELS:2002 base-year data file user’s manual (Ingels et al. 2004) were not used to guide the items 

picked. Design effects were not expected to change much from the base year to the first followup, and it is more important to compare design effects across cohorts (e.g., ELS:2002 versus 

NELS:88) than to compare design effects from the base year with those from the first follow-up. 

The student variables used were the versions after imputation, and all variables used were 

after disclosure avoidance (see sections 3.6 and 3.7). Also, the public versions of the variables 

were used when the public version differed from the restricted version. For all respondents and 

for dropouts, the standard errors and design effects were calculated using both the cross-sectional 

weight (F1QWT) and the panel weight (F1PNLWT). When using the panel weight, only panel 

respondents were included. The difference between the cross-sectional and panel respondents is 

26 “White and all other races” is predominantly White, with a very small number of individuals from other race 

categories. All race categories exclude individuals of Hispanic or Latino origin. 

93 
 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

that base-year expanded sample students who responded in the first follow-up and freshened 

respondents are cross-sectional respondents but are not panel respondents. Expanded sample 

students were excluded from the analyses. 

Appendix I contains tables of design effects for all respondents and dropouts. Each table 

includes the survey item (or composite variable), variable name and value, percent estimate, 

design standard error, simple random sample standard error, sample size (N), design effect 

(DEFF), and square root of the design effect (DEFT). Tables 25 and 26 summarize the average 

DEFFs and DEFTs for the full sample and panel sample, respectively, for all respondents, 

dropouts, and each subgroup. The reader should note that the mean DEFTs reported in tables 25, 

26, and 27 were not calculated directly from the mean DEFF but, rather, are based on the 

summary statistics from the tables in appendix I. 

Table 25. Mean design effects (DEFFs) and root design effects (DEFTs) for the first follow-up full 

sample, by selected characteristics: 2004 

Characteristic Mean DEFF Mean DEFT

 All respondents 2.26 1.47 

Dropouts 1.31 1.14 

Male 1.90 1.37 

Female 1.94 1.37 

American Indian or Alaska Native 1.51 1.22 

Asian or Pacific Islander 2.14 1.44 

Black or African American 1.49 1.21 

Hispanic or Latino 1.59 1.25 

More than one race 1.71 1.30 

White and all other races1

 1.84 1.35 

Public schools 1.97 1.37 

Catholic schools 2.25 1.46 

Other private schools 3.02 1.66 

Low socioeconomic status (SES) 1.66 1.27 

Middle SES 1.68 1.29 

High SES 1.91 1.38 

Urban 2.85 1.64 

Suburban 2.08 1.41 

Rural 1.71 1.29 

1

 “White and all other races” is predominantly White, with a very small number of individuals from other race 

categories. All race categories exclude individuals of Hispanic or Latino origin. 

NOTE: The mean DEFT was not calculated directly from the mean DEFF but, rather, is the average DEFT over 

selected items. See appendix I of this document for more information. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 26. Mean design effects (DEFFs) and root design effects (DEFTs) for the first follow-up 

panel sample, by selected characteristics: 2004 

Characteristic Mean DEFF Mean DEFT

 All respondents 2.23 1.46 

Dropouts 1.31 1.14 

Male 1.88 1.37 

Female 1.93 1.37 

American Indian or Alaska Native 1.50 1.21 

Asian or Pacific Islander 2.17 1.44 

Black or African American 1.49 1.22 

Hispanic or Latino 1.60 1.25 

More than one race 1.70 1.30 

White and all other races1

 1.83 1.35 

Public schools 1.94 1.37 

Catholic schools 2.25 1.46 

Other private schools 3.00 1.65 

Low socioeconomic status (SES) 1.64 1.26 

Middle SES 1.67 1.29 

High SES 1.92 1.38 

Urban 2.80 1.63 

Suburban 2.08 1.42 

Rural 1.71 1.30 

1

 “White and all other races” is predominantly White, with a very small number of individuals from other race 

categories. All race categories exclude individuals of Hispanic or Latino origin. 

NOTE: The mean DEFT was not calculated directly from the mean DEFF but, rather, is the average DEFT over 

selected items. See appendix I of this document for more information. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Table 27 shows the design effects from the BY for subgroups. The first follow-up design 

effects are lower for all respondents and for most of the subgroups than the base year design 

effects. For the full sample, the design effect for males is the same as in the base year, the design 

effects for American Indian or Alaska Native and for multiracial respondents are greater than in 

the base year, and the design effects for the other 14 subgroups are less than in the base year. 

For the panel sample, the design effects for American Indian or Alaska Native and for multiracial 

respondents are greater than in the base year, and the design effects for the other 15 subgroups 

are less than in the base year. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Table 27. Mean design effects (DEFFs) and root design effects (DEFTs) for base-year student 

questionnaire data, by selected characteristics: 2002 

Group Mean DEFF Mean DEFT

 All students 2.35 1.50 

Male 1.90 1.37 

Female 2.01 1.40 

American Indian or Alaska Native 1.42 1.18 

Asian or Pacific Islander 2.27 1.49 

Black or African American 1.67 1.28 

Hispanic or Latino 1.82 1.32 

More than one race 1.63 1.27 

White and all other races1

 2.03 1.41 

Public schools 2.07 1.41 

Catholic schools 2.43 1.51 

Other private schools 3.53 1.78 

Low socioeconomic status (SES) 1.70 1.29 

Middle SES 1.73 1.31 

High SES 1.99 1.39 

Urban 2.88 1.64 

Suburban 2.15 1.44 

Rural 1.94 1.37 

1

 “White and all other races” is predominantly White, with a very small number of individuals from other race 

categories. All race categories exclude individuals of Hispanic or Latino origin. 

NOTE: The mean DEFT was not calculated directly from the mean DEFF but, rather, is the average DEFT over 

selected items. See appendix I of this document for more information. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

The smaller design effects in the first follow-up compared with those in the base year 

may be due to the general tendency in longitudinal studies for design effects to lessen over time, 

as dispersion reduces the original clustering. In the first follow-up, as some of the sample 

members left the base-year school, the clusters of students within schools dispersed to an extent. 

Social characteristics of the sample members potentially varied to a greater extent as the clusters 

dispersed. Also, the weight trimming was more aggressive in the first follow-up than in the base 

year (i.e., the weights were trimmed for a larger percentage of the sample members in the first 

follow-up than in the base year). As discussed in section 3.4, trimming weights reduces the 

variance, which, by definition, reduces the design effect. Additionally, the items used to 

compute the mean design effects were different in the first follow-up than in the base year, 

because the design effects were not expected to change much between the two rounds of the 

study. It is more important to compare design effects across cohorts, as described below, so the 

items were chosen to be as comparable to NELS:88 second follow-up items as possible. 

The design effects indicate that the ELS:2002 first follow-up full sample was more 

efficient than the NELS:88 second follow-up full sample and the HS&B first follow-up 

sophomore cohort full sample. For means and proportions based on first follow-up questionnaire 

96 
 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

data for all respondents, the average design effect in ELS:2002 was 2.26; the comparable figures 

were 3.71 for the NELS:88 second follow-up and 3.59 for the HS&B sophomore cohort first 

follow-up. Figure 4 shows the mean design effects and root design effects for the HS&B first 

follow-up sophomore cohort, NELS:88 second follow-up, and ELS:2002 first follow-up. The 

difference in design effects is also apparent for some subgroup estimates. Ingels et al. (1994) 

present design effects for 16 subgroups defined similarly to those in table 25 above. For all 16 

subgroups, the ELS:2002 design effects are smaller on average than those for the NELS:88 

second follow-up. 

Figure 4. Full sample mean design effects and root design effects, by longitudinal study: Selected 

years, 1972–2004 

Value 

3.6 3.7 

2.3 

1.8 1.9 

1.5 

0.0 

0.5 

1.0 

1.5 

2.0 

2.5 

3.0 

3.5 

4.0 

Design effects 

Root design effects 

HS&B first follow-up NELS:88 second ELS:2002 first 

sophomore cohort follow-up follow-up 

Study 

SOURCE: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B), 

“First Follow-up, 1980”; National Education Longitudinal Study of 1988 (NELS:88), “Second Follow-up, 1992”; and 

Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 2004.” 

The design effects indicate that the ELS:2002 panel sample (sophomore cohort) was 

more efficient than the NELS:88 F1F2 panel sample (sophomore cohort). For means and 

proportions based on first follow-up questionnaire data for all respondents, the average design 

effect in ELS:2002 was 2.23; the comparable figure was 3.73 for the NELS:88 sophomore 

cohort. Figure 5 shows the mean design effects and root design effects for the NELS:88 second 

follow-up and the ELS:2002 first follow-up sophomore cohort. The difference in design effects 

is also apparent for some subgroup estimates. Ingels et al. (1994) present design effects for 16 

subgroups defined similarly to those in table 26 above. For all 16 subgroups, the ELS:2002 

design effects are smaller on average than those for the NELS:88 sophomore cohort. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Figure 5. Mean design effects and root design effects, by NELS:88 and ELS:2002 panel sample 

(sophomore cohort): Selected years, 1988–2004 

Value 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

3.7 

2.3 

1.9 

1.5 

NELS:88 second follow-up ELS:2002 first follow-up 

Study 

Design effects 

Root design effects 

SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal 

Study of 1988 (NELS:88), “Second Follow-up, 1992”; and Education Longitudinal Study of 2002 (ELS:2002), “First 

Follow-up, 2004.” 

The design effects indicate that the ELS:2002 full and panel samples were also more 

efficient than the NELS:88 sample for dropouts. For means and proportions based on first 

follow-up questionnaire data for dropouts, the average design effect in ELS:2002 was 1.31 for 

both the full and panel samples; the comparable figures were 2.9 and 2.8 for the NELS:88 second 

follow-up full and F1F2 panel samples, respectively. 

The smaller design effects in ELS:2002 compared with those for NELS:88 and HS&B 

are probably due to subsampling. Nonrespondents were subsampled in the ELS:2002 first 

follow-up, but additional subsampling was done in the other studies. In NELS:88, no 

subsampling was done in the second follow-up, but several types of sample members, including 

nonrespondents, were subsampled. Additionally, disproportional strata representation was 

introduced by subsampling in the NELS:88 first follow-up. Dropouts were retained with 

certainty, whereas other students were subsampled at different rates. See Ingels et al. (1994) for 

more details. In HS&B, the sophomore cohort members who were no longer in the base-year 

school were subsampled. See Spencer et al. (1987) for more details. As mentioned above, the 

general tendency in longitudinal studies is for design effects to lessen over time, as dispersion 

reduces the original clustering. Subsampling increases design effects because it introduces 

additional variability into the weights with an attendant loss in sample efficiency. 

The smaller design effects in ELS:2002 compared with those for the HS&B sophomore 

cohort also may reflect the somewhat smaller cluster size used in the later survey in the base 

year. Although the clusters were reduced somewhat in the first follow-up for both studies, a 

number of students remained in the base-year school. The HS&B base-year sample design 

98 
DEFFc = 1 + (b – 1) rho,

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

called for 36 sophomores selected from each school. The ELS:2002 sample design called for 

about 26 sophomores selected from each school. Clustering tends to increase the variance of 

survey estimates because the observations within a cluster are similar and therefore add less 

information than independently selected observations. The impact of clustering depends mainly 

on two factors: the number of observations within each cluster and the degree of within-cluster 

homogeneity. When cluster sizes vary, the impact of clustering (DEFFc) can be estimated by 

DEFFc = 1 + (b – 1) rho, 

where b refers to the average cluster size (the average number of students selected from each 

school) and rho refers to the intraclass correlation coefficient, a measure of the degree of withincluster homogeneity. If the value of rho (which varies from one variable to the next) averaged 

about 0.05 in both studies, then the reduced cluster size in ELS:2002 would almost exactly 

account for the reduction in the design effects relative to HS&B. 

If one must perform a quick analysis of ELS:2002 data without using one of the software 

packages for analysis of complex survey data, the design effects tables in appendix I can be used 

to make approximate adjustments to the standard errors of survey statistics computed using the 

standard software packages that assume simple random sampling designs. One cannot be 

confident regarding the actual design-based standard error without performing the analysis using 

one of the software packages specifically designed for analysis of data from complex sample 

surveys. 

Standard errors for a proportion can be estimated from the standard error computed using 

the formula for the standard error of a proportion based on a simple random sample and the 

appropriate DEFT: 

SE = DEFT * (p(1-p)/n)1/2. 

Similarly, the standard error of a mean can be estimated from the weighted variance of 

the individual scores and the appropriate mean DEFT: 

SE = DEFT * (Var/n)1/2. 

Tables 25, 26, and 27 make it clear that the DEFFs and DEFTs vary considerably by 

subgroup. It is therefore important to use the mean DEFT for the relevant subgroup in 

calculating approximate standard errors for subgroup statistics. 

Standard error estimates may be needed for subgroups that are not shown in the 

appendix. One rule of thumb may be useful in such situations. The general rule states that 

design effects will generally be smaller for groups that are formed by subdividing the subgroups 

listed in the tables. (Smaller subgroups will be affected less by clustering than larger subgroups; 

in terms of the equation for DEFFc, b will be reduced.) Estimates for Hispanic males, for 

example, will generally have smaller design effects than the corresponding estimates for all 

Hispanics or all males. For this reason, it will usually be conservative to use the subgroup mean 

DEFT to approximate standard errors for estimates concerning a portion of the subgroup. This 

rule only applies when the variable used to subdivide a subgroup crosscuts schools. Sex is one 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

such variable because most schools include students of both sexes. It will not reduce the average 

cluster size to form groups that are based on subsets of schools. 

Standard errors may also be needed for other types of estimates than the simple means 

and proportions that are the basis for the results presented in the above tables. A second method 

can be used to estimate approximate standard errors for comparisons between subgroups. If the 

subgroups crosscut schools, then the design effect for the difference between the subgroup means 

will be somewhat smaller than the design effect for the individual means; consequently, the 

variance of the difference estimate will be less than the sum of the variances of the two subgroup 

means from which it is derived: 

Var(b-a) = Var(b) + Var(a) 

where Var(b-a) refers to the variance of the estimated difference between the subgroup means, 

and Var(a) and Var(b) refer to the variances of the two subgroup means. This equation assumes 

that the covariance of the subgroup means is negligible. It follows from this equation that Var(a) 

+ Var(b) can be used in place of Var(b-a) with conservative results. 

A final principle is that more complex estimators show smaller design effects than simple 

estimators (Kish and Frankel 1974). Thus, correlation and regression coefficients tend to have 

smaller design effects than subgroup comparisons, and subgroup comparisons have smaller 

design effects than means. This principle implies that it will be conservative to use the DEFTs in 

the above tables in calculating approximate standard errors for complex statistics, such as 

multiple regression coefficients. The procedure for calculating such approximate standard errors 

is the same as with simpler estimates: first, a standard error is calculated using the formula for 

data from a simple random sample; then the standard error is multiplied by the appropriate 

DEFT. 

One analytic strategy for accommodating complex survey designs is to use the mean 

design effect to adjust for the effective sample size resulting from the design. For example, one 

could create a weight that is the multiplicative inverse of the design effect and use that weight (in 

conjunction with sampling weights) to deflate the obtained sample size to take into account the 

inefficiencies due to a sample design that is a departure from a simple random sample. Using 

this procedure, statistics calculated by a statistical program such as SAS or SPSS will reflect the 

reduction in sample size in the calculation of standard errors and degrees of freedom. Such 

techniques capture the effect of the sample design on sample statistics only approximately. 

However, while not providing a full accounting of the sample design, this procedure provides 

some adjustment for the sample design and is probably better than conducting analysis that 

assumes the data were collected from a simple random sample. The analyst applying this 

correction procedure should carefully examine the statistical software being used and assess 

whether or not the program treats weights in such a way as to produce the effect described above. 

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3.6 Imputation 

3.6.1 Imputation Variables 

Eighteen key analysis variables were selected for imputation for the ELS:2002 first 

follow-up study. These variables included the same variables that were chosen for imputation in 

the base-year study and two new variables from the first follow-up study. Table 28 lists the 

selected variables. The two new variables selected for imputation include the spring 2004 

student ability estimate for mathematics and the spring 2004 student enrollment status (e.g., in 

school in grade 12, in school in other grade or ungraded or early graduate, out of school because 

of dropout or homeschooled, or out of scope/ineligible). These variables were chosen because 

they are standard classification variables used in most data reporting. 

Table 28. First follow-up imputation variables, by number and weighted proportion imputed: 2004 

Weighted percent Number of cases 

Variable imputed imputed1 

Student sex 1 0.01 

Student race/ethnicity 6 0.04 

Student language minority status 33 0.20 

Student Hispanic subgroup2

 14 0.09 

Student Asian subgroup2

 12 0.07 

School program type 651 4.01 

Student postsecondary educational expectations 91 0.56 

Parental aspirations for student postsecondary achievement 687 4.23 

Family composition 65 0.40 

Mother’s educational attainment3

 111 0.68 

Mother’s occupation3

 166 1.02 

Father’s educational attainment3

 183 1.13 

Father’s occupation3

 237 1.46 

Family income (2001)3

 868 5.34 

Enrollment status (in school vs. out) 86 0.53 

Spring 2004 student ability estimate (theta) for mathematics4

 2,707 16.66 

10th-grade student ability estimate (theta) for mathematics4

 651 4.01 

10th-grade student ability estimate (theta) for reading4

 651 4.01 

1 The denominator used in calculating the weighted percent missing varies by variable due to restrictions on eligibility 

for imputation. 

2 All race categories exclude individuals of Hispanic or Latino origin. 

3 Used to construct socioeconomic status (SES). 

4 Used to construct scale, quartile, and proficiency scores. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

3.6.2 Imputation Methodologies 

The ELS:2002 base-year and first follow-up data files were imputed using three 

imputation methods including logical imputation, weighted sequential hot deck imputation, and 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

multiple imputation. This section gives a brief summary of each of these methods and outlines 

how each one was used in the imputation plan. 

All possible logical imputations were performed before any other imputation method was 

applied to the data files. For instance, student sex was imputed by assigning a value based on 

student name. This assignment mapping was developed using the distribution of the known 

student names and sex values. Student sex and student race were the only two variables that 

were logically imputed. 

After all logical imputations were completed, weighted sequential hot deck imputation 

(Cox 1980) was used to impute 13 categorical variables. Sequential hot deck imputation is a 

common procedure used for item nonresponse. This method uses the respondent survey data 

(donors) to provide imputed values for records with missing values. The basic principle of 

sequential hot deck imputation involves defining imputation classes, which generally consist of a 

cross-classification of covariates, and then replacing missing values sequentially from a single 

pass through the survey data within the imputation classes. When sequential hot deck imputation 

is performed using the sampling weights of the item respondents and nonrespondents, the 

procedure is called weighted sequential hot deck imputation. This procedure takes into account 

the unequal probabilities of selection in the original sample by using the sampling weight to 

specify the expected number of times a particular respondent’s answer was used to replace a 

missing item. These expected selection frequencies are specified so that, over repeated 

applications of the algorithm, the expected value of the weighted distribution of the imputed 

values will equal in expectation within imputation class the weighted distribution of the reported 

answers. Weighted sequential hot deck imputation was chosen for most of the variables because 

this procedure works well for categorical data. 

The last imputation procedure used was multiple imputation (MI). The MI procedure 

was chosen for three continuous variables: the 10th-grade student ability estimate (theta) for 

mathematics, the 10th-grade student ability estimate (theta) for reading, and the spring 2004 

student ability estimate (theta) for mathematics. MI is a technique that requires imputing missing 

values several times and creating m complete datasets. These are created such that regular 

complete-case analyses can be performed. The parameters of interest, then, can be calculated by 

averaging the parameter estimators from each augmented dataset. The SAS PROC MI procedure 

was used to impute these three variables. The Markov Chain Monte Carlo (MCMC) model 

option, which assumes the data are from a multivariate normal distribution, was used to estimate 

the entire joint posterior probability distribution of the unknown quantities. Random draws from 

this distribution were taken to fill in the missing values. 

The SAS PROC MI procedure was selected for these three variables because it provides 

an imputation variance based on MI theory and does not require any additional computation. The 

“theta” value (which is only a restricted-use variable) is used in the construction of various 

ELS:2002 test variables, and this construction process requires an imputation variance. 

It should be noted that MI was not chosen for imputation of all variables, because the MI 

procedure is currently restricted to imputation of continuous variables and most of the variables 

selected for imputation are categorical. Therefore, the weighted sequential hot deck was the 

preferred method for these variables. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

3.6.3 Definition of Eligibility for Imputation 

Table C-1 in appendix C shows the set of respondents for which each variable was 

imputed. The appendix lists the different respondent types, including the questionnaireineligible (expanded sample) students. As shown in the appendix, all variables were imputed for 

base-year nonrespondents who became first follow-up respondents (651 students) and for baseyear respondents who were also first follow-up respondents (14,062). For base-year 

questionnaire-incapable students whose status had changed 2 years later and who were 

successfully surveyed in the first follow-up (n = 105), all variables were imputed except school 

program type and the 10th-grade ability estimates. For freshened respondents (171), all variables 

were imputed except school program type, the 10th-grade ability estimates, and parental 

aspirations. For the first follow-up questionnaire-incapable students (100), all variables were 

imputed except school program type, student postsecondary educational expectations, and the 

student ability estimates. 

The 16 variables that were imputed in the base-year study were not imputed again for 

base-year nonrespondents because their data were either reported or imputed in the base year. 

These variables were only imputed in the first follow-up when not provided by respondents in 

the new participant supplement by first follow-up respondents who were base-year 

nonrespondents, 12th-grade freshened sample members, or base-year questionnaire-ineligible 

students who became first follow-up respondents. The base-year nonrespondents who became 

first follow-up respondents included students still at the base-year school, transfer students, 

dropouts, early graduates, and homeschooled students. 

The spring 2004 student ability estimate for mathematics was imputed for students who 

were considered in school (i.e., students still attending the sample school as of spring 2004 and 

transfer students as of spring 2004). The estimate was not imputed for out-of-school respondents 

(i.e., dropouts, early graduates, and homeschooled students). For the 651 students who became 

first follow-up respondents and were base-year nonrespondents, the 10th-grade student ability 

estimates for mathematics and reading were imputed. 

Finally, spring 2004 enrollment status for all first follow-up sample members 

(respondents and nonrespondents) on the restricted-use file (16,374) was imputed. Most 

information was imputed using data available from school records, and any remaining missing 

data were imputed using the weighted sequential hot deck procedure. This variable was imputed 

because it is an analysis variable that will be used frequently, and it provides a better picture of 

the cohort dropout rate. 

3.6.4 Imputation Results 

Of the 15 categorical variables, 2 variables (sex and race/ethnicity) were imputed using 

logical imputation, and 13 were imputed using a weighted sequential hot deck procedure. The 

remaining 3 continuous variables were imputed using MI. 

Table 28 lists the variables in the order in which they were imputed and shows the 

number of cases that were imputed. The order in which the variables were imputed depended on 

whether the response of one variable was dependent upon the response of another variable. For 

instance, Hispanic and Asian subgroup could only be imputed after the race/ethnicity variable 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

was determined. Similarly, family income was dependent on parent education and occupation; 

therefore, parent education and occupation were imputed prior to family income. Within these 

dependencies, the variables were imputed in the same order that they were imputed in the baseyear study, generally starting with the variable containing the lowest percentage missing up to 

the variable with the highest percentage missing. 

The basic MI model used for imputing the student ability estimates included the 

following predictor variables: sex, race/ethnicity, student language, student postsecondary 

aspirations, parental aspirations for student, family composition, mother’s and father’s 

occupation and education level, household income, school type, urbanicity, and census region. 

The model used for predicting the spring 2004 student ability estimate in mathematics also 

contained the 10th-grade ability estimate for both mathematics and reading. Similarly, the model 

used for predicting the 10th-grade ability estimate for both mathematics and reading contained 

the spring 2004 student ability estimate in mathematics. 

Table C-2 presents the imputation classes and sorting variables used for all of the 

variables imputed by the weighted sequential hot deck approach, and table C-3 presents the 

variables used in the MI models. Table C-4 presents the before- and after-imputation 

distributions. 

3.6.5 Imputation Evaluation 

The key measure for determining whether the imputation methods produce acceptable 

results is that the before- and after-imputation weighted distributions are similar. For evaluation 

of the imputation results, distributions were considered to be similar when absolute differences 

are less than 5 percent where the absolute difference is calculated by subtracting the beforeimputation weighted percent from the after-imputation weighted percent. If absolute differences 

are greater than 5 percent, then the unweighted distributions were examined to see if the large 

differences are due to small sample sizes. Any large differences were evaluated and corrected 

when possible (perhaps by using different imputation classes) and documented when no 

resolution is possible. 

MI inference assumes that the analyst’s model is the same as the imputer’s model. 

However, the two models may not be the same. Therefore, a general practice is to include as 

many variables as possible when doing MI. The precision that is lost when unimportant 

predictors are included is usually relatively small compared with the general validity of analyses 

of the resultant multiply imputed dataset. The PROC MI procedure provides the betweenimputation, within-imputation, and total variances for the model. Additionally, it provides the 

degrees of freedom for the total variance, the relative increase in variance due to missing values, 

and the fraction of missing information for each parameter estimate. These statistics were used 

in the evaluation of the MI. 

3.7 Disclosure Risk Analysis and Protections 

Because of the paramount importance of protecting the confidentiality of NCES data that 

contain information about specific individuals, ELS:2002 first follow-up data were subject to 

various procedures to minimize disclosure risk. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

As a first step, all ELS:2002 data files (school and student) were reviewed to identify 

high-risk variables. Some variables were identified as unsuitable for the public-use file in any 

form; these variables appear only on the restricted-use files. Public-use variables that might 

point to specific individuals or schools (e.g., some fine-grained variables, particularly those in 

continuous form, and variables with extreme outliers) were altered through data coarsening 

techniques, such as top coding, bottom coding, or recasting into categorical form. 

As a second step, a technique called “data swapping” was carried out, both for schoollevel data and for student-level data. Schools and respondents were randomly selected for 

swapping to achieve a specific, but undisclosed, swapping rate. In data swapping, some 

variables for a sample case that has been paired with another case will be exchanged. By so 

doing, even if a tentative identification of an individual is made, because every case in the file 

has some undisclosed probability of having been swapped, uncertainty remains about the 

accuracy and interpretation of the match. The swapping was done independently of the 

swapping conducted in the base year. 

As a final step, the ELS:2002 data underwent a disclosure risk analysis. In this analysis, 

school characteristics information available on the data files was compared to information on 

publicly available universe files of schools. A distance measure was used to compute risk of 

deductive disclosure, and techniques to minimize disclosure risk were applied until school 

identities were appropriately masked. Specific techniques employed included both perturbation 

(perturbation directly alters individual respondent data for some variables) and coarsening of the 

data (coarsening reduces the level of detail, for example, by making a continuous variable 

categorical).27 

In the case of the coarsening applied to certain variables on the public-use file, more finegrained detail for these variables may be found on the restricted-use files. In the case of 

perturbation of the data (including swapping), all changes imposed on the public-use files were 

also implemented in the restricted-use files. Although perturbation techniques such as swapping 

do result in changes in estimates generated from the data, before-and-after weighted distributions 

and correlations for swapped variables show that, after applying the disclosure limitation 

techniques, the analytic utility of the data files has not been compromised in any way. 

3.8 Student Nonresponse Bias Analysis 

Unit nonresponse causes bias in survey estimates when the outcomes of respondents and 

nonrespondents are different. For ELS:2002, student response is defined as the sample member 

completing at least a specified portion of the questionnaire. The response rate was above 85 

percent overall and for most domains (see section 3.4 for a description of the domains). 

However, the response rate was below 85 percent for four domains (spring 2002 sophomores 

who were dropouts, transfer students, homeschooled, or early graduates), so a student-level 

nonresponse bias analysis was conducted for these domains. The final overall student weighted 

response rate was 88.7 percent. Although the overall response rate was above 85 percent and a 

27 The NCES Statistical Standards (Seastrom 2003) (http://nces.ed.gov/statprog/2002/std4_2.asp), specifically NCES 

Standard 4-2, provide information both about the legislative background and legal requirements of maintaining 

confidentiality, and definitions of key terms (perturbation, coarsening, disclosure risk analysis, data swapping, and so 

forth). 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

nonresponse bias analysis is not required by NCES standards, a bias analysis for base-year 

sophomores was conducted for the purposes of quality and completeness using both the crosssectional and panel weights. 

The nonresponse bias was estimated for variables known for both respondents and 

nonrespondents. In the base year, information was not collected for nonresponding students 

other than what was received on the student enrollment lists. On these lists, data were obtained 

on IEP status, race/ethnicity, and sex. These data were not provided by all schools (in particular, 

information on IEP status was often missing, and IEP information was typically relevant only for 

public schools). In consequence, only the school-supplied race/ethnicity and sex data were used. 

For most of the first follow-up freshened students, race/ethnicity and sex data were available. 

The student’s spring 2004 enrollment status was also used and defined as follows: 

• in school, in grade (in grade 12); 

• in school, out of grade (in grade 10 or 11, ungraded, or graduated early); and 

• out of school (dropout or homeschooled). 

There were also extensive data available for schools from the base-year school 

administrator questionnaire, so these data were used to help reduce potential nonresponse bias. 

Students were linked to the base-year school from which they were sampled. The first follow-up 

administrator data were not used when available because it is possible that student nonresponse 

is correlated with school nonresponse. It was safer to use the base-year administrator data for all 

students. The school sampling frame constructed from the CCD and PSS also contains data for 

all base-year schools. School data used included the following: 

• school sector; 

• urbanicity; 

• region; 

• sophomore enrollment; 

• total enrollment; 

• number of minutes per class; 

• number of class periods; 

• number of school days; 

• number of students receiving free or reduced-price lunch; 

• number of full-time teachers; 

• percentage of full-time teachers certified; 

• number of part-time teachers; 

• number of different grades taught at the school; 

• school level; 

• coeducational status; 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

• percentage of students with an IEP; 

• percentage of students with LEP; 

• percentage Hispanic or Latino sophomores; 

• percentage Asian sophomores; 

• percentage Black or African American sophomores; and 

• percentage All other race sophomores (includes White). 

The procedures used for the nonresponse bias analysis were similar to those used in the 

base year. First, for the school and student data known for most respondents and 

nonrespondents, the nonresponse bias was estimated and tested to determine if the bias was 

significant at the 5 percent level. Second, nonresponse adjustments were computed, and 

variables known for most respondents and nonrespondents were included in the nonresponse 

models. The nonresponse adjustments described in section 3.4 were designed to significantly 

reduce or eliminate nonresponse bias for variables included in the models. Variables not known 

for most respondents and nonrespondents could not be included in the nonresponse adjustments, 

and therefore nonresponse bias could not explicitly be reduced for these variables. However, 

many of the variables in the nonresponse models are correlated with many of the other variables. 

Third, after the school and student weights were computed, remaining bias for data 

known for most respondents and nonrespondents was estimated and statistically tested to check 

if there was any remaining significant nonresponse bias. Fourth, the remaining bias after student 

weight adjustments was divided by the standard error, that is, bias/standard error. 

The bias in an estimated mean based on respondents, yR , is the difference between this 

mean and the target parameter, B (i.e., the mean that would be estimated if a complete census of 

the target population was conducted). This bias can be expressed as follows: 

B( ) y = y R r −π

The estimated mean based on nonrespondents, yNR , can be computed if data for the 

particular variable for most of the nonrespondents are available. The estimation of π is as 

follows: 

ˆ (1 η) yR +ηy π =− N R

where η is the weighted unit nonresponse rate. For the variables that are from the frame rather 

than from the sample, B can be estimated without sampling error. Therefore, the bias can be 

estimated as follows: 

B yR = y −πˆ ˆ () R 

or equivalently 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Bˆ ( y R ) =η ( y R N − y R )

This formula shows that the estimate of the nonresponse bias is the difference between 

the mean for respondents and nonrespondents multiplied by the weighted nonresponse rate. The 

variance of the bias was computed using Taylor series estimation in RTI’s software package 

SUDAAN. 

Tables 29 and 30 show the nonresponse bias before and after weight adjustments for 

selected variables for base-year sophomores. The first set of columns in each table shows the 

estimated bias before nonresponse adjustment for the variables available for most responding and 

nonresponding students. The results are identical for these two tables. Statistical tests (t tests) 

were used to test each level of the variables for significance of the bias at the 0.05/(c-1) 

significance level, where c is the number of categories (levels) within the primary variable. 

Below is a summary of the before-adjustment significant bias for tables 29 and 30: 

• At least one level of 9 of the 25 variables was biased in each table. 

• Sixteen levels of variables were found to be significantly biased in both tables 29 and 

30. 

• Significant biases were usually small. 

The second set of columns in tables 29 and 30 shows the estimated bias after weight 

adjustments (using F1QWT for table 29 and F1PNLWT for table 30) for the variables available 

for most responding and nonresponding students. The bias after weight adjustments was 

computed as the difference between the estimate using nonresponse-adjusted (final) weights and 

the estimate using the design (base) weights prior to nonresponse adjustment. This latter 

estimate is an estimate of B because it is the estimate of the target population using the design 

weights. Similar to the testing of before-adjustment bias, t tests were performed to test the 

significance of the bias for each level of the variables. In both tables 29 and 30, the estimated 

bias usually decreased after weight adjustments. Therefore, the number of significantly biased 

levels of variables decreased from 16 before adjustment to zero after adjustment in both tables. 

Tables 31, 32, 33, and 34 show the nonresponse bias before and after weight adjustments 

for selected variables in domains where the response rate was less than 85 percent. Table 31 

refers to the domain of transfer students, table 32 refers to the domain of dropouts, table 33 refers 

to the domain of early graduates, and table 34 refers to the domain of homeschooled students. 

As in tables 29 and 30, the first set of columns in each table shows the estimated bias before 

nonresponse adjustment for the variables available for most responding and nonresponding 

students. Statistical tests (t tests) were again used to test the significance of the bias at the 

0.05/(c-1) significance level. Below is a summary of the before-adjustment significant bias for 

tables 31, 32, 33, and 34: 

• At least one level of three variables and a total of four levels were found to be 

significantly biased in table 31. 

• One level of two variables was found to be significantly biased in table 32. 

• One level of five variables was found to be significantly biased in table 33. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

• At least one level of four variables and a total of five levels were found to be 

significantly biased in table 34. 

• Significant biases were usually small. 

As in tables 29 and 30, the second set of columns in tables 31, 32, 33, and 34 shows the 

estimated bias after weight adjustments (using F1QWT) for the variables available for most 

responding and nonresponding students. The bias after weight adjustments was computed the 

same way as described above for tables 29 and 30. Statistical tests (t tests) were performed to 

test the significance of the bias for each level of the variables. In all four tables, the estimated 

bias sometimes decreased after weight adjustments and sometimes increased after weight 

adjustments. In tables 31 and 32, the amount of significant bias actually increased to seven 

levels of four variables and five levels of three variables, respectively. In tables 33 and 34, the 

amount of significant bias decreased to one and zero variables, respectively. Note that sample 

members are assigned to these domains based on data known for respondents and 

nonrespondents, and sample members may actually be in different domains. Therefore, these 

bias estimates are approximate. Also, the weight adjustments accounted for enrollment status, as 

described in section 3.4, but enrollment status combined the categories of transfer student, 

dropout, early graduate, or homeschooled student. Enrollment status was used because it was 

considered the more important analysis variable and to be consistent with NELS:88. 

The student nonresponse bias analyses in conjunction with the weighting adjustments 

described below do not eliminate all bias. They reduce bias for some of the variables known for 

most respondents and nonrespondents, which are considered to be some of the analytically 

important variables and are correlated with many of the other variables. Significant bias after 

weight adjustments is minimal for the variables analyzed. Some of these variables are used to 

help create composite (or derived) variables. There may be bias remaining in other variables. 

Figures 6 through 11 compare the estimated relative bias before nonresponse adjustment 

with the estimated relative bias after nonresponse adjustment for base-year sophomores using 

F1QWT, base-year sophomores using F1PNLWT, transfer students using F1QWT, dropouts 

using F1QWT, early graduates using F1QWT, and homeschooled students using F1QWT, 

respectively. Relative bias is the bias of the estimate divided by the estimate. It provides an 

indication of the order of magnitude of the bias with respect to the estimate. Figures 6 and 7 

indicate that when the relative bias was large before nonresponse adjustment, it was almost 

always reduced after nonresponse adjustment. When the relative bias was small before 

nonresponse adjustment, it stayed small after nonresponse adjustment with occasional small 

increases. These two figures clearly show that the nonresponse adjustment reduced bias for 

base-year sophomores. Figures 8 through 11 show somewhat of a random pattern. Sometimes 

relative bias decreased after nonresponse adjustment, sometimes relative bias increased after 

nonresponse adjustment, and sometimes, relative bias did not change much after nonresponse 

adjustment. As shown in tables 32 through 34, the bias is frequently not significant after 

nonresponse adjustment. Also, as mentioned above, sample members are assigned to these 

domains based on data known for respondents and nonrespondents, and sample members may 

actually be in different domains. 

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Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Nonresponse bias can have an effect on significance testing. Tables 29 through 34 

include an estimate of the bias ratio (student bias divided by the standard error). If this ratio is 

larger than 2 percent, then the probability of a Type I error is greater than 0.05. Figures 12 

through 17 show the student bias ratio by the Type I error rate for base-year sophomores using 

F1QWT, base-year sophomores using F1PNLWT, transfer students, dropouts, early graduates, 

and homeschooled students, respectively. Figures 12 and 13 show that for many of the student 

variables included in the nonresponse bias analysis, the Type I error rate is at or close to 0.05, 

and outliers were not graphed. These results are similar to the base-year results for spring 2002 

sophomores. Figures 14 through 17 show that although some variables have a Type I error rate 

at or near 0.05, there are more variables that have a higher Type I error rate. These figures do 

not take the school bias ratio into account. The school bias ratio varies by school variable, as 

shown in the ELS:2002 base-year data file user’s manual (Ingels et al. 2004). If it is assumed 

that the school bias ratio is zero, then there is no effect on the student bias ratio. However, if the 

school bias ratio is large, then the Type I error rates are larger. Although the tables above show 

that nonresponse bias is minimal, the data user should exercise caution when conducting 

statistical tests. 

110 
Table 29. Nonresponse bias before and after nonresponse adjustment for base-year sophomores using the cross

selected categorical variables: 2004 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Description 

Unweighted 

respondents 

Unweighted 

nonrespondents 

Respondent 

mean 

weighted1 

respondent 

mean 

weighted1 

Estimated 

bias 

Relative 

bias SIG2 

before 

adjustments1 

after 

adjustments3 

Est

mate

bia

Asian 10th-grade enrollment percent 

≤ 2 percent 5,684 547 38.298 39.784 -0.167 -0.004 N 38.465 38.686 -0.22

> 2 percent 9,029 943 61.702 60.216 0.167 0.003 N 61.535 61.314 0.22

Black or African American 10thgrade enrollment percent 

≤ 4 percent 5,043 459 34.669 32.029 0.296 0.009 N 34.373 34.296 0.07

> 4 percent 9,670 1031 65.331 67.971 -0.296 -0.005 N 65.627 65.704 -0.07

Minutes per class period 

≤ 45 3,540 368 18.887 21.081 -0.246 -0.013 N 19.133 19.144 -0.01

 46–50 3,166 312 21.924 20.110 0.203 0.009 N 21.720 21.662 0.05

 51–80 3,925 394 28.983 29.746 -0.086 -0.003 N 29.068 29.036 0.03

 81+ 4,082 416 30.206 29.063 0.128 0.004 N 30.078 30.159 -0.08

Class periods per day 

1–4 4,219 421 31.191 28.889 0.258 0.008 N 30.933 31.021 -0.08

5–6 3,612 403 26.901 29.533 -0.295 -0.011 N 27.196 27.129 0.06

 7 3,984 400 24.649 25.304 -0.073 -0.003 N 24.722 24.742 -0.01

8–9 2,898 266 17.259 16.274 0.111 0.006 N 17.149 17.109 0.04

Is the school coeducational?  Yes 13,937 1,425 97.909 98.506 -0.067 -0.001 N 97.976 97.970 0.00

No, all-female school 361 35 1.004 0.890 0.013 0.013 N 0.992 0.991 

No, all-male school 415 30 1.087 0.604 0.054 0.052 N 1.032 1.039 -0.00

Student race/ethnicity4 

All other races 9,120 830 67.450 62.158 0.594 0.009 Y 66.857 66.818 0.03

 Asian 1,619 173 3.891 3.623 0.030 0.008 N 3.861 3.853 0.00

Black or African American 2,169 261 15.708 18.383 -0.300 -0.019 N 16.008 15.985 0.02

Hispanic or Latino 1,805 226 12.951 15.836 -0.324 -0.024 N 13.275 13.344 -0.06

10th-grade enrollment  0–99 2,888 255 12.689 9.164 0.395 0.032 Y 12.294 12.337 -0.04

 100–249 3,823 293 22.457 16.644 0.652 0.030 Y 21.805 21.947 -0.14

 250–499 4,704 485 36.049 38.916 -0.322 -0.009 N 36.371 36.355 0.01

 500+ 3,298 457 28.805 35.276 -0.726 -0.025 Y 29.530 29.361 0.17

111 

See notes at end of table. 
Table 29. Nonresponse bias before and after nonresponse adjustment for base-year sophomores using the cross

selected categorical variables: 2004—Continued 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Description 

Unweighted 

respondents 

Unweighted 

nonrespondents 

Respondent 

mean 

weighted1 

respondent 

mean 

weighted1 

Estimated 

bias 

Relative 

bias SIG2 

before 

adjustments1 

after 

adjustments3 

Es

mate

bia

Total enrollment 

≤ 600 3,511 278 17.875 12.259 0.630 0.037 Y 17.245 17.327 -0.08

 601–1,200 4,453 406 27.547 25.559 0.223 0.008 N 27.324 27.438 -0.11

 1,201–1,800 3,321 364 25.828 29.082 -0.365 -0.014 N 26.193 26.117 0.07 > 1,800 3,428 442 28.750 33.099 -0.488 -0.017 N 29.238 29.117 0.12

Enrollment status 

In school, in grade (grade 12) 12,305 888 81.268 58.075 2.602 0.033 Y 78.666 78.622 0.04

In school, out of grade 1,697 350 13.235 23.153 -1.112 -0.078 Y 14.348 14.339 0.00

Out of school 711 252 5.497 18.772 -1.489 -0.213 Y 6.986 7.039 -0.05

Free or reduced-price lunch 0 

2,667 258 8.150 7.875 0.031 0.004 N 8.119 8.065 0.05

 1–10 3,347 306 25.022 26.694 -0.188 -0.007 N 25.210 25.211 -0.00

 11–30 4,386 450 35.984 32.895 0.347 0.010 N 35.638 35.574 0.06

 > 30 4,313 476 30.843 32.536 -0.190 -0.006 N 31.033 31.150 -0.11

Number of full-time teachers  1–40 3,857 319 18.359 13.996 0.489 0.027 Y 17.870 17.963 -0.09

 41–70 3,776 314 23.521 19.526 0.448 0.019 N 23.073 23.168 -0.09

 71–100 3,759 436 30.240 32.764 -0.283 -0.009 N 30.523 30.421 0.10

 101+ 3,321 421 27.880 33.714 -0.654 -0.023 Y 28.535 28.448 0.08

Number of grades within the school 4 11,206 1,131 79.654 79.374 0.031 # N 79.623 79.660 -0.03

> or < 4 3,507 359 20.346 20.626 -0.031 -0.002 N 20.377 20.340 0.03

Types of grades within the school 

K–12, PreK–10th, 1st–12th, PreK/ 

 1st–9th/12th and PreK–12 schools 980 91 5.024 3.745 0.143 0.029 N 4.881 4.829 0.05

Middle grades but no elementary 1,555 153 7.595 6.502 0.123 0.016 N 7.473 7.575 -0.10

Only high school 12,178 1,246 87.380 89.753 -0.266 -0.003 N 87.647 87.596 0.05

Hispanic 10th-grade enrollment 

percent 

≤ 3 percent 5,716 533 37.928 37.304 0.070 0.002 N 37.858 38.088 -0.23

> 3 percent 8,997 957 62.072 62.696 -0.070 -0.001 N 62.142 61.912 0.23

112 

See notes at end of table. 
Table 29. Nonresponse bias before and after nonresponse adjustment for base-year sophomores using the cross

selected categorical variables: 2004—Continued 

Before nonresponse adjustment After nonres

Overall Overall 

Non- mean, mean, 

Description 

Unweighted 

respondents 

Unweighted 

nonrespondents 

Respondent 

mean 

weighted1 

respondent 

mean 

weighted1 

Estimated 

bias 

Relative 

bias SIG2 

before 

adjustments1 

after 

adjustments3 

Es

mate

bia

IEP5 percentage 

≤ 5 percent 5,801 537 26.532 25.632 0.101 0.004 N 26.431 26.541 -0.11

 6–10 percent 3,760 389 32.843 34.502 -0.186 -0.006 N 33.029 32.884 0.14

 11–5 percent 3,213 352 26.441 25.356 0.122 0.005 N 26.319 26.294 0.02

 > 15 percent 1,939 212 14.184 14.510 -0.037 -0.003 N 14.221 14.281 -0.06

LEP6 percentage 

0 percent 6,457 532 34.559 28.762 0.650 0.019 Y 33.909 33.896 0.01

1 percent 2,897 289 23.189 22.184 0.113 0.005 N 23.076 23.079 -0.00

2–5 percent 2,447 282 18.577 23.160 -0.514 -0.027 N 19.091 19.101 -0.00

> 6 percent 2,912 387 23.675 25.894 -0.249 -0.010 N 23.924 23.924 0.00

Urbanicity Urban 4,950 588 29.567 35.077 -0.618 -0.020 Y 30.185 30.078 0.10

 Suburban 7,063 674 50.183 49.843 0.038 0.001 N 50.145 50.183 -0.03

 Rural 2,700 228 20.250 15.080 0.580 0.029 Y 19.670 19.739 -0.06

All other races6 10th-grade 

enrollment percent 

≤ 80 percent 7,245 845 50.735 55.048 -0.484 -0.009 Y 51.219 51.153 0.06

 > 80 percent 7,468 645 49.265 44.952 0.484 0.010 Y 48.781 48.847 -0.06

Number of part-time teachers  0–1 4,205 479 30.682 32.753 -0.232 -0.008 N 30.914 30.956 -0.04

 2–3 4,241 379 28.906 24.858 0.454 0.016 N 28.452 28.465 -0.01

 4–6 3,569 370 21.502 22.411 -0.102 -0.005 N 21.604 21.658 -0.05

 7+ 2,698 262 18.910 19.978 -0.120 -0.006 N 19.030 18.922 0.10

Full-time teacher certified  0–90 percent 3,797 360 16.138 14.344 0.201 0.013 N 15.937 15.980 -0.04

91–99 percent 2,577 313 20.008 22.786 -0.312 -0.015 N 20.320 20.211 0.10

100 percent 8,339 817 63.854 62.871 0.110 0.002 N 63.744 63.808 -0.06

Geocode  Census division (public schools) 

 Public-New England/Middle

 Atlantic5 1,991 222 16.407 18.446 -0.229 -0.014 N 16.636 16.671 -0.03

 Public-East North Central 1,782 213 14.241 16.450 -0.248 -0.017 N 14.489 14.359 0.13

113 

See notes at end of table. 
Table 29. Nonresponse bias before and after nonresponse adjustment for base-year sophomores using the cross

selected categorical variables: 2004—Continued 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Est

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mate

bia

Geocode—Continued 

 Public-West North Central 953 70 8.056 5.497 0.287 0.037 N 7.769 8.020 -0.25

 Public-South Atlantic 2,112 223 16.390 16.654 -0.030 -0.002 N 16.420 16.440 -0.02

 Public-East South Central 850 53 6.216 3.967 0.252 0.042 N 5.964 5.936 0.02

 Public-West South Central 1,318 141 9.459 9.451 0.001 0.000 N 9.458 9.407 0.05

 Public-Mountain 601 94 6.747 9.777 -0.340 -0.048 N 7.087 7.202 -0.11 Public-Pacific 1,853 202 14.705 13.160 0.173 0.012 N 14.531 14.313 0.21

Census region (private schools)

 Private-Northwest 724 59 1.916 1.248 0.075 0.041 N 1.841 1.817 0.02

 Private-Midwest 964 78 1.990 1.910 0.009 0.005 N 1.981 1.910 0.07

 Private-South 1,031 79 2.374 1.637 0.083 0.036 N 2.291 2.440 -0.14

 Private-West 534 56 1.499 1.803 -0.034 -0.022 N 1.533 1.486 0.04

Number of days in school year 

Less than 180 days 3,856 357 24.501 21.603 0.325 0.013 N 24.176 24.277 -0.10

180 days 8,135 841 56.940 56.859 0.009 # N 56.931 56.815 0.11

More than 180 days 2,722 292 18.558 21.538 -0.334 -0.018 N 18.893 18.908 -0.01

School sector Public 11,460 1,218 92.221 93.402 -0.132 -0.001 N 92.353 92.346 0.00

 Catholic 1,898 129 4.438 2.902 0.172 0.040 Y 4.266 4.268 -0.00

Other private 1,355 143 3.341 3.696 -0.040 -0.012 N 3.381 3.385 -0.00

Student sex Male 7,335 744 50.198 50.193 0.001 # N 50.198 50.433 -0.23

 Female 7,378 746 49.802 49.807 -0.001 # N 49.802 49.567 0.23

114 

# Rounds to zero. 1 Design weight is used before nonresponse adjustment. This is the distribution to each response category. 2 “Y” denotes statistical significance at p < .05. “N” denotes no statistical significance. 3 Weight after nonresponse adjustment. 4 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and Multiracial. All race categories exclude indiv

origin. 

5 IEP = Individualized Education Program. 6 LEP = limited English proficient. 7 Collapsed category comprising two Census divisions. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 20
Table 30. Nonresponse bias before and after nonresponse adjustment for base-year sophomores using the panel 

categorical variables: 2004 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Description 

Unweighted 

respondents 

Unweighted 

nonrespondents 

Respondent 

mean 

weighted1 

respondent 

mean 

weighted1 

Estimated 

bias 

Relative 

bias SIG2 

before 

adjustments1 

after 

adjustments3 

Es

mate

bi

Asian 10th-grade enrollment percent 

≤ 2 percent 5,684 547 38.298 39.784 -0.167 -0.004 N 38.465 38.700 -0.23

> 2 percent 9,029 943 61.702 60.216 0.167 0.003 N 61.535 61.300 0.23

Black or African American 10th-grade enrollment percent 

≤ 4 percent 5,043 459 34.669 32.029 0.296 0.009 N 34.373 34.346 0.02

> 4 percent 9,670 1031 65.331 67.971 -0.296 -0.005 N 65.627 65.654 -0.02

Minutes per class period 

≤ 45 3,540 368 18.887 21.081 -0.246 -0.013 N 19.133 19.153 -0.0

 46–50 3,166 312 21.924 20.110 0.203 0.009 N 21.720 21.650 0.07

 51–80 3,925 394 28.983 29.746 -0.086 -0.003 N 29.068 29.028 0.04

 81+ 4,082 416 30.206 29.063 0.128 0.004 N 30.078 30.169 -0.09

Class periods per day 

1–4 4,219 421 31.191 28.889 0.258 0.008 N 30.933 31.005 -0.07

5–6 3,612 403 26.901 29.533 -0.295 -0.011 N 27.196 27.110 0.08

 7 3,984 400 24.649 25.304 -0.073 -0.003 N 24.722 24.743 -0.02

8–9 2,898 266 17.259 16.274 0.111 0.006 N 17.149 17.141 0.00

Is the school coeducational?  Yes 13,937 1,425 97.909 98.506 -0.067 -0.001 N 97.976 97.965 0.0

No, all-female school 361 35 1.004 0.890 0.013 0.013 N 0.992 0.993 -0.00

No, all-male school 415 30 1.087 0.604 0.054 0.052 N 1.032 1.042 -0.00

Student race/ethnicity4 

All other races 9,120 830 67.450 62.158 0.594 0.009 Y 66.857 66.817 0.04

Asian 1,619 173 3.891 3.623 0.030 0.008 N 3.861 3.853 0.00

Black or African American 2,169 261 15.708 18.383 -0.300 -0.019 N 16.008 15.985 0.02

Hispanic or Latino 1,805 226 12.951 15.836 -0.324 -0.024 N 13.275 13.345 -0.07

10th-grade enrollment  0–99 2,888 255 12.689 9.164 0.395 0.032 Y 12.294 12.345 -0.05

 100–249 3,823 293 22.457 16.644 0.652 0.030 Y 21.805 21.952 -0.14

 250–499 4,704 485 36.049 38.916 -0.322 -0.009 N 36.371 36.384 -0.0

 500+ 3,298 457 28.805 35.276 -0.726 -0.025 Y 29.530 29.320 0.2

115 

See notes at end of table. 
Table 30. Nonresponse bias before and after nonresponse adjustment for base-year sophomores using the panel 

categorical variables: 2004—Continued 

Description Total enrollment 

Unweighted 

respondents 

Before nonresponse adjustment 

NonUnweighted Respondent respondent 

non- mean mean 

respondents weighted1 weighted1 

Estimated 

bias 

Relative 

bias SIG2 

Overall 

mean, 

before 

adjustments1 

After nonresp

Overall 

mean, 

after Es

adjust- mate

ments3 bi

≤ 600 

 601–1,200  1,201–1,800  > 1,800 

3,511 

4,453 3,321 

3,428 

278 

406 364 

442 

17.875 

27.547 25.828 

28.750 

12.259 

25.559 29.082 

33.099 

0.630 

0.223 

-0.365 

-0.488 

0.037 

0.008 

-0.014 

-0.017 

Y 

N 

N 

N 

17.245 

27.324 

26.193 

29.238 

17.341 

27.416 

26.225 

29.018 

-0.09

-0.09

-0.03

0.22

Enrollment status 

In school, in grade (grade 12) In school, out of grade Out of school 

12,305 

1,697 

711 

888 

350 252 

81.268 

13.235 

5.497 

58.075 

23.153 

18.772 

2.602 

-1.112 

-1.489 

0.033 

-0.078

-0.213

 Y 

Y 

Y 

78.666 

14.348 

6.986 

78.715 

14.289 

6.996 

-0.04

0.05

-0.0

Free or reduced-price lunch 

0 

 1–10  11–30  > 30 

2,667 

3,347 

4,386 

4,313 

258 

306 

450 

476 

8.150 

25.022 

35.984 

30.843 

7.875 

26.694 

32.895 

32.536 

0.031 

-0.188 

0.347 

-0.190 

0.004 

-0.007 

0.010 

-0.006 

N 

N N 

N 

8.119 

25.210 35.638 

31.033 

8.067 

25.211 35.603 

31.120 

0.05

-0.00

0.03

-0.08

Number of full-time teachers  1–40  41–70  71–100  101+ 

3,857 

3,776 3,759 

3,321 

319 

314 436 

421 

18.359 

23.521 30.240 

27.880 

13.996 

19.526 32.764 

33.714 

0.489 

0.448 

-0.283 

-0.654 

0.027 

0.019 

-0.009 

-0.023 

Y 

N 

N 

Y 

17.870 

23.073 

30.523 

28.535 

17.976 

23.120 

30.451 

28.453 

-0.10

-0.04

0.07

0.08

Number of grades within the school 4 

> or < 4 

11,206 

3,507 

1,131 

359 

79.654 

20.346 

79.374 

20.626 

0.031 

-0.031 

# 

-0.002

N 

N 

79.623 20.377 

79.619 

20.381 

0.00

-0.00

Types of grades within the school  K–12, PreK–10th, 1st–12th, 

 PreK/1st-9th/12th and PreK–12 schools 980 91 5.024 3.745 0.143 0.029 N 4.881 4.829 0.05

Middle grades but no elementary Only high school 

1,555 

12,178 

153 

1,246 

7.595 

87.380 

6.502 

89.753 

0.123 

-0.266 

0.016 

-0.003

 N N 

7.473 

87.647 

7.556 

87.615 

-0.08

0.03

Hispanic 10th-grade enrollment percent 

≤ 3 percent > 3 percent 

5,716 

8,997 

533 

957 

37.928 

62.072 

37.304 

62.696 

0.070 

-0.070 

0.002 

-0.001

 N 

N 

37.858 

62.142 

38.059 

61.941 

-0.20

0.20

116 

See notes at end of table. 
Table 30. Nonresponse bias before and after nonresponse adjustment for base-year sophomores using the panel 

categorical variables: 2004—Continued 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Description 

Unweighted 

respondents 

Unweighted 

nonrespondents 

Respondent 

mean 

weighted1 

respondent 

mean 

weighted1 

Estimated 

bias 

Relative 

bias SIG2 

before 

adjustments1 

after 

adjustments3 

Es

mate

bi

IEP5 percentage 

≤ 5 percent 5,801 537 26.532 25.632 0.101 0.004 N 26.431 26.567 -0.13

 6–10 percent 3,760 389 32.843 34.502 -0.186 -0.006 N 33.029 32.877 0.15

11–15 percent 3,213 352 26.441 25.356 0.122 0.005 N 26.319 26.356 -0.03

 > 15 percent 1,939 212 14.184 14.510 -0.037 -0.003 N 14.221 14.200 0.02

LEP6 percentage 

0 percent 6,457 532 34.559 28.762 0.650 0.019 Y 33.909 33.927 -0.0

1 percent 2,897 289 23.189 22.184 0.113 0.005 N 23.076 23.136 -0.06

2–5 percent 2,447 282 18.577 23.160 -0.514 -0.027 N 19.091 19.051 0.04

> 6 percent 2,912 387 23.675 25.894 -0.249 -0.010 N 23.924 23.886 0.03

Urbanicity Urban 4,950 588 29.567 35.077 -0.618 -0.020 Y 30.185 30.020 0.16 Suburban 7,063 674 50.183 49.843 0.038 0.001 N 50.145 50.252 -0.10

 Rural 2,700 228 20.250 15.080 0.580 0.029 Y 19.670 19.728 -0.05

All other races 10th-grade 

 enrollment percent 

≤ 80 percent 7,245 845 50.735 55.048 -0.484 -0.009 Y 51.219 51.104 0.1

 > 80 percent 7,468 645 49.265 44.952 0.484 0.010 Y 48.781 48.896 -0.1

Number of part-time teachers  0–1 4,205 479 30.682 32.753 -0.232 -0.008 N 30.914 30.925 -0.0

 2–3 4,241 379 28.906 24.858 0.454 0.016 N 28.452 28.440 0.0

 4–6 3,569 370 21.502 22.411 -0.102 -0.005 N 21.604 21.704 -0.10

 7+ 2,698 262 18.910 19.978 -0.120 -0.006 N 19.030 18.931 0.09

Full-time teacher certified  0–90 percent 3,797 360 16.138 14.344 0.201 0.013 N 15.937 15.903 0.03

91–99 percent 2,577 313 20.008 22.786 -0.312 -0.015 N 20.320 20.210 0.1

100 percent 8,339 817 63.854 62.871 0.110 0.002 N 63.744 63.887 -0.14

117 

See notes at end of table. 
Table 30. Nonresponse bias before and after nonresponse adjustment for base-year sophomores using the panel 

categorical variables: 2004—Continued 

118 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Es

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mate

bi

Geocode 

Census division (public schools) 

 Public-New England/Middle 

 Atlantic7 1,991 222 16.407 18.446 -0.229 -0.014 N 16.636 16.686 -0.05

 Public-East North Central 1,782 213 14.241 16.450 -0.248 -0.017 N 14.489 14.357 0.13

 Public-West North Central 953 70 8.056 5.497 0.287 0.037 N 7.769 8.019 -0.25

 Public-South Atlantic 2,112 223 16.390 16.654 -0.030 -0.002 N 16.420 16.407 0.0

 Public-East South Central 850 53 6.216 3.967 0.252 0.042 N 5.964 5.934 0.03 Public-West South Central 1,318 141 9.459 9.451 0.001 # N 9.458 9.429 0.03 Public-Mountain 601 94 6.747 9.777 -0.340 -0.048 N 7.087 7.217 -0.13

 Public-Pacific 1,853 202 14.705 13.160 0.173 0.012 N 14.531 14.297 0.23

Census region (private schools)

 Private-Northwest 724 59 1.916 1.248 0.075 0.041 N 1.841 1.802 0.03

 Private-Midwest 964 78 1.990 1.910 0.009 0.005 N 1.981 1.911 0.07

 Private-South 1,031 79 2.374 1.637 0.083 0.036 N 2.291 2.455 -0.16

 Private-West 534 56 1.499 1.803 -0.034 -0.022 N 1.533 1.485 0.04

Number of days in school year 

Less than 180 days 3,856 357 24.501 21.603 0.325 0.013 N 24.176 24.313 -0.13180 days 8,135 841 56.940 56.859 0.009 # N 56.931 56.839 0.09More than 180 days 2,722 292 18.558 21.538 -0.334 -0.018 N 18.893 18.848 0.04

School sector Public 11,460 1,218 92.221 93.402 -0.132 -0.001 N 92.353 92.346 0.00 Catholic 1,898 129 4.438 2.902 0.172 0.040 Y 4.266 4.268 -0.00

Other private 1,355 143 3.341 3.696 -0.040 -0.012 N 3.381 3.386 -0.00

Student sex Male 7,335 744 50.198 50.193 0.001 # N 50.198 50.435 -0.23

 Female 7,378 746 49.802 49.807 -0.001 # N 49.802 49.565 0.23

# Rounds to zero. 1 Design weight is used before nonresponse adjustment. This is the distribution to each response category. 2 “Y” denotes statistical significance at p < .05. “N” denotes no statistical significance. 3 Weight after nonresponse adjustment. 4 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and Multiracial. All race categories exclude individuals of H5 IEP = Individualized Education Program. 6 LEP = limited English proficient. 7 Collapsed category comprising two Census divisions. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 2004.” 
Table 31. Nonresponse bias before and after nonresponse adjustment for transfer students, by selected categoric

Description Asian 10th-grade enrollment percent 

≤ 2 percent > 2 percent 

Unweighted 

respondents 

499 

764 

Before nonresponse adjustment 

NonUnweighted Respondent respondent 

non- mean mean 

respondents weighted1 weighted1 

207 38.940 43.595 

316 61.060 56.405 

Estimated 

bias 

-1.484 

1.484 

Relative 

bias

-0.037

0.025 

 SIG2 

N 

N 

Overall 

mean, 

before 

adjustments1 

40.425 

59.575 

After nonresp

Overall 

mean, 

after Esti

adjust- mated 

ments3 bias 

39.200 1.225 

60.800 -1.225 

Black or African American 10th-grade enrollment percent 

≤ 4 percent > 4 percent 

388 

875 

157 

366 

31.132 

68.868 

30.075 

69.925 

0.337 

-0.337 

0.011 

-0.005

 N 

N 

30.795 

69.205 

31.231 

68.769 

-0.436 

0.436 

Minutes per class period 

≤ 45 292 118 16.627 17.673 -0.334 -0.020 N 16.961 16.365 0.596 

 46–50 256 94 18.943 19.014 -0.023 -0.001 N 18.966 18.806 0.160  51–80 382 160 34.360 31.884 0.790 0.024 N 33.570 35.087 -1.517  81+ 333 151 30.070 31.428 -0.433 -0.014 N 30.503 29.742 0.761 

Class periods per day 

1–4 338 139 31.118 28.703 0.770 0.025 N 30.347 30.483 -0.136 5–6 369 160 32.535 35.291 -0.879 -0.026 N 33.414 33.882 -0.469 

 7 343 139 22.398 23.522 -0.359 -0.016 N 22.757 22.052 0.704 

8–9 213 85 13.949 12.484 0.467 0.035 N 13.482 13.582 -0.100 

Is the school coeducational?  Yes 

No, all-female school No, all-male school 

1,205 

23 

35 

508 

8 

7 

98.063 

0.798 

1.139 

98.997 

0.566 

0.436 

-0.298 

0.074 

0.224 

-0.003 

0.102 

0.245 

N 

N 

N 

98.361 

0.724 

0.915 

98.121 

0.785 

1.094 

0.240 

-0.061 

-0.179 

Student race/ethnicity4 

All other races 632 251 53.508 55.613 -0.672 -0.012 N 54.179 53.159 1.020 

 Asian 131 61 3.716 3.731 -0.005 -0.001 N 3.721 3.723 -0.002 

Black or African American 292 115 24.884 22.887 0.637 0.026 N 24.247 24.881 -0.634 

Hispanic or Latino 208 96 17.892 17.769 0.039 0.002 N 17.853 18.237 -0.384 

10th-grade enrollment  0–99 267 105 12.298 10.865 0.457 0.039 N 11.841 12.647 -0.807 

 100–249 283 95 17.840 15.690 0.686 0.040 N 17.155 18.089 -0.934 

 250–499 395 166 35.178 37.831 -0.846 -0.023 N 36.024 34.762 1.261 

 500+ 318 157 34.684 35.615 -0.297 -0.008 N 34.981 34.501 0.480 

119 

See notes at end of table. 
Table 31. Nonresponse bias before and after nonresponse adjustment for transfer students, by selected categoric

Continued 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

Total enrollment 

≤ 600 301 107 15.675 12.404 1.043 0.071 N 14.632 15.917 -1.286 

 601–1,200 346 140 23.330 27.929 -1.467 -0.059 N 24.797 24.139 0.658 

 1,201–1,800 287 120 27.049 26.741 0.098 0.004 N 26.951 26.619 0.332 

 > 1,800 329 156 33.945 32.926 0.325 0.010 N 33.620 33.325 0.295 

Enrollment status 

In school, in grade (grade 12) 1,069 410 83.088 77.862 1.667 0.020 N 81.421 80.386 1.035 

In school, out of grade 193 85 16.829 16.362 0.149 0.009 N 16.680 19.496 -2.816 

Out of school ‡ 28 0.083 5.776 -1.816 -0.956 Y 1.899 0.118 1.781 

Free or reduced-price lunch 0 

251 84 9.756 7.616 0.683 0.075 N 9.074 9.721 -0.647 

 1–10 246 94 20.829 23.433 -0.831 -0.038 N 21.660 20.808 0.851  11–30 353 157 35.491 31.627 1.233 0.036 N 34.259 34.415 -0.157 

 > 30 413 188 33.923 37.324 -1.085 -0.031 N 35.008 35.055 -0.047 

Number of full-time teachers  1–40 362 125 18.597 15.696 0.925 0.052 N 17.672 18.950 -1.279 

 41–70 320 107 21.113 17.826 1.048 0.052 N 20.065 21.556 -1.491 

 71–100 298 153 30.407 33.530 -0.996 -0.032 N 31.403 30.339 1.064 

 101+ 283 138 29.883 32.948 -0.978 -0.032 N 30.861 29.154 1.707 

Number of grades within the school 4 968 382 80.539 76.561 1.269 0.016 N 79.270 80.289 -1.019 

> or < 4 295 141 19.461 23.439 -1.269 -0.061 N 20.730 19.711 1.019 

Types of grades within the school 

K–12, PreK–10th, 1st–12th, PreK/ 

 1st–9th/12th and PreK–12 schools 101 36 5.501 3.928 0.502 0.100 N 5.000 5.861 -0.861 

Middle grades but no elementary 123 63 6.554 7.838 -0.410 -0.059 N 6.963 6.566 0.398 

Only high school 1,039 424 87.945 88.233 -0.092 -0.001 N 88.037 87.574 0.463 

Hispanic 10th-grade enrollment 

percent 

≤ 3 percent 411 163 31.872 32.595 -0.231 -0.007 N 32.102 31.158 0.945 

> 3 percent 852 360 68.128 67.405 0.231 0.003 N 67.898 68.842 -0.945 

120 

See notes at end of table. 
Table 31. Nonresponse bias before and after nonresponse adjustment for transfer students, by selected categoric

Continued 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

IEP5 percentage 

≤ 5 percent 527 178 28.194 24.358 1.224 0.045 N 26.971 28.403 -1.432 

 6–10 percent 307 148 33.959 33.567 0.125 0.004 N 33.834 31.945 1.889 

11–15 percent 257 118 24.193 26.080 -0.602 -0.024 N 24.795 25.161 -0.366 

 > 15 percent 172 79 13.653 15.995 -0.747 -0.052 N 14.400 14.492 -0.092 

LEP6 percentage 

0 percent 526 185 30.619 28.095 0.805 0.027 N 29.814 31.079 -1.266 

1 percent 230 110 22.575 27.127 -1.452 -0.060 N 24.027 21.628 2.399 

2–5 percent 194 85 17.641 18.103 -0.147 -0.008 N 17.788 18.090 -0.301 

> 6 percent 313 143 29.165 26.676 0.794 0.028 N 28.371 29.203 -0.832 

Urbanicity Urban 516 224 37.376 38.433 -0.337 -0.009 N 37.713 37.955 -0.242 

 Suburban 551 221 46.516 44.836 0.536 0.012 N 45.980 45.783 0.197 

 Rural 196 78 16.109 16.730 -0.198 -0.012 N 16.307 16.262 0.045 

All other races 10th-grade  enrollment percent 

≤ 80 percent 714 321 59.812 58.717 0.349 0.006 N 59.463 59.696 -0.233 

 > 80 percent 549 202 40.188 41.283 -0.349 -0.009 N 40.537 40.304 0.233 Number of part-time teachers  0–1 404 191 34.307 38.381 -1.299 -0.036 N 35.607 35.162 0.445 

 2–3 312 121 23.970 20.679 1.050 0.046 N 22.920 23.503 -0.583 

 4–6 319 130 23.523 22.978 0.174 0.007 N 23.349 23.159 0.191 

 7+ 228 81 18.200 17.962 0.076 0.004 N 18.124 18.176 -0.053 

Full-time teacher certified  0–90 percent 389 135 20.484 15.960 1.443 0.076 N 19.041 21.254 -2.214 

91–99 percent 221 81 19.054 16.203 0.909 0.050 N 18.144 19.208 -1.064 

100 percent 653 307 60.463 67.838 -2.352 -0.037 Y 62.815 59.537 3.278 

121 

See notes at end of table. 
Table 31. Nonresponse bias before and after nonresponse adjustment for transfer students, by selected categoric

Continued 

122 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

Geocode  Census division (public schools) 

 Public-New England/Middle

 Atlantic7 

112 51 10.274 13.368 -0.987 -0.088 N 11.261 10.045 1.216 

 Public-East North Central 142 93 12.613 18.875 -1.997 -0.137 N 14.610 13.065 1.545 

 Public-West North Central 76 25 7.650 5.417 0.712 0.103 N 6.938 7.451 -0.514 

 Public-South Atlantic 185 74 17.128 15.128 0.638 0.039 N 16.490 17.128 -0.638 

 Public-East South Central 49 19 4.375 5.068 -0.221 -0.048 N 4.596 4.392 0.204 

 Public-West South Central 124 45 11.451 8.235 1.026 0.098 N 10.425 10.703 -0.278 

 Public-Mountain 47 28 6.326 9.953 -1.157 -0.155 N 7.483 7.085 0.398 

 Public-Pacific 216 91 20.574 17.834 0.874 0.044 N 19.700 20.407 -0.707 

Census region (private schools)

 Private-Northwest 57 19 1.766 0.985 0.249 0.164 N 1.517 1.710 -0.194  Private-Midwest 95 30 2.386 2.155 0.074 0.032 N 2.313 2.464 -0.151  Private-South 94 35 2.665 1.978 0.219 0.090 N 2.446 2.524 -0.078 

 Private-West 66 13 2.791 1.005 0.570 0.256 N 2.221 3.024 -0.803 

Number of days in school year 

Less than 180 days 308 117 23.265 21.733 0.489 0.021 N 22.776 23.502 -0.725 

180 days 729 303 57.790 59.478 -0.538 -0.009 N 58.329 59.391 -1.062 

More than 180 days 226 103 18.945 18.789 0.050 0.003 N 18.895 17.108 1.787 

School sector Public 951 426 90.392 93.877 -1.112 -0.012 Y 91.504 90.277 1.226 

 Catholic 157 42 4.376 2.388 0.634 0.169 Y 3.742 4.169 -0.427 

Other private 155 55 5.232 3.734 0.478 0.100 N 4.754 5.554 -0.800 

Student sex Male 635 252 51.073 45.767 1.692 0.034 N 49.380 53.053 -3.672 

 Female 628 271 48.927 54.233 -1.692 -0.033 N 50.620 46.947 3.672 

# Rounds to zero. 

‡ Reporting standards not met.

1 Design weight is used before nonresponse adjustment. This is the distribution to each response category. 2 “Y” denotes statistical significance at p < .05. “N” denotes no statistical significance. 

3 Weight after nonresponse adjustment. 4 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and Multiracial. All race categories exclude individuals of Hispanic or 5 IEP = Individualized Education Program. 6 LEP = limited English proficient. 7 Collapsed category comprising two Census divisions. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 2004.” 
Table 32. Nonresponse bias before and after nonresponse adjustment for dropouts, by selected categorical variab

Description Asian 10th-grade enrollment percent 

≤ 2 percent > 2 percent 

Unweighted 

respondents 

275 

395 

Before nonresponse adjustment 

NonUnweighted Respondent respondent 

non- mean mean 

respondents weighted1 weighted1 

78 40.915 45.749 

112 59.085 54.251 

Estimated 

bias 

-1.318 

1.318 

Relative 

bias

-0.031

0.023 

 SIG2 

N N 

Overall 

mean, 

before 

adjustments1 

42.233 57.767 

After nonrespo

Overall 

mean, 

after Esti

adjust- mated 

ments3 bias 

39.881 2.352 

60.119 -2.352 

Black or African American 10th-grade 

enrollment percent 

≤ 4 percent > 4 percent 

129 

541 

57 

133 

22.543 

77.457 

31.284 

68.716 

-2.383 

2.383 

-0.096

0.032 

N N 

24.926 75.074 

23.577 

76.423 

1.349 

-1.349 

Minutes per class period 

≤ 45 131 34 18.673 19.662 -0.270 -0.014 N 18.943 19.312 -0.369 

46–50 129 27 19.172 10.406 2.390 0.142 Y 16.783 19.727 -2.945 

51–80 189 58 30.253 34.481 -1.153 -0.037 N 31.405 31.572 -0.167 

81+ 221 71 31.902 35.451 -0.968 -0.029 N 32.869 29.388 3.481 

Class periods per day 

1–4 224 77 32.916 38.421 -1.501 -0.044 N 34.417 30.577 3.840 

5–6 182 49 28.673 28.892 -0.060 -0.002 N 28.733 30.829 -2.097 

7 167 38 24.750 20.837 1.067 0.045 N 23.683 24.023 -0.340 

8–9 97 26 13.661 11.850 0.494 0.037 N 13.167 14.571 -1.404 

Is the school coeducational? 

Yes 

 No, all-female school  No, all-male school 

667 

‡ ‡ 

189 

‡ ‡ 

99.665 

0.231 0.104 

99.888 

0.112 

# 

-0.061 

0.033 

# 

-0.001

0.164 

# 

N 

N N 

99.726 

0.199 0.075 

99.669 

0.237 0.094 

0.058 

-0.038 -0.019 

Student race/ethnicity4 

All other races 298 112 49.471 60.197 -2.924 -0.056 N 52.395 51.548 0.847 Asian 52 15 2.688 1.908 0.213 0.086 N 2.475 2.451 0.024 

Black or African American 167 31 24.174 18.869 1.446 0.064 N 22.728 23.645 -0.918 

Hispanic or Latino 153 32 23.667 19.026 1.265 0.056 N 22.402 22.355 0.046 

10th-grade enrollment 

0–99 103 23 9.173 9.058 0.031 0.003 N 9.142 9.475 -0.334 

100–249 131 40 20.203 19.590 0.167 0.008 N 20.036 18.413 1.622 

250–499 232 69 34.173 42.579 -2.292 -0.063 N 36.465 34.561 1.904 500+ 204 58 36.451 28.773 2.093 0.061 N 34.358 37.550 -3.192 

123 

See notes at end of table. 
Table 32. Nonresponse bias before and after nonresponse adjustment for dropouts, by selected categorical variab

Continued 

Description Total enrollment 

Unweighted 

respondents 

Before nonresponse adjustment 

NonUnweighted Respondent respondent 

non- mean mean 

respondents weighted1 weighted1 

Estimated 

bias 

Relative 

bias SIG2 

Overall 

mean, 

before 

adjustments1 

After nonrespo

Overall 

mean, 

after Esti- 

adjust- mated 

ments3 bias 

≤ 600 130 33 14.925 13.395 0.417 0.029 N 14.508 14.201 0.307 

601–1,200 1,201–1,800 > 1,800 

149 189 

202 

48 56 

53 

21.800 29.132 

34.143 

26.169 29.408 

31.027 

-1.191 -0.075 

0.849 

-0.052-0.003

0.026 

N N 

N 

22.991 29.207 

0.023 

21.263 27.968 

0.028 

1.728 1.239 

-0.006 

Enrollment status 

Out of school 669 190 99.969 100.000 -0.008 # N 99.977 99.972 0.006 

Free or reduced-price lunch 

0 27 6 2.288 1.554 0.200 0.096 N 2.088 2.848 -0.760 

1–10 106 37 18.462 21.421 -0.807 -0.042 N 19.268 18.439 0.829 

11–30 220 65 34.551 33.426 0.307 0.009 N 34.244 35.744 -1.499 > 30 317 82 44.699 43.599 0.300 0.007 N 44.400 42.969 1.430 

Number of full-time teachers 

1–40 140 35 15.869 10.743 1.398 0.097 N 14.472 15.023 -0.551 

41–70 129 46 18.489 25.637 -1.949 -0.095 N 20.438 17.780 2.657 

71–100 194 57 30.095 32.966 -0.783 -0.025 N 30.877 31.115 -0.237 

101+ 207 52 35.547 30.654 1.334 0.039 N 34.213 36.082 -1.869 

Number of grades within the school 

4 533 152 81.030 78.088 0.802 0.010 N 80.228 82.281 -2.053 

> or < 4 137 38 18.970 21.912 -0.802 -0.041 N 19.772 17.719 2.053 

Types of grades within the school  K–12, PreK–10th, 1st–12th, 

 PreK/1st–9th/12th and  PreK–12 schools 18 4 2.146 5.069 -0.797 -0.271 N 2.943 2.666 0.277 

Middle grades but no elementary Only high school 

60 

592 

13 

173 

8.457 

89.396 

4.499 

90.432 

1.079 

-0.282 

0.146 

-0.003

 N 

N 

7.378 

89.679 

6.427 

90.907 

0.952 

-1.228 

Hispanic 10th-grade enrollment percent 

≤ 3 percent > 3 percent 

248 

422 

67 

123 

34.371 

65.629 

37.298 

62.702 

-0.798 

0.798 

-0.023

0.012 

N 

N 

35.169 

64.831 

31.527 

68.473 

3.642 

-3.642 

124 

See notes at end of table. 
Table 32. Nonresponse bias before and after nonresponse adjustment for dropouts, by selected categorical variab

Continued 

Before nonresponse adjustment After nonrespo

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

IEP5 percentage 

≤ 5 percent 158 51 19.525 26.546 -1.914 -0.089 N 21.439 19.635 1.804 

6–10 percent 186 54 34.592 32.195 0.653 0.019 N 33.938 32.693 1.245 11–15 percent 203 57 30.611 27.038 0.974 0.033 N 29.637 31.111 -1.474 

> 15 percent 123 28 15.272 14.221 0.287 0.019 N 14.986 16.561 -1.575 

LEP6 percentage 

0 percent 201 70 24.965 34.034 -2.472 -0.090 N 27.437 26.449 0.989 

1 percent 154 29 24.395 12.640 3.205 0.151 Y 21.190 22.109 -0.919 

2–5 percent 118 36 19.251 23.948 -1.280 -0.062 N 20.531 18.538 1.994 

> 6 percent 197 55 31.389 29.378 0.548 0.018 N 30.841 32.905 -2.064 

Urbanicity 

Urban 263 64 37.056 36.739 0.086 0.002 N 36.970 40.296 -3.326 

Suburban 276 92 43.284 47.660 -1.193 -0.027 N 44.477 41.161 3.316 

Rural 131 34 19.659 15.601 1.106 0.060 N 18.553 18.544 0.009 

All other races 10th-grade enrollment 

percent 

≤ 80 percent 434 109 65.990 59.564 1.752 0.027 N 64.238 64.267 -0.029 

> 80 percent 236 81 34.010 40.436 -1.752 -0.049 N 35.762 35.733 0.029 

Number of part-time teachers 

0–1 272 82 42.411 45.376 -0.808 -0.019 N 43.219 42.483 0.736 

2–3 167 39 24.305 20.452 1.050 0.045 N 23.254 24.895 -1.641 

4–6 156 49 22.251 23.508 -0.343 -0.015 N 22.594 21.402 1.192 

7+ 75 20 11.033 10.664 0.101 0.009 N 10.933 11.220 -0.288 

Full-time teacher certified 

0–90 percent 135 49 16.745 21.346 -1.254 -0.070 N 18.000 16.025 1.975 

91–99 percent 134 34 23.639 22.456 0.323 0.014 N 23.317 23.962 -0.645 

100 percent 401 107 59.615 56.197 0.932 0.016 N 58.683 60.013 -1.330 

125 

See notes at end of table. 
Table 32. Nonresponse bias before and after nonresponse adjustment for dropouts, by selected categorical variab

Continued 

126 

Before nonresponse adjustment After nonrespo

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

Geocode  Census division (public schools) 

 Public-New England/Middle

 Atlantic7 115 38 17.689 22.811 -1.396 -0.073 N 19.085 19.386 -0.301 

 Public-East North Central 98 27 14.843 15.744 -0.246 -0.016 N 15.089 14.436 0.652 

 Public-West North Central 40 11 5.665 5.934 -0.073 -0.013 N 5.739 5.874 -0.136 

 Public-South Atlantic 132 38 20.966 19.734 0.336 0.016 N 20.630 18.943 1.687 

 Public-East South Central 64 10 8.380 6.045 0.637 0.082 N 7.744 7.531 0.213 

 Public-West South Central 66 18 10.038 5.776 1.162 0.131 N 8.876 8.817 0.059 

 Public-Mountain 31 18 6.531 10.065 -0.963 -0.129 N 7.494 8.102 -0.608 

 Public-Pacific 87 22 14.159 8.955 1.419 0.111 N 12.741 15.109 -2.368 

Census region (private schools)

 Private-Northwest ‡ ‡ 0.177 0.112 0.018 0.112 N 0.159 0.214 -0.055 

 Private-Midwest 12 3 0.576 0.243 0.091 0.187 N 0.485 0.643 -0.158 

 Private-South 18 3 0.544 0.361 0.050 0.101 N 0.494 0.629 -0.135 

 Private-West 5 ‡ 0.432 4.220 -1.033 -0.705 N 1.464 0.315 1.149 

Number of days in school year 

Less than 180 days 167 41 23.478 18.139 1.455 0.066 N 22.023 22.108 -0.085 

180 days 379 115 55.810 66.759 -2.985 -0.051 N 58.795 57.642 1.153 

More than 180 days 124 34 20.712 15.101 1.530 0.080 N 19.183 20.250 -1.068 

School sector Public 633 182 98.272 95.064 0.874 0.009 N 97.397 98.199 -0.802 

 Catholic 9 ‡ 0.469 0.112 0.098 0.262 N 0.372 0.419 -0.047 

Other private 28 7 1.259 4.824 -0.972 -0.436 N 2.231 1.382 0.848 

Student sex Male 375 107 53.769 49.503 1.163 0.022 N 52.606 56.236 -3.629 

Female 295 83 46.231 50.497 -1.163 -0.025 N 47.394 43.764 3.629 

— Not available. # Rounds to zero. ‡ Reporting standards not met. 1 Design weight is used before nonresponse adjustment. This is the distribution to each response category. 2 “Y” denotes statistical significance at p < .05. “N” denotes no statistical significance. 3 Weight after nonresponse adjustment. 4 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and Multiracial. All race categories exclude individuals of Hispanic or 5 IEP = Individualized Education Program. 6 LEP = limited English proficient. 7 Collapsed category comprising two Census divisions. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 2004.” 
Table 33. Nonresponse bias before and after nonresponse adjustment for early graduates, by selected categorica

Before nonresponse adjustment After nonrespo

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

Asian 10th-grade enrollment percent 

≤ 2 percent 266 62 44.452 56.500 -2.339 -0.050 N 46.791 46.602 0.189 

> 2 percent 291 65 55.548 43.500 2.339 0.044 N 53.209 53.398 -0.189 

Black or African American 10th-grade 

enrollment percent 

≤ 4 percent 124 26 26.134 28.875 -0.532 -0.020 N 26.667 24.581 2.086 

> 4 percent 433 101 73.866 71.125 0.532 0.007 N 73.333 75.419 -2.086 

Minutes per class period 

≤ 45 59 16 9.856 13.018 -0.614 -0.059 N 10.470 10.196 0.274 

 46–50 96 29 19.762 21.386 -0.315 -0.016 N 20.078 17.716 2.361 

 51–80 148 21 28.707 16.765 2.318 0.088 N 26.389 28.081 -1.692 

 81+ 254 61 41.674 48.831 -1.389 -0.032 N 43.063 44.007 -0.944 

Class periods per day 

1–4 248 63 41.117 50.504 -1.822 -0.042 N 42.939 42.784 0.155 5–6 142 26 28.331 15.394 2.511 0.097 N 25.820 27.243 -1.424 

 7 115 29 21.095 25.131 -0.783 -0.036 N 21.878 19.087 2.792 

8–9 52 9 9.457 8.972 0.094 0.010 N 9.363 10.886 -1.523 

Is the school coeducational?  Yes 551 127 99.503 100.000 -0.096 -0.001 N 99.599 99.452 0.147  No, all-female school 4 # 0.388 # # # N 0.313 0.444 -0.131 

 No, all-male school ‡ # 0.109 # # # N 0.088 0.104 -0.016 

Student race/ethnicity4 

All other races 300 65 59.404 52.710 1.299 0.022 N 58.105 59.572 -1.468 

Asian 52 8 3.048 1.989 0.206 0.072 N 2.843 2.745 0.098 

Black or African American 114 29 19.738 22.655 -0.566 -0.028 N 20.304 21.077 -0.773 

Hispanic or Latino 91 25 17.810 22.646 -0.939 -0.050 N 18.748 16.606 2.142 

10th-grade enrollment 

0–99 86 11 11.487 3.285 1.592 0.161 Y 9.895 9.847 0.048 

 100–249 121 25 20.199 18.169 0.394 0.020 N 19.805 20.090 -0.286 

 250–499 198 54 34.678 47.137 -2.418 -0.065 N 37.097 37.600 -0.504 

 500+ 152 37 33.636 31.409 0.432 0.013 N 33.204 32.462 0.742 

127 

See notes at end of table. 
Table 33. Nonresponse bias before and after nonresponse adjustment for early graduates, by selected categorica

Continued 

Before nonresponse adjustment After nonrespo

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

Total enrollment 

≤ 600 103 13 15.111 6.145 1.741 0.130 Y 13.370 13.700 -0.329 

 601–1,200 151 35 25.051 30.838 -1.124 -0.043 N 26.174 25.852 0.322  1,201–1,800 148 41 26.563 32.032 -1.062 -0.038 N 27.625 28.634 -1.009 

 > 1,800 155 38 33.275 30.985 0.445 0.014 N 32.831 31.814 1.017 

Enrollment status 

In school, out of grade 556 127 99.675 100.000 -0.063 -0.001 N 99.738 99.698 0.040 

Free or reduced-price lunch 

0 41 7 5.414 2.513 0.563 0.116 N 4.851 4.757 0.094 

 1–10 95 22 17.009 19.831 -0.548 -0.031 N 17.557 17.057 0.500 

 11–30 187 38 35.179 33.020 0.419 0.012 N 34.760 37.276 -2.516 

 > 30 234 60 42.398 44.636 -0.434 -0.010 N 42.832 40.910 1.922 

Number of full-time teachers  1–40 120 14 16.257 5.781 2.034 0.143 Y 14.224 16.944 -2.720  41–70 128 24 19.676 22.688 -0.585 -0.029 N 20.261 20.663 -0.402  71–100 146 46 31.117 37.338 -1.208 -0.037 N 32.325 28.015 4.309 

 101+ 163 43 32.949 34.193 -0.241 -0.007 N 33.191 34.378 -1.187 

Number of grades within the school 4 446 104 81.820 86.231 -0.856 -0.010 N 82.676 82.047 0.629 

> or < 4 111 23 18.180 13.769 0.856 0.049 N 17.324 17.953 -0.629 

Types of grades within the school  K–12, PreK–10th, 1st–12th, 

 PreK/1st–9th/12th and  PreK–12 schools 28 ‡ 5.560 0.941 0.897 0.192 N 4.663 3.843 0.820 

Middle grades but no elementary 37 10 4.392 5.150 -0.147 -0.032 N 4.539 4.514 0.025 

Only high school 492 115 90.048 93.908 -0.749 -0.008 N 90.798 91.642 -0.844 

Hispanic 10th-grade enrollment 

percent 

≤ 3 percent 217 55 35.238 38.850 -0.701 -0.020 N 35.939 37.863 -1.924 

> 3 percent 340 72 64.762 61.150 0.701 0.011 N 64.061 62.137 1.924 

128 

See notes at end of table. 
Table 33. Nonresponse bias before and after nonresponse adjustment for early graduates, by selected categorica

Continued 

Before nonresponse adjustment After nonresp

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

IEP5 percentage 

≤ 5 percent 157 30 27.199 18.845 1.622 0.063 N 25.577 24.098 1.479 

 6–10 percent 150 34 28.786 37.260 -1.645 -0.054 N 30.431 31.620 -1.189 

11–15 percent 168 39 31.728 28.597 0.608 0.020 N 31.120 29.794 1.326 

 > 15 percent 82 24 12.288 15.297 -0.584 -0.045 N 12.872 14.488 -1.616 

LEP6 percentage 

0 percent 188 31 27.250 21.913 1.036 0.040 N 26.214 28.934 -2.721 

1 percent 126 29 22.890 19.536 0.651 0.029 N 22.239 23.025 -0.786 

2–5 percent 95 39 16.827 37.069 -3.929 -0.189 Y 20.757 17.924 2.832 

> 6 percent 148 28 33.033 21.482 2.242 0.073 N 30.791 30.116 0.675 

Urbanicity Urban 210 56 41.696 40.710 0.191 0.005 N 41.504 38.237 3.267  Suburban 256 53 43.490 45.431 -0.377 -0.009 N 43.867 47.048 -3.181  Rural 91 18 14.814 13.859 0.186 0.013 N 14.629 14.715 -0.086 

All other races 10th-grade 

enrollment percent 

≤ 80 percent 345 79 64.455 56.894 1.468 0.023 N 62.987 61.991 0.996 

 > 80 percent 212 48 35.545 43.106 -1.468 -0.040 N 37.013 38.009 -0.996 

Number of part-time teachers  0–1 188 45 37.980 34.827 0.612 0.016 N 37.368 37.224 0.144 

 2–3 181 31 31.809 26.096 1.109 0.036 N 30.700 30.708 -0.008 

 4–6 125 33 17.981 22.667 -0.910 -0.048 N 18.890 20.229 -1.338  7+ 63 18 12.230 16.410 -0.812 -0.062 N 13.041 11.839 1.202 

Full-time teacher certified  0–90 percent 117 21 21.369 10.265 2.156 0.112 Y 19.214 17.568 1.646 

91–99 percent 108 37 19.441 32.139 -2.465 -0.113 N 21.906 18.970 2.936 

100 percent 332 69 59.190 57.596 0.309 0.005 N 58.880 63.462 -4.581 

129 

See notes at end of table. 
Table 33. Nonresponse bias before and after nonresponse adjustment for early graduates, by selected categorica

Continued 

Before nonresponse adjustment After nonrespo

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

Geocode 

Census division (public schools) 

 Public-New England/Middle

 Atlantic7 56 13 10.900 7.828 0.596 0.058 N 10.304 10.698 -0.394 

 Public-East North Central 80 17 14.068 17.413 -0.649 -0.044 N 14.717 15.377 -0.660 

 Public-West North Central 33 10 5.546 6.753 -0.234 -0.041 N 5.780 6.403 -0.623 

 Public-South Atlantic 113 31 20.509 22.393 -0.366 -0.018 N 20.875 20.364 0.511 

 Public-East South Central 43 10 6.434 8.027 -0.309 -0.046 N 6.743 7.004 -0.261 

 Public-West South Central 89 26 14.695 23.286 -1.668 -0.102 N 16.363 13.900 2.463 

 Public-Mountain 34 6 8.080 7.974 0.021 0.003 N 8.060 10.474 -2.414 

 Public-Pacific 66 6 15.517 4.786 2.083 0.155 N 13.434 13.072 0.362 

Census region (private schools)

 Private-Northwest 4 ‡ 0.418 0.272 0.028 0.073 N 0.390 0.389 0.001 

 Private-Midwest 12 5 0.597 0.922 -0.063 -0.096 N 0.660 0.685 -0.025 

 Private-South 18 ‡ 0.899 0.346 0.107 0.136 N 0.791 0.993 -0.201  Private-West 9 # 2.336 # # # N 1.883 0.642 1.240 

Number of days in school year 

Less than 180 days 131 39 22.161 34.883 -2.470 -0.100 N 24.631 23.966 0.665 180 days 330 71 60.394 51.556 1.716 0.029 N 58.678 59.943 -1.264 

More than 180 days 96 17 17.445 13.561 0.754 0.045 N 16.691 16.092 0.599 

School sector Public 514 119 95.750 98.460 -0.526 -0.005 N 96.276 97.291 -1.015 

 Catholic 8 ‡ 0.413 0.402 0.002 0.005 N 0.411 0.418 -0.007 

Other private 35 6 3.837 1.138 0.524 0.158 N 3.313 2.291 1.022 

130 

See notes at end of table. 
Table 33. Nonresponse bias before and after nonresponse adjustment for early graduates, by selected categorica

Continued 

Before nonresponse adjustment After nonresp

Description 

Unweighted 

respondents 

Unweighted 

nonrespondents 

Respondent 

mean 

weighted1 

Nonrespondent 

mean 

weighted1 

Estimated 

bias 

Relative 

bias SIG2 

Overall 

mean, 

before 

adjustments1 

Overall 

mean, 

after 

adjustments3 

Est

mate

bia

Student sex Male 275 60 50.330 50.115 0.042 0.001 N 50.288 51.578 -1.289  Female 282 67 49.670 49.885 -0.042 -0.001 N 49.712 48.422 1.289 

# Rounds to zero. ‡ Reporting standards not met. 1 Design weight is used before nonresponse adjustment. This is the distribution to each response category. 2 “Y” denotes statistical significance at p < .05. “N” denotes no statistical significance. 3 Weight after nonresponse adjustment. 4 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and Multiracial. All race categories exclude individuals of H5 IEP = Individualized Education Program. 6 LEP = limited English proficient. 7 Collapsed category comprising two Census divisions. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 2004.” 

131 
Table 34. Nonresponse bias before and after nonresponse adjustment for homeschooled students, by selected ca

2004 

Description Asian 10th-grade enrollment 

percent ≤ 2 percent > 2 percent 

Unweighted 

respondents 

14 

26 

Before nonresponse adjustment 

NonUnweighted Respondent respondent 

non- mean mean 

respondents weighted1 weighted1 

9 36.443 42.093 

11 63.557 57.907 

Estimated 

bias 

-1.802 

1.802 

Relative 

bias

-0.047

0.029 

 SIG2 

N 

N 

Overall 

mean, 

before 

adjustments1 

38.245 

61.755 

After nonrespon

Overall 

mean, 

after Esti

adjust- mated 

ments3 bias 

38.227 0.017 

61.773 -0.017 

Black or African American 10th-grade 

enrollment percent 

≤ 4 percent > 4 percent 

20 

20 

‡ 

18 

55.692 

44.308 

13.165 

86.835 

13.564 

-13.564 

0.322 

-0.234

 Y Y 

42.128 57.872 

52.812 47.188 

-10.684 

10.684 

Minutes per class period 

≤ 45 13 5 25.215 19.633 1.781 0.076 N 23.435 29.804 -6.370 

46–50 11 ‡ 24.487 7.603 5.385 0.282 N 19.102 26.071 -6.969 

51–80 9 3 31.145 26.231 1.567 0.053 N 29.578 23.887 5.690 

81+ 7 10 19.152 46.534 -8.733 -0.313 N 27.886 20.237 7.648 

Class periods per day 

1–4 8 10 27.383 46.534 -6.108 -0.182 N 33.491 20.526 12.965 

5–6 11 3 32.682 26.231 2.058 0.067 N 30.624 33.051 -2.427 

7 11 4 22.604 12.312 3.283 0.170 N 19.321 25.490 -6.169 

8–9 10 3 17.331 14.924 0.768 0.046 N 16.564 20.933 -4.370 

Is the school coeducational? 

Yes 

 No, all-female school  No, all-male school 

37 

‡ ‡ 

20 

# # 

97.424 

1.651 0.925 

100.000 

# # 

-0.822 

# # 

-0.008 

# # 

N 

N N 

98.246 

1.125 0.630 

97.834

1.335 0.831 

 0.411 

-0.210-0.201

Student race/ethnicity4 

All other races 

 Asian 

29 

‡ 

15 

‡ 

66.631 

0.395 

72.847 

2.952 

-1.982 

-0.815 

-0.029

-0.673 

N 

N 

68.614 

1.211 

67.280 

0.323 

1.334 

0.887 

Black or African American 4 3 12.742 17.877 -1.638 -0.114 N 14.379 12.116 2.263 Hispanic or Latino 6 ‡ 20.232 6.325 4.436 0.281 N 15.796 20.281 -4.485 

10th-grade enrollment 

0–99 17 3 28.730 10.048 5.959 0.262 N 22.772 23.878 -1.106 

100–249 6 5 14.005 23.254 -2.950 -0.174 N 16.955 14.172 2.783 250–499 9 9 30.745 40.466 -3.101 -0.092 N 33.845 33.079 0.766 500+ 8 3 26.521 26.231 0.092 0.003 N 26.428 28.871 -2.443 

132 

See notes at end of table. 
Table 34. Nonresponse bias before and after nonresponse adjustment for homeschooled students, by selected ca

2004—Continued 

Before nonresponse adjustment After nonrespon

Overall Overall 

Non- mean, mean, 

Description 

Unweighted 

respondents 

Unweighted 

nonrespondents 

Respondent 

mean 

weighted1 

respondent 

mean 

weighted1 

Estimated 

bias 

Relative 

bias SIG2 

before 

adjustments1 

after 

adjustments3 

Esti

mated 

bias 

Total enrollment 

≤ 600 16 ‡ 31.545 5.108 8.432 0.365 Y 23.113 26.242 -3.129 

601–1,200 11 9 25.027 40.729 -5.008 -0.167 N 30.035 27.181 2.854 

1,201–1,800 5 6 17.913 27.932 -3.196 -0.151 N 21.109 19.222 1.887 

> 1,800 8 3 25.515 26.231 -0.228 -0.009 N 25.743 27.355 -1.612 

Enrollment status 

Out of school 40 20 100.000 100.000 # # Y 100.000 100.000 # 

Free or reduced-price lunch 

0 12 3 16.342 8.028 2.652 0.194 N 13.690 10.209 3.481 

1–10 4 ‡ 11.845 16.425 -1.461 -0.110 N 13.306 12.785 0.521 

11–30 14 7 48.928 41.624 2.330 0.050 N 46.599 53.474 -6.875 

> 30 10 8 22.884 33.923 -3.521 -0.133 N 26.405 23.532 2.873 

Number of full-time teachers 

1–40 20 6 40.155 26.043 4.501 0.126 N 35.654 36.874 -1.220 41–70 6 5 12.676 24.320 -3.714 -0.227 N 16.390 12.970 3.420 

71–100 8 4 24.545 18.941 1.787 0.079 N 22.758 26.851 -4.093 

101+ 6 5 22.623 30.696 -2.575 -0.102 N 25.198 23.305 1.893 

Number of grades within the 

school 

4 24 15 72.682 75.849 -1.010 -0.014 N 73.692 76.647 -2.955 

> or < 4 16 5 27.318 24.151 1.010 0.038 N 26.308 23.353 2.955 

Types of grades within the school  K–12, PreK–10th, 1st–12th, 

 PreK/1st–9th/12th and  PreK–12 schools 10 ‡ 16.196 7.694 2.712 0.201 N 13.484 10.936 2.549 

 Middle grades but no

 elementary 5 # 8.692 # # # N 5.920 8.908 -2.988 

Only high school 25 18 75.112 92.306 -5.484 -0.068 N 80.596 80.157 0.439 

Hispanic 10th-grade enrollment 

percent 

≤ 3 percent 12 8 25.884 35.141 -2.953 -0.102 N 28.837 25.398 3.439 > 3 percent 28 12 74.116 64.859 2.953 0.041 N 71.163 74.602 -3.439 

133 

See notes at end of table. 
Table 34. Nonresponse bias before and after nonresponse adjustment for homeschooled students, by selected ca

2004—Continued 

Before nonresponse adjustment After nonrespon

Overall Overall 

Non- mean, mean, 

Description 

Unweighted 

respondents 

Unweighted 

nonrespondents 

Respondent 

mean 

weighted1 

respondent 

mean 

weighted1 

Estimated 

bias 

Relative 

bias SIG2 

before 

adjustments1 

after 

adjustments3 

Esti

mated 

bias 

IEP5 percentage 

≤ 5 percent 19 6 34.812 19.899 4.757 0.158 N 30.056 27.945 2.111 

6–10 percent 9 6 30.592 34.188 -1.147 -0.036 N 31.739 32.697 -0.958 

11–15 percent 7 3 21.799 23.138 -0.427 -0.019 N 22.226 23.538 -1.312 

> 15 percent 5 5 12.797 22.776 -3.183 -0.199 N 15.979 15.820 0.159 

LEP6 percentage 

0 percent 22 12 43.965 50.930 -2.222 -0.048 N 46.187 41.181 5.005 

1 percent 5 3 12.355 21.332 -2.863 -0.188 N 15.218 11.807 3.411 

2–5 percent 7 4 23.244 21.025 0.708 0.031 N 22.536 26.781 -4.245 

> 6 percent 6 ‡ 20.436 6.713 4.377 0.273 N 16.059 20.231 -4.172 

Urbanicity 

Urban 10 3 17.742 8.028 3.098 0.212 N 14.644 20.807 -6.163 

Suburban 19 10 57.755 57.268 0.155 0.003 N 57.599 52.817 4.783 

Rural 11 7 24.503 34.705 -3.254 -0.117 N 27.757 26.376 1.380 

All other races 10th-grade 

enrollment percent 

≤ 80 percent 17 12 49.219 55.986 -2.158 -0.042 N 51.377 52.461 -1.084 

> 80 percent 23 8 50.781 44.014 2.158 0.044 N 48.623 47.539 1.084 

Number of part-time teachers 

0–1 14 9 40.492 43.889 -1.083 -0.026 N 41.575 44.061 -2.485 

2–3 9 5 26.332 21.688 1.481 0.060 N 24.851 18.514 6.337 

4–6 9 4 19.750 20.768 -0.325 -0.016 N 20.075 22.015 -1.941 

7+ 8 ‡ 13.426 13.655 -0.073 -0.005 N 13.499 15.410 -1.911 

Full-time teacher certified 

0–90 percent 17 3 36.817 8.028 9.182 0.332 Y 27.635 31.377 -3.743 

91–99 percent ‡ 3 9.238 17.397 -2.602 -0.220 N 11.841 9.107 2.734 100 percent 21 14 53.945 74.576 -6.580 -0.109 N 60.525 59.516 1.009 

134 

See notes at end of table. 
Table 34. Nonresponse bias before and after nonresponse adjustment for homeschooled students, by selected ca

2004—Continued 

135 

Before nonresponse adjustment After nonrespon

Overall Overall 

Non- mean, mean, 

Unweighted Respondent respondent Esti- before after Esti

Description 

Unweighted 

respondents 

nonrespondents mean weighted1 mean weighted1 mated bias Relative bias SIG2 adjust- ments1 adjust- ments3 mated bias 

Geocode 

Census division (public schools) 

 Public-New England/Middle

 Atlantic7 ‡ 4 7.581 21.468 -4.429 -0.369 N 12.010 10.267 1.743 

Public-East North Central 8 ‡ 25.700 5.612 6.407 0.332 N 19.293 25.966 -6.673 

Public-West North Central ‡ # 4.704 # # # N 3.204 4.541 -1.337 

 Public-South Atlantic 4 4 12.578 25.748 -4.201 -0.250 N 16.778 12.706 4.072 

Public-East South Central ‡ 4 6.223 16.928 -3.414 -0.354 N 9.637 6.385 3.252 

Public-West South Central 4 ‡ 11.439 2.727 2.779 0.321 N 8.660 12.417 -3.757 

 Public-Mountain ‡ # 5.854 # # # N 3.987 6.805 -2.818 

 Public-Pacific 3 ‡ 9.184 17.136 -2.536 -0.216 N 11.720 10.379 1.341 

Census region (private schools)

 Private-Northwest ‡ # 0.887 # # # N 0.604 0.960 -0.356 

 Private-Midwest ‡ 2 0.395 4.709 -1.376 -0.777 N 1.771 0.323 1.448 

 Private-South 6 2 11.413 5.673 1.831 0.191 N 9.582 4.064 5.518 

 Private-West 5 # 4.042 # # # N 2.753 5.185 -2.432 

Number of days in school year 

Less than 180 days 14 3 30.146 8.273 6.976 0.301 Y 23.170 32.305 -9.136 

180 days 23 13 60.270 68.624 -2.664 -0.042 N 62.935 58.376 4.559 

More than 180 days 3 4 9.584 23.103 -4.312 -0.310 N 13.895 9.319 4.577 

School sector 

Public 27 16 83.262 89.618 -2.027 -0.024 N 85.290 89.467 -4.178 

 Catholic ‡ ‡ 2.181 2.920 -0.236 -0.098 N 2.416 1.842 0.574 

Other private 11 3 14.557 7.462 2.263 0.184 N 12.294 8.690 3.604 

See notes at end of table. 
Table 34. Nonresponse bias before and after nonresponse adjustment for homeschooled students, by selected ca

2004—Continued 

136 

Description 

Unweighted 

respondents 

Before nonresponse adjustment 

NonUnweighted Respondent respondent 

non- mean mean 

respondents weighted1 weighted1 

Estimated 

bias 

Relative 

bias SIG2 

Overall 

mean, 

before 

adjustments1 

After nonrespon

Overall 

mean, 

after Esti

adjust- mated 

ments3 bias 

Student sex 

Male 20 8 56.978 44.752 3.899 0.073 N 53.079 56.685 -3.607 

Female 20 12 43.022 55.248 -3.899 -0.083 N 46.921 43.315 3.607 

— Not available. 

# Rounds to zero. 

‡ Reporting standards not met. 

1 Design weight is used before nonresponse adjustment. This is the distribution to each response category. 2 “Y” denotes statistical significance at p < .05. “N” denotes no statistical significance. 3 Weight after nonresponse adjustment. 4 “All other races” includes White, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, and Multiracial. All race categories exclude individuals of H5 IEP = Individualized Education Program. 6 LEP = limited English proficient. 7 Collapsed category comprising two Census divisions. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 2004.” 
 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Figure 6. Before versus after nonresponse adjustment estimates for relative bias for base-year 

sophomores using the cross-sectional weight: 2004 

Estimated relative bias 

after nonresponse 

adjustment 

0.08 

0.07 

0.06 

0.05 

0.04 

0.03 

0.02 

0.01 

0 

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 

Estimated relative bias before nonresponse adjustment 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Figure 7. Before versus after nonresponse adjustment estimates for relative bias for base-year 

sophomores using the panel weight: 2004 

Estimated relative bias 

after nonresponse 

adjustment 

0 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

0.07 

0.08 

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 

Estimated relative bias before nonresponse adjustment 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

137 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Figure 8. Before versus after nonresponse adjustment estimates for relative bias for transfer 

students using the cross-sectional weight: 2004 

Estimated relative bias 

after nonresponse 

adjustment 

0.08 

0.07 

0.06 

0.05 

0.04 

0.03 

0.02 

0.01 

0 

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 

Estimated relative bias before nonresponse adjustment 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Figure 9. Before versus after nonresponse adjustment estimates for relative bias for dropouts 

using the cross-sectional weight: 2004 

Estimated relative bias 

after nonresponse 

adjustment 

0.08 

0.07 

0.06 

0.05 

0.04 

0.03 

0.02 

0.01 

0 

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 

Estimated relative bias before nonresponse adjustment 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

138 
Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Figure 10. Before versus after nonresponse-adjustment estimates for relative bias for early 

graduates using the cross-sectional weight: 2004 

Estimated relative bias 

after nonresponse 

adjustment 

0 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

0.07 

0.08 

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 

Estimated relative bias before nonresponse adjustment 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Figure 11. Before versus after nonresponse adjustment estimates for relative bias for 

homeschooled students using the cross-sectional weight: 2004 

Estimated relative bias 

after nonresponse 

adjustment 

0.08 

0.07 

0.06 

0.05 

0.04 

0.03 

0.02 

0.01 

0 

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 

Estimated relative bias before nonresponse adjustment 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

139 
 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Figure 12. Minimum bias ratio by Type I error rate for base-year sophomores using the crosssectional weight: 2004 

Type I error rate 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

0 0.2 0.4 0.6 0.8 1 1.2 

Bias ratio 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Figure 13. Minimum bias ratio by Type I error rate for base-year sophomores using the panel 

weight: 2004 

Type I error rate 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

0 0.2 0.4 0.6 0.8 1 1.2 

Bias ratio 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

140 
 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Figure 14. Minimum bias ratio by Type I error rate for transfer students using the cross-sectional 

weight: 2004 

Type I error rate 

0 

0.05 

0.1 

0.15 

0.2 

0.25 

0 0.2 0.4 0.6 0.8 1 1.2 

Bias ratio 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Figure 15. Minimum bias ratio by Type I error rate for dropouts using the cross-sectional weight: 

2004 

Type I error rate 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

0 0.2 0.4 0.6 0.8 1 1.2 

Bias ratio 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

141 
 

 

Chapter 3: Sample Design, Weighting, Design Effects, and Data Quality 

Figure 16. Minimum bias ratio by Type I error rate for early graduates using the cross-sectional 

weight: 2004 

Type I error rate 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

0 0.2 0.4 0.6 0.8 1 1.2 

Bias ratio 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Figure 17. Minimum bias ratio by Type I error rate for homeschooled students using the crosssectional weight: 2004 

Type I error rate 

0 

0.05 

0.1 

0.15 

0.2 

0.25 

0 0.2 0.4 0.6 0.8 1 1.2 

Bias ratio 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

142 
 

Chapter 4 

Data Collection Methodology and Results 

4.1 Data Collection Overview 

This chapter briefly describes data collection for the Education Longitudinal Study of 

2002 (ELS:2002) base-year survey and, more expansively, data collection for the first follow-up. 

The discussion of first follow-up data collection includes both in-school student data collection 

and out-of-school follow-up for those no longer in school or unable to take part in the school 

setting. 

The base-year survey collected data from students, parents, teachers, librarians, and 

school administrators. Pre-data-collection activities included securing endorsements from 

educational organizations and gaining cooperation from state education agencies, school 

districts, and individual schools. Self-administered questionnaires and cognitive tests were the 

principal mode of data collection. Data collection primarily took place during in-school survey 

sessions conducted by an RTI field interviewer or team. 

Base-year data were collected in spring term 2002. A total of 752 high schools 

participated, resulting in a weighted school response rate of 67.8 percent. A total of 15,362 

students participated, primarily in in-school sessions, for an 87.3 percent weighted response 

rate.28 Each sampled student’s mathematics teacher and English teacher were given a 

questionnaire to complete. Weighted student-level coverage rates for teacher data were 91.6 

percent (indicating receipt of a report from either the math teacher, the English teacher, or both). 

School administrators and library media coordinators also completed a questionnaire (weighted 

response rates were 98.5 percent and 95.9 percent, respectively). Mail questionnaires were sent 

to parents with a telephone follow-up for nonresponders. Student coverage for parent 

questionnaires was 87.5 percent (weighted). Survey administrators (SAs) completed a facilities 

checklist at each school. Full details about the base-year study may be found in the Education 

Longitudinal Study of 2002: Base Year Data File User’s Manual (NCES 2004–405) (Ingels et al. 

2004), available on the NCES website. The number of completed instruments and both weighted 

and unweighted completion rates are summarized in table 35. 

28 In a two stage-sample, a final response rate should be viewed as the product of both levels of participation. For 

example, with a school response rate of 67.8 percent and a student response rate of 87.3 percent, the final response 

rate taking both stages of the design into account is 67.8 * 87.3 = 59.2 percent. A school nonresponse analysis was 

conducted in the base year to establish that nonresponse bias at the school level was minimal and to provide a fuller 

basis for nonresponse adjustments in the final weighting. Similar analysis and adjustment were undertaken at the 

student level. For details see Ingels et al. (2004), Education Longitudinal Study of 2002: Base Year Data File User’s 

Manual (NCES 2004–405), chapter 3, section 3.2.6. 

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Table 35. Summary of ELS:2002 base-year completion and coverage rates, by instrument: 2002 

Weighted Unweighted 

Instrument Selected Participated percent percent 

Student questionnaire 17,591 15,362 87.28 87.33 

Student assessment1

 15,362 14,543 95.08 94.67 

Parent questionnaire2

 15,362 13,488 87.45 87.80 

Teacher ratings of students3

 15,362 14,081 91.64 91.66 

School administrator questionnaire 752 743 98.53 98.80 

Library media center questionnaire 752 718 95.93 95.48 

Facilities checklist 752 752 100.00 100.00 

1 Percentage of cases for which a student questionnaire and cognitive test were obtained. When a test was not 

obtained, test results were imputed. 

2 Indicates a coverage rate: the proportion of participating students with a parent report. More parents participated; 

completed case numbers reflect the records in the public-use data file, where parent (and teacher) data were 

excluded for students who did not complete a base-year student questionnaire. 

3 Indicates a coverage rate: ratings obtained from at least one teacher. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

First follow-up in-school data collection occurred between January and June 2004. Outof-school data collection took place between February and August 2004 and included telephone 

and in-person interviews. Results are summarized in table 36. 

Table 36. Summary of ELS:2002 first follow-up completion and coverage rates, by instrument: 

2004 

Weighted Unweighted 

Instrument Selected Participated percent percent 

Total sample for public-use file 16,515 14,989 88.70 90.76 

Student questionnaire 13,092 12,427 93.39 94.92 

Student assessment1

 12,427 10,995 87.40 88.48 

School administrator questionnaire2

 12,427 11,856 95.90 95.41 

Transfer questionnaire 1,799 1,275 68.36 70.87 

Dropout questionnaire 876 686 73.20 78.31 

Early graduate questionnaire 687 560 80.64 81.51 

Homeschool questionnaire 61 41 61.46 67.21 

1

 Indicates a coverage rate: percentage of cases for which a student questionnaire and cognitive test were obtained. 

When a test was not obtained, test results were imputed. 

2

 Indicates a coverage rate: percentage of students affiliated with base-year (2002) schools in 2004 (student 

questionnaire completers) for whom a school administrator report was obtained. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

Tables 37 and 38 give further information about response rates for each of the first 

follow-up questionnaires. Table 37 shows that overall about 89 percent (weighted; or 91 percent 

unweighted) of the total ELS:2002 sample (comprising both 2002 sophomores 2 years later and 

2004 freshened seniors) were successfully surveyed—whether through completion of a student, 

transfer student, dropout, homeschool, or early graduate questionnaire. 

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Table 37. Summary of ELS:2002 first follow-up completion and coverage rates, overall results by student question

assessment, and school questionnaire, by selected characteristics: 2004 

145

Characteristic  Total 

Total1

Weighted Unweighted 

88.70 90.76 

 Student questionnaire 

Weighted Unweighted 

93.39 94.92 

Coverage: math 

assessment2 

Weighted Unweighted 

87.40 88.48 

Participated Sampled 

14,989 

16,515 

12,427 

13,092 

10,995 

12,427 

Base-year school sector  Public  Catholic  Other private 

88.57 

92.36 

87.97 

90.33 

93.64 

90.57 

93.38 

93.85 

92.90 

94.94 

95.40 

94.05 

86.92 

92.92 

92.73 

87.23 92.46 

92.89 

Base-year school urbanicity  Urban  Suburban  Rural 

86.74 88.89 

91.30 

89.25 91.31 

92.17 

92.18 

93.18 95.60 

93.93 

95.18 96.00 

85.91 

88.10 87.73 

88.73 

88.92 86.87 

Base-year school region  Northeast  Midwest  South  West 

88.07 

88.75 

89.55 

87.91 

90.56 

90.96 91.47 

89.45 

92.02 

93.78 

94.45 92.61 

93.75 95.51 95.98 93.48 

84.88 89.58 89.53 83.98 

86.70 89.68 90.27 85.49 

Race/ethnicity4 

American Indian or Alaska Native Asian or Pacific Islander Black or African American 

 Hispanic or Latino 

More than one race 

 White 

89.83 

89.39 87.78 88.25 

81.26 

89.63 

90.44 

90.54 89.36 

89.91 

83.64 

92.04 

95.98 

93.35 94.55 

94.16 86.69 

93.50 

94.90 94.08 95.38 

95.10 88.85 

95.46 

87.85 

 85.39 

 86.25 

85.22 88.06 

88.23 

84.95 

87.42 87.11 

86.19 88.32 

89.51 

1 A student, transfer, early graduate, dropout, or homeschool questionnaire was obtained. 2 Percentage of participating student cohort members in base-year schools who completed the math test. 3 Percentage of participating (i.e., questionnaire completers) student cohort members in base-year schools for whom administrator data were obtained. 4 All race categories exclude individuals of Hispanic or Latino origin. 

NOTE: Coverage rates are based on questionnaire completers associated with base-year (2002) schools in 2004 (i.e., sophomore cohort members who 

schools or freshened seniors at the same schools). SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 20
Table 38. Summary of ELS:2002 first follow-up completion and coverage rates, overall results by transfer, dropout

homeschool questionnaire, by selected characteristics: 2004 

146 

Characteristic  Total 

Transfer questionnaire 

Weighted Unweighted 

68.36 70.87 

Dropout questionnaire 

Weighted Unweighted 

73.20 78.31 

Early graduate 

questionnaire 

Weighted Unweighted 

80.64 81.51 

Participated Sampled 

1,275 

1,799 

686 

876 

560 

687 

Base-year school sector  Public  Catholic  Other private 

67.57 

79.64 

74.94 

69.28 

78.89 

73.81 

73.85 

91.89 

40.92 

78.10 

90.00 

80.00 

80.19 

81.03 

93.43 

81.26 80.00 

85.71 

Base-year school urbanicity  Urban  Suburban  Rural 

67.67 69.13 

67.75 

69.93 71.43 

71.84 

73.26 71.50 

77.20 

80.72 

75.60 79.64 

81.17 

79.80 81.60 

79.18 

82.85 83.49 

Base-year school region  Northeast  Midwest  South  West 

64.15 

64.81 

71.79 

69.07 

70.71 

67.89 

72.63 

71.55 

67.88 

72.22 

77.62 71.01 

75.32 

78.87 

80.78 75.15 

85.30 

76.77 

76.77 89.42 

81.08 

79.62 

79.34 90.16 

Race/ethnicity1 

American Indian or Alaska Native Asian or Pacific Islander Black or African American 

 Hispanic or Latino 

More than one race 

 White 

78.18 

67.01 

70.08 69.04 

62.39 

67.82 

80.00 

70.06 

72.16 

70.36 

61.95 

71.72 

50.25 

82.28 

77.15 

78.96 68.26 

68.80 

60.00 

75.00 

81.42 

84.58 75.93 

74.37 

100.00

76.84 

78.45 

78.18 69.82 

83.03 

 100.00 

81.48 

79.39 

80.29 75.00 

83.18 

# Rounds to zero. 1 All race categories exclude individuals of Hispanic or Latino origin. 

NOTE: Coverage rates are based on questionnaire completers associated with base-year (2002) schools in 2004 (i.e., sophomore cohort members who 

schools or freshened seniors at the same schools). SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 20
Chapter 4: Data Collection Methodology and Results 

Student questionnaire completers comprise those participating first follow-up sample 

members then currently (spring term 2004) associated with a base-year (2002) school. In other 

words, the student questionnaire sample was drawn from base-year sophomore cohort members 

who remained at their base-year school or seniors brought in through the freshening process at 

those same schools. There were 13,092 individuals in the sample eligible to complete a student 

questionnaire, and 12,427 did so. The overall response rate for this group was 93.4 percent, 

weighted (94.9 percent unweighted). 

The mathematics assessment was administered to about 87 percent (weighted) of the 

student questionnaire sample (again, the individuals who remained in, or were freshened in, the 

base-year schools). For this same sample (students associated with a base-year school 2 years 

later), school administrator data are available 95 percent (weighted) of the time. 

Of course, not all sophomore cohort members remained in their base-year schools. Many 

transferred to a new school. These students completed a transfer student questionnaire. 

(Although they did not complete the mathematics assessment, a mathematics score was imputed 

for them.) Table 38 shows that for transfer students, a 68.4 percent weighted (70.9 percent 

unweighted) questionnaire completion rate was achieved. 

Dropouts were defined in ELS:2002 as sample members who were absent from school 

for 4 consecutive weeks or more at the time of the survey, and not absent due to accident or 

illness. Table 38 also shows that the sophomore cohort dropout participation rate was about 73 

percent (though over 78 percent unweighted). Early graduates were defined as sample members 

who had graduated from high school or obtained certification of high school equivalency (e.g., 

obtained the General Educational Development [GED] credential) on or before March 15, 2004. 

Table 38 also provides information about early graduates (80.6 percent weighted response rate) 

and the small number (61) of 2002 sophomores who were in a homeschool situation 2 years 

later. 

Although it is of interest to examine response rates in terms of the various first follow-up 

questionnaires, it is also of interest to examine questionnaire response in terms of such analytic 

populations as high school seniors. This examination requires that response rates for two 

different questionnaires, the student questionnaire and the transfer student questionnaire, be 

combined. The senior cohort comprises sophomore cohort members 2 years later who were 

spring-term seniors in 2004, regardless of whether they remained at the base-year school or 

transferred to a new school. It also includes a freshening sample of seniors who were not eligible 

for selection into the sophomore cohort (either because they were not in 10th grade in 2002 or 

were not in the country). Table 39 shows that over 94 percent of 2004 seniors completed a 

questionnaire. The table also reports separately on student questionnaire completers (the 

“stayers” who remained in the base-year schools and were seniors, and the freshened seniors in 

the same schools) and transfer questionnaire completers (the “movers” who went to another 

school and were also seniors in 2004). 

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Chapter 4: Data Collection Methodology and Results 

Table 39. Questionnaire completion rate for ELS:2002 senior cohort, by selected characteristics: 

2004 

Characteristic 

Total (2004 seniors) 

Completed student 

questionnaire 

Weighted Unweighted 

96.84 96.78 

Completed transfer 

questionnaire 

Weighted Unweighted 

74.32 73.79 

Overall completion rate 

Weighted Unweighted 

94.42 94.25 

Participated 

Sampled 

12,269 

12,677 

1,157 

1,568 

13,426 

14,245 

Base-year school sector 

 Public 

 Catholic 

 Other private 

96.77 

97.69 

97.66 

96.57 

97.61 

97.29

73.63 

83.91 

79.47 

72.07 

80.77 

77.89 

94.30 

96.45 

94.86 

93.89 

96.04 

94.59 

Base-year school urbanicity

 Urban 

 Suburban 

 Rural 

96.87 

96.58 

97.46 

96.78 

96.72 

96.94 

74.23 

75.03 

72.55 

73.27 

74.52 

73.09 

93.78 

94.44 

95.25 

93.63 

94.52 

94.66 

Base-year school region

 Northeast 

 Midwest 

South 

West 

96.11 

97.54 

97.14 

96.28 

96.14

97.53 

97.14 

95.84 

80.92 

69.40 

75.61 

74.62 

77.60 

70.36 

74.60 

74.38 

95.17 

94.44 

94.71 

93.37 

94.80 

94.41 

94.63 

92.92 

Race/ethnicity1 

American Indian or Alaska Native 

Asian or Pacific Islander 

Black or African American 

 Hispanic or Latino 

More than one race 

White 

96.86 

97.10 

97.13 

96.94 

96.60 

96.77 

95.79 

96.86 

96.90 

96.86 

95.19 

96.87 

83.78 

72.86 

77.37 

74.22 

67.61 

73.48 

83.33 

74.03 

76.47 

73.08 

63.74 

73.91 

93.79 

94.40 

93.72 

93.77 

93.19 

94.82 

93.81 

94.51 

93.40 

93.36 

91.07 

94.87 

1

 All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 

(ELS:2002), “First Follow-up, 2004.” 

4.2 First Follow-up Pre-Data-Collection Activities 

Although the results of data collection have been described above, section 4.2 describes 

pre-data-collection activities, and sections 4.3 through 4.5 discuss in-school student and 

administrator and out-of-school data collection procedures. Prior to beginning first follow-up 

data collection, it was necessary to recruit schools and have the school coordinator (the point of 

contact for ELS:2002 at the school) fill out enrollment status update information (in anticipation 

of tracing and sampling activities). Additionally, SAs had to be hired and trained. 

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Chapter 4: Data Collection Methodology and Results 

4.2.1 School Recruitment 

States and districts had been informed in the base year that there would be a first followup 2 years later. For that reason, the states were not recontacted. For most districts, a courtesy 

letter was mailed reminding them about ELS:2002 and stating that their schools would be 

contacted to gain permission to collect follow-up data. A few districts required that a research 

application be resubmitted to return to the schools in their jurisdiction. 

After receiving district approval (or 1 week after sending the district courtesy letter for 

those districts that did not require applications), each school was sent an information package by 

Federal Express. The package was addressed to the principal. It contained a letter from the 

project officer and a list of the students who had been sampled from the base year. The letter 

contained a request that the school update this student list with current student status and return it 

to RTI. The package also contained an endorsement letter from the district, if the district had 

provided a letter, and a district-level approval to conduct research if applicable. 

Several days after the package was sent, schools were contacted by telephone. After 

determining the appropriate person with whom to speak, the recruiter discussed details of the 

study and answered any questions. If the school agreed to participate again, a school coordinator 

was identified. This person served as a point of contact at the school and was responsible for 

handling the logistical arrangements. Dates for Survey Day and two Makeup Days were 

scheduled. Also, the name of the staff member who should receive the school administrator 

questionnaire was obtained. The type of parental consent that the school required was 

determined, and approval was obtained for a student incentive. 

Some 752 schools participated in the base-year study. When base-year schools were 

recontacted for the first follow-up, it was learned that five of the schools no longer had 

ELS:2002 sample members (enrolled in any grade at the school) or high school seniors (hence no 

freshening sample). These schools, therefore, were no longer eligible for the study. Of the 

eligible schools, 698 (93.4 percent) allowed RTI to return to collect data in the schools. In 44 

cases, the school refused to allow RTI to return to the school to collect data. Three districts 

(representing a total of five schools) also refused to allow RTI to return to their schools to collect 

data. Data from students enrolled at these schools/districts were collected outside of the school 

setting. Students at the base-year schools completed student questionnaires and a math test at the 

in-school administration. School administrator questionnaires were collected. 

A handful of base-year schools split into multiple schools between 2002 and 2004. Thus, 

in addition to schools that participated in the base year, five schools that received pools of 

students from base-year schools were included as new schools in survey activities but were not 

added to the probability sample. All five of these schools agreed to participate in the first 

follow-up. The students who had moved to these new schools in en masse transfers from a baseyear school were asked to complete both the student questionnaires and the mathematics 

assessment. An in-school administration was held, with the full complement of makeup days. 

School administrator questionnaires were also collected to provide student contextual data; no 

school weight has been generated for the five new schools. 

As expected, there were numerous instances in which students had transferred from the 

base-year school to another school. If five or more students had transferred to the same school, 

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Chapter 4: Data Collection Methodology and Results 

an attempt was made to conduct a Survey Day at the destination school. These schools were 

called “convenience” schools. Ten schools were identified as convenience schools and agreed to 

participate. Students at convenience schools completed transfer student questionnaires at the inschool administration but did not complete the math test. School administrator questionnaires 

were not collected at convenience schools. 

At the time schools were recruited, recruiters ascertained the type of consent required by 

the school. A total of 91.2 percent of the schools allowed passive consent. Private, non-Catholic 

schools (other private) had the lowest rate of passive consent (88.6 percent allowing passive 

consent compared with 91.3 percent and 92.3 percent, respectively, for public and Catholic 

schools). 

4.2.2 Presurvey Contacts With Schools 

In the spring and again in the autumn of 2003, each base-year school was provided a list 

of ELS:2002 base-year sample members from their school. The school was asked to indicate 

whether each sample member was still enrolled at the school. For any sample member who was 

no longer enrolled, the school was asked to indicate the reason and date the student left. If the 

student had transferred to another school, the base-year school was asked to indicate the name 

and location of the transfer school. This information was gathered again in the spring of 2004, 

prior to the school’s scheduled Survey Day. 

In the fall of 2003, each base-year school was also asked to provide a list of the 12thgraders enrolled at that school, so this information could be used as part of the freshening 

process. 

4.2.3 Tracing the Student Sample 

As noted in the prior section, schools were asked to identify sample members who no 

longer attended the base-year school. At the time, contact information for those individuals was 

collected. 

A postcard update was mailed to all ELS:2002 sample members in the early fall of 2003. 

Sample members were asked to update contact information and return the postcard to RTI. A 

total of 3,830 postcards were returned by sample members. In addition, 280 mailings were 

returned from the post office with forwarding address information, and 1,028 were returned with 

no forwarding information. 

Prior to the start of first follow-up data collection, location information for the sample 

members was processed through locating databases (including the U.S. Postal Service National 

Change of Address [NCOA] file). In addition, the following types of cases were sent through 

Telematch (a national database that provides telephone number): 

• any case that had a new address from NCOA; 

• any base-year nonrespondent or questionnaire-ineligible case; 

• any base-year respondent that was a candidate for out-of-school data collection 

(because the base-year school identified the respondent as having left the school); and 

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Chapter 4: Data Collection Methodology and Results 

• any base-year respondent or freshened eligible 12th-grader with an address but no 

phone number. 

During the course of data collection, cases were sent to the tracing unit for intensive 

tracing if the telephone unit was unsuccessful in locating the sample member. Information 

crucial to tracing was collected at the time of the base-year data collection and was made 

available to the tracing staff. The students were asked to provide the following information 

when they completed the in-school questionnaire in the base year: 

• student’s full name, address, and current telephone number; 

• student’s Social Security number; 

• full name, address, telephone number (both home and work), and e-mail address of 

mother/father or female/male guardian; 

• full name, address, and telephone number of a close relative or friend not currently 

living with the student who is likely to know how to locate the student should the 

student relocate; and 

• student’s nickname, if any. 

The questionnaire the parents completed contained a similar set of questions: 

• student’s Social Security number; 

• parent’s Social Security number; 

• full name, address, and telephone number (home and work) of mother/father or 

female/male guardian; and 

• full name, address, and telephone number of a close relative or friend not currently 

living with the family who is likely to know how to locate the student or the parent 

should the student or family relocate. 

The tracing unit updated addresses and telephone numbers produced by these tracing 

activities directly into the ELS:2002 locator database. The database maintained the most current 

location information for the students (i.e., name, address, telephone number, and Social Security 

number) as well as historical data generated from various tracing activities. 

Tracing sources included Fastdata (for name, address, and change of address searches), 

Experian (for address or Social Security number searches), LexisNexis (for Social Security 

number, address, and reverse phone searches), and Trans Union (to develop Social Security 

numbers from other information). In addition, the Department of Education’s Central Processing 

System (CPS) was checked to see if sample members had applied for postsecondary financial aid 

using the Free Application for Federal Student Aid (FAFSA). Some 1,666 cases were sent for 

matching, with information located for 271 cases. 

The in-house tracing unit was able to locate 1,137 sample members. Of the 1,611 cases 

that went through tracing, interviews were completed with 964 of them. 

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Chapter 4: Data Collection Methodology and Results 

Cases that could not be located via telephone or database search were sent to the field for 

tracing. Field staff used sources such as apartment complex management, Department of Motor 

Vehicles’ databases, real estate agents, voter registration rolls, and neighbors to try to locate 

sample members or their families. Field staff also often returned to the base-year school to see if 

any additional locating information could be provided. 

Peer locating was also attempted. The locator inquired whether the sample member knew 

the whereabouts of other sample members from the same (base-year) school. 

4.2.4 Training 

4.2.4.1 Field Staff Recruitment and Training 

In the first follow-up, 10 field supervisors (FS) and 85 survey administrators (SA) were 

hired and trained. Staff were identified from RTI’s National Interviewer File, a database that 

contains information about available field staff across the country. Five of the FSs had worked 

on the base-year ELS:2002 study. A number of the SAs had worked on previous rounds of 

ELS:2002 (either as SAs or as SA assistants). The others had experience on a variety of other 

research studies. 

Prior to training, each SA was mailed a copy of the SA manual and a home study 

exercise. The SAs were instructed to read the manual and complete the home study exercise 

before the first day of training. Project staff conducted training in Durham, North Carolina, on 

January 9–11, 2004. Table 40 presents the SA training agenda. 

Each SA signed a confidentiality agreement and an affidavit of nondisclosure at the 

beginning of training. During training, contacts that had already been made with the schools 

were discussed, as well as contacts that each SA would need to make with the school coordinator 

prior to Survey Day. Survey Day logistics were covered as well as administration instructions 

for the student questionnaire and cognitive tests. Criteria for scheduling Makeup Days were also 

covered as well as how to schedule the Makeup Days with the coordinator. The field supervisor 

discussed the recruitment, hiring, and training procedures for SA assistants (SAs were 

responsible for hiring their own assistant). While explaining active and passive consent 

procedures, there was a discussion about contacting parents for gaining active permission and 

converting refusals. 

Before the beginning of out-of-school field data collection, the field supervisors and 

regional supervisors identified SAs and other field personnel who would be suited to conduct inperson interviews with sample members. These staff received additional training by telephone. 

Training focused extensively on field tracing techniques and on administering a screener to 

sample members to determine the correct questionnaire to use for the interview. A total of 92 

staff were trained to collect data in the field. 

152 
 

Chapter 4: Data Collection Methodology and Results 

Table 40. Survey administration training agenda: 2004 

Friday, January 9, 2004 

• Introductions 

• Confidentiality 

• Prior NCES studies/overview of ELS:2002 

• Prior contacts with schools 

• Consent types 

• Types of schools (base year, new, convenience) 

• CAC and CAC exercise 

• Roster 

• Working with the school coordinator 

• Role play—going over roster 

− Recognizing and dealing with reluctant coordinators 

• Types of students 

• Survey Day logistics 

• Student and new participant student questionnaires (NPSQs) 

• Math test 

• Editing and edit exercise 

Saturday, January 10, 2004 

• Questions about previous day 

• Unusual situations 

• Coordinator honorarium 

• Other end of Survey Day activities—collect school administrator, catalogs, nonresponding student form 

• Transmittal form and transmittal form exercise 

• Packing list 

• Shipping materials 

• Phone report to field supervisors 

• Survey Day from start to finish 

• Makeup Days 

• Contacting parents (with role play) 

• Hiring and training survey administrator (SA) assistants 

Sunday, January 11, 2004 

• Questions from previous days 

• Student nonresponse follow-up 

• Dealing with paperwork 

• Dealing with disruptive students/other problems at schools 

• Institutional Review Board (IRB) training 

• Headway procedures 

• Certification 

• Distribution of assignments 

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Chapter 4: Data Collection Methodology and Results 

4.2.4.2 Telephone Interviewer Training 

Telephone interviewers were trained beginning on February 7, 2004. Several training 

classes were held in conjunction with the graduated release of sample to the telephone 

interviewers. Table 41 presents the telephone interviewer training agenda. 

Telephone interviewer training included an overview of the study, frequently asked 

questions, practice with the various questionnaires, and practice with the computer-assisted 

telephone interview (CATI) Case Management System (how to get into the computer system, 

coding various call outcomes, etc.). Interviewers also had extensive practice on refusal 

avoidance and refusal conversion. Questionnaire practice took the form of “round robins” 

(where the entire group took turns asking various questions from the questionnaire and keying 

responses) and paired mocks (where two interviewers were paired together—one acted as the 

respondent and the other acted as the interviewer). Prior to beginning calling, interviewers had 

to pass a certification process to prove that they had mastered the training material. Certification 

included answering frequently asked questions as well as demonstrating proficiency in two 

practice interviews and refusal avoidance. 

4.3 Data Collection Procedures—In-School 

After training, each SA recruited, hired, and trained an SA assistant to help in the school. 

In some cases, the SA was able to use the same assistant for all of the assigned schools. 

However, in a few cases, the schools were far enough away from where the SA lived that it 

involved an overnight stay. In that case, the SA hired an SA assistant who lived close to the 

school. 

The SAs received case assignment cards for each of their assigned schools. The case 

assignment cards contained information about the school, including the name and phone number 

of the school coordinator and the designated Survey Day and Makeup Days. Prior to the 

designated Survey Day, the SA contacted the coordinator to make sure that the Survey Day 

supplies had arrived and the arrangements were in place. The SA also asked for an update of 

sample members’ status (e.g., if anyone had transferred/dropped out or if any of the students who 

had left the school had returned since the fall update) and determined the eligibility of students 

on the freshened 12th-grader list. At the same time, the SA determined if the coordinator had 

received any parental refusals. If so, the SA began refusal conversion efforts if the school 

coordinator was willing to provide a telephone number for the parent. In active consent schools, 

the SA also determined from the coordinator which parents had not yet returned permission 

forms. If the school was willing to provide telephone numbers and/or if contact information was 

available from base-year data collection, the SA began calling those parents to prompt them to 

return the forms. 

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Chapter 4: Data Collection Methodology and Results 

Table 41. Telephone interviewer training agenda: 2004 

Saturday, February 7, 2004 

• Welcome/introduction of staff 

• Introduction to ELS:2002/overview 

• Demo interview (student interview) 

• Group questions 

− Divide into groups; scripted questions asked by project staff; group discussion about reactions to 

questionnaire, project, etc. 

• Frequently asked questions (FAQs) 

− Cover key FAQs in detail, present certification questions 

• Confidentiality forms 

• Screener—eligibility 

− Presentation about screener 

• Round robin mock: Homeschool 

• Incentives 

• Round robin mock: Transfer 

• Front-end practice 

− Intro to the front-end and computer-assisted telephone interview (CATI) Case Management System 

(CMS). Discuss what happens when no one answers the phone. Review how to code a refusal, intro 

to roster lines, etc. 

• Round robin mock: Early graduate 

• Quality control (QC) 

− QC meetings, monitoring, etc. 

Sunday, February 8, 2004 

• Questions from previous day 

• Refusal avoidance presentation 

− Generic refusal avoidance presentation, tone of voice, listening skills, etc.; some project-specific 

material 

• Round robin mock: Dropout 

• Front-end practice 

− Brief review of previous day; in depth about roster lines, adding roster lines, informed consent, etc. 

• Refusal avoidance practice (mocks) 

− Divide into groups or pairs; telephone interviewers (TIs) have scripted sheets and FAQs to use; mock 

phone calls with respondents; how to address concerns (some project-specific concerns based on field 

test) 

• Round robin: New participant student 

• Refusal avoidance practice (mocks) 

− Continuation from above; focus on project-specific concerns 

• Scripted paired mocks 

− Divide into pairs—one TI is interviewer, the other is respondent 

• Certification 

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Chapter 4: Data Collection Methodology and Results 

On Survey Day at each school, the SA checked in with the school coordinator and 

collected any parental permission forms that had been received. In active consent schools, the 

SA checked the student roster to make sure that only students who had returned signed 

permission forms were allowed to participate. In both active and passive consent schools, the 

SA made sure that no one for whom the school received a parental refusal was allowed to 

participate unless the parent had rescinded that decision in writing. As students entered the 

testing room, they were checked off on the student roster. After the majority of the sampled 

students arrived, the school coordinator was asked to try to locate the students who were not 

present. 

Survey Day at each school was staffed with one SA and one SA assistant. The SA 

labeled questionnaires and tests with each student’s identification number. Prior to beginning 

data collection, the students were read a script describing the study, giving the elements of 

informed consent and instructions for completing the questionnaires/tests. Students who were 

base-year respondents received student questionnaires. Students who had not participated in the 

base year received new participant student questionnaires (NPSQs). 

Each student was given a questionnaire to complete during a 45-minute group 

administration. After the questionnaires were collected, the SA handed out math tests. There 

were four different math test forms. Base-year mathematics test results determined which test 

form each base-year respondent received in the first follow-up (high, medium, or low difficulty). 

Students who did not participate in the base year were given a math test that encompassed a 

range of questions from high to low math ability. Students were given 26 minutes to complete 

the math test. While the students were taking the tests, the SA and SA assistant checked the 

student questionnaires for critical items. After the tests had been completed, the SA asked 

students who missed critical items to complete them before returning to class. 

At the conclusion of the group administration, the SA gave each participating student an 

incentive if preapproved by the school. Nearly three-fourths of the schools (72.9 percent) 

approved a $20 cash incentive for each participating student. Approximately 20 percent of the 

schools did not approve cash but allowed a $20 gift certificate to be presented to each 

participating student. Approximately 4 percent of the schools did not allow either incentive but 

had alternative arrangements (such as a cash equivalent donation to the student council, school 

library, etc.). Fewer than 3 percent of the schools would not permit an incentive of any type. 

After distributing the incentive and sending the students back to class, the SA determined 

whether a Makeup Day was necessary. A Makeup Day was automatically scheduled if three or 

more students who had permission to participate were not present for Survey Day. If fewer 

students missed the session, a decision was made on a case-by-case basis. If a Makeup Day was 

deemed necessary, the SA informed the school coordinator. Makeup Days had been scheduled 

during the recruitment phase of the study. During the first follow-up, 190 schools had only a 

Survey Day administration. A Survey Day and one Makeup Day were held at 320 schools. A 

total of 203 schools required a Survey Day and at least two Makeup Days. Because of the 

smaller number of students in makeup sessions, only one person covered Makeup Days. 

Generally, the SA conducted Makeup Days unless the SA assistant lived substantially closer to 

the school. 

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Chapter 4: Data Collection Methodology and Results 

As expected, the first day of data collection at a school was the most productive. Of 

those who participated in in-school survey sessions, 87.5 percent (9,737) were interviewed on 

Survey Day, and the remaining 12.5 percent on a Makeup Day. Some 10.1 percent (1,126) 

participated on the first Makeup Day, and 2.4 percent (262) on a subsequent Makeup Day. 

School coordinators were given a base honorarium of $50. However, as a graduatedresults-based incentive, additional honorarium amounts of up to $50 were given for schools with 

high student response rates. 

Table 42 shows the proportion of student questionnaire cases completed in in-school 

sessions versus those that had to be completed outside school. Table 43 reports on the 

completion rates for sample members who were classified as currently enrolled students at 

schools that allowed in-school survey administration for the first follow-up. Of the 12,161 

students sampled from schools that allowed in-school survey administration, 89.0 percent who 

participated took part during the in-school administration. Even though these schools allowed at 

least one in-school Survey Day (and often one or more Makeup Days), it was nevertheless 

necessary to pursue some students outside school. An additional 5.8 percent were surveyed 

outside the school setting, to achieve an overall weighted participation rate of 94.8 percent. 

Although questionnaire completion defines participation in ELS:2002, student 

questionnaire completers were also asked to complete a mathematics assessment. There is little 

difference in the ultimate questionnaire-defined response rates according to whether the school 

allowed a survey session (94.8 percent) or did not (93.4 percent), but a greater difference exists 

for test completion. Math test completion rates are shown in tables 44 and 45. Table 44 shows 

test completion rates of all sample members classified as currently (spring 2004) enrolled 

students in base-year schools regardless of whether the school allowed in-school data collection 

for the first follow-up. Math tests were collected for 93.1 percent (weighted) of all sample 

members classified as currently enrolled. Table 45 reports on test completion rates of currently 

enrolled student respondents at high schools where in-school Survey Days were held. As 

expected, the rate of test completion among questionnaire completers who attended schools that 

permitted in-school survey administration was quite high—99.1 percent. 

157 
Table 42. Proportion of student questionnaire cases completed in-school versus out-of-school, by selected chara

158 

Characteristic Total 

Total 

Weighted Unweighted 

93.39 94.92 

In-school 

Weighted Unweighted 

82.83 84.72 

Out of school 

Weighted Unweighted 

10.56 10.20 

Participated Sampled 

12,427 

13,092 

11,092 13,092 

1,335 

13,092 

Base-year school sector Public Catholic Other private 

93.38 

93.85 

92.90 

94.94 

95.40 

94.05 

82.39 88.34 

87.25 

83.62 89.04 

87.44 

10.99 

5.52 

5.66 

11.32 

6.37 

6.61 

Base-year school urbanicity Urban  Suburban Rural 

92.18 

93.18 95.60 

93.93 

95.18 

96.00 

81.79 

82.20 

85.89 

84.30 

85.12 

84.44 

10.39 

10.98 

9.72 

9.63 

10.06 

11.57 

Base-year school region  Northeast Midwest South West 

92.02 

93.78 

94.45 

92.61 

93.75 

95.51 

95.98 

93.48 

78.46 

84.74 

86.43 

79.27 

81.59 

86.68 

87.69 

80.19 

13.55 

9.04 

8.02 

13.34 

12.16 

8.83 

8.29 

13.30 

Race/ethnicity1 

American Indian or Alaska Native Asian or Pacific Islander Black or African American 

Hispanic 

or Latino 

More than one race 

White 

95.98 

93.35 

94.55 

94.16 

86.69 

93.50 

94.90 

94.08 

95.38 

95.10 

88.85 

95.46 

85.33 

80.16 

83.38 

81.87 

74.80 

83.73 

80.61 

82.68 

84.06 

82.78 

79.24 

86.19 

10.65 

13.19 

11.17 

12.29 

11.89 

9.78 

14.29 

11.40 

11.32 

12.33 

9.62 9.27 

1 All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, 20
 

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 4: Data Collection Methodology and Results 

Table 43. Student questionnaire completion rates at base-year schools that allowed in-school 

data collection in the first follow-up, by selected characteristics: 2004 

Characteristic 

 Total 

In-school 

Weighted Unweighted 

88.97 91.21 

Out-of-school 

Weighted Unweighted 

5.78 4.98 

Participated 

Sampled 

11,093 

12,161 

606 

12,161 

Base-year school sector 

 Public 

 Catholic 

 Other private 

88.73 

91.88 

91.24 

90.64 

93.38 

92.57 

6.06 

2.84 

2.46 

5.70 

2.85 

2.42 

Base-year school urbanicity 

 Urban 

 Suburban 

 Rural 

85.66 

89.20 

93.16 

88.95 

91.71 

94.11 

7.25 

5.74 

3.74 

5.97 

4.79 

3.66 

Base-year school region 

 Northeast 

 Midwest 

 South 

 West 

85.49 

90.21 

92.42 

85.46 

88.38 

92.49 

93.86 

87.66 

8.01 

4.71 

3.48 

8.44 

7.11 

4.14 

3.22 

7.08 

Race/ethnicity1 

American Indian or Alaska Native 

Asian or Pacific Islander 

Black or African American 

 Hispanic or Latino 

More than one race 

 White 

91.69 

86.72 

89.53 

87.42 

84.25 

89.67 

88.76 

88.62 

91.50 

88.92 

85.36 

92.68 

6.87 

8.24 

6.07 

8.26 

5.33 

5.03 

8.99 

6.88 

5.24 

7.49 

5.43 

3.93 

1

 All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

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Chapter 4: Data Collection Methodology and Results 

Table 44. Math test completion—all eligible students (students still associated with a base-year 

school at time of data collection, regardless of whether the school permitted an inschool survey session), by selected characteristics: 2004 

Characteristic 

 Total 

Weighted 

93.06 

Tests completed 

Unweighted 

93.99 

Participated 

Sampled 

10,995 

11,698 

Base-year school sector 

 Public 

 Catholic 

 Other private 

92.75 

95.96 

97.32 

93.18 

96.14 

97.36 

Base-year school urbanicity 

 Urban 

 Suburban 

 Rural 

91.01 

93.41 

95.06 

92.65 

94.50 

95.07 

Base-year school region 

 Northeast 

 Midwest 

 South 

 West 

91.03 

93.90 

95.13 

90.73 

92.20 

94.57 

95.52 

92.22 

Race/ethnicity1 

American Indian or Alaska Native 

Asian or Pacific Islander 

Black or African American 

 Hispanic or Latino 

More than one race 

 White 

93.03 

90.82 

92.65 

90.26 

92.16 

93.97 

90.80 

92.32 

93.48 

91.34 

93.12 

95.11 

1

 All race categories exclude individuals of Hispanic or Latino origin. 

NOTE: Students were eligible to take the mathematics test only if they were enrolled in their base-year (2002) school 

2 years later (2004) or were a 2004 freshened senior in a base-year school that participated in the first follow-up. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

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Chapter 4: Data Collection Methodology and Results 

Table 45. Math test completion—only base-year schools allowing survey days in the first followup, as a percentage of questionnaire completers, by selected characteristics: 2004 

Characteristic 

 Total 

Weighted 

99.10 

Tests completed 

Unweighted 

99.12 

Participated 

Sampled 

10,995 

11,093 

Base-year school sector 

 Public 

 Catholic 

 Other private 

99.08 

98.86 

99.95 

99.04 

99.01 

99.91 

Base-year school urbanicity 

 Urban 

 Suburban 

 Rural 

98.71 

99.41 

98.87 

98.87 

99.41 

98.77 

Base-year school region 

 Northeast 

 Midwest 

 South 

 West 

99.56 

98.79 

98.72 

99.69 

99.62 

98.78 

98.81 

99.67 

Race/ethnicity1 

American Indian or Alaska Native 

Asian or Pacific Islander 

Black or African American 

 Hispanic or Latino 

More than one race 

 White 

100.00 

99.45 

98.93 

98.80 

97.99 

99.25 

100.00 

99.48 

98.84 

99.03 

99.04 

99.12 

1

 All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

4.4 Data Collection Procedures—School Administrator Survey 

When schools were recruited for the first follow-up, school coordinators were asked to 

name an individual who would be responsible for completing the school administrator survey. 

Because the bulk of the questions were of a general nature about the school and its policies, any 

knowledgeable staff member was permitted to complete the majority of the questionnaire. It was 

required that the final section be filled out by the principal of the school. Because this section 

only took about 5 minutes to complete, it reduced the burden on the principal by allowing 

someone else in the school to complete the greater part of the questionnaire. 

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Chapter 4: Data Collection Methodology and Results 

School administrator questionnaires were mailed to the designated survey recipients in 

February 2004. These questionnaires were sent to the base-year participating schools, including 

those schools that did not permit in-school data collection for the first follow-up (with the 

exception of 13 that refused both in-school administration and the school administrator 

questionnaire). Prompting for school administrator questionnaires was done during SA contacts 

with the schools. After the last Survey Day in the school, institutional recruiters prompted for 

questionnaires. In an attempt to boost participation, a small subset of the schools was offered the 

option of completing an abbreviated questionnaire designed to gather key data points. Schools 

were also offered the option of e-mailing or faxing the completed questionnaire instead of 

sending it by mail. A total of 712 questionnaires (94.7 percent) were received. Completed 

school administrator questionnaires were received from 98.9 percent of all of the schools that 

allowed first follow-up data collection in the schools and 47.8 percent of the schools that refused 

in-school data collection. 

4.5 Data Collection Procedures—Out-of-School 

During the school recruitment process, base-year schools were asked to provide 

enrollment status updates for sample members at three points in time: spring term of 2003, fall 

term of 2003, and several weeks prior to the scheduled Survey Day in spring term of 2004. For 

those who left their base-year school, the school was asked to provide contact information to 

allow for out-of-school data collection during the first follow-up survey period. 

Telephone data collection began in February 2004. Sample members who were 

identified for initial contact by the telephone unit included those no longer enrolled at the baseyear school and those who attended base-year schools that did not grant permission to conduct an 

in-school survey session. Other cases were identified for telephone follow-up after the Survey 

Day and all Makeup Days had taken place at the school the sample members attended. 

For sample members under the age of 18, parental permission was obtained by telephone 

prior to initiating contact with the sample member. Once parental permission was obtained (and 

for those sample members aged 18 or older), a screener was administered to the sample member 

to determine eligibility and which type of questionnaire to administer (student, transfer, dropout, 

early graduate, or homeschool). Sample members who did not participate in the base year were 

also administered a new participant supplement (NPS). 

Some nonresponding sample members were assigned to SAs for field follow-up. The 

determination of which cases were sent to the field was based on the distance of the sample 

member from the SA, the SA’s availability, and whether telephone leads on the sample member 

had been exhausted. In March 2004, SAs were identified to work cases in the field that had 

proved difficult to reach by telephone. A total of 1,803 cases were assigned to field staff. The 

SAs were sent tracing information on each sample member. As with the telephone interviewing, 

SAs obtained parental permission for sample members under the age of 18. SAs also screened 

sample members prior to interviewing them to determine eligibility and which questionnaire to 

administer. A total of 797 sample members were interviewed in the field. An additional 80 field 

cases were completed either by mailed questionnaire or telephone interview and were withdrawn 

from the field assignment. (Questionnaires were mailed to sample members at their request, but 

there was no mass mailing.) 

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Chapter 4: Data Collection Methodology and Results 

4.6 First Follow-up Yield 

Tables 46 and 47 summarize additional data collection results for the ELS:2002 first 

follow-up that are of methodological interest. (Because they are not concerned with population 

estimates but have a methodological focus, the tables present only unweighted percentages.) 

Overall yield by method of data collection is shown in table 46. The majority of those 

who responded did so during the in-school Survey or Makeup Day. Approximately 20 percent 

participated as a result of the telephone interview follow-up. Just over 5 percent were 

interviewed by a field interviewer. 

Table 46. Overall yield, by method of data collection (unweighted percents): 2004 

Method 

 Total responses 

Number of responses 

14,989 

Percent of total response 

100.00 

In school 

Mail 

Telephone 

Field 

11,125 

43 

3024 

797 

74.21 

0.29 

20.17 

5.33 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

Table 47 summarizes response rates by the sample members’ base-year status. As 

expected, response rates were higher for sample members who had been base-year respondents 

than for those who had not participated in 2002. Nevertheless, about two-thirds of the sample of 

base-year nonparticipants took part in the first follow-up, a high percentage given the past 

response propensities of this group. More specifically, the first follow-up response rate for baseyear respondents was 92.4 percent, compared with 66.7 percent for base-year nonrespondents. 

Freshened sample members were enrolled in the 12th grade in the spring term of 2004 but had 

not been eligible to participate in the base year because they were either out of the country or 

were not high school sophomores at that time. Some 82.6 percent of freshened seniors 

completed a first follow-up student questionnaire. Finally, some sophomores sampled in the 

base year were unable, for reasons of their language status (insufficient command of English) or 

owing to a severe disability, to participate through questionnaire completion at the time 

(nonetheless, contextual data were collected for these individuals, who appear only on the baseyear restricted-use file). A subset of these individuals was reclassified in the spring of 2004 as 

able to complete a questionnaire and took part in the study. These 105 individuals are included 

in the total row only; they are included on the public-use file ECB. 

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Chapter 4: Data Collection Methodology and Results 

Table 47. Overall unweighted response rates, by base-year status: 2004 

Base-year status 

 Total 

Eligible sample 

16,5151

Respondents 

 14,9891

Response rate 

(unweighted percent) 

90.76 

Base-year nonrespondent 

Base-year respondent 

Freshened 12th-grader 

976 

15,227 

207 

651 

14,062 

171 

66.70 

92.35 

82.61 

1 Includes (shown only in totals) 105 sophomore cohort members who were classified as incapable of completing a 

questionnaire in 2002 but were reevaluated in 2004, found to be capable, and responded to the first follow-up survey. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

164 
Chapter 5 

Data Preparation and Processing 

This chapter describes the automated systems used to control survey processes for the 

Education Longitudinal Study of 2002 (ELS:2002), including procedures used to maintain 

receipt control; aspects of data preparation (such as coding); and the various procedures for data 

capture, cleaning, and editing. The chapter is organized into seven sections: (1) overview of 

systems design, development, and testing; (2) data receipt; (3) coding for hardcopy instruments; 

(4) data capture for optically scanned instruments; (5) data cleaning and editing; (6) data capture 

and editing for computer-assisted telephone interviewing (CATI); and (7) data processing and 

file preparation. 

5.1 Overview of Systems Design, Development, and Testing 

Most systems were developed in the base year, then redesigned if necessary during the 

first follow-up field test with concern for the processes needed for the first follow-up main study. 

The effort was to test systems in a smaller environment to reveal points in which improvements 

could be implemented on a larger scale. After the field test, improvements were implemented 

and checked in a test environment. 

The following systems were developed in the base year and refined and tested in the first 

follow-up field test: 

• a recruiting system; 

• a Survey Control System (SCS); 

• a Survey Day materials generation program; 

• a questionnaire receipt application; 

• a web-based Integrated Management System (IMS); 

• production reports; 

• TELEform (application used for scanning questionnaires); 

• a mail return application; 

• an incentive tracking application; 

• a field reporting system to help field supervisors track the status of in-school data 

collection and field interviewing; 

• a Structured Query Language (SQL) server database to store scanned data responses; 

• a scanned image database; and 

• a student CATI instrument. 

A full development process, including design, programming, testing, and implementation, 

was used in the creation of these systems. Specifications were developed in word processing 

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Chapter 5: Data Preparation and Processing 

documents and flowchart applications, and progress was tracked using Microsoft Project and 

Microsoft Excel. Specifications for questionnaires were designed in word processing documents 

and were updated to reflect what changed between the field test questionnaires and the full-scale 

questionnaires. 

Between the field test and full-scale studies, systems and procedures were evaluated, and 

the following functionality was added to the full-scale operations: 

• a field assignment system; 

• a field materials generation system; 

• mail generation invoked by requests in CATI; 

• a computer-assisted data entry (CADE) program for the field screener; 

• TELEform versions of out-of-school hardcopy questionnaires (i.e., transfer, dropout, 

early graduate); 

• quality control steps implemented during scanning, rather than later during data 

delivery processes; 

• data cleaning and editing programs; 

• a scanned image archive server that allowed instant access to scanned questionnaires 

during the data cleaning and review process; 

• a cleaning and editing application that allowed editors to review and correct 

questionnaire data as appropriate, working in conjunction with actual scanned images 

in cases in which inconsistent data occurred; 

• a data review system that allowed reviewers to randomly review questionnaires with 

data to detect data deficiencies (e.g., scanning problems); and 

• an occupation coding application. 

5.2 Data Receipt 

The data preparation facility received all materials returned to RTI after a school’s survey 

was complete or school officials sent in completed questionnaires. Procedures were established 

to systematically receive and record all required forms; this process included the scanning of barcoded labels. Receipt events were available for the full-scale study to identify questionnaires 

that were not completed fully or accurately and to allow project staff to follow up promptly. 

Different versions of questionnaires (e.g., student, transfer, early graduate, etc.) were easily 

distinguishable within the receipt process and were automatically batched separately based on 

the questionnaire type. 

After questionnaires were received and added to the receipt system, a batch number was 

assigned to the questionnaire. To assist the project team in cases that required referring to a 

questionnaire, the system was able to access dynamically the status of an individual 

questionnaire and provide its batch number. If the questionnaire had moved beyond the scanning 

stage, the scanned image could be accessed as well. Questionnaires were occasionally identified 

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Chapter 5: Data Preparation and Processing 

for data removal (e.g., when parental consent was lacking). Rather than deal with the removal 

process manually, a spreadsheet was developed to document these cases, and case removal was 

integrated into the data delivery process. This approach was useful because it did not disrupt the 

questionnaire processes and provided the ability to add cases back to final data files when 

appropriate (e.g., when parental permission was obtained). 

5.3 Coding for Hardcopy Instruments 

The following text items were obtained in the questionnaires: 

• mother/female guardian occupation (from student’s new participant supplement); 

• father/male guardian occupation (from student’s new participant supplement); 

• expected occupation after high school (from student questionnaire); and 

• expected occupation at age 30 (from student questionnaire). 

Occupation text was loaded into a coding application in which a coding specialist could 

select the correct code from the 16 occupation categories. The resulting codes were merged back 

into the data files. 

5.4 Data Capture for Optically Scanned Instruments 

The following questionnaires were developed for optical scanning: 

• a student questionnaire; 

• a new participant student questionnaire (new participant supplement joined with an 

abbreviated student questionnaire); 

• four math tests; 

• a school administrator questionnaire; 

• an abbreviated student questionnaire; 

• a new participant supplement; 

• a transfer questionnaire; 

• a not currently in school (dropout) questionnaire; 

• an early graduate questionnaire; and 

• a homeschool questionnaire. 

After questionnaires were received and batched, they were ready for TELEform 

scanning. A TELEform questionnaire contained text fields that could be recognized by scanning 

machines and interpreted forms text to data through optical character recognition. Verifiers 

reviewed data that were not interpreted accurately by the scanning machines or were not 

consistent with expected ranges. Once verification was complete, the data were converted to an 

American Standard Code for Information Interchange (ASCII) file, and the questionnaire image 

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Chapter 5: Data Preparation and Processing 

was written to the server. This process provided immediate access to raw questionnaire data and 

a repository of images accessible by ELS:2002 staff. 

TELEform development began with the field test TELEform document and specifications 

in Microsoft Word that indicated changes made between the field test and the full-scale study. 

Modifications were easily made, and variable names were updated appropriately. Any new 

TELEform documents were first developed in Microsoft Word as a specification. As changes in 

the TELEform document were required, the corresponding Microsoft Word document was 

updated using the “Track Changes” tool. Reviewers would compare the specifications to the 

printed version of the TELEform document to ensure that all questionnaires were the latest 

version. When a TELEform document was confirmed as final, internal testing of the scanning 

and data-writing processes occurred. About 10 forms were printed and filled out for testing 

purposes. The test forms were scanned so that the resulting data could be compared to the 

original questionnaire; this comparison would detect problems with the printed questionnaire, the 

scanning program, or the SQL server database. 

Scanning procedures were evaluated after the field test in an effort to streamline the 

scanning process for the full-scale study. Different stages of the scanning process were timed, 

and averages across each stage (i.e., cutting, scanning, evaluation, verification, data/image 

commit) for each questionnaire were used to analyze system and staffing needs. The need for 

efficient archiving procedures arose from the large amount of space taken by scanned images on 

the server and the need for access to the image for review. An application was developed to 

control the archiving process across the tens of thousands of scanned images. Archive 

procedures were modified from those used during the field test, and an SQL database was 

created to track what had been archived (and to which CD volume) for easy image retrieval. 

Questionnaire data were committed to ASCII data files and loaded with a scheduled 

process into an SQL server database each night. Raw SQL server data were compared to the 

original questionnaires to ensure that scanning procedures were accurately storing data to the 

SQL server. The SCS tracked each form that was scanned by indicating a scanned event 

whenever the SQL server database was updated for a questionnaire. If a record was not 

transmitted successfully before or during the commit (i.e., nightly loading process) to the SQL 

server, a scanned event would be lacking for the questionnaire and could be easily identified later 

for rescanning. This approach ultimately ensured that all questionnaires had a corresponding 

data record and could not be dropped without detection. 

5.5 Data Cleaning and Editing 

An application was developed in which case/item-specific issues were reviewed and new 

values were recorded for subsequent data cleaning and editing. Records were selected for review 

based on one of the following criteria: random selection, suspicious values during frequency 

reviews, values out of expected ranges, and values not adhering to a particular skip pattern. The 

review application provided the case/item-level information, reasons for review, and a link to the 

scanned image of the questionnaire. Reviewers determined scanning corrections, recommended 

changes (if respondents had misinterpreted the question), and reviewed items randomly to spot 

potential problems that would require more widespread review. 

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Chapter 5: Data Preparation and Processing 

The application was built on an SQL server database that contained all records for review 

and stored the recommended data changes. Editing programs built in SAS read the SQL server 

database to obtain the edits and applied the edits to the questionnaire data. Questionnaire data 

were stored at multiple stages across cleaning and editing programs, so comparison across each 

stage of data cleaning could be easily confirmed with the documentation on recommended edits. 

Raw data were never directly updated, so changes were always stored cumulatively and applied 

each time a cleaned dataset was produced. This process provided the ability to document all 

changes and easily fix errors or reverse decisions upon further review. 

Editing programs also contained procedures that output inconsistent items across logical 

patterns within the questionnaire. For example, instructions to skip items could be based on 

previously answered questions; however, the respondent may not have followed the proper 

pattern based on the previous answers. These items were reviewed, and rules were written to 

either correct previously answered (or unanswered) questions to match the dependent items or 

blank out subsequent items to stay consistent with previously answered items. 

5.6 Data Capture and Editing for CATI 

For the out-of-school data collection effort, the following CATI instruments were 

developed to administer to sample members: student (developed from the TELEform abbreviated 

version), transfer, not currently in school (dropout), early graduate, and homeschool. A screener 

at the beginning of the CATI survey was responsible for determining which questionnaire 

module a respondent was to be administered. 

CATI logic was designed such that the TELEform and CATI records could be 

concatenated into one data file. CATI instruments were developed with logic based on the skip 

patterns in the questionnaires. Questions were automatically skipped during administration. The 

questionnaire development program (Blaise) stored data for each item answered, but respondents 

were allowed to go back to previously answered items. In rare cases, a previously answered item 

could be changed in such a way that the questionnaire logic was inconsistent with data already 

answered from a different logical path. Blaise automatically corrected the previously 

administered responses so that the skip logic was consistent. 

5.7 Data Processing and File Preparation 

All TELEform questionnaire scans were stored in an SQL server database. CATI data 

were exported nightly to ASCII files. Cleaning programs were designed to concatenate CATI 

and TELEform SQL server data into SAS datasets, adjusting and cleaning variables when 

formats were not consistent. Special attention was focused on this concatenation to verify that 

results stayed consistent and to rule out possible format problems. 

Once questionnaire data were concatenated and cleaned across modes and versions, the 

following cleaning and editing steps were implemented: 

• anomalous data cleaning based on review of data with original questionnaire image 

(e.g., scanning errors); 

169 
Chapter 5: Data Preparation and Processing 

• rule-based cleaning (changes that were made based on patterns in data, rather than 

review of images); 

• hard-coded edits based on changes recommended by a reviewer if respondents 

misunderstood the questionnaire (e.g., respondent was instructed to enter a 

percentage, but there was strong evidence that the respondent entered a count rather 

than the percentage); and 

• edits based on logical patterns in questionnaire (e.g., skip pattern relationships 

between gate and dependent questions). 

All respondent records in the final dataset were verified with the SCS to spot 

inconsistencies. For example, it was possible that data were collected for a respondent who later 

was set to an ineligible status. It would not be appropriate to include that data, and the SCS 

served as a safeguard to ensure data integrity. Furthermore, the data files served as a check 

against the SCS to ensure that all respondent information was included in production reports. 

Item documentation procedures were developed to capture variable and value labels for 

each item. Item wording for each question was also provided as part of the documentation. This 

information was loaded into a documentation database that could export final data file layouts 

and format statements used to produce formatted frequencies for review. The documentation 

database also had tools to produce final electronic codebook input files. 

170 
 

Chapter 6 

Data File Contents 

This chapter provides a concise account of the Education Longitudinal Study of 2002 

(ELS:2002/04) base-year to first follow-up longitudinal data file contents. It addresses the 

following six topics: (1) structure of the electronic codebook (ECB) system, (2) analysis 

populations, (3) weights and flags, (4) composite and classification variables, (5) variable 

naming conventions, and (6) the hardcopy student component codebook. 

6.1 Data Structure 

ELS:2002/04 first follow-up data have been made available in public- and (for licensed 

users) restricted-use versions29 in an ECB format on CD-ROM. The ECB is designed to be run 

in a Windows environment. The ECB (NCES 2006–346) is available at no cost from the 

National Center for Education Statistics (NCES). Appendix A supplies a brief introduction to 

the ECB, including its installation. 

The ECB system serves as an electronic version of a fully documented survey codebook. 

It allows the data user to browse through all ELS:2002/04 variables contained on the data files, 

search variable and value names for key words related to particular research questions, review 

the wording of these items along with notes and other pertinent information related to them, 

examine the definitions and programs used to develop composite and classification variables, 

and “output” the data for statistical analysis. The ECB also provides an electronic display of the 

distribution of counts and percentages for each variable in the dataset. Analysts can use the ECB 

to select or “tag” variables of interest, print hardcopy codebooks that display the distributions of 

the tagged variables, and generate SAS and SPSS program code (including variable and value 

labels) that can be used with the analyst’s own statistical software. 

The ECB comprises two megafiles, one at the student level (with other data sources 

supplying contextual data for analysis of the student) and one at the school level. The megafile 

at the student level encompasses base-year student (student questionnaire and test, parent, and 

teacher questionnaires) and school (administrator, library, facilities) data in conjunction with first 

follow-up student (student, transfer, dropout, early graduate, and homeschool questionnaires, and 

student tests) and school administrator data. 

The second megafile, at the school level, encompasses base-year data (facilities checklist, 

the school administrator questionnaire, and the library media center questionnaire) and first 

follow-up school administrator questionnaire data. Analysts should be aware that the base-year 

school data may be used as a stand-alone, nationally representative sample of 2001–02 schools 

with 10th grades, but that the school data for the 2003–04 school year are not generalizable to the 

nation’s high schools with 12th grades. 

29 A license is required to access the restricted-use ECB (http://nces.ed.gov/statprog/confid6.asp). 

171 
Chapter 6: Data File Contents 

6.2 First Follow-up Analysis Populations 

There are several first follow-up populations of analytic interest. One may be interested 

in longitudinal analyses of the sophomore cohort 2 years later or in analyses of selected subsets 

of the cohort (e.g., dropouts, students who remained in the base-year schools and for whom 

school effects can be measured). One may also be interested in cross-sectional analysis of the 

senior class of 2004. In turn, cross-sectional cross-cohort analyses may be undertaken, 

comparing the ELS:2002 senior cohort (2004) with that of the National Longitudinal Study of 

the High School Class of 1972 (NLS-72) (1972), the High School and Beyond Longitudinal 

Study of 1980 (HS&B) (1980), and the National Education Longitudinal Study of 1988 

(NELS:88) (1992), and longitudinal cross-cohort analyses, comparing panels of the HS&B 

sophomore cohort (1980–82), the NELS:88 sophomore cohort (1990–92), and the ELS:2002 

sophomore cohort (2002–04). 

6.3 First Follow-up Weights and Flags 

In addition to the base-year school and student weights (further described in chapter 3), 

two weights have been created for the public-use file in the ELS:2002 first follow-up: a crosssectional first follow-up weight for sample members who completed a questionnaire in the first 

follow-up (F1QWT), and a first follow-up panel weight for sample members who completed a 

questionnaire in both the base year and first follow-up or who completed a questionnaire in the 

first follow-up and completed selected base-year items in the first follow-up (the standard 

classification variables) and had base-year assessment results imputed (F1PNLWT). It should be 

noted that F1QWT generalizes to no meaningful population. It encompasses both 2002 

sophomores 2 years later and 2004 seniors, including freshened seniors (who were not part of the 

sophomore cohort). 

Participation flags (which are always dichotomous) and status variables (which have 

more than two values), as well as weights, may be used for subsetting—in other words, they can 

be used to select the subset of respondents that the analyst intends to examine. For example, if 

one wishes to select only those students for whom there are math assessment data, the status 

variable F1TSTAT would be invoked (a “1” means a math test was completed; a “2” or “3” 

reflects imputed test data). 

6.4 Composite and Classification Variables 

Composite variables—also called constructed, derived, or created variables—are usually 

generated using responses from two or more questionnaire items or from recoding of a variable 

(typically for disclosure avoidance reasons). Some are copied from another source (e.g., a 

variable supplied in sampling or imported from an external database). Examples of composite 

variables include school variables (school sector, urbanicity, region of the country), math 

assessment scores (achievement quartile in math), and demographic variables (sex, race, 

Hispanic ethnicity, and month and year of birth). 

Most of the composite variables can be used as classification variables or independent 

variables in data analysis. For better estimation in cross-sectional analysis, many of the 

172 
 

 

Chapter 6: Data File Contents 

composites have undergone imputation procedures for missing data (all imputed versions of 

variables have been flagged). 

6.5 Naming Conventions 

Data users should find naming conventions for variables, flags, and weights intuitive and 

quite similar to those employed in NELS:88. Most variables begin with an indicator of the wave 

(e.g., base-year variables begin with BY). Weights follow the same wave-naming convention 

and also contain the suffix WT (e.g., BYSTUWT is the name for the final student weight for 

base-year questionnaire completion, F1QWT is the equivalent first follow-up questionnaire 

completion weight, and BYSCHWT is the name for the base-year final school weight). Just as 

first follow-up variables begin with the prefix F1, second follow-up (2006) variables will begin 

with F2, and so on. 

Variable names also distinguish (in their third character) among components and 

questionnaire types. F1S, for example, indicates a first follow-up student questionnaire variable, 

whereas F1A stands for administrator questionnaire items. Likewise, F1T is used for the transfer 

questionnaire, F1D for the dropout questionnaire, F1E for the early graduate questionnaire, F1H 

for the homeschool questionnaire, and F1N for new participant supplement items. Variables that 

reflect specific items in the questionnaire carry the question number in the variable name, 

immediately after the component indicator. Hence, F1S58 would be item 58 from the first 

follow-up student questionnaire, and F1D19 would be item 19 in the dropout instrument. 

The round-specific constructed variables are typically not anchored in a single 

questionnaire item and may sometimes reflect nonquestionnaire sources of information, such as 

the assessments. First follow-up test scores carry the prefix F1TX. F1TXMQU, for example, 

indicates the quartile score for the first follow-up mathematics test. Flags are indicated by the 

suffix FLG or FG. Variable names also distinguish between the public (P) and restricted (R) use 

forms, where variables differ between them. 

6.6 Guide to the Hardcopy Codebooks 

Although for most purposes the flexibility of the ECB will best meet users’ needs, in 

some situations it may be helpful to have access to a specialized hardcopy codebook of the 

student data. The hardcopy codebooks appear as PDF files for the web-published version of this 

manual (see http://nces.ed.gov/surveys/els2002) and correspond to appendix G of this document. 

The codebook supplies a comprehensive description of the student data file. For each variable 

on the student component data file, the codebook provides a summary of the related information, 

including the question number and wording, the variable name, and the responses to the item, 

along with their unweighted frequency and percent and weighted percent. It also provides 

missing data frequencies sorted by the following reserve codes:30 

30 The reserve codes are used throughout the ECB. The description is added to the first variable of each section to 

help users understand the meaning of each code. 

173 
Chapter 6: Data File Contents 

• -1: “Don’t know” represents respondents who indicated that they did not know the 

answer to the question. 

• -2: “Refused” represents respondents who indicated that they refused to answer the 

question. 

• -3: “Item legitimate skip/NA” is filled for questions that are not answered because 

prior answers route the respondent elsewhere. 

• -4: “Nonrespondent” is filled for all variables across the entire questionnaire when a 

sample member did not respond to the questionnaire. 

• -5: “Out of range” represents hardcopy questionnaire respondents who reported 

values that are out of range. 

• -6: “Multiple response” represents hardcopy questionnaire respondents who clearly 

reported more than one response for an item that requires only one response. 

• -7: “Partial interview-breakoff” is filled for questions that are not answered because 

the respondent does not wish to continue the interview or they have run out of time. 

This also includes particular items that are not included on an abbreviated version 

questionnaire. 

• -8: “Survey component legitimate skip/NA” is filled for all items within a survey 

component for sample members who were not administered that component by 

design for one of the following reasons: (1) the component was not administered 

based on their status (e.g., transfer students did not receive certain items on the inschool survey), (2) the sample member was not yet included in the study at the time 

of administration (e.g., first follow-up freshened sample members did not participate 

in the base-year survey), or (3) the sample member was not capable of completing the 

survey component (e.g., students who were ineligible due to a language barrier or 

disability at the time of the survey were not administered a questionnaire). 

• -9: “Missing” is filled for questions that are not answered within the hardcopy 

questionnaire when the routing suggests that they should have filled a response. 

Information on obtaining the ELS:2002/04 Base-Year to First Follow-up ECB (and other 

NCES ECBs) can be found by reviewing the data products for the study at 

http://nces.ed.gov/pubsearch. Information on applying for a restricted-use license also appears 

on the NCES website at http://nces.ed.gov/pubsearch/licenses.asp. 

174 
References 

Burns, L.J., Heuer, R., Ingels, S.J., Pollack, J.M., Pratt, D.J., Rock, D., Rogers, J., Scott, L.A., 

Siegel, P., and Stutts, E. (2003). ELS:2002 Base Year Field Test Report (NCES 

Working Paper 2003–03). U.S. Department of Education. Washington, DC: National 

Center for Education Statistics. 

Carlson, B.L., Johnson, A.E., and Cohen, S.B. (1993). An Evaluation of the Use of Personal 

Computers for Variance Estimation with Complex Survey Data. Journal of Official 

Statistics, 9(4): 795–814. 

Chromy, J.R. (1981). Variance Estimators for a Sequential Sample Selection Procedure. In D. 

Krewski, R. Platek, and J.N.K. Rao (Eds.), Current Topics in Survey Sampling (pp. 329– 

347). New York: Academic Press. 

Cohen, S.B. (1997). An Evaluation of Alternative PC-Based Software Packages Developed for 

the Analysis of Complex Survey Data. The American Statistician, 57(13): 285–292. 

Cox, B.G. (1980). The Weighted Sequential Hot Deck Imputation Procedure. Proceedings of 

the Section on Survey Research Methods (pp. 721–726). The American Statistical 

Association. 

Curtin, T.R., Ingels, S.J., Wu, S., and Heuer, R. (2002). NELS:88 Base Year to Fourth Followup Data File User’s Manual (NCES 2002–323). U.S. Department of Education, National 

Center for Education Statistics. Washington, DC: U.S. Government Printing Office. 

Deville, J.C., and Särndal, C-E. (1992). Calibration Estimating in Survey Sampling. Journal of 

the American Statistical Association, 87: 376–382. 

Fetters, W.B., Stowe, P.S., and Owings, J.A. (1984). Quality of Responses of High School 

Students to Questionnaire Items, High School and Beyond (NCES 84–216). U.S. 

Department of Education, National Center for Education Statistics. Washington, DC: 

U.S. Government Printing Office. 

Folsom, R.E., and A.C. Singh. (2000). The Generalized Exponential Model for Sampling 

Weight Calibration for Extreme Values, Nonresponse, and Poststratification. 

Proceedings of the Section on Survey Research Methods (pp. 598–603). The American 

Statistical Association. 

Hambleton, R.K. (1989). Principles and Selected Applications of Item Response Theory. In 

R.L. Linn (Ed.), Educational Measurement, 3rd ed. (pp. 147–200). New York: 

MacMillan. 

Hambleton, R.K., Swaminathan, H., and Rogers, H.J. (1991). Fundamentals of Item Response 

Theory. Newbury Park, CA: Sage. 

175 
 

References 

Hurst, D., Tan, A., Meek, A., and Sellers, J. (2003). Overview and Inventory of State Education 

Reforms: 1990 to 2000 (NCES 2003–020). U.S. Department of Education, National 

Center for Education Statistics. Washington, DC: U.S. Government Printing Office. 

Ingels, S.J. (1996). Sample Exclusion in NELS:88—Characteristics of Base Year Ineligible 

Students: Changes in Eligibility Status After Four Years (NCES 96–723). U.S. 

Department of Education, National Center for Education Statistics. Washington, DC: 

U.S. Government Printing Office. 

Ingels, S.J., Burns, L.J., Chen, X., Cataldi, E.F., and Charleston, S. (2005). A Profile of the 

American High School Sophomore in 2002 (NCES 2005–338). U.S. Department of 

Education, National Center for Education Statistics. Washington, DC: U.S. Government 

Printing Office. 

Ingels, S.J., Curtin, T.R., Kaufman, P., Alt, M.N., and Chen, X. (2002). Coming of Age in the 

1990s: The Eighth-Grade Class of 1988 12 Years Later (NCES 2002–321). U.S. 

Department of Education, National Center for Education Statistics. Washington, DC: 

U.S. Government Printing Office. 

Ingels, S.J., Dowd, K.L., Baldridge, J.D., Stipe, J.L., Bartot, V.H., and Frankel, M.R. (1994). 

NELS:88 Second Follow-up: Student Component Data File User’s Manual (NCES 94– 

374). U.S. Department of Education. Washington, DC: National Center for Education 

Statistics. 

Ingels, S.J., Pratt, D.J., Rogers, J., Siegel, P.H., and Stutts, E.S. (2004). Education Longitudinal 

Study of 2002: Base Year Data File User’s Manual (NCES 2004–405). U.S. 

Department of Education. Washington, DC: National Center for Education Statistics. 

Available: http://nces.ed.gov/pubsearch. 

Kaufman, P., and Rasinski, K. (1991). Quality of the Responses of Eighth-Grade Students in 

NELS:88 (NCES 91–487). U.S. Department of Education, National Center for Education 

Statistics. Washington, DC: U.S. Government Printing Office. 

Kish, L., and Frankel, M.R. (1974). Inference from Complex Samples. Journal of the Royal 

Statistical Society, Series B (Methodological), 36: 2–37. Reprinted: G. Kalton and S. 

Heeringa, eds., Leslie Kish: Selected Papers (New York: Wiley, 2003). 

Lemke, M., Lippman, L., Bairu, G., Calsyn, C., Kruger, T., Jocelyn, L., Kastberg, D., Liu, Y., 

Roey, S., and Williams, T. (2001). Outcomes of Learning: Results from the 2000 

Program for International Student Assessment of 15-Year-Olds in Reading, Mathematics, 

and Science Literacy (NCES 2002–115). U.S. Department of Education, National Center 

for Education Statistics. Washington, DC: U.S. Government Printing Office. 

McLaughlin, D.H., and Cohen, J. (1997). NELS:88 Survey Item Evaluation Report (NCES 97– 

052). U.S. Department of Education, National Center for Education Statistics. 

Washington, DC: U.S. Government Printing Office. 

176 
References 

Rasinski, K., Ingels, S.J., Rock, D.A., and Pollack, J. (1993). America’s High School 

Sophomores: A Ten-Year Comparison (NCES 93–087). U.S. Department of Education, 

National Center for Education Statistics. Washington, DC: U.S. Government Printing 

Office. 

Riccobono, J.A., Henderson, L.B., Burkheimer, G.J., Place, C., and Levinsohn, J.R. (1981). 

National Longitudinal Study: Base Year (1972) through Fourth Follow-up (1979) Data 

File User’s Manual. U.S. Department of Education, National Center for Education 

Statistics. Washington, DC: U.S. Government Printing Office. 

Rock, D.A., and Pollack, J.M. (1995a). Psychometric Report for the NELS:88 Base Year 

Through Second Follow-up (NCES 95–382). U.S. Department of Education, National 

Center for Education Statistics. Washington, DC: U.S. Government Printing Office. 

Rock, D.A., and Pollack, J.M. (1995b). Mathematics Course-Taking and Gains in Mathematics 

Achievement (NCES 95–714). U.S. Department of Education, National Center for 

Education Statistics. Washington, DC: U.S. Government Printing Office. 

Scott, L.A., Rock, D.A., Pollack, J.M., and Ingels, S.J. (1995). Two Years Later: Cognitive 

Gains and School Transitions for NELS:88 Eighth Graders (NCES 95–436). U.S. 

Department of Education, National Center for Education Statistics. Washington, DC: 

U.S. Government Printing Office. 

Seastrom, M. (2003). NCES Statistical Standards (NCES 2003–601). U.S. Department of 

Education, National Center for Education Statistics. Washington, DC: U.S. Government 

Printing Office. Available: http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2003601. 

Spencer, B.D., Frankel, M.R., Ingels, S.J., Rasinski, K., and Tourangeau, R. (1990). NELS:88 

Base Year Sample Design Report (NCES 90–463). U.S. Department of Education, 

National Center for Education Statistics. Washington, DC: U.S. Government Printing 

Office. 

Spencer, B.D., Sebring, P., and Campbell, B. (1987). High School and Beyond Third Follow-up 

(1986) Sample Design Report (NCES 88402). U.S. Department of Education. 

Washington, DC: National Center for Education Statistics. 

Tourangeau, R., Sebring, P., Campbell, B., Glusberg, M., Spencer, B.D., and Singleton, M. 

(1987). The National Longitudinal Study of the High School Class of 1972 (NLS-72) 

Fifth Follow-up (1986) Data File User’s Manual (NCES 87–406c). U.S. Department of 

Education, National Center for Education Statistics. Washington, DC: U.S. Government 

Printing Office. 

Wolter, K. (1985). Introduction to Variance Estimation. New York: Springer-Verlag. 

Woodruff, R.S. (1971). A Simple Method for Approximating the Variance of a Complicated 

Estimate. Journal of the American Statistical Association, 66: 411–414. 

177 
References 

Zahs, D., Pedlow, S., Morrissey, M., Marnell, P., and Nichols, B. (1995). High School and 

Beyond Fourth Follow-up Methodology Report (NCES 95–426). U.S. Department of 

Education, National Center for Education Statistics. Washington, DC: U.S. Government 

Printing Office. 

178 
Appendix A 

Introduction to the Electronic Codebook 

A-1 

 

Appendix A: 

Introduction to the Electronic Codebook 

Appendix A 

Introduction to the Electronic Codebook 

This appendix supplies a brief introduction to the Education Longitudinal Study of 2002 

(ELS:2002/04) base-year to first follow-up data in electronic codebook (ECB) format. General 

instructions are provided for using the ELS:2002/04 data, along with an orientation to ECB and 

variance estimation software that can be used to manipulate the data. 

A.1 Obtaining the ELS:2002/04 ECB 

The ELS:2002/04 base-year to first follow-up ECB on CD-ROM carries the National 

Center for Education Statistics (NCES) product/publication number NCES 2006–346. This data 

product contains 

• ELS:2002/04 data from the base year and first follow-up; 

• ECB software; and 

• documentation. 

A single copy of an ELS:2002/04 public-use CD-ROM may be obtained without cost 

from the Education Publications Center (ED Pubs), until supplies are exhausted. This group can 

be contacted by telephone at 1-877-4ED-PUBS or by writing 

ED Pubs 

P.O. Box 1398 

Jessup, MD 20794–1398 

Requests can also be made electronically to http://www.edpubs.org/ or to 

customerservice@edpubs.org. Requesters will need the title of the data product and the NCES 

number (NCES 2006–346 for the ELS:2002/04 base-year to first follow-up ECB). 

A restricted-use version of the ECB is available to institutionally based users in the 

United States whose research requires this additional level of information. A restricted-use 

license agreement is required for this version. Contact NCES at 

http://nces.ed.gov/pubsearch/licenses.asp. 

A.2 Features and Content of the ELS:2002/04 ECB 

ECBs allow the user to 

• search a list of variables based on keywords or labels; 

• tag (i.e., select) variables for analysis; 

• generate SAS and SPSS syntax for system files; 

• produce printed codebooks of selected variables; 

A-3 
 

Appendix A: 

Introduction to the Electronic Codebook 

• import tag files; and 

• access database files for extraction. 

The overall organization of data reflects two integrated and comprehensive data files, or 

megafiles. One megafile is at the student level; the other is at the school level. School-level 

variables include information collected in the base-year school administrator questionnaire, 

library media center questionnaire, and facilities checklist, as well as data from the first followup school administrator questionnaire. Users are cautioned that only the base-year school-level 

files generalize to the nation’s high schools (specifically, to regular high schools with a 

10th grade in the 2001–02 school year). First follow-up school-level data do not provide 

national estimates for the nation’s high schools with a 12th grade in the 2003–04 school year. 

Nonetheless, because the first follow-up returned to the base-year schools, the first follow-up 

school data permit analysis of the nation’s high schools 2 years later, in 2002 (also, of course, 

providing contextual data that can be attached to the student record). 

At the student level, data from the base-year and first follow-up student (and related1

 ) 

questionnaires, the base-year and first follow-up assessments, the base-year teacher and parent 

questionnaires, and school-level variables at the individual level are represented. Universe 

variables, weights, participation flags and status indicators, and composite variables (also called 

constructed variables, derived variables, or created variables) are located at the beginning of the 

file, followed by the questionnaire variables. 

Some important variable naming conventions (typically embedded in the first three to 

four characters of each variable name) may be noted. Normally, the first three to four characters 

of each variable name identify the instrument from which the variable is taken. BYS stands for 

base-year student; BYS21 stands for question 21 in the student questionnaire. BYP stands for 

base-year parent, BYA for the base-year administrator questionnaire, and so on. Likewise, F1 is 

the prefix used for first follow-up variables (hence, F1S45 represents question 45 in the first 

follow-up student questionnaire). A label with the terminal characters WT is indicative of a 

weight (e.g., BYSTUWT is the final or nonresponse-adjusted student weight for the base year). 

Test variables contain the characters TX, while flags are indicated by FLG or FG and status 

variables by STAT (e.g., BYTXSTAT refers to test completion status in the base year). The 

contents of the student and school megafiles are described more specifically in the sections 

below. 

A.2.1 Student Megafile 

The student-level file contains variables from the base-year student, parent, and teacher 

questionnaires, as well as scores for the assessments in reading and mathematics. The studentlevel file also contains questionnaire and assessment data for the first follow-up sample. Schoollevel data are also included, attached to the student record. 

The main contents of the student file, in order of appearance, and associated naming 

conventions are as follows: 

1 The first follow-up data represent two student cohorts: sophomores in 2002 and seniors in 2004. Not all 

sophomore cohort members were high school students 2 years later. Some were dropouts, some were early 

graduates, and some were being homeschooled. Data for these individuals are provided on the “student” file 

regardless of whether the individual was a student, dropout, or early graduate or was being homeschooled in 2004. 

A-4 
 

Appendix A: 

Introduction to the Electronic Codebook 

• IDs and universe variables. Student and school identifications (IDs) and universe 

variables are at the beginning of the data file. 

• BY weights and composites. The weights (BYSCHWT, BYSTUWT) lead this 

section. They are followed by student-level composites, participation flags, status 

flags, imputation flags, school-level composites, and Common Core of Data (CCD) 

and Private School Study (PSS) data, as well as confidential geocode data and 

linkages to external sources. 

• F1 weights and composites. The weights (F1QWT and F1PNLWT) lead this section. 

They are followed by student-level composites, participation flags, status flags, 

imputation flags, school-level composites, and CCD/PSS data, as well as confidential 

geocode data and linkages to external sources. 

• BY student questionnaire (BYS*). These data come from scanned forms filled out by 

the student or from the computer-assisted telephone interview (CATI). 

• F1 student questionnaire (F1S*, F1D*, F1T*, F1E*, F1N*). These data come from 

currently enrolled students, dropouts, transfer students, early graduates, or 

homeschoolers. Data come from completed forms or from the CATI interview. 

• BY school (BYA*, BYL*, BYF*). These data come from BY school administrator 

questionnaires, library and facilities questionnaires, and facilities checklists. The data 

are linked to BY eligible students and replicated at the student level. 

• F1 school (F1A*). These data come from F1 school administrator questionnaires. 

The data are linked to F1 currently enrolled students and replicated at the student 

level. 

• BY parent questionnaire (BYP*). These data come from scanned forms filled out by 

the parent or from the CATI interview. 

• BY teacher questionnaire—English (BYTE*). These data come from scanned teacher 

questionnaires filled out by the student sample member’s English teacher. English 

teacher data have been linked to the appropriate student(s). 

• BY teacher questionnaire—math (BYTM*). These data come from the scanned 

teacher questionnaire and have been linked to the appropriate student(s). 

A.2.2 School Megafile 

The school-level file contains all questionnaires administered at the school level. This 

includes the school administrator questionnaires (base year and first follow-up) and the base-year 

library media center questionnaire and facilities checklist. 

Variable prefixes on the school file identify the contents: 

• IDs and weights. Student and school IDs and the school weight (BYSCHWT) are at 

the beginning of the data file. Note that there is no first follow-up school weight. 

• BY school-level composites. School-level composites are produced from 

questionnaire data, allowing an analyst access to data in an easier format. 

A-5 
Appendix A: 

Introduction to the Electronic Codebook 

• BY data from outside sources. Licensed users of the restricted-use file will have 

access to CCD/PSS data via the NCES identification number (NCESID), geocodes, 

and other information for linking to external sources. 

• F1 school-level composites. School-level composites are produced from 

questionnaire data, allowing an analyst access to data in an easier format. 

• F1 data from outside sources. Licensed users of the restricted-use file will have 

access to CCD/PSS data via the NCESID, geocodes, and other information for linking 

to external sources. 

• BY school administrator data (BYA*). These data come from scanned forms filled 

out by the BY school principal and other administrative staff. 

• F1 school administrator data (F1A*). These data come from scanned forms filled out 

by the F1 school principal and other administrative staff. 

• BY library section data (BYL*). These data come from scanned forms filled out by 

the librarian or library media center specialist. 

• BY school facilities data (BYF*). These data come from scanned forms filled out by 

the survey administrator during the student surveys at the school. 

The school ID is constructed such that student file records can merge with the school 

data. 

A.3 Installing the ECB 

A.3.1 Hardware/Software Requirements 

The ECB program is designed to run on a PC with Windows 95 or higher versions. 

A.3.2 Installation Procedures 

To install the ECB, complete the following steps: 

1. Close all applications on your computer. 

2. Place the CD-ROM into the CD-ROM drive. 

3. From Windows, click on “START” and then “RUN.” 

4. Browse through the CD-ROM drive for the “ecbw” folder and open the “SETUP.exe” 

file. 

5. Setup will guide you through the installation of the ECB. 

6. Click on the ECB icon to run. 

A-6 
 

 

Appendix A: 

Introduction to the Electronic Codebook 

A.4 Using the ECB 

A.4.1 Understanding the File Structure and Capacity 

The ECB is ready to use once it is installed. Familiarity with the “hot” keys and some 

practice can help the user to more quickly understand the structure of the file and the power 

provided by the ECB to produce data files: 

1. On the toolbar found at the top of the ECB screen, click on each “hot” key. 

2. Consult the “Electronic Codebook Help Guide” available on the CD-ROM (file 

named “HELP.pdf”) for an overview of the ECB functions. 

A.4.2 Examining the Frequencies Available for Each Variable on the ECB 

By examining these data descriptions, the ELS:2002 user will begin to appreciate the 

complexity of collecting data from respondents (legitimate values, legitimate skips, refusals, 

etc.). It is important to realize that some respondents 

• did not respond to an entire instrument; 

• skipped individual items; 

• refused to complete selected items; 

• did not reach the end of the questionnaire in the time they were given; 

• completed abbreviated versions of the instrument; 

• made illegal skips; and/or 

• responded outside predefined valid ranges. 

The following reserve code conventions are used in the ELS:2002 data files: 

• –1 = “Don’t know” 

This reserve code applies to questions in the hardcopy questionnaires that allow a 

“Don’t know” response. The CATI interview by default allows “Don’t know” for 

most questions that a respondent does not know so that the subsequent question can 

be administered. 

• –2 = “Refused” 

Respondents are free to refuse to answer any question. In the hardcopy questionnaire, 

such refusals are explicitly captured only for critical items (items that, because of 

their importance, are subject to onsite edit and retrieval). CATI interviews, by 

default, allow refusals to be recorded on a question-by-question basis. 

• –3 = “Item legitimate skip/NA” 

Questions that are not answered because prior answers route the respondent 

elsewhere are filled with “Legitimate skip/NA.” This value applies to variables from 

all data collection modes. 

A-7 
Appendix A: 

Introduction to the Electronic Codebook 

• –4 = “Nonrespondent” 

“Nonrespondent” variables from questionnaires that have no respondent are filled 

with the “Nonrespondent” reserve code. This code applies to both the student file and 

the school file, because each file is composed of multiple interviews. For example, 

the school file may contain school administrator questionnaire data and facilities data, 

but the school’s librarian may not have responded to the library media questionnaire; 

hence, all library media variables appear with the “Nonrespondent” reserve code. 

• –5 = “Out of range” 

This code applies to values reported by the respondent that are out of range. 

Responses are set to this value if they are beyond the reasonable limits for the given 

item. For example, a teacher may have indicated teaching at a particular school for a 

longer period of time than he/she taught overall. 

• –6 = “Multiple response” 

Non-CATI applications are unable to prevent respondents from giving multiple 

responses to a question that requires one answer. The scanning process for hardcopy 

questionnaires routes these instances to a verifier to determine whether the respondent 

“intended” to choose one answer (e.g., eraser marks interpreted by the optical 

scanning equipment as a second answer). If the verifier cannot determine a single 

unique answer, the item is assigned the reserve code for “Multiple response.” 

• –7 = “Partial interview-breakoff” 

Questions that are not answered because the respondent does not wish to continue the 

interview, or, in timed sessions, because they have run out of time, are filled with a 

“Partial/not reached” reserve code. This code is also used for CATI interviews that 

encounter breakoffs during the interview (and the respondent cannot be reached for 

completion of the interview). The code is also used for an abbreviated version of the 

questionnaire in which particular items are not included. 

• –8 = “Survey component legitimate skip/NA” 

Survey components that do not apply to the sample member will have questions with 

values of -8. For example, a student who is currently enrolled would not be 

administered the early graduate questionnaire, so questions that are specific to that 

questionnaire will have values of -8. Another example are freshened students, who 

will have values of -8 for questions that were administered in BY questionnaires. 

• –9 = “Missing” 

This code applies to questions that are not answered in the scanned hardcopy 

questionnaires. These questions are typically missed accidentally (e.g., respondent 

did not understand the routing pattern) and are not an indication of the respondent 

filling out only part of the questionnaire. This reserve code can also apply to CATI 

data where, for reasons associated with different versions, an item is not 

administered. 

A-8 
Appendix A: 

Introduction to the Electronic Codebook 

A.4.3 Creating a Taglist, Extracting Data, and Generating Program Code 

The following procedures can be used to tag variables, extract data, and generate program 

codes on the ECB: 

1. Tag variables of interest by clicking on the “tag box” next to each variable. 

2. Choose the appropriate weights and flags for the population of interest. In each 

megafile, flags can be selected to identify a particular part of the population. For 

example, flags are available to identify whether a student questionnaire completer 

also completed a test. Weights are variables placed on the dataset to compensate for 

the unequal probabilities of selection and to adjust for nonresponse. When used with 

flags, weights allow the analyst to make generalizations about the national 

populations represented by the various ELS:2002 samples (e.g., schools versus 

students within schools). When weights are not used or a flag is used inappropriately, 

the estimates generated will not be representative of the population. 

3. After tagging the variables of interest, go to “File” and then “Output.” 

4. Select the program (e.g., SPSS to generate SPSS program code). 

5. Specify the directory and the name of the program code file. 

6. Select the appropriate button in the “Confirmation” box. 

7. To view the program code, select “File” and then “View Output.” 

8. Open the program code in the appropriate software (e.g., SPSS) to generate a working 

system file and run analyses. It may be necessary to modify the program slightly 

(check for “execute” statements, period locations, and file names). The code should 

identify the ASCII data file location, which will be the CD-ROM. Users should be 

aware of a possible SPSS syntax error associated with continuous variables: the 

“VALUE LABELS” statement is missing when the first tagged item for a data file is 

continuous and has no reserve codes. 

A.4.4 Variance Estimation 

Because the ELS:2002 sample design involved stratification, disproportionate sampling 

of certain strata (e.g., oversampling of Asians and of private schools), and clustered (e.g., 

students within a school) probability sampling, the resulting statistics are more variable than they 

would have been had they been based on data collected from a simple random sample of the 

same size. A number of statistical packages (e.g., SUDAAN, WesVar, Stata, and AM) take 

account of complex sampling designs in the calculation of standard errors. (For an assessment of 

strengths and limitations of SUDAAN, Stata, and WesVar, see Broene and Rust 2000.) AM 

variance estimation software can be downloaded for free from the following website: 

http://am.air.org/. 

A.5 Additional Sources of Information (NCES Reports, Bibliographic 

Resources) 

A number of reports using ELS:2002 data have been produced to date. ELS:2002 reports 

can be found in electronic format on the NCES website under 

A-9 
Appendix A: 

Introduction to the Electronic Codebook 

http://nces.ed.gov/surveys/els2002/. From that website, documents can be searched and 

downloaded. The NCES website also includes an ELS:2002 Bibliography 

(http://nces.ed.gov/surveys/els2002/ ), noting these and additional reports, articles, and 

conference papers on or using the study. In addition, many of the National Education 

Longitudinal Study of 1988 (NELS:88) reports may be of interest, both for what they suggest 

about possible cross-cohort analyses and for issues that can be examined cross-sectionally and 

longitudinally in ELS:2002 and NELS:88. In addition to the ELS:2002 Bibliography, the 

NELS:88 Bibliography may be of interest to data users (http://nces.ed.gov/surveys/nels88/). 

A.6 Appendix A Reference 

Broene, P., and Rust, K. (2000). Strengths and Limitations of Using SUDAAN, Stata, and 

WesVarPC for Computing Variances from NCES Data Sets (NCES 2000–03). U.S. 

Department of Education. Washington, DC: National Center for Education Statistics. 

A-10 
Appendix B 

Base-Year and First Follow-up Questionnaires 

B-1 

Appendix B: 

Base-Year and First Follow-up Questionnaires 

Appendix B 

Base-Year and First Follow-up Questionnaires 

Web-published PDF files of the Education Longitudinal Study of 2002 (ELS:2002) 

base-year and first follow-up questionnaires are available at 

http://nces.ed.gov/surveys/els2002/index.asp. 

B-3 

Appendix C 

Documentation for Imputed Variables 

C-1 

Appendix C: 

Documentation for Imputed Variables 

Appendix C 

Documentation for Imputed Variables 

C.1 Introduction 

Appendix C comprises tables that provide further documentation of imputation 

procedures. Table C-1 reports on the imputation status of eight groups of sample members, 

based on their combined base-year and first follow-up response and eligibility status. (Note that 

the expanded “contextual” sample members are individuals deemed incapable, owing to limited 

English proficiency or a severe disability, of completing the questionnaire. For these students, 

only contextual information, such as parent or school or teacher reports, was collected.) With the 

imputation variables forming the rows and sample disposition the columns, the table indicates 

whether imputation for these cases was performed in the base year or the first follow-up. 

Table C-2 provides further information about the questionnaire variables imputed through 

the weighted sequential hotdeck method. It lists each imputation variable, the imputation class, 

and the sort variables. 

Table C-3 provides further information about the assessment variables (the ability 

estimate, or theta) that were approached through multiple imputation. Specifically, the table lists 

all variables included in the multiple imputation model. 

Table C-4 shows before-and-after distributions (sample size and weighted percent) for all 

imputed questionnaire variables. 

Finally, tables C-5 through C-39B show the comparisons between unimputed and 

imputed point estimates for select variables and the respective standard errors. A discussion 

outlining the analytical approach and general findings follows on page C-12. The comparisons 

are based on the forthcoming report: United States High School Sophomores: A Twenty-Two 

Year Comparison, 1980-2002. 

C-3 
Table C-1. ELS:2002 imputation variables, by respondent status: 2004 

C-4 

 Sample disposition 

BY 

BY respondent/ BY BY 

nonrespondent/ F1 BY contextual/ Freshened contextual/ nonrespondent/ 

Imputation variable 

Sample size1 

F1 respondent 

651 

respondent 

14,062 

F1 respondent 

105 

respondent 

171 

F1 contextual 

53 

F1 contextual 

2 

F

Student sex X X X X X X Student race/ethnicity X X X X X X 

Student language minority X X X X X X 

status 

Student Hispanic subgroup X X X X X X 

Student Asian subgroup X X X X X X School program type X O Ø Ø Ø Ø 

Student postsecondary X X X X Ø Ø 

educational expectations 

Parental aspirations for student X O X Ø X X 

postsecondary achievement 

Family composition X X X X X X 

Mother’s educational X X X X X X 

attainment 

Mother’s occupation X X X X X X 

Father’s educational X X X X X X 

attainment 

Father’s occupation X X X X X X 

Family income (2001) X O X X X X 

Enrollment status (in school vs. X X X X X X 

out, grade) 

12th-grade student ability X X X X Ø Ø 

estimates (theta) for mathematics 

10th-grade student ability X O Ø Ø Ø Ø 

estimates (theta) for mathematics 

10th-grade student ability X O Ø Ø Ø Ø 

estimates (theta) for reading 

1 Total sample size is 15,089. 

NOTE: BY = base year; F1 = first follow-up. X denotes that these cases were imputed in the first follow-up study, O denotes that these base-year study, and Ø denotes that these cases were not imputed in either the base-year or first follow-up study. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002). 
Table C-2. ELS:2002 imputation variables, by imputation class and sort variables: 2004 

Imputation variable Imputation class variables Sort variables 

Student race (F1RACE) English as native language (F1STLANG) Student Hispanic origin (F1HISPAN) Student Asian origin (F1ASIAN) Type of school program (BYSCHPRG) Student postsecondary aspirations (F1STEXP) Parental aspirations for student postsecondary 

achievement (BYPARASP) 

School identifier (SCHOOLID) 

Student race (F1RACE) Student race (F1RACE) English as native language (F1STLANG) 

Student race (F1RACE) 

English as native language (F1STLANG) 

School coed status (BYA11) 

Percent 10th-graders in general high school 

(BYA14A) 

Percent 10th-graders in college prep (BY14B) Percent 10th-graders in voc/tech (BYA14D) 

Student sex (F1SEX) 

PROGTYPE (program) Student race (F1RACE) Student postsecondary aspirations (F1STEXP) 

Census region (BYREGIO

Urbanicity (BYURBAN) School type (BYSCTRL) 

Census region (BYREGIO

Urbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) 

C-5 

See note at end of table. 
Table C-2. ELS:2002 imputation variables, by imputation class and sort variables: 2004—Continued 

Imputation variable Imputation class variables Sort variables 

C-6 

Family composition (F1FCOMP) Mother’s educational attainment (F1MOTHED) Father’s educational attainment (F1FATHED) Mother’s occupation (F1OCCUM) Father’s occupation (F1OCCUF) Household income (BYINCOME) Student enrollment status (F1RISTAT) 

Student race (F1RACE) 

English as native language (F1STLANG) 

Student race (F1RACE) 

Student postsecondary aspirations (F1STEXP) 

Student race (F1RACE) 

Student postsecondary aspirations (F1STEXP) 

Student race (F1RACE) 

Mother’s educational attainment (F1MOTHED) 

Student race (F1RACE) 

Father’s educational attainment (F1FATHED) 

Mother’s educational attainment (F1MOTHED) 

Father’s educational attainment (F1FATHED) Family composition (F1FCOMP) 

Student grade (GRADE) 

Student final F1 enrollment status (F1ENRFIN) 

Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) Census region (BYREGIOUrbanicity (BYURBAN) School type (BYSCTRL) Student race (F1RACE) IMPGRP School identifier (SCHOO

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002). 
 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-3. Variables included in multiple imputation model for student ability estimates for 

reading and mathematics: 2002 and 2004 

Imputation variable Variables included in multiple imputation model 

Student ability estimates (theta) for base-year 

mathematics and reading and first follow-up 

mathematics 

School type (BYSCTRL) 

Census region (BYREGION) 

Census urbanicity (BYURBAN) 

Student sex (F1SEX) 

Student race (F1RACE) 

Student language (F1STLANG) 

Mother’s occupation (F1OCCUM) 

Father’s occupation (F1OCCUF) 

Student postsecondary aspirations (F1STEXP) 

Parental aspirations for student postsecondary achievement 

(BYPARASP) 

Mother’s educational attainment (F1MOTHED) 

Father’s educational attainment (F1FATHED) 

Household income (BYINCOME) 

Family composition (F1FCOMP) 

10th-grade student ability estimates for math and reading 

12th-grade student ability estimates for math 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

C-7 
 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-4. ELS:2002 imputation variable distributions before and after imputation: 2004 

Before imputation After imputation 

Weighted Weighted 

Characteristic Sample size percent Sample size percent 

Student sex (F1SEX) 15,086 100.00 15,089 100.00 

Male 7,537 50.64 7,538 50.63 

Female 7,549 49.36 7,551 49.37 

Student race (F1RACE) 15,074 100.00 15,089 100.00 

American Indian 127 0.98 127 0.98 

Asian 1,536 4.23 1,537 4.23 

Black 1,996 14.32 1,999 14.34 

Hispanic, no race specified 1,004 7.27 1,005 7.27 

Hispanic, race specified 1,229 9.15 1,232 9.18 

Multiracial, non-Hispanic 679 4.02 679 4.02 

White 8,503 60.02 8,510 60.00 

English as native language (F1STLANG) 14,970 100.00 15,089 100.00 

No 2,608 14.49 2,632 14.47 

Yes 12,362 85.51 12,457 85.53 

Student Hispanic origin (F1HISPAN) 14,274 100.00 15,089 100.00 

Not applicable 12,066 82.84 12,077 78.26 

Mexican, Mexican-American, Chicano 1,423 11.55 1,907 14.39 

Cuban 87 0.64 116 0.83 

Dominican 81 0.79 102 0.90 

Puerto Rican 286 1.93 413 2.71 

Central American 161 1.16 226 1.41 

South American 170 1.08 248 1.50 

Student Asian origin (F1ASIAN) 14,270 100.00 15,089 100.00 

Not applicable 12,459 93.97 12,473 87.33 

Chinese 402 1.30 554 2.53 

Filipino 277 1.17 482 2.78 

Japanese 131 0.46 225 1.27 

Korean 277 0.89 391 1.81 

Southeast Asian 450 1.28 568 2.28 

South Asian 274 0.92 396 2.00 

Type of school program (BYSCHPRG) 14,438 100.00 15,089 100.00 

Missing 362 2.99 362 2.84 

General 4,845 37.06 5,088 37.10 

College preparatory, academic 7,888 49.79 8,229 49.84 

Vocational, including technical/business 1,343 10.16 1,410 10.22 

See note at end of table. 

C-8 
 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-4. ELS:2002 imputation variable distributions before and after imputation: 2004— 

Continued 

Before imputation After imputation 

Weighted Weighted 

Characteristic Sample size percent Sample size percent 

Student postsecondary aspirations 

 (F1STEXP) 14,998 100.00 15,089 100.00 

Missing 100 0.69 100 0.69 

Less than high school graduation 48 0.42 50 0.44 

GED only 182 1.68 185 1.70 

High school graduation 669 4.95 679 4.99 

Attend or complete a 2-year school 2,041 15.34 2,055 15.33 

Attend college, but not complete a 4-year 

degree 500 3.90 506 3.90 

Graduate from college 4,780 31.23 4,796 31.14 

Obtain a master’s degree or equivalent 3,286 20.12 3,294 20.05 

Obtain a PhD, MD, or other advanced 

degree 2,089 12.06 2,100 12.07 

Other 1,303 9.61 1,324 9.69 

Parental aspirations for student 

postsecondary achievement 

 (BYPARASP) 14,367 100.00 15,089 100.00 

Missing 202 1.93 202 1.82 

Less than high school graduation 11 0.08 12 0.07 

High school graduation or GED only 473 3.77 504 3.83 

Attend or complete a 2-year school 1,061 8.54 1,117 8.53 

Attend college, but not complete a 4-year 

degree 132 1.04 142 1.05 

Graduate from college 6,278 44.29 6,596 44.36 

Obtain a master’s degree or equivalent 3,003 19.80 3,162 19.86 

Obtain a PhD, MD, or other advanced 

degree 3,207 20.56 3,354 20.48 

Family composition (F1FCOMP) 14,959 100.00 15,089 100.00 

Mother and father 9,066 57.66 9,138 57.62 

Mother and male guardian 1,752 13.03 1,763 12.96 

Father and female guardian 454 3.12 458 3.13 

Two guardians 240 1.73 243 1.73 

Mother only 2,612 18.54 2,642 18.61 

Father only 445 3.19 450 3.20 

Female guardian only 190 1.37 194 1.39 

Male guardian only 48 0.29 49 0.29 

Lives with student less than half time 152 1.07 152 1.06 

See note at end of table. 

C-9 
 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-4. ELS:2002 imputation variable distributions before and after imputation: 2004— 

Continued 

Before imputation After imputation 

Weighted Weighted 

Characteristic Sample size percent Sample size percent 

Mother’s educational attainment 

 (F1MOTHED) 14,911 100.00 15,089 100.00 

Did not finish high school 1,872 13.31 1,909 13.43 

Graduated from high school or GED 3,960 27.84 4,016 27.88 

Attended 2-year school, no degree 1,789 12.77 1,813 12.78 

Graduated from 2-year school 1,574 11.04 1,587 11.04 

Attended college, no 4-year degree 1,517 10.06 1,526 9.99 

Graduated from college 2,801 16.82 2,821 16.72 

Completed master’s degree or equivalent 1,088 6.37 1,106 6.39 

Completed PhD, MD, advanced degree 310 1.79 311 1.77 

Father’s educational attainment 

(F1FATHED) 14,839 100.00 15,089 100.00 

Did not finish high school 1,946 14.01 1,998 14.12 

Graduated from high school or GED 4,175 30.22 4,249 30.26 

Attended 2-year school, no degree 1,366 9.65 1,384 9.58 

Graduated from 2-year school 1,144 7.93 1,159 7.93 

Attended college, no 4-year degree 1,346 8.97 1,364 8.94 

Graduated from college 2,705 17.14 2,741 17.13 

Completed master’s degree or equivalent 1,296 7.60 1,323 7.60 

Completed PhD, MD, advanced degree 861 4.48 871 4.45 

Mother’s occupation (F1OCCUM) 14,846 100.00 15,089 100.00 

No job 567 3.08 582 3.11 

Clerical 2,400 16.46 2,442 16.52 

Craftsperson 331 2.34 338 2.33 

Farmer, farm manager 73 0.53 75 0.54 

Homemaker 768 5.39 785 5.43 

Laborer 632 4.56 652 4.65 

Manager, administrator 1,590 10.77 1,612 10.76 

Military 27 0.17 27 0.17 

Operative 605 4.42 623 4.47 

Professional A 2,158 13.87 2,181 13.77 

Professional B 568 3.64 575 3.62 

Proprietor, owner 348 2.27 357 2.29 

Protective service 107 0.70 108 0.70 

Sales 640 4.36 654 4.36 

Schoolteacher 999 6.37 1,004 6.27 

Service 2,282 16.03 2,317 16.02 

Technical 742 4.93 748 4.88 

Other 9 0.10 9 0.09 

See note at end of table. 

C-10 
 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-4. ELS:2002 imputation variable distributions before and after imputation: 2004— 

Continued 

Before imputation After imputation 

Sample Weighted Sample Weighted 

Characteristic size percent size percent 

Father’s occupation (F1OCCUF) 14,794 100.00 15,089 100.00

 No job 162 0.75 167 0.75 

Clerical 349 2.51 358 2.52 

Craftsperson 1,860 13.62 1,904 13.69 

Farmer, farm manager 284 2.07 296 2.14 

Homemaker 354 2.45 360 2.45 

Laborer 1,519 10.68 1,561 10.76 

Manager, administrator 2,206 14.88 2,248 14.82 

Military 187 1.26 191 1.25 

Operative 1,696 12.23 1,728 12.24 

Professional A 1,599 9.94 1,624 9.94 

Professional B 892 4.90 906 4.91 

Proprietor, owner 902 5.90 910 5.83 

Protective service 503 3.41 512 3.40 

Sales 772 5.18 783 5.15 

Schoolteacher 213 1.45 216 1.44 

Service 600 3.96 614 3.95 

Technical 685 4.65 700 4.64 

Other 11 0.14 11 0.14 

Household income (BYINCOME) 14,154 100.00 15,089 100.00 

None 67 0.41 73 0.46 

$1,000 or less 154 1.13 161 1.09 

$1,001–$5,000 252 1.73 273 1.78 

$5,001–$10,000 293 2.17 318 2.22 

$10,001–$15,000 594 4.24 630 4.27 

$15,001–$20,000 668 4.86 707 4.83 

$20,001–$25,000 872 6.55 933 6.61 

$25,001–$35,000 1,625 12.07 1,725 11.98 

$35,001–$50,000 2,652 19.81 2,826 19.74 

$50,001–$75,000 2,929 21.19 3,132 21.17 

$75,001–-$100,000 1,922 12.99 2,057 13.08 

$100,001–$200,000 1,611 10.15 1,710 10.10 

$200,001 or more 515 2.67 544 2.68 

Student enrollment status (F1RISTAT) 15,944 100.00 16,374 100.00 

In school and in grade 12 13,899 85.50 14,305 85.72 

In school and not in grade 12 1,015 7.33 1,033 7.25 

Out of school 909 6.49 915 6.36 

Out of scope 121 0.68 121 0.67 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002). 

C-11 
Appendix C: 

Documentation for Imputed Variables 

C.2 Examining the Effects of Imputation 

Surveys often produce an incomplete data record due to respondent item nonresponse. 

Even though most of the questionnaire was completed, the respondent may choose to ignore 

some items, refuse to answer a particular question, provide an improbable response, break off an 

interview, fail to complete the last items of a timed interview, or mistakenly skip a question. The 

greatest concern with item nonresponse is that respondent answers are systematically different 

from nonrespondent answers, resulting in biased estimates of means, proportions, variances, and 

covariances (Federal Committee on Statistical Methodology 2001; Groves 1989; Seastrom 

2003). 

For items with some level of nonresponse, the researchers can address the potential for 

bias after the collection process has ended through imputation. Imputation is the process of 

estimating the value that a respondent might have reported. Typically, the information used to 

impute data is based on other responses the respondent gave during the interview or from 

information based on other respondents. 

Following the standards developed by the National Center for Education Statistics 

(NCES), key items for the Education Longitudinal Study of 2002 (ELS:2002) were statistically 

or logically imputed for missing data. Although past studies, such as the National Education 

Longitudinal Study of 1988 (NELS:88) and the High School and Beyond Study (HS&B), had 

data editing and some logical imputations, statistical imputations (the multiple imputations and 

hotdeck imputations of ELS:2002 provide relevant examples) were generally not performed (see 

chapter 3 for more detail on the ELS:2002 imputation strategy). 

To assess the impact that imputation has on point estimates, the distributions for key 

items were compared before and after imputation. The organization and selection of the 

variables were driven in part by the intercohort comparisons between ELS:2002, NELS:88, and 

HS&B made in the forthcoming NCES report, United States High School Sophomores: A 

Twenty-Two Year Comparison, 1980-2002. The reason for employing the tables in this 

particular report reflects the concern with whether, for cross-cohort comparisons, ELS:2002 

imputed data should be used. The imputed data should be the most precise and accurate but may 

not be as strictly comparable as the unimputed version of the ELS:2002 data, in that imputation 

was not performed in the prior studies. (Tables C-39A and C-39B speak in particular to this 

issue.) 

Because the variables (unimputed and imputed) are dependent and can be thought of as 

paired, the difference of these two variables is treated as if it were a single sample. In other 

words, the two variables are treated as repeated measures. The comparisons were tested in 

SUDAAN using t-test statistics. To guard against errors of inference based upon multiple 

comparisons, the Bonferroni procedure adjusts significance tests for multiple contrasts. This 

method corrects the significance (or alpha) level for the total number of contrasts made with a 

particular classification variable. For each classification variable, there are (K * (K −1))/ 2 

possible contrasts (or nonredundant pairwise comparisons), where K is the number of categories. 

For example, if a classification variable such as race has six categories, K=6 and there are 

(6*5)/2=15 possible comparisons between the categories. The Bonferroni procedure divides the 

C-12 
Appendix C: 

Documentation for Imputed Variables 

alpha-level for a single t-test (in this case, .05) by the number of possible pairwise comparisons 

(15) to derive a new alpha corrected for the fact that multiple contrasts are being made. 

The reader should recognize that because of the dependent observations and large sample 

size, many small differences were found to be statistically significant. As presented in the tables 

below, these small differences would not normally be thought of as having substantive or 

practical significance. The sample sizes between the two variables being compared differed by 

only the amount of missing data. Table C-4 presents the sample sizes and weighted distributions 

for key variables. 

The analysis was divided by item topic: student demographics (tables C-6A to C-12B), 

school experiences (C-13A to C-17B), tested achievement (C-18A to C-26B), afterschool 

activities (C-27A to C-30B), life values (C-31A to C-34B), and plans and expectations (C-35A to 

C-38B). A sample of items was selected from the questionnaire for each topic. Summary 

statistics for the differences between imputed and unimputed estimates are presented in table C-5 

by topic area. A final analysis shown in tables C-39A and 39B compares the NELS:88 and 

HS&B sophomore cohorts with both the imputed and unimputed ELS:2002 sophomore cohort 

data. These comparisons demonstrate to some degree the potential impact that imputation has on 

bivariate statistics in intercohort analysis. 

An important analytical variable is the socioeconomic status indicator (SES). This 

variable is not imputed directly but contains elements from five other variables that were 

imputed. As with the other comparisons, SES was recomputed using the unimputed values, and 

these estimates were compared to the imputed estimates, testing for differences. 

Table C-5 summarizes the general findings for the imputation comparisons by ELS:2002 

topical area. Generally, differences were very small, ranging from an average of 0.01 percent for 

life value items to 0.06 percent for tested achievement. Of these differences, only a fraction of 

comparisons, were statistically significant. For example, out of 53 student demographics 

comparisons, only 9 percent (or 5) were statistically significant, the largest being a 0.6 

percentage point difference (mother and father family living arrangement and Asian/Pacific 

Islander race categories). 

However, for one variable in particular, there were significant and large differences for 

students by parents’ education level, especially those students who had at least one parent with a 

graduate/professional degree. Table C-16A shows a moderate decline (-5.1 percentage points) in 

the percentage of students in this category who felt disruptions interfered with learning. Larger 

differences between imputed and unimputed estimates for this group of students were seen in the 

use of calculators (table C-17A, -14.2 percentage points) and in tested achievement (C-20A, C21A, C-23A, C-24A, C-25A, C-26A). For example, students who had a least one parent with a 

graduate or professional degree saw significant differences in the probability of proficiency in 

reading level 2 (18.7 percentage points or a 40 percent increase from the unimputed score), 

reading level 3 (10.7 percentage points or a 153 percent increase), math level 2 (12.4 percentage 

points or an 18 percent increase), math level 3 (21.9 percentage points or a 47 percent increase), 

math level 4 (19.6 percentage points or a 107 percent increase), and math level 5 (2.5 percentage 

points or a 417 percent increase). In each case, the imputed mean was significantly greater than 

the unimputed mean. It is of interest to note that the univariate distributions for mother’s and 

C-13 
Appendix C: 

Documentation for Imputed Variables 

father’s education levels (tables C-8A and C-9A) did not show any large differences. These 

findings must be tempered by the fact that they are the exception. Most comparisons did not 

reveal any difference between imputed and unimputed estimates, and any difference that was 

detected was usually very small (< 1 percentage point). 

An important issue is how imputation affected SES, particularly whether the results 

would differ greatly depending on whether it was constructed in accordance with the earlier (e.g., 

NELS:88) specifications or constructed in accord with the new ELS:2002 specifications. This 

composite variable is critical for research on tested achievement and other educational outcomes 

as a major nonschool factor that correlates highly with school success. For ELS:2002 and for its 

predecessor studies as well, the five components of the SES variable are mother’s and father’s 

education, mother’s and father’s occupation, and family income. In prior studies (such as 

NELS:88), a student-derived household items index was substituted when parent-reported 

income data were missing. In addition, when parent-reported data were missing for parent 

education or occupation, student-reported data were substituted. 

In ELS:2002 (unlike NELS:88), the household items index was not used in the 

construction of SES. If missing from the parent survey, family income was directly imputed. 

However, as in NELS:88, if parental occupation and education reports were missing from the 

parent survey, student-reported data were substituted. Only if the occupation and education 

variables were missing from both the parent and student surveys were these data elements 

imputed. 

Examination of the relationships between SES and race, and SES and school sector, 

reveals no statistically significant differences between SES in ELS:2002 when constructed 

according to the NELS:88 criteria (inclusion of the household index when income data are 

missing) versus the ELS:2002 criteria (direct imputation of missing income data (see tables 

C-11A and C-12A). Other tables using SES as a row variable show small differences—all 

smaller than 2 percentage points (and most below 1 percentage point). 

Finally, to demonstrate to some degree the potential impact that imputation has on 

bivariate statistics in intercohort analysis, tables C-39A and C-39B provide comparisons between 

imputed and unimputed point estimates from ELS:2002 to NELS:88 and HS&B data files. Of 

the 36 comparisons between the unimputed ELS:2002 and NELS:88, only 2 changed in 

statistical significance (one became significantly different and the other was no longer 

significant). For the ELS:2002 and HS&B comparisons, no changes in the number of 

statistically significant comparisons or in the direction of these differences were detected. This 

limited analysis suggests that imputation has a limited impact on intercohort comparisons. 

This appendix examines the potential impact that imputation had on point estimates and 

intercohort comparisons. The general findings reveal a number of differences, but these 

differences were very small and in most cases lack any practical or substantive magnitude. Some 

estimates experienced significant differences, but these estimates were not part of a larger pattern 

and usually involved a relatively small, select population, suggesting a limited impact from 

imputation. Although this analysis cannot specify how well the imputation worked, it 

demonstrates, in general, that the imputation did not introduce large shifts from unimputed point 

C-14 
Appendix C: 

Documentation for Imputed Variables 

estimates, allowing analysts to take advantage of the larger sample sizes when conducting 

statistical analyses. 

C.3 Appendix C References 

Federal Committee on Statistical Methodology. (2001). Measuring and Reporting Sources of 

Error in Surveys (Statistical Policy Working Paper 31). Washington, DC: Office of 

Management and Budget. 

Groves, R.M. (1989). Survey Errors and Survey Costs. New York: Wiley. 

Seastrom, M. (2003). NCES Statistical Standards (NCES 2003–601). U.S. Department of 

Education, National Center for Education Statistics. Washington, DC: U.S. Government 

Printing Office. Available: http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2003601 

C-15 
Appendix C: 

Documentation for Imputed Variables 

Table C-5. Summary of differences between imputed and unimputed data, by topic: 2002 

Percentage of 

Percentage of 

unimputed 

estimates > 

Mean Median 

unimputed 

estimates > 

imputed 

estimates 

Topic 

relative 

difference 

(percent) 

relative 

difference 

(percent) 

Number of 

comparisons 

made 

Percentage 

of significant 

differences 

imputed 

estimates (all 

estimates) 

(estimates with 

significant 

differences) 

Student demographics 0.0158 0.0082 53 9.4 49.1 40.0 

School experiences 0.0256 0.0053 219 12.8 60.7 67.9 

Tested achievement 0.0617 0.0163 192 57.8 55.7 49.5 

Afterschool activity 0.0068 0.0025 117 0.9 46.2 100.0 

Life values 0.0058 0.0026 108 12.0 54.6 30.8 

Plans and expectations 0.0092 0.0011 146 4.8 49.3 28.6 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-16 
Appendix C: 

Documentation for Imputed Variables 

Table C-6A. Percentage of high school sophomores, by sex: 2002 

Sex Unimputed Imputed Difference 

Male 50.5 50.5 # 

Female 49.5 49.5 # 

# Rounds to zero. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

Table C-6B. Standard errors for table C-6A estimates (percentage of high school sophomores, by 

sex): 2002 

Sex Unimputed Imputed 

Male 0.53 0.53 

Female 0.53 0.53 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-17 
Appendix C: 

Documentation for Imputed Variables 

Table C-7A. Percentage of high school sophomores, by family living arrangement: 2002 

Family living arrangement Unimputed Imputed Difference 

Mother and father 57.4 56.8 -0.6* 

Mother and guardian 13.3 13.4 0.1 

Father and guardian 3.1 3.2 0.1 

Mother only 18.9 19.0 0.1 

Father only 3.2 3.2 # 

Other relative or nonrelative 4.1 4.3 0.2 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

Table C-7B. Standard errors for table C-7A estimates (percentage of high school sophomores, by 

family living arrangement): 2002 

Family living arrangement Unimputed Imputed 

Mother and father 0.58 0.57 

Mother and guardian 0.37 0.36 

Father and guardian 0.18 0.16 

Mother only 0.46 0.44 

Father only 0.21 0.20 

Other relative or nonrelative 0.22 0.21 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-18 
Appendix C: 

Documentation for Imputed Variables 

Table C-8A. Percentage of high school sophomores, by mother’s highest level of education: 2002 

Highest level of education Unimputed Imputed Difference 

Did not finish high school 12.9 13.2 0.3* 

Graduated from high school or GED 27.8 27.9 0.1 

Some postsecondary education (PSE) 34.8 34.6 -0.2* 

Graduated from college 16.7 16.6 -0.1 

Completed master’s or equivalent 6.0 6.0 # 

Completed Ph.D., M.D., or other advanced degree 1.7 1.7 # 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

Table C-8B. Standard errors for table C-8A estimates (percentage of high school sophomores, by 

mother’s highest level of education): 2002 

Highest level of education Unimputed Imputed 

Did not finish high school 0.53 0.54 

Graduated from high school or GED 0.50 0.49 

Some postsecondary education (PSE) 0.54 0.53 

Graduated from college 0.48 0.46 

Completed master’s or equivalent 0.28 0.27 

Completed Ph.D., M.D., or other advanced degree 0.15 0.15 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-19 
Appendix C: 

Documentation for Imputed Variables 

Table C-9A. Percentage of high school sophomores, by father’s highest level of education: 2002 

Highest level of education Unimputed Imputed Difference 

Did not finish high school 13.6 13.9 0.3* 

Graduated from high school or GED 29.9 30.1 0.2 

Some postsecondary education (PSE) 27.7 27.4 -0.2 

Graduated from college 16.9 16.7 -0.2 

Completed master’s or equivalent 7.5 7.4 -0.1 

Completed Ph.D., M.D., or other advanced degree 4.5 4.4 -0.1 

* Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

Table C-9B. Standard errors for table C-9A estimates (percentage of high school sophomores, by 

father’s highest level of education): 2002 

Highest level of education Unimputed Imputed 

Did not finish high school 0.57 0.54 

Graduated from high school or GED 0.59 0.53 

Some postsecondary education (PSE) 0.52 0.48 

Graduated from college 0.46 0.43 

Completed master’s or equivalent 0.33 0.30 

Completed Ph.D., M.D., or other advanced degree 0.28 0.26 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-20 
Appendix C: 

Documentation for Imputed Variables 

Table C-10A. Percentage of high school sophomores whose native language is English, by 

race/ethnicity: 2002 

Race/ethnicity1 Unimputed Imputed Difference 

American Indian or Alaska Native 83.8 83.7 -0.1 

Asian or Pacific Islander 36.3 36.9 0.6* 

Black or African American 94.5 94.4 -0.1 

Hispanic or Latino 47.6 47.7 0.1 

More than one race 92.8 92.5 -0.3 

White 97.0 97.0 # 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1

 All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

Table C-10B. Standard errors for table C-10A estimates (percentage of high school sophomores 

whose native language is English, by race/ethnicity): 2002 

Race/ethnicity1 Unimputed Imputed 

American Indian or Alaska Native 4.55 4.46 

Asian or Pacific Islander 2.07 2.01 

Black or African American 0.62 0.64 

Hispanic or Latino 1.97 1.93 

More than one race 0.99 1.04 

White 0.28 0.28 

1

 All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-21 
 

Appendix C: 

Documentation for Imputed Variables 

Table C-11A. Percentage of high school sophomores, by socioeconomic status and 

race/ethnicity: 2002 

Unimputed Imputed Difference 

Race/ethnicity1 Low Middle High Low Middle High Low Middle High 

American Indian or 

 Alaska Native 33.8 52.7 13.5 31.4 54.9 13.7 -2.4 2.3 0.2 

Asian or Pacific Islander 28.5 39.9 31.6 28.0 40.5 31.5 -0.5 0.6 -0.1 

Black or African American 36.5 50.5 13.0 35.2 51.9 12.9 -1.3 1.4 -0.1 

Hispanic or Latino 49.9 40.4 9.7 50.1 40.2 9.7 0.2 -0.2 # 

More than one race 23.2 55.4 21.4 23.6 56.0 20.4 0.4 0.6 -1.0 

White 15.5 52.7 31.8 15.6 52.3 32.0 0.1 -0.3 0.2 

# Rounds to zero. 

1

 All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

Table C-11B. Standard errors for table C-11A estimates (percentage of high school sophomores, 

by socioeconomic status and race/ethnicity): 2002 

Unimputed Imputed 

Race/ethnicity1 Low Middle High Low Middle High 

American Indian or Alaska 

Native 4.73 4.87 3.78 5.42 4.87 3.48 

Asian or Pacific Islander 2.24 1.91 2.34 2.16 1.69 2.15 

Black or African American 1.44 1.30 0.95 1.38 1.37 0.89 

Hispanic or Latino 1.78 1.46 0.87 1.86 1.54 0.86 

More than one race 2.01 2.41 1.95 2.01 2.33 1.76 

White 0.66 0.79 0.95 0.63 0.80 0.94 

1

 All race categories exclude individuals of Hispanic or Latino origin. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-22 
Appendix C: 

Documentation for Imputed Variables 

Table C-12A. Percentage of high school sophomores, by school sector and socioeconomic 

status: 2002 

Unimputed Imputed Difference 

Socioeconomic Other Other Other 

status Public Catholic private Public Catholic private Public Catholic private 

Lowest quarter 98.1 0.9 1.0 98.0 1.0 1.0 -0.1 0.1 # 

Middle quarters 94.0 3.5 2.5 94.0 3.5 2.6 # -0.1 0.1 

Highest quarter 83.3 9.1 7.6 83.5 9.0 7.5 0.2 -0.1 -0.2 

# Rounds to zero. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

Table C-12B. Standard errors for table C-12A estimates (percentage of high school sophomores, 

by school sector and socioeconomic status): 2002 

Unimputed Imputed 

Other Other 

Socioeconomic status Public Catholic private Public Catholic private 

Lowest quarter 0.23 0.14 0.18 0.24 0.16 0.17 

Middle quarters 0.30 0.19 0.23 0.31 0.19 0.25 

Highest quarter 1.00 0.60 0.82 0.98 0.59 0.81 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-23 
Table C-13A. Percentage of high school sophomores, by high school program and selected student characteristic

C-24 

Unimputed 

Imputed 

D

Academic/ Academic/ 

Characteristic General 

college 

preparatory Vocational General 

college 

preparatory Vocational General 

p

Sex 

Male 39.0 48.1 12.9 39.3 47.9 12.8 0.3 

Female 37.7 53.7 8.5 37.8 53.5 8.7 0.1 

Socioeconomic status 

Lowest quarter 43.8 39.9 16.4 42.8 41.6 15.7 -1.0 

Middle quarters 40.2 49.0 10.8 40.8 48.3 10.9 0.6 

Highest quarter 29.0 65.9 5.0 29.9 64.5 5.5 0.9 

Composite achievement 

test score 

Lowest quarter 48.6 34.1 17.3 48.0 35.0 17.0 -0.6 

Second quarter 44.8 41.8 13.4 44.5 42.4 13.1 -0.3 

Third quarter 36.3 56.1 7.7 36.8 55.3 7.9 0.5 

Highest quarter 24.4 70.7 4.8 24.9 69.9 5.1 0.5 

* Denotes statistical significance at p < .05. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 
Table C-13B. Standard errors for table C-13A estimates (percentage of high school sophomores, by high school p

student characteristics): 2002 

Unimputed Imputed 

Characteristic General 

Academic/college 

preparatory Vocational General 

Academic/co

prepar

Sex 

Male 0.80 0.88 0.66 0.79 

Female 0.80 0.81 0.44 0.79 

Socioeconomic status 

Lowest quarter 1.10 1.07 0.93 1.04 

Middle quarters 0.82 0.86 0.56 0.77 Highest quarter 1.11 1.19 0.50 1.13 

Composite achievement test score 

Lowest quarter 1.06 0.94 0.90 1.01 

Second quarter 1.12 1.08 0.76 1.07 

Third quarter 1.07 1.17 0.60 1.07 

Highest quarter 1.06 1.16 0.55 1.04 

C-25 SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 
Table C-14A. Percentage of high school sophomores who report having been in various kinds of courses or progra

by selected student characteristics: 2002 

C-26 

Unimputed Imputed 

Bilingual Bilingual 

or or 

Remedial Remedial bicultural Advanced Remedial Remedial bicultural Advanced Remedial Reme

Characteristic English math education Placement English math education Placement English m

Sex 

Male 10.1 11.5 26.9 17.1 10.1 11.5 26.9 17.1 -0.002 

Female 6.9 8.4 29.5 18.3 6.9 8.4 29.5 18.3 0.006 

Socioeconomic 

status 

Lowest 

quarter 10.5 12.4 19.1 12.6 10.1 12.0 20.0 13.2 -0.408 -

Middle 

quarters 8.1 9.4 28.9 16.5 8.4 9.7 28.3 16.1 0.267 

Highest 

quarter 7.0 8.5 35.9 25.2 7.2 8.4 36.0 25.1 0.162 

Composite 

achievement test score 

Lowest 

quarter 14.5 16.0 12.8 9.5 14.6 16.0 12.8 9.5 0.017 

Second 

quarter 7.3 9.6 21.1 11.6 7.4 9.6 20.9 11.5 0.037 

Third quarter 6.8 8.6 34.6 17.9 6.7 8.5 34.3 17.9 -0.099* 

Highest 

quarter 5.4 5.7 44.2 31.6 5.4 5.7 44.1 31.4 -0.050 

# Rounds to zero. * Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 
Table C-14B. Standard errors for table C-14A estimates (percentage of high school sophomores who report havin

kinds of courses or programs in high school, by selected student characteristics): 2002 

Unimputed Imputed 

Bilingual or B

Remedial Remedial bicultural Advanced Remedial Remedial 

Characteristic English math education Placement English math 

Sex 

Male 0.44 0.51 0.76 0.60 0.44 0.51 Female 0.37 0.44 0.80 0.68 0.37 0.44 

Socioeconomic status 

Lowest quarter 0.66 0.74 0.83 0.71 0.66 0.76 

Middle quarters 0.43 0.47 0.76 0.62 0.43 0.46 

Highest quarter 0.52 0.58 1.02 1.06 0.51 0.55 

Composite achievement 

test score 

Lowest quarter 0.77 0.89 0.71 0.60 0.76 0.88 

Second quarter 0.50 0.61 0.91 0.70 0.49 0.60 

Third quarter 0.51 0.57 1.05 0.88 0.50 0.56 Highest quarter 0.48 0.48 1.10 1.26 0.47 0.48 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B

C-27 
Table C-15A. Percentage of high school sophomores saying they usually or often come to school unprepared, by 

characteristics: 2002 

C-28 

Unimputed 

Imputed 

Dif

Come Come 

Come to Come to to Come to to 

Come school school Come school school Come school 

to without Come to without to without Come to without to without 

Characteristic 

school 

without 

books 

paper, 

pen, or 

pencil 

school 

without 

homework 

books 

and/or 

homework 

school 

without 

books 

paper, 

pen, or 

pencil 

school 

without 

homework 

books 

and/or 

homework 

school 

without 

books 

paper, 

pen, or 

pencil 

Sex 

Male 18.5 22.0 30.5 35.2 18.5 22.0 30.5 35.2 # # 

Female 15.1 13.1 21.3 25.6 15.1 13.1 21.3 25.6 # # 

Socioeconomic 

status 

Lowest quarter 22.0 21.8 31.7 37.3 21.8 21.1 31.8 37.1 -0.2 -0.7 

Middle quarters 16.2 16.9 25.8 30.4 16.1 17.1 25.8 30.4 -0.1 0.3 

Highest quarter 12.9 14.6 20.1 23.7 13.4 14.9 20.2 24.0 0.5 0.3 

Composite 

achievement test score 

Lowest quarter 29.6 29.6 37.9 44.4 29.5 29.6 37.8 44.4 # # Second quarter 16.0 16.2 26.1 30.7 15.9 16.4 26.1 30.6 # 0.2 

Third quarter 12.3 13.0 22.1 26.2 12.2 13.0 22.1 26.1 -0.1 # 

Highest quarter 9.7 11.0 17.7 20.8 9.7 11.1 17.7 20.8 # # 

# Rounds to zero. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 
Table C-15B. Standard errors for table C-15A estimates (percentage of high school sophomores saying they usua

school unprepared, by selected student characteristics): 2002 

Unimputed Imputed 

Come to school 

Come to school Come to school without books Come to Come to school Come 

Characteristic 

Come to school 

without books 

without paper, 

pen, or pencil 

without 

homework 

and/or 

homework 

school without 

books 

without paper, 

pen, or pencil 

h

Sex 

Male 0.57 0.60 0.71 0.77 0.57 0.60 

Female 0.57 0.51 0.64 0.69 0.57 0.51 

Socioeconomic status 

Lowest quarter 0.90 0.78 0.99 1.06 0.88 0.78 

Middle quarters 0.54 0.54 0.66 0.70 0.55 0.54 

Highest quarter 0.70 0.82 0.89 0.92 0.71 0.78 

Composite 

achievement test score 

Lowest quarter 1.01 0.93 1.09 1.14 1.01 0.93 

Second quarter 0.80 0.82 1.02 1.04 0.78 0.81 

Third quarter 0.66 0.64 0.82 0.87 0.65 0.64 

Highest quarter 0.57 0.62 0.75 0.80 0.57 0.62 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-29 
Table C-16A. Percentage of high school sophomores who agreed or strongly agreed with various statements abo

and teaching, by selected student characteristics: 2002 

C-30 

Unimputed Imputed 

Characteristic 

I don’t 

feel safe 

at this 

school 

Disruptions 

by other 

students get 

in the way of 

my learning 

The 

teaching is 

good 

I don’t feel 

safe at this 

school 

Disruptions by other students 

get in the 

way of my 

learning 

The 

teaching is 

good 

I don’t feel 

safe at this 

school 

Do

Sex Male 

12.7 43.6 79.0 12.7 43.6 79.0 # 

Female 11.1 47.8 82.2 11.1 47.8 82.2 # 

Socioeconomic status 

Lowest quarter 17.0 52.5 79.3 16.4 51.9 79.4 -0.5 

Middle quarters 11.5 44.9 79.7 12.0 45.4 79.6 0.5 

Highest quarter 7.5 40.3 84.0 7.3 40.2 83.9 -0.2 

Parents’ education 

High school or less 18.5 53.7 84.3 15.2 50.4 79.7 -3.4* 

Some college 13.1 48.7 78.8 12.0 45.6 79.4 -1.1* 

College graduation 12.3 44.8 79.6 9.7 42.5 82.2 -2.6* 

Graduate or professional 

degree 11.1 47.4 78.3 9.2 42.3 82.7 -1.9* 

Native language1 

English 11.0 44.0 80.2 11.1 44.3 80.1 0.2* 

Non-English 16.4 54.3 83.8 16.7 54.4 83.6 0.2 

Student’s educational 

expectations 

High school or less 22.6 50.2 68.5 22.8 50.7 68.6 0.2 

Some college 16.6 46.2 76.1 16.7 46.6 76.2 # 

College graduation 9.3 44.5 81.7 9.6 44.7 81.5 0.3* 

Graduate or professional 

degree 9.1 44.8 85.6 9.1 45.0 85.4 # 

Don’t know 16.4 46.4 74.6 16.2 46.6 74.5 -0.1 

See notes at end of table. 
Table C-16A. Percentage of high school sophomores who agreed or strongly agreed with various statements abo

and teaching, by selected student characteristics: 2002—Continued 

Unimputed Imputed 

Characteristic 

I don’t 

feel safe 

at this 

school 

Disruptions 

by other 

students get 

in the way of 

my learning 

The 

teaching is 

good 

I don’t feel 

safe at this 

school 

Disruptions by other students 

get in the 

way of my 

learning 

The 

teaching is 

good 

I don’t feel 

safe at this 

school 

Do

Composite achievement test 

score 

Lowest quarter 21.1 55.2 73.8 21.1 55.3 73.9 -0.1 

Second quarter 12.4 48.9 78.5 12.7 49.0 78.3 0.3 

Third quarter 8.7 42.7 82.5 8.6 42.5 82.5 # 

Highest quarter 5.2 35.8 87.7 5.2 35.9 87.7 # 

# Rounds to zero. * Denotes statistical significance at p < .05. 

1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-31 
Table C-16B. Standard errors for table C-16A estimates (percentage of high school sophomores who agreed or st

various statements about the school’s climate and teaching, by selected student characteristics): 20

Unimputed Imputed 

I don’t feel Disruptions by other Disruptions by 

Characteristic 

safe at this 

school 

students get in the 

way of my learning 

The teaching is 

good 

I don’t feel safe at 

this school 

students get 

way of my lea

Sex 

Male 0.5 0.8 0.7 0.5 

Female 0.5 0.8 0.6 0.5 

Socioeconomic status 

Lowest quarter 0.8 0.9 0.9 0.8 

Middle quarters 0.5 0.8 0.7 0.5 

Highest quarter 0.6 1.1 0.8 0.6 

Parents’ education 

High school or less 1.5 1.8 1.4 0.7 

Some college 0.8 1.2 1.0 0.6 College graduation 1.0 1.5 1.3 0.7 

Graduate or professional degree 1.0 1.6 1.4 0.7 

Native language1 

English 0.4 0.6 0.6 0.4 Non-English 1.1 1.2 0.9 1.1 

Student’s educational expectations 

High school or less 1.6 1.7 1.7 1.6 

Some college 1.2 1.8 1.4 1.2 College graduation 0.5 0.9 0.8 0.5 

Graduate or professional degree 0.5 0.9 0.7 0.5 

Don’t know 1.2 1.6 1.4 1.2 

Composite achievement test score 

Lowest quarter 0.8 1.0 1.0 0.8 

Second quarter 0.7 1.0 0.9 0.7 

Third quarter 0.6 1.1 0.8 0.6 

Highest quarter 0.5 1.0 0.7 0.5 

C-32 

1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 
Table C-17A. Percentage of high school sophomores’ use of calculators and computers, by selected student char

Unimputed Imputed D

Characteristic 

Use 

calculators 

Use graphic 

calculators 

Use 

computers 

Use 

calculators 

Use graphic 

calculators 

Use 

computers 

Use 

calculators 

U

Sex Male 

7.4 31.6 58.5 7.4 31.6 58.5 # 

Female 4.6 33.7 62.8 4.6 33.7 62.8 # 

Socioeconomic status 

Lowest quarter 8.6 38.5 54.8 8.7 38.1 54.8 0.1 

Middle quarters 5.9 34.4 62.4 5.8 34.7 62.1 # Highest quarter 3.4 22.9 63.2 3.7 23.1 63.5 0.2 

Parents’ education 

High school or less 13.2 40.7 57.2 8.5 37.6 57.8 -4.7 

Some college 7.1 36.8 59.4 5.7 35.5 60.3 -1.4 College graduation 5.9 35.5 60.3 5.0 28.2 62.6 -0.9 

Graduate or professional 

degree 5.7 38.0 61.9 3.8 23.8 63.5 -1.9 

Native language1 

English 5.2 31.7 61.9 5.3 31.7 61.6 0.1 

Non-English 10.6 38.4 55.1 10.6 38.3 54.8 # 

Student’s educational 

expectations 

High school or less 12.9 42.9 54.8 13.3 42.6 54.0 0.4 

Some college 8.1 40.3 57.7 8.0 40.2 57.0 # 

College graduation 4.7 32.5 61.6 4.7 32.4 61.2 # 

Graduate or professional 

degree 3.9 26.2 62.1 4.0 26.3 61.7 0.1 

Don’t know 10.0 40.2 64.1 9.9 40.1 63.8 -0.1 

Composite achievement test 

score 

Lowest quarter 10.9 39.7 48.4 10.9 39.6 48.4 # 

Second quarter 5.8 36.5 61.4 5.7 36.5 61.1 -0.1* 

Third quarter 4.2 33.3 65.1 4.2 32.9 64.7 -0.1* 

Highest quarter 3.3 21.7 68.7 3.3 21.6 68.4 # 

C-33 

# Rounds to zero. * Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 
Table C-17B. Standard errors for table C-17A estimates (percentage of high school sophomores’ use of calculator

selected student characteristics): 2002 

Unimputed Imputed 

Characteristic Use calculators 

Use graphic 

calculators Use computers Use calculators 

Use gr

calcu

Sex Male 

0.46 0.94 0.92 0.46 

Female 0.34 1.01 0.86 0.34 

Socioeconomic status 

Lowest quarter 0.66 1.17 1.11 0.68 

Middle quarters 0.40 1.06 0.94 0.39 

Highest quarter 0.43 1.10 1.24 0.44 

Parents’ education 

High school or less 1.54 2.20 2.13 0.61 

Some college 0.68 1.34 1.33 0.41 

College graduation 0.76 1.74 1.62 0.47 Graduate or professional degree 0.72 1.84 1.76 0.47 

Native language1 

English 0.32 0.91 0.82 0.32 

Non-English 1.01 1.63 1.50 1.02 

Student’s educational expectations 

High school or less 1.27 1.82 1.99 1.24 

Some college 0.90 1.92 1.77 0.89 

College graduation 0.42 1.08 1.02 0.41 

Graduate or professional degree 0.32 0.96 1.02 0.32 

Don’t know 1.09 1.91 1.68 1.06 

Composite achievement test score 

Lowest quarter 0.67 1.13 1.24 0.67 

Second quarter 0.50 1.16 1.19 0.50 

Third quarter 0.44 1.32 1.26 0.44 

Highest quarter 0.42 1.15 1.24 0.41 

C-34 

1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-18A. Item Response Theory (IRT)-estimated number-right scores for mathematics, by 

selected student characteristics: 2002 

Unimputed Difference 

Characteristic (mean) Imputed (mean) (mean) 

Sex

 Male 37.6 38.0 0.5 

Female 38.0 37.1 -1.0 

Socioeconomic status 

Lowest quarter 31.2 31.5 0.3* 

Middle quarters 37.6 37.3 -0.3* 

Highest quarter 44.1 44.0 -0.1 

High school program 

General 35.1 35.0 -0.1 

Academic/college preparatory 40.8 40.5 -0.4* 

Vocational 33.0 33.0 0.1 

* Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-35 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-18B. Standard errors for table C-18A estimates (Item Response Theory [IRT]-estimated 

number-right scores for mathematics, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex

 Male 0.24 0.24 

Female 0.25 0.25 

Socioeconomic status 

Lowest quarter 0.29 0.28 

Middle quarters 0.19 0.21 

Highest quarter 0.25 0.25 

High school program 

General 0.26 0.26 

Academic/college preparatory 0.24 0.24 

Vocational 0.46 0.44 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-36 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-19A. High school sophomore probability of proficiency at reading level 1, by selected 

student characteristics: 2002 

Unimputed Imputed Difference 

Characteristic (mean) (mean) (mean) 

Sex

 Male 87.1 87.6 0.5* 

 Female 91.0 91.3 0.3* 

Socioeconomic status 

 Lowest quarter 79.0 80.7 1.7* 

 Middle quarters 90.9 90.4 -0.5* 

 Highest quarter 95.8 96.2 0.4 

Parents’ education 

High school or less 77.5 83.5 6.0 

 Some college 85.9 89.8 3.9* 

 College graduation 89.4 92.5 3.1* 

 Graduate or professional degree 91.7 94.5 2.8* 

Student’s educational expectations 

High school or less 68.5 69.8 1.3* 

 Some college 82.6 82.7 0.1 

 College graduation 91.9 91.6 -0.2 

 Graduate or professional degree 95.4 95.2 -0.2 

 Don’t know 84.7 84.0 -0.7* 

Native language1

 English 91.5 91.5 -0.1 

 Non-English 76.7 76.8 0.1 

Composite achievement test score 

 Lowest quarter 59.5 60.1 0.5* 

 Second quarter 97.7 97.8 0.1* 

 Third quarter 99.9 99.9 #* 

 Highest quarter 100.0 100.0 #* 

High school program 

 General 87.4 87.1 -0.3 

 Academic/college preparatory 93.1 92.5 -0.6* 

 Vocational 82.9 83.1 0.2 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-37 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-19B. Standard errors for table C-19A estimates (high school sophomore probability of 

proficiency at reading level 1, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex

 Male 0.50 0.48 

 Female 0.45 0.44 

Socioeconomic status 

 Lowest quarter 0.83 0.77 

 Middle quarters 0.36 0.39 

 Highest quarter 0.42 0.38 

Parents’ education 

High school or less 1.46 0.70 

 Some college 0.78 0.46 

 College graduation 0.74 0.53 

 Graduate or professional degree 0.69 0.56 

Student’s educational expectations 

High school or less 1.46 1.37 

 Some college 0.96 0.93 

 College graduation 0.48 0.47 

 Graduate or professional degree 0.34 0.34 

 Don’t know 1.01 0.99 

Native language1

 English 0.33 0.33 

 Non-English 1.16 1.13 

Composite achievement test score 

 Lowest quarter 0.83 0.82 

 Second quarter 0.15 0.14 

 Third quarter 0.02 0.02 

 Highest quarter # # 

High school program 

 General 0.56 0.55 

 Academic/college preparatory 0.40 0.41 

 Vocational 1.00 0.96 

# Rounds to zero. 

1 The first language students learned to speak when they were children. 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-38 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-20A. High school sophomore probability of proficiency at reading level 2, by selected 

student characteristics: 2002 

Unimputed Imputed Difference 

Characteristic (mean) (mean) (mean) 

Sex

 Male 43.6 44.2 0.5* 

Female 48.3 48.2 -0.1 

Socioeconomic status 

Lowest quarter 25.0 26.2 1.2* 

Middle quarters 46.1 45.2 -0.9* 

Highest quarter 67.6 68.0 0.4 

Parents’ education 

High school or less 21.6 30.5 8.8* 

Some college 34.6 43.6 9.0* 

College graduation 42.7 56.2 13.5* 

Graduate or professional degree 46.3 65.0 18.7* 

Student’s educational expectations 

High school or less 15.7 15.5 -0.2 

Some college 26.8 27.1 0.3 

College graduation 46.9 46.4 -0.5* 

Graduate or professional degree 62.0 61.9 -0.1 

Don’t know 35.1 34.2 -0.9* 

Native language1 

English 49.7 49.2 -0.5* 

Non-English 27.3 27.6 0.3 

Composite achievement test score 

Lowest quarter 2.6 2.6 # 

Second quarter 22.7 22.6 -0.2 

Third quarter 65.3 65.5 0.2* 

Highest quarter 93.8 94.0 0.1* 

High school program 

General 38.6 38.5 -0.1 

Academic/college preparatory 56.7 55.6 -1.1* 

Vocational 28.9 29.2 0.4 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-39 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-20B. Standard errors for table C-20A estimates (high school sophomore probability of 

proficiency at reading level 2, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex 

Male 0.80 0.78 

Female 0.88 0.85 

Socioeconomic status 

Lowest quarter 0.85 0.84 

Middle quarters 0.70 0.68 

Highest quarter 0.89 0.88 

Parents’ education 

High school or less 1.37 0.80 

Some college 0.98 0.83 

College graduation 1.29 0.96 

Graduate or professional degree 1.32 1.17 

Student’s educational expectations 

High school or less 1.07 0.98 

Some college 1.11 1.07 

College graduation 0.89 0.85 

Graduate or professional degree 0.89 0.85 

Don’t know 1.27 1.22 

Native language1 

English 0.70 0.68 

Non-English 1.21 1.18 

Composite achievement test score 

Lowest quarter 0.12 0.12 

Second quarter 0.45 0.42 

Third quarter 0.56 0.53 

Highest quarter 0.22 0.21 

High school program 

General 0.86 0.83 

Academic/college preparatory 0.82 0.79 

Vocational 1.46 1.39 

1 The first language students learned to speak when they were children. 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-40 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-21A. High school sophomore probability of proficiency at reading level 3, by selected 

student characteristics: 2002 

Unimputed Imputed Difference 

Characteristic (mean) (mean) (mean) 

Sex 

Male 8.2 8.1 -0.1 

Female 8.8 8.5 -0.3* 

Socioeconomic status 

Lowest quarter 2.5 2.5 # 

Middle quarters 6.7 6.3 -0.4* 

Highest quarter 18.2 17.8 -0.3 

Parents’ education 

High school or less 1.3 3.0 1.7* 

Some college 3.8 6.1 2.2* 

College graduation 6.0 11.5 5.4* 

Graduate or professional degree 7.0 17.7 10.7* 

Student’s educational expectations 

High school or less 0.8 0.8 # 

Some college 2.1 2.1 -0.1 

College graduation 7.6 7.2 -0.4* 

Graduate or professional degree 13.9 13.5 -0.4* 

Don’t know 5.8 5.6 -0.2 

Native language1 

English 9.4 9.0 -0.4* 

Non-English 3.9 3.8 -0.1 

Composite achievement test score 

Lowest quarter # # #* 

Second quarter 0.1 0.1 #* 

Third quarter 2.2 2.1 -0.1* 

Highest quarter 31.4 30.9 -0.5* 

High school program 

General 4.9 4.8 -0.1 

Academic/college preparatory 12.7 12.1 -0.6* 

Vocational 2.8 2.8 # 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-41 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-21B. Standard errors for table C-21A estimates (high school sophomore probability of 

proficiency at reading level 3, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex 

Male 0.34 0.32 

Female 0.39 0.37 

Socioeconomic status 

Lowest quarter 0.25 0.23 

Middle quarters 0.26 0.25 

Highest quarter 0.72 0.71 

Parents’ education 

High school or less 0.33 0.24 

Some college 0.34 0.28 

College graduation 0.53 0.57 

Graduate or professional degree 0.60 0.84 

Student’s educational expectations 

High school or less 0.23 0.20 

Some college 0.31 0.29 

College graduation 0.39 0.36 

Graduate or professional degree 0.51 0.48 

Don’t know 0.62 0.59 

Native language1 

English 0.31 0.29 

Non-English 0.44 0.41 

Composite achievement test score 

Lowest quarter # # 

Second quarter 0.01 0.01 

Third quarter 0.11 0.11 

Highest quarter 0.66 0.63 

High school program 

General 0.30 0.28 

Academic/college preparatory 0.46 0.43 

Vocational 0.42 0.39 

# Rounds to zero. 

1 The first language students learned to speak when they were children. 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-42 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-22A. High school sophomore probability of proficiency at math level 1, by selected 

student characteristics: 2002 

Unimputed Imputed Difference 

Characteristic (mean) (mean) (mean) 

Sex 

Male 91.4 91.7 0.3* 

Female 91.3 91.6 0.3* 

Socioeconomic status 

Lowest quarter 83.2 84.5 1.4* 

Middle quarters 92.8 92.5 -0.3 

Highest quarter 97.2 97.1 -0.1 

Parents’ education 

High school or less 81.6 87.4 5.7 

Some college 89.5 91.6 2.2* 

College graduation 90.4 94.3 3.9* 

Graduate or professional degree 93.8 95.6 1.8* 

Student’s educational expectations 

High school or less 77.0 77.6 0.6 

Some college 86.0 85.7 -0.2 

College graduation 93.7 93.6 -0.1 

Graduate or professional degree 96.0 95.9 -0.1 

Don’t know 87.9 87.5 -0.4 

Native language1 

English 93.0 93.0 -0.1 

Non-English 83.8 83.7 -0.1 

Composite achievement test score 

Lowest quarter 69.1 69.5 0.4* 

Second quarter 97.5 97.6 0.1* 

Third quarter 99.7 99.7 #* 

Highest quarter 100.0 100.0 #* 

High school program 

General 89.8 89.5 -0.2 

Academic/college preparatory 94.7 94.3 -0.4* 

Vocational 87.1 87.1 0.1 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-43 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-22B. Standard errors for table C-22A estimates (high school sophomore probability of 

proficiency at math level 1, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex 

Male 0.36 0.35 

Female 0.36 0.35 

Socioeconomic status 

Lowest quarter 0.66 0.56 

Middle quarters 0.30 0.33 

Highest quarter 0.26 0.26 

Parents’ education 

High school or less 1.24 0.48 

Some college 0.53 0.37 

College graduation 0.68 0.37 

Graduate or professional degree 0.52 0.46 

Student’s educational expectations 

High school or less 1.11 1.05 

Some college 0.87 0.85 

College graduation 0.34 0.33 

Graduate or professional degree 0.30 0.30 

Don’t know 0.72 0.72 

Native language1 

English 0.25 0.26 

Non-English 0.84 0.81 

Composite achievement test score 

Lowest quarter 0.67 0.66 

Second quarter 0.09 0.09 

Third quarter 0.01 0.01 

Highest quarter # # 

High school program 

General 0.42 0.41 

Academic/college preparatory 0.30 0.30 

Vocational 0.80 0.76 

# Rounds to zero. 

1 The first language students learned to speak when they were children. 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-44 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-23A. High school sophomore probability of proficiency at math level 2, by selected 

student characteristics: 2002 

Unimputed Imputed Difference 

Characteristic (mean) (mean) (mean) 

Sex 

Male 68.0 68.4 0.4* 

Female 65.3 65.7 0.5* 

Socioeconomic status 

Lowest quarter 44.9 46.4 1.4* 

Middle quarters 68.2 67.8 -0.4 

Highest quarter 86.3 86.2 -0.1 

Parents’ education 

High school or less 41.6 52.0 10.5* 

Some college 57.0 65.9 8.9* 

College graduation 64.1 76.1 11.9* 

Graduate or professional degree 70.5 82.9 12.4* 

Student’s educational expectations 

High school or less 32.2 32.4 0.2 

Some college 48.1 48.3 0.3 

College graduation 70.7 70.1 -0.6* 

Graduate or professional degree 81.0 80.9 -0.1 

Don’t know 55.3 54.4 -0.9* 

Native language1 

English 70.2 69.9 -0.3* 

Non-English 49.7 49.7 0.1 

Composite achievement test score 

Lowest quarter 9.9 9.8 -0.2* 

Second quarter 63.4 63.2 -0.2 

Third quarter 95.1 95.3 0.2* 

Highest quarter 99.9 99.9 #* 

High school program 

General 59.2 59.2 -0.1 

Academic/college preparatory 77.5 76.4 -1.1* 

Vocational 50.9 51.2 0.3 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-45 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-23B. Standard errors for table C-23A estimates (high school sophomore probability of 

proficiency at math level 2, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex 

Male 0.86 0.84 

Female 0.90 0.89 

Socioeconomic status 

Lowest quarter 1.19 1.15 

Middle quarters 0.73 0.75 

Highest quarter 0.75 0.73 

Parents’ education 

High school or less 2.00 1.02 

Some college 1.19 0.88 

College graduation 1.40 0.91 

Graduate or professional degree 1.31 1.09 

Student’s educational expectations 

High school or less 1.50 1.42 

Some college 1.53 1.48 

College graduation 0.92 0.89 

Graduate or professional degree 0.83 0.81 

Don’t know 1.51 1.46 

Native language1 

English 0.71 0.71 

Non-English 1.66 1.62 

Composite achievement test score 

Lowest quarter 0.46 0.44 

Second quarter 0.75 0.72 

Third quarter 0.28 0.26 

Highest quarter 0.01 0.01 

High school program 

General 0.99 0.99 

Academic/college preparatory 0.76 0.75 

Vocational 1.83 1.77 

1 The first language students learned to speak when they were children. 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-46 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-24A. High school sophomore probability of proficiency at math level 3, by selected 

student characteristics: 2002 

Unimputed Imputed Difference 

Characteristic (mean) (mean) (mean) 

Sex 

Male 48.0 48.0 # 

Female 44.7 44.7 -0.1 

Socioeconomic status 

Lowest quarter 24.8 25.1 0.3 

Middle quarters 45.5 44.7 -0.8* 

Highest quarter 70.7 70.9 0.2 

Parents’ education 

High school or less 20.8 29.5 8.7* 

Some college 34.3 42.9 8.6* 

College graduation 41.7 56.6 14.9* 

Graduate or professional degree 46.9 68.8 21.9* 

Student’s educational expectations 

High school or less 13.7 13.2 -0.4 

Some college 24.0 23.9 -0.1 

College graduation 48.4 47.5 -1.0* 

Graduate or professional degree 63.4 63.1 -0.3 

Don’t know 33.7 32.9 -0.7* 

Native language1 

English 49.6 49.0 -0.6* 

Non-English 30.2 30.1 -0.1 

Composite achievement test score 

Lowest quarter 0.7 0.7 #* 

Second quarter 17.3 16.4 -0.9* 

Third quarter 69.5 69.6 0.1 

Highest quarter 98.6 98.6 0.1* 

High school program 

General 36.5 36.3 -0.2 

Academic/college preparatory 59.0 57.5 -1.5* 

Vocational 29.7 29.8 0.1 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-47 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-24B. Standard errors for table C-24A estimates (high school sophomore probability of 

proficiency at math level 3, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex 

Male 0.93 0.92 

Female 0.95 0.92 

Socioeconomic status 

Lowest quarter 0.96 0.96 

Middle quarters 0.82 0.81 

Highest quarter 1.04 1.02 

Parents’ education 

High school or less 1.57 0.95 

Some college 1.17 0.93 

College graduation 1.48 1.13 

Graduate or professional degree 1.50 1.32 

Student’s educational expectations 

High school or less 1.22 1.13 

Some college 1.39 1.33 

College graduation 1.04 1.00 

Graduate or professional degree 1.04 1.00 

Don’t know 1.50 1.44 

Native language1 

English 0.80 0.79 

Non-English 1.48 1.44 

Composite achievement test score 

Lowest quarter 0.11 0.11 

Second quarter 0.62 0.59 

Third quarter 0.79 0.76 

Highest quarter 0.14 0.13 

High school program 

General 0.98 0.97 

Academic/college preparatory 0.94 0.91 

Vocational 1.76 1.64 

1 The first language students learned to speak when they were children. 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-48 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-25A. High school sophomore probability of proficiency at math level 4, by selected 

student characteristics: 2002 

Unimputed Imputed Difference 

Characteristic (mean) (mean) (mean) 

Sex 

Male 22.7 22.3 -0.3* 

Female 18.9 18.5 -0.4* 

Socioeconomic status 

Lowest quarter 7.5 7.6 0.1 

Middle quarters 18.6 17.7 -0.9* 

Highest quarter 39.1 38.7 -0.4 

Parents’ education 

High school or less 5.3 9.8 4.5* 

Some college 12.2 16.4 4.2* 

College graduation 16.5 27.4 10.8* 

Graduate or professional degree 18.4 38.0 19.6* 

Student’s educational expectations 

High school or less 3.2 3.1 -0.1 

Some college 6.6 6.5 -0.1 

College graduation 20.2 19.4 -0.9* 

Graduate or professional degree 32.4 31.7 -0.7* 

Don’t know 13.0 12.7 -0.3 

Native language1 

English 22.4 21.7 -0.7* 

Non-English 12.8 12.6 -0.2 

Composite achievement test score 

Lowest quarter 0.1 0.1 #* 

Second quarter 1.5 1.4 -0.1* 

Third quarter 14.7 14.1 -0.6* 

Highest quarter 66.6 66.1 -0.5* 

High school program 

General 13.9 13.6 -0.2 

Academic/college preparatory 28.9 27.7 -1.2* 

Vocational 10.7 10.6 -0.1 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-49 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-25B. Standard errors for table C-25A estimates (high school sophomore probability of 

proficiency at math level 4, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex 

Male 0.65 0.63 

Female 0.66 0.63 

Socioeconomic status 

Lowest quarter 0.46 0.45 

Middle quarters 0.52 0.52 

Highest quarter 1.01 0.95 

Parents’ education 

High school or less 0.66 0.54 

Some college 0.73 0.56 

College graduation 0.97 0.86 

Graduate or professional degree 1.03 1.19 

Student’s educational expectations 

High school or less 0.46 0.43 

Some college 0.65 0.61 

College graduation 0.70 0.66 

Graduate or professional degree 0.85 0.82 

Don’t know 0.99 0.95 

Native language1 

English 0.57 0.55 

Non-English 0.95 0.91 

Composite achievement test score 

Lowest quarter 0.01 0.01 

Second quarter 0.10 0.09 

Third quarter 0.45 0.43 

Highest quarter 0.68 0.67 

High school program 

General 0.59 0.56 

Academic/college preparatory 0.77 0.73 

Vocational 0.90 0.87 

1 The first language students learned to speak when they were children. 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-50 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-26A. High school sophomore probability of proficiency at math level 5, by selected 

student characteristics: 2002 

Unimputed Imputed Difference 

Characteristic (mean) (mean) (mean) 

Sex 

Male 1.4 1.3 -0.1* 

Female 0.6 0.6 #* 

Socioeconomic status 

Lowest quarter 0.2 0.2 # 

Middle quarters 0.5 0.5 -0.1 

Highest quarter 2.7 2.6 -0.1 

Parents’ education 

High school or less 0.2 0.2 #* 

Some college 0.3 0.4 0.1* 

College graduation 0.3 1.2 0.9 

Graduate or professional degree 0.6 3.1 2.5* 

Student’s educational expectations 

High school or less # # # 

Some college 0.1 # # 

College graduation 0.6 0.6 #* 

Graduate or professional degree 2.0 1.9 -0.1* 

Don’t know 0.6 0.6 #* 

Native language1 

English 1.0 0.9 -0.1* 

Non-English 1.2 1.1 -0.1* 

Composite achievement test score 

Lowest quarter # # # 

Second quarter # # # 

Third quarter # # #* 

Highest quarter 3.9 3.8 -0.1* 

High school program 

General 0.4 0.4 #* 

Academic/college preparatory 1.6 1.5 -0.1* 

Vocational 0.3 0.3 #* 

# Rounds to zero. 

* Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-51 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-26B. Standard errors for table C-26A estimates (high school sophomore probability of 

proficiency at math level 5, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic SE (mean) SE (mean) 

Sex 

Male 0.14 0.13 

Female 0.07 0.07 

Socioeconomic status 

Lowest quarter 0.05 0.05 

Middle quarters 0.06 0.06 

Highest quarter 0.25 0.23 

Parents’ education 

High school or less 0.12 0.05 

Some college 0.06 0.06 

College graduation 0.07 0.17 

Graduate or professional degree 0.14 0.31 

Student’s educational expectations 

High school or less 0.03 0.02 

Some college 0.03 0.02 

College graduation 0.08 0.07 

Graduate or professional degree 0.18 0.17 

Don’t know 0.17 0.16 

Native language1 

English 0.09 0.08 

Non-English 0.21 0.19 

Composite achievement test score 

Lowest quarter # # 

Second quarter # # 

Third quarter 0.01 0.01 

Highest quarter 0.28 0.27 

High school program 

General 0.07 0.06 

Academic/college preparatory 0.14 0.13 

Vocational 0.12 0.11 

# Rounds to zero. 

1 The first language students learned to speak when they were children. 

NOTE: SE = standard error. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-52 
Table C-27A. Percentage of high school sophomores who participate in academic clubs, athletics, and cheerleadi

selected student characteristics: 2002 

Unimputed Imputed 

Characteristic 

Academic 

clubs Athletics 

Cheerleading/ 

drill team 

Academic 

clubs Athletics 

Cheerleading/ 

drill team 

Academic 

clubs 

Sex 

Male 6.8 61.0 8.1 6.8 61.0 8.1 # 

Female 9.9 48.5 19.2 9.9 48.5 19.2 # 

Socioeconomic status 

Lowest quarter 5.5 44.1 13.0 5.6 44.9 13.5 0.1 

Middle quarters 7.2 55.4 14.1 7.2 54.9 14.2 # 

Highest quarter 13.6 64.0 13.1 13.3 64.3 12.8 -0.3 

Composite achievement 

test score 

Lowest quarter 4.2 47.9 15.0 4.3 47.7 15.0 0.1 

Second quarter 5.0 52.6 14.7 5.2 52.5 14.5 0.2 Third quarter 7.8 56.6 13.8 8.2 56.5 13.7 0.4 Highest quarter 15.6 62.2 11.7 15.5 62.3 11.6 -0.1 

# Rounds to zero. * Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B

C-53 
Table C-27B. Standard errors for table C-27A estimates (percentage of high school sophomores who participate in 

athletics, and cheerleading/drill team, by selected student characteristics): 2002 

 Unimputed Imp

Characteristic Academic clubs Athletics 

Cheerleading/ 

drill team Academic clubs A

Sex 

Male 0.38 0.81 0.52 0.38 

Female 0.46 0.85 0.63 0.46 

Socioeconomic status 

Lowest quarter 0.46 1.13 0.72 0.46 

Middle quarters 0.38 0.86 0.58 0.38 

Highest quarter 0.73 1.08 0.78 0.74 

Composite achievement test score 

Lowest quarter 0.42 1.04 0.83 0.42 

Second quarter 0.42 1.05 0.75 0.44 

Third quarter 0.55 1.07 0.75 0.55 

Highest quarter 0.83 1.04 0.74 0.80 

C-54 SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-28A. Percentage of high school sophomores who participate in hobby clubs, music, and vocational clubs

characteristics: 2002 

Unimputed Imputed 

Characteristic 

Hobby 

clubs Music 

Vocational 

clubs 

Hobby 

clubs Music 

Vocational 

clubs 

Hobby 

clubs 

Sex 

Male 8.1 16.2 7.6 8.1 16.3 7.6 # 

Female 10.9 26.8 9.1 10.9 26.8 9.1 # 

Socioeconomic status 

Lowest quarter 6.5 15.2 9.1 6.7 15.6 9.2 0.2 

Middle quarters 8.8 21.6 8.6 8.8 21.6 8.6 0.1 

Highest quarter 13.8 27.4 7.0 13.5 27.1 7.0 -0.3 

Composite achievement test 

score 

Lowest quarter 6.3 15.4 8.8 6.4 15.4 8.8 # 

Second quarter 7.0 19.1 9.3 7.2 18.8 9.5 0.2 Third quarter 10.3 22.7 7.8 10.8 22.7 7.7 0.4 

Highest quarter 13.1 28.9 7.5 13.4 28.7 7.4 0.4 

# Rounds to zero. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B

C-55 
Table C-28B. Standard errors for table C-28A estimates (percentage of high school sophomores who participate in 

and vocational clubs, by selected student characteristics): 2002 

Characteristic Sex 

Hobby clubs 

Unimputed 

Music Vocational clubs Hobby clubs 

Imputed 

Mu

Male 0.41 0.60 0.53 0.41 0

Female 0.50 0.71 0.53 0.50 0

Socioeconomic status 

Lowest quarter Middle quarters Highest quarter 

0.47 

0.40 

0.80 

0.74 

0.67 

1.02 

0.72 

0.54 

0.56 

0.50 

0.39 

0.79 

001

Composite achievement 

test score 

Lowest quarter Second quarter Third quarter Highest quarter 

0.53 

0.50 

0.63 

0.75 

0.79 

0.82 

0.92 

1.04 

0.63 

0.73 

0.68 0.70 

0.52 

0.49 

0.64 0.75 

0

0

01

C-56 SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-29A. Percentage of high school sophomores, by employment status and selected student characteristics

Unimputed Imputed D

Worked 

Worked more Worked 

more than 15 Worked more 

Worked than 10 hours Worked more than than 15 Work

Characteristic 

Ever 

worked 

for pay or 

employed 

for pay or 

employed 

at time of 

survey 

hours 

per week 

at time 

of survey 

per 

week at 

time of 

survey 

Ever 

worked 

for pay or 

employed 

for pay or 

employed 

at time of 

survey 

10 hours 

per week 

at time of 

survey 

hours 

per week 

at time 

of survey 

Ever 

worked for 

pay or 

employed 

for pay 

employ

at time 

surv

Sex 

Male 62.9 27.6 66.2 57.2 62.9 27.7 66.2 57.2 # 

Female 56.6 23.6 57.0 44.4 56.6 23.6 57.0 44.5 # 

Socioeconomic 

status 

Lowest 

quarter 54.0 22.9 70.2 61.4 55.1 23.4 69.9 60.5 1.0 0

Middle 

quarters 62.2 27.3 65.3 53.2 61.8 27.2 65.3 53.4 -0.4 -0

Highest 

quarter 59.7 24.6 47.6 38.4 59.8 24.5 47.8 38.7 0.1 -0

Composite 

achievement test score 

Lowest 

quarter 56.1 25.3 69.8 61.9 56.1 25.3 69.8 61.7 0.1 

Second 

quarter 60.2 26.3 70.8 60.4 59.9 26.2 71.0 60.6 -0.3 -0

Third quarter 60.9 25.8 61.1 49.9 60.9 25.8 61.3 50.0 -0.1 

Highest 

quarter 61.3 25.0 47.2 34.7 61.3 25.1 47.1 34.8 # 0

# Rounds to zero. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-57 
Table C-29B. Standard errors for table C-29A estimates (percentage of high school sophomores, by employment s

student characteristics): 2002 

Unimputed Imputed 

Worked for Worked for 

Ever pay or Worked more Worked more pay or Worke

worked for employed at than 10 hours than 15 hours Ever worked employed at than 1

Characteristic 

pay or 

employed 

time of 

survey 

per week at 

time of survey 

per week at 

time of survey 

for pay or 

employed 

time of 

survey 

per w

time of 

Sex 

Male 0.82 0.71 1.45 1.52 0.82 0.71 

Female 0.80 0.69 1.50 1.55 0.80 0.69 

Socioeconomic 

status 

Lowest quarter 1.31 1.00 2.08 2.21 1.26 0.98 

Middle quarters 0.75 0.72 1.34 1.39 0.77 0.73 

Highest quarter 1.05 0.93 2.29 2.23 1.05 0.91 

Composite 

achievement test score 

Lowest quarter 1.24 1.01 1.95 2.01 1.25 1.01 

Second quarter 1.08 0.96 1.82 1.93 1.07 0.94 

Third quarter 1.05 0.92 2.21 2.03 1.04 0.91 

Highest quarter 1.05 0.97 2.06 2.07 1.04 0.97 

C-58 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-30A. Percentage of high school sophomores who report that they engage in various activities at least onc

selected student characteristics: 2002 

Unimputed Imputed 

Characteristic 

Driving or 

riding 

around 

Visiting with 

friends or 

meeting at a 

hangout 

Talking with 

friends on the 

telephone 

Driving or 

riding 

around 

Visiting with 

friends or 

meeting at a 

hangout 

Talking with 

friends on the 

telephone 

Driving or 

riding 

around 

V

m

Sex 

Male 59.5 80.3 64.9 59.5 80.3 64.9 # Female 57.1 78.6 83.3 57.1 78.6 83.3 # 

Socioeconomic status 

Lowest quarter 56.3 73.5 69.6 56.6 74.3 69.7 0.2 Middle quarters 60.6 81.1 75.9 60.5 80.5 75.9 -0.1 Highest quarter 55.9 82.2 75.0 55.7 82.2 74.7 -0.2 

Composite 

achievement test score 

Lowest quarter 59.4 74.4 72.1 59.2 74.3 72.1 -0.2 

Second quarter 63.7 81.9 76.2 63.6 81.7 75.9 # Third quarter 59.5 82.6 75.1 59.4 82.6 75.1 # 

Highest quarter 51.0 79.1 73.0 51.2 79.1 73.2 0.2 

# Rounds to zero. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-59 
Table C-30B. Standard errors for table C-30A estimates (percentage of high school sophomores who report that th

activities at least once or twice a week, by selected student characteristics): 2002 

Unimputed Imputed 

Visiting with Visiting w

Characteristic 

Driving or 

riding around 

friends at a 

hangout 

Talking with friends 

on the telephone 

Driving or 

riding around 

friends a

hango

Sex 

Male 0.76 0.65 0.68 0.76 0

Female 0.84 0.65 0.56 0.84 0

Socioeconomic status 

Lowest quarter 1.05 0.98 0.94 1.07 0Middle quarters 0.72 0.58 0.69 0.72 0

Highest quarter 1.14 0.81 0.86 1.15 0

Composite achievement test 

score 

Lowest quarter 1.10 1.02 0.93 1.10 1Second quarter 1.05 0.81 0.85 1.05 0Third quarter 1.04 0.80 0.87 1.03 0Highest quarter 1.14 0.85 0.88 1.13 0

C-60 SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-31A. Percentage of high school sophomores who report that various life values related to work are very i

selected student characteristics: 2002 

Unimputed Imputed 

Being Being able Being Being able Being 

successful to find Having successful to find Having successful 

Characteristic 

in my line of 

work 

steady 

work 

lots of 

money 

in my line 

of work 

steady 

work 

lots of 

money 

in my line 

of work 

Sex 

Male 84.1 82.0 51.0 84.1 81.9 51.0 # 

Female 88.5 86.7 33.3 88.5 86.7 33.3 # 

Socioeconomic status 

Lowest quarter 81.9 81.2 46.8 81.9 81.9 47.3 # 

Middle quarters 86.9 85.2 42.4 86.9 84.6 42.6 # 

Highest quarter 89.2 85.7 36.5 89.1 85.9 36.2 -0.1 

Composite achievement 

test score 

Lowest quarter 76.7 76.8 55.8 76.7 76.9 55.5 # 

Second quarter 85.9 86.6 47.4 85.8 86.5 46.9 -0.1 

Third quarter 90.6 87.9 38.0 90.6 87.6 37.6 # 

Highest quarter 91.4 85.6 29.4 91.4 85.7 29.5 # 

# Rounds to zero. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B

C-61 
Table C-31B. Standard errors for table C-31A estimates (percentage of high school sophomores who report that v

related to work are very important to them, by selected student characteristics): 2002 

Unimputed Imp

Being Being able to Being Bei

Characteristic 

successful in 

my line of work 

find steady 

work 

Having lots of 

money 

successful in 

my line of work 

fi

Sex 

Male 0.52 0.58 0.79 0.52 

Female 0.49 0.45 0.71 0.49 

Socioeconomic status 

Lowest quarter 0.81 0.79 0.97 0.82 

Middle quarters 0.47 0.50 0.75 0.48 

Highest quarter 0.67 0.77 0.92 0.65 

Composite achievement test score 

Lowest quarter 0.83 0.85 1.10 0.82 

Second quarter 0.74 0.66 1.11 0.72 Third quarter 0.60 0.69 0.95 0.58 

Highest quarter 0.61 0.77 1.02 0.60 

C-62 SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-32A. Percentage of high school sophomores who report that various life values related to family are very 

selected student characteristics: 2002 

Unimputed Imputed 

Finding right Finding right Finding right 

person to Being able to person to Being able to person to 

marry and give my marry and give my marry and 

Characteristic 

having a 

happy family 

life 

Having 

children 

children better 

opportunities than I’ve had 

having a 

happy family 

life 

Having 

children 

children better 

opportunities than I’ve had 

having a 

happy family 

life 

Sex 

Male 73.4 45.1 78.9 73.4 45.1 78.9 # 

Female 79.3 49.7 81.6 79.3 49.7 81.6 # 

Socioeconomic status 

Lowest quarter 71.6 42.0 83.4 71.4 43.5 83.6 -0.2 Middle quarters 76.5 48.0 81.9 76.6 47.3 81.6 # 

Highest quarter 81.0 51.3 73.9 80.9 51.2 74.4 -0.2 

Composite achievement test 

score 

Lowest quarter 70.3 44.5 80.3 70.4 44.2 80.6 0.1 

Second quarter 75.3 46.4 84.0 75.2 46.4 84.4 -0.1 

Third quarter 79.2 49.0 82.2 79.0 48.7 82.5 -0.2 Highest quarter 80.7 50.2 73.2 80.7 50.0 73.6 # 

# Rounds to zero. * Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-63 
Table C-32B. Standard errors for table C-32A estimates (percentage of high school sophomores who report that v

related to family are very important to them, by selected student characteristics): 2002 

Unimputed Imputed 

Being able to give 

Finding right my children Finding right 

person to marry better person to marry 

and having a opportunities than and having a 

happy family life Having children I’ve had happy family life Having childr

Sex 

Male 0.68 0.82 0.63 0.68 0

Female 0.60 0.76 0.57 0.60 0

Socioeconomic status 

Lowest quarter 0.87 1.11 0.74 0.89 1

Middle quarters 0.64 0.82 0.58 0.61 0

Highest quarter 0.84 0.97 0.87 0.87 0

Composite achievement test 

score 

Lowest quarter 0.87 1.00 0.85 0.85 0

Second quarter 0.95 1.09 0.76 0.91 1

Third quarter 0.84 1.04 0.73 0.82 1

Highest quarter 0.86 1.05 0.95 0.84 1

C-64 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-33A. Percentage of high school sophomores who report that various life values related to friendships and 

important to them, by selected student characteristics: 2002 

Unimputed Imputed 

Having Having 

Having 

leisure time 

to enjoy 

Getting 

away from Having 

leisure time 

to enjoy 

Getting 

away from Having 

Characteristic 

strong 

friendships 

own 

interests 

this area of the country 

strong 

friendships 

own 

interests 

this area of the country 

strong 

friendships 

Sex 

Male 79.3 68.8 21.5 79.3 68.8 21.5 # 

Female 86.2 67.4 21.1 86.2 67.4 21.1 # 

Socioeconomic status 

Lowest quarter 75.3 59.1 22.9 76.0 59.6 22.7 0.6 

Middle quarters 83.9 68.8 22.5 83.5 69.0 22.6 -0.3 

Highest quarter 87.7 75.3 17.2 87.7 74.4 17.4 # 

Composite achievement test 

score 

Lowest quarter 73.5 58.8 27.3 73.8 58.9 27.1 0.3* 

Second quarter 82.1 68.2 21.8 81.9 68.1 21.8 -0.2 Third quarter 86.9 71.3 19.9 86.8 71.0 19.7 -0.2 

Highest quarter 88.2 73.6 17.1 88.0 73.8 17.0 -0.2 

# Rounds to zero. * Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-65 
Table C-33B. Standard errors for table C-33A estimates (percentage of high school sophomores who report that v

related to friendships and leisure time are very important to them, by selected student characteristic

Unimputed Imputed 

Having leisure Getting away from Having leis

Characteristic 

Having strong 

friendships 

time to enjoy 

own interests 

this area of the 

country 

Having strong 

friendships 

time to en

own intere

Sex 

Male 0.58 0.70 0.64 0.58 0

Female 0.51 0.69 0.61 0.51 0

Socioeconomic status 

Lowest quarter 0.84 1.02 0.84 0.91 0Middle quarters 0.51 0.65 0.68 0.54 0Highest quarter 0.63 0.84 0.73 0.61 0

Composite achievement test 

score 

Lowest quarter 0.94 0.96 0.97 0.91 0Second quarter 0.82 0.91 0.93 0.80 0

Third quarter 0.73 0.93 0.87 0.69 0Highest quarter 0.61 0.89 0.73 0.59 0

C-66 SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-34A. Percentage of high school sophomores who report that various life values related to community are 

them, by selected student characteristics: 2002 

Unimputed Imputed 

Characteristic 

Helping other 

people in 

community 

Working to 

correct social 

and economic 

inequalities 

Living close 

to parents 

and 

relatives 

Helping other 

people in 

community 

Working to 

correct social 

and economic 

inequalities 

Living close 

to parents 

and 

relatives 

Helping other 

people in 

community 

c

a

Sex 

Male 29.9 18.7 28.0 29.9 18.7 28.0 #* 

Female 42.6 20.0 31.3 42.6 20.0 31.3 # 

Socioeconomic 

status 

Lowest quarter 38.3 25.5 35.6 38.7 25.2 35.2 0.4 

Middle quarters 35.0 17.9 29.4 35.0 18.2 29.4 # 

Highest quarter 36.9 16.0 24.3 36.6 16.0 24.8 -0.3 

Composite 

achievement test score 

Lowest quarter 41.6 28.6 40.5 41.9 28.7 40.7 0.3 

Second quarter 35.3 19.5 31.5 36.7 20.5 31.8 1.3* 

Third quarter 32.6 14.9 27.1 33.7 15.3 27.0 1.1* 

Highest quarter 32.4 13.1 19.9 33.3 13.5 20.0 0.9* 

# Rounds to zero. * Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-67 
Table C-34B. Standard errors for table C-34A estimates (percentage of high school sophomores who report that v

related to community are very important to them, by selected student characteristics): 2002 

 Unimputed Imputed 

Working to Wor

Helping other correct social and Living close to Helping other correct 

Characteristic 

people in 

community 

economic 

inequalities 

parents and 

relatives 

people in 

community 

and eco

ineq

Sex 

Male 0.63 0.60 0.69 0.63 

Female 0.70 0.65 0.70 0.70 

Socioeconomic status 

Lowest quarter 0.93 1.00 0.95 0.93 Middle quarters 0.71 0.60 0.69 0.71 

Highest quarter 0.87 0.69 0.88 0.87 

Composite achievement test 

score 

Lowest quarter 1.03 0.97 1.07 1.02 Second quarter 1.01 0.86 0.96 0.99 

Third quarter 0.97 0.76 0.95 0.92 

Highest quarter 0.88 0.64 0.86 0.88 

C-68 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-35A. Percentage of high school sophomores who expect to attain various levels of education, by selected 

student characteristics: 2002 

Unimputed 

Imputed 

Two 

High 

Two years 

or less of High 

years or 

less of High 

year

les

Characteristic 

school 

diploma 

or less 

college or 

vocational 

school 

College 

graduate 

Graduate/ 

professional 

degree 

school 

diploma 

or less 

college or 

vocational 

school 

College 

graduate 

Graduate/ 

professional 

degree 

school 

diploma 

or less 

colleg

vocati

sc

Sex 

Male 11.9 13.3 41.7 33.2 12.5 13.2 41.5 32.8 0.6* -

Female 5.6 9.7 38.0 46.7 5.8 9.7 37.8 46.6 0.2* 

Socioeconomic 

status 

Lowest 

quarter 17.4 17.7 37.3 27.6 16.8 17.0 38.2 28.0 -0.6 -

Middle 

quarters 8.1 12.1 42.2 37.6 8.9 12.4 41.5 37.2 0.8* 

Highest 

quarter 2.0 4.5 37.4 56.1 2.5 4.6 37.6 55.2 0.5 

Composite 

achievement test score 

Lowest 

quarter 23.2 20.7 35.2 20.9 24.1 20.1 35.3 20.5 0.9* -

Second 

quarter 8.3 15.7 44.6 31.4 9.1 15.3 44.9 30.8 0.7* -

Third quarter 3.5 7.8 43.1 45.5 3.7 8.0 43.1 45.2 0.2 

Highest 

quarter 1.0 3.4 35.7 59.9 1.0 3.4 35.5 60.1 0.1 

# Rounds to zero. * Denotes statistical significance at p < .05. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-69 
Table C-35B. Standard errors for table C-35A estimates (percentage of high school sophomores who expect to att

education, by selected student characteristics): 2002 

Unimputed Imputed 

Two years or Two years or 

Characteristic 

High school 

diploma or less 

less of college 

or vocational 

school 

College 

graduate 

Graduate/ 

professional 

degree 

High school 

diploma or less 

less of college or 

vocational 

school 

Sex 

Male 0.53 0.53 0.74 0.73 0.52 0.52 

Female 0.37 0.47 0.70 0.80 0.38 0.47 

Socioeconomic status 

Lowest quarter 0.89 0.81 1.09 1.00 0.82 0.75 

Middle quarters 0.41 0.47 0.74 0.78 0.43 0.48 

Highest quarter 0.32 0.42 0.96 0.98 0.35 0.42 

Composite achievement 

test score 

Lowest quarter 1.04 0.91 1.04 1.00 0.98 0.87 

Second quarter 0.58 0.92 1.15 1.10 0.60 0.85 

Third quarter 0.40 0.57 1.04 1.07 0.39 0.55 Highest quarter 0.20 0.39 1.00 1.07 0.20 0.38 

C-70 SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 
Table C-36A. Percentage of high school sophomores who report various intentions with regard to entering colleg

graduation, by selected student characteristics: 2002 

Unimputed Imputed D

After After 

Characteristic 

Right 

after high 

school 

After a 

year 

more 

than a 

year 

No/don’t 

know 

Right 

after high 

school 

After a 

year 

more 

than a 

year 

No/don’t 

know 

Right 

after high 

school 

Afte

ye

Sex 

Male 71.7 16.6 3.2 8.5 71.7 16.6 3.2 8.6 # 

Female 78.8 14.3 0.8 6.1 78.8 14.3 0.8 6.1 # 

Socioeconomic 

status 

Lowest quarter 66.9 20.2 2.5 10.4 67.2 20.1 2.6 10.2 0.3 -0

Middle quarters 73.6 16.9 1.9 7.6 73.3 17.2 1.8 7.7 -0.3 0

Highest quarter 85.4 9.0 1.4 4.3 85.7 8.7 1.4 4.2 0.4 -0

Composite 

achievement test score 

Lowest quarter 67.9 20.4 3.1 8.6 67.9 20.4 3.1 8.6 0.1 Second quarter 68.7 21.4 1.8 8.1 68.7 21.4 1.8 8.1 # -0

Third quarter 76.4 14.7 1.7 7.2 76.4 14.7 1.7 7.2 # -0

Highest quarter 85.3 7.9 1.3 5.5 85.3 7.8 1.3 5.5 # 

# Rounds to zero. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-71 
Table C-36B. Standard errors for table C-36A estimates (percentage of high school sophomores who report variou

regard to entering college after high school graduation, by selected student characteristics): 2002 

Unimputed Imputed 

Characteristic 

Right after 

high school After a year 

After more 

than a year 

No/don’t 

know 

Right after 

high school After a year 

Sex 

Male 0.75 0.60 0.31 0.49 0.75 0.60 

Female 0.67 0.57 0.13 0.35 0.67 0.57 

Socioeconomic status 

Lowest quarter 1.16 1.05 0.35 0.78 1.21 1.05 

Middle quarters 0.75 0.63 0.21 0.45 0.79 0.67 

Highest quarter 0.74 0.61 0.28 0.44 0.70 0.58 

Composite achievement test  score 

Lowest quarter 1.24 1.06 0.46 0.72 1.23 1.04 

Second quarter 1.08 0.97 0.28 0.63 1.07 0.96 

Third quarter 1.02 0.82 0.26 0.57 1.00 0.81 

Highest quarter 0.80 0.58 0.28 0.53 0.80 0.57 

C-72 SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Table C-37A. Percentage of high school sophomores who report that fathers, mothers, school counselors, and tea

the most important thing for them to do right after high school, by selected student characteristics: 

Unimputed 

Imputed 

Teacher Teacher 

or or 

School favorite School favorite 

Characteristic Father Mother counselor teacher 

Father 

Mother counselor teacher 

Father 

Mot

Sex 

Male 67.5 72.4 60.7 61.3 67.5 72.4 60.7 61.3 # Female 74.2 78.6 68.8 70.4 74.2 78.5 68.8 70.5 # 

Socioeconomic 

status 

Lowest quarter 61.1 68.6 59.9 62.9 60.8 67.9 59.7 63.0 -0.3 

Middle quarters 69.3 74.4 63.8 64.2 69.5 74.9 63.8 64.0 0.2 

Highest quarter 82.5 83.7 71.6 72.5 82.3 83.5 71.6 72.6 -0.2 

Composite 

achievement test score 

Lowest quarter 58.7 65.4 57.5 58.4 58.6 65.2 57.4 58.2 -0.1 

Second quarter 67.6 73.4 65.4 64.4 67.6 73.5 65.4 64.5 # 

Third quarter 76.1 79.9 67.5 68.8 76.0 79.7 67.5 68.7 -0.1 

Highest quarter 79.1 81.9 68.1 71.2 79.1 81.9 68.1 71.2 # 

# Rounds to zero. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “Base Year, 2002.” 

C-73 
Table C-37B. Standard errors for table C-37A estimates (percentage of high school sophomores who report that fa

school counselors, and teachers think college is the most important thing for them to do right after selected student characteristics): 2002 

C-74 

Unimputed Imputed 

Teacher or 

School favorite 

Characteristic Father Mother counselor teacher Father Mother co

Sex 

Male 0.80 0.74 0.83 0.76 0.80 0.74 

Female 0.68 0.67 0.72 0.63 0.68 0.67 

Socioeconomic status 

Lowest quarter 1.13 1.09 1.08 1.06 1.08 1.08 

Middle quarters 0.70 0.67 0.79 0.70 0.68 0.65 

Highest quarter 0.81 0.86 1.02 0.95 0.80 0.88 

Composite achievement test  score 

Lowest quarter 1.28 1.18 1.23 1.13 1.28 1.17 Second quarter 1.04 1.01 1.03 1.04 1.02 0.99 

Third quarter 0.92 0.83 1.03 1.02 0.92 0.82 

Highest quarter 0.86 0.90 1.03 1.01 0.85 0.89 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “B
Appendix C: 

Documentation for Imputed Variables 

Table C-38A. Percentage of high school sophomores’ expected occupation at age 30, by sex: 

2002 

Unimputed Imputed Difference 

Occupation Male Female Male Female Male Female 

Clerical 0.1 0.4 0.1 0.4 # # 

Craftsman 4.6 0.8 4.6 0.8 # # 

Farmer, farm manager 0.2 # 0.2 # # # 

Homemaker # 0.2 # 0.2 # # 

Laborer 0.7 # 0.7 # # # 

Manager, administrator 2.2 1.7 2.2 1.7 # # 

Military 1.7 0.2 1.7 0.2 # # 

Operative 1.1 0.1 1.1 0.1 # # 

Professional (1) 25.5 23.9 25.5 23.9 # # 

Professional (2) 11.6 28.5 11.6 28.5 # # 

Proprietor or owner 2.6 1.6 2.6 1.6 # # 

Protective service 3.3 1.2 3.3 1.2 # # 

Sales 0.8 0.3 0.8 0.3 # # 

School teacher 0.6 2.6 0.6 2.6 # # 

Service 0.4 4.6 0.4 4.6 # # 

Technical 4.5 2.2 4.5 2.2 # # 

Plan not to work 0.5 0.5 0.5 0.5 # # 

Other 1.1 0.9 1.1 0.9 # # 

Don’t know 38.4 30.3 38.4 30.3 # # 

# Rounds to zero. 

NOTE: The occupational list given to sophomores was as follows: Clerical such as bank teller, bookkeeper, 

secretary, typist, mail carrier, ticket agent; Craftsman such as baker, automobile mechanic, machinist, painter, 

plumber, telephone installer, carpenter; Farmer, farm manager; Homemaker or housewife only; Laborer such as 

construction worker, car washer, sanitary worker, farm laborer; Manager, administrator such as sales manager, office 

manager, school administrator, buyer, restaurant manager, government official; Military such as career officer, 

enlisted man or woman in the Armed Forces; Operative such as meat cutter, assembly worker, machine operator, 

welder, taxicab, bus or truck driver; Professional (1) such as accountant, artist, registered nurse, engineer, librarian, 

writer, social worker, actor, actress, athlete, politician, but not including school teacher; Professional (2) such as 

clergyman, dentist, physician, lawyer, scientist, college teacher; Proprietor or owner such as owner of small business, 

contractor, restaurant owner; Protective service such as detective, police officer or guard, sheriff, fire fighter; Sales 

such as salesperson, advertising or insurance agent, real estate broker; School teacher such as elementary or 

secondary; Service such as barber, beautician, practical nurse, private household worker, janitor, waiter; Technical 

such as draftsman, medical or dental technician, computer programmer; Plan not to work; and Other. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-75 
Appendix C: 

Documentation for Imputed Variables 

Table C-38B. Standard errors for table C-38A estimates (percentage of high school sophomores’ 

expected occupation at age 30, by sex): 2002 

Unimputed Imputed 

Occupation Male Female Male Female 

Clerical 0.04 0.10 0.04 0.10 

Craftsman 0.32 0.13 0.32 0.13 

Farmer, farm manager 0.06 0.03 0.06 0.03 

Homemaker 0.01 0.06 0.01 0.06 

Laborer 0.12 # 0.12 # 

Manager, administrator 0.22 0.19 0.22 0.19 

Military 0.19 0.08 0.19 0.08 

Operative 0.19 0.04 0.19 0.04 

Professional (1) 0.70 0.61 0.70 0.61 

Professional (2) 0.49 0.61 0.49 0.61 

Proprietor or owner 0.23 0.19 0.23 0.19 

Protective service 0.32 0.15 0.32 0.15 

Sales 0.14 0.08 0.14 0.08 

School teacher 0.12 0.24 0.12 0.24 

Service 0.09 0.33 0.09 0.33 

Technical 0.32 0.22 0.32 0.22 

Plan not to work 0.11 0.10 0.11 0.10 

Other 0.14 0.16 0.14 0.16 

Don’t know 0.73 0.68 0.73 0.68 

# Rounds to zero. 

NOTE: The occupational list given to sophomores was as follows: Clerical such as bank teller, bookkeeper, 

secretary, typist, mail carrier, ticket agent; Craftsman such as baker, automobile mechanic, machinist, painter, 

plumber, telephone installer, carpenter; Farmer, farm manager; Homemaker or housewife only; Laborer such as 

construction worker, car washer, sanitary worker, farm laborer; Manager, administrator such as sales manager, office 

manager, school administrator, buyer, restaurant manager, government official; Military such as career officer, 

enlisted man or woman in the Armed Forces; Operative such as meat cutter, assembly worker, machine operator, 

welder, taxicab, bus or truck driver; Professional (1) such as accountant, artist, registered nurse, engineer, librarian, 

writer, social worker, actor, actress, athlete, politician, but not including school teacher; Professional (2) such as 

clergyman, dentist, physician, lawyer, scientist, college teacher; Proprietor or owner such as owner of small business, 

contractor, restaurant owner; Protective service such as detective, police officer or guard, sheriff, fire fighter; Sales 

such as salesperson, advertising or insurance agent, real estate broker; School teacher such as elementary or 

secondary; Service such as barber, beautician, practical nurse, private household worker, janitor, waiter; Technical 

such as draftsman, medical or dental technician, computer programmer; Plan not to work; and Other. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002.” 

C-76 
Table C-39A. Comparison of estimates between ELS:2002 imputed and unimputed data, NELS:88 data, and HS&B 

student characteristics: 1980, 1990, and 2002 ELS:2002 ELS:2002 ELS:2002 ELS:2002 

Variable Unimputed Imputed NELS:88 HS&B unimputed –NELS:88 imputed– NELS:88 Change in significance? unimputed –HS&B 

Father’s education (composite) 

Did not finish high school 13.6 13.9 15.2 22.6 -1.6 -1.3 No -9.0* 

Graduated from high school or GED 29.9 30.1 25.8 31.1 4.1* 4.3* No -1.2 

Some postsecondary education (PSE) 27.7 27.4 33.3 23.5 -5.6* -5.9* No 4.2* 

 Graduated from college 16.9 16.7 14.2 12.3 2.7* 2.5* No 4.6* 

Completed master’s or equivalent 7.5 7.4 6.5 6.2 1.0 0.9 No 1.3* 

Completed Ph.D., M.D., or other 

 advanced degree 4.5 4.4 5.0 4.3 -0.5 -0.6 No 0.2 

Mother’s education (composite) 

Did not finish high school 12.9 13.2 13.0 17.8 -0.1 0.2 No -4.9* 

Graduated from high school or GED 27.8 27.9 30.8 46.5 -3.0* -2.9* No -18.7* Some postsecondary education (PSE) 34.8 34.6 39.0 21.9 -4.2* -4.4* No 12.9* 

 Graduated from college 16.7 16.6 11.9 9.1 4.8* 4.7* No 7.6* 

Completed master’s or equivalent 6.0 6.0 4.5 3.4 1.5* 1.5* No 2.6* 

Completed Ph.D., M.D., or other 

 advanced degree 1.7 1.7 0.7 1.3 1.0* 1.0* No 0.4 

Native language1

 English 86.2 86.0 90.2 94.6 -4.0* -4.2* No -8.4* 

Non-English 13.8 14.0 9.8 5.4 4.0* 4.2* No 8.4* 

IRT-estimated number-right score in 

mathematics 37.2 37.2 36.5 32.8 0.7* 0.7* No 4.4* 

Probability of proficiency in reading2

 Level 1 89.0 89.4 91.1 † -2.1* -1.7* No †  Level 2 46.0 46.2 49.9 † -3.9* -3.7* No † 

 Level 3 8.5 8.3 12.7 † -4.2* -4.4* No † 

See notes at end of table. 

C-77 
Table C-39A. Comparison of estimates between ELS:2002 imputed and unimputed data, NELS:88 data, and HS&B 

student characteristics: 1980, 1990, and 2002—Continued 

C-78 

ELS:2002 

ELS:2002 ELS:2002 ELS:2002 

Variable Unimputed Imputed NELS:88 HS&B 

unimputed 

–NELS:88 

imputed– 

NELS:88 

Change in 

significance? 

unimputed 

–HS&B 

Probability of proficiency in mathematics3

 Level 1 91.4 91.7 90.7 † 0.7 1.0* Yes † 

 Level 2 66.6 67.1 63.0 † 3.6* 4.1* No † 

 Level 3 46.4 46.4 43.5 † 2.9* 2.9* No † 

 Level 4 20.8 20.4 19.0 † 1.8* 1.4* No † 

 Level 5 1.0 1.0 0.4 † 0.6* 0.6* No † 

Family composition Mother 

and father 57.4 56.8 67.2 70.2 -9.8* -10.4* No -12.8* 

Mother 

and guardian 13.3 13.4 11.2 6.9 2.1* 2.2* No 6.4* 

 Father and guardian 3.1 3.2 2.7 2.1 0.4 0.5 No 1.0* 

Mother 

only 18.9 19.0 13.9 15.5 5.0* 5.1* No 3.4* 

 Father only 3.2 3.2 2.5 3.1 0.7 0.7 No 0.1 

 Other relative or nonrelative 4.1 4.3 2.5 2.2 1.6* 1.8* No 1.9* 

Student’s educational expectations 

High school or less 8.7 9.2 10.2 26.5 -1.5* -1.0 Yes -17.8* 

 Some college 11.5 11.5 30.3 32.9 -18.8* -18.8* No -21.4* 

 College graduation 39.8 39.7 32.1 22.7 7.7* 7.6* No 17.1* 

Graduate or professional degree 40.0 39.7 27.4 17.9 12.6* 12.3* No 22.1* 

High school program  General 38.4 38.6 49.6 46.0 -11.2* -11.0* No -7.6*  Academic/college preparatory 50.9 50.7 39.3 33.1 11.6* 11.4* No 17.8* 

Vocational 

10.8 10.8 11.1 21.0 -0.3 -0.3 No -10.2* 

† Not applicable. * Denotes statistical significance at p < .05. 1 The first language students learned to speak when they were children. 

2 Level 1 = simple comprehension; level 2 = simple inference; level 3 = complex inference. 3 Level 1 = simple arithmetic operations on whole numbers; level 2 = simple operations with decimals, fractions, powers, and roots; level 3 = simple proble

understanding of low-level mathematical concepts; level 4 = understanding of intermediate-level mathematical concepts and/or having the ability to formuword problems; level 5 = proficiency in solving complex multistep word problems and/or the ability to demonstrate knowledge of mathematics material foucourses. NOTE: IRT = Item Response Theory. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002); National Education 

(NELS:88); High School and Beyond (HS&B). 

C-78 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-39B. Standard errors for table C-39A estimates (comparison of estimates between 

ELS:2002 imputed and unimputed data, NELS:88 data, and HS&B data, by selected 

student characteristics): 1980, 1990, and 2002 

ELS:2002 

Variable Unimputed Imputed NELS:88 HS&B 

Father’s education (composite) 

Did not finish high school 0.57 0.54 0.62 0.53 

Graduated from high school or GED 0.59 0.53 0.61 0.49 

Some postsecondary education (PSE) 0.52 0.48 0.63 0.40 

Graduated from college 0.46 0.43 0.49 0.38 

Completed master’s or equivalent 0.33 0.30 0.41 0.25 

Completed Ph.D., M.D., or other advanced degree 0.28 0.26 0.38 0.26 

Mother’s education (composite) 

Did not finish high school 0.53 0.54 0.54 1.14 

Graduated from high school or GED 0.50 0.49 0.64 1.28 

Some postsecondary education (PSE) 0.54 0.53 0.68 1.00 

Graduated from college 0.48 0.46 0.46 0.75 

Completed master’s or equivalent 0.28 0.27 0.25 0.44 

Completed Ph.D., M.D., or other advanced degree 0.15 0.15 0.08 0.30 

Native language1 

English 0.60 0.60 0.68 0.31 

Non-English 0.60 0.60 0.68 0.31 

IRT-estimated number-right score in mathematics 0.23 0.23 0.21 0.22 

Probability of proficiency in reading2 

Level 1 0.40 0.39 0.40 † 

Level 2 0.72 0.70 0.70 † 

Level 3 0.29 0.28 0.50 † 

Probability of proficiency in mathematics3 

Level 1 0.31 0.30 0.30 † 

Level 2 0.77 0.77 0.80 † 

Level 3 0.82 0.81 0.80 † 

Level 4 0.56 0.54 0.50 † 

Level 5 0.08 0.08 # † 

Family composition 

Mother and father 0.58 0.57 0.72 0.49 

Mother and guardian 0.37 0.36 0.45 0.29 

Father and guardian 0.18 0.16 0.32 0.10 

Mother only 0.46 0.44 0.48 0.37 

Father only 0.21 0.20 0.31 0.12 

Other relative or nonrelative 0.22 0.21 0.22 0.12 

See notes at end of table. 

C-79 
 

 

 

 

 

 

 

 

 

 

 

Appendix C: 

Documentation for Imputed Variables 

Table C-39B. Standard errors for table C-39A estimates (comparison of estimates between 

ELS:2002 imputed and unimputed data, NELS:88 data, and HS&B data, by selected 

student characteristics): 1980, 1990, and 2002—Continued 

ELS:2002 

Variable Unimputed Imputed NELS:88 HS&B 

Student’s educational expectations 

High school or less 0.36 0.36 0.42 0.50 

Some college 0.38 0.37 0.65 0.39 

College graduation 0.50 0.50 0.59 0.38 

Graduate or professional degree 0.62 0.60 0.64 0.40 

High school program 

General 0.63 0.63 0.95 0.71 

Academic/college preparatory 0.68 0.68 0.96 0.74 

Vocational 0.46 0.46 0.37 0.61 

† Not applicable. 

# Rounds to zero. 

1 The first language students learned to speak when they were children. 

2

 Level 1 = simple comprehension; level 2 = simple inference; level 3 = complex inference. 

3

 Level 1 = simple arithmetic operations on whole numbers; level 2 = simple operations with decimals, fractions, 

powers, and roots; level 3 = simple problem solving, requiring the understanding of low-level mathematical concepts; 

level 4 = understanding of intermediate-level mathematical concepts and/or having the ability to formulate multistep 

solutions to word problems; level 5 = proficiency in solving complex multistep word problems and/or the ability to 

demonstrate knowledge of mathematics material found in advanced mathematics courses. 

NOTE: IRT = Item Response Theory. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002); National Education Longitudinal Study of 1988 (NELS:88); High School and Beyond (HS&B). 

C-80 
Appendix D 

Public-Use Masked/Suppressed Variables 

Available on Restricted Files for Licensed Users 

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Appendix D: 

Public-Use Marked/Suppressed Variables on Restricted Files 

Appendix D 

Public-Use Masked/Suppressed Variables Available on 

Restricted Files for Licensed Users 

The restricted-use electronic codebook (ECB) files contain all variables on the public-use 

file. However, to protect confidentiality, versions may differ in the amount of available detail 

(e.g., a given variable may appear in categorical form in the public-use file but appear in 

continuous form in the restricted-use file, or it may include additional breakouts of collapsed 

categories, such as a restricted-use breakout for Native Hawaiians). In addition, a number of 

variables appear on the restricted file that have no counterpart on the public-use files (e.g., 

various geocode variables below the level of the four U.S. Census regions reported on the publicuse file). The list provided in table D-1 follows the variable position order on the ECB. 

Table D-1. Restricted-use unique variables in base-year to first follow-up student-level and 

school-level megafiles: 2004 

Student-level restricted-use only variables 

F1UNIVR1 Sample member status in BY and F1 rounds (restricted) 

F1UNIVR2 How sample member entered study 

BYEXPWT Student expanded sample weight 

BYRACE_R Student’s race/ethnicity—composite (restricted) 

BYRACE2 Student’s race/ethnicity—64 category 

BYSARACE Student’s race/ethnicity—school roster 

BYRACE_1 Student is White—composite 

BYRACE_2 Student is Black or African American—composite 

BYRACE_3 Student is Asian—composite 

BYRACE_4 Student is Native Hawaiian/Pacific Islander—composite 

BYRACE_5 Student is American Indian/Alaska Native—composite 

BYHISPAN Student’s Hispanic subgroup—composite 

BYASIAN Student’s Asian subgroup—composite 

BYDOB_R Student’s date of birth: Year-month-day 

BYPARACR Parent’s race/ethnicity—composite (restricted) 

BYQXDATR Date of base-year student questionnaire administration 

PISARFLG Whether included in PISA reading score concordance sample 

PISAMFLG Whether included in PISA math score concordance sample 

BYIEPTYP Federal disability category for base-year IEPs 

BYACCTYP Base-year questionnaire/test accommodations 

BYTXMTH Math test theta T score 

BYTXMTI1 Math theta T score—multiple imputation value 1 of 5 

BYTXMTI2 Math theta T score—multiple imputation value 2 of 5 

BYTXMTI3 Math theta T score—multiple imputation value 3 of 5 

BYTXMTI4 Math theta T score—multiple imputation value 4 of 5 

BYTXMTI5 Math theta T score—multiple imputation value 5 of 5 

BYTXRTH Reading test theta T score 

BYTXRTI1 Reading theta T score—multiple imputation value 1 of 5 

BYTXRTI2 Reading theta T score—multiple imputation value 2 of 5 

BYTXRTI3 Reading theta T score—multiple imputation value 3 of 5 

See note at end of table. 

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Appendix D: 

Public-Use Marked/Suppressed Variables on Restricted Files 

Table D-1. Restricted-use unique variables in base-year to first follow-up student-level and 

school-level megafiles: 2004—Continued 

Student-level restricted-use only variables—Continued 

BYTXRTI4 Reading theta T score—multiple imputation value 4 of 5 

BYTXRTI5 Reading theta T score—multiple imputation value 5 of 5 

BYRESZIP Residential zip code for student/family 

BYSF1R_R 1st friend’s race (restricted) 

BYSF2R_R 2nd friend’s race (restricted) 

BYSF3R_R 3rd friend’s race (restricted) 

BYERAC_R English teacher’s race/ethnicity—composite (restricted) 

BYMRAC_R Math teacher’s race/ethnicity—composite (restricted) 

BYG10ER Grade 10 enrollment—2001–02 school roster 

BYCENDIV Census division of school locale 

BYSTATE State code for school locale 

BYCOUNTY County code for school locale 

BYSCHZIP School zip code 

BYHISPIM Imputation flag—HISPANIC 

BYASNIM Imputation flag—ASIAN 

F1EXPWT F1 expanded sample weight 

F1XPNLWT F1 expanded sample panel weight 

F1DOB_R F1 student’s date of birth: Year-month-day 

 F1ESSTAT F1 expanded sample status 

F1EXPFLG F1 expanded sample member dropout 

F1DOFLG F1 dropout status in spring term 2004 

F1RDSTAT F1 dropout status (restricted) 

F1SEPS03 Date separated from BY school—spring 2003 

F1SEPF03 Date separated from BY school—fall 2003 

F1SEPS04 Date separated from BY school—spring 2004 

F1TXMTH F1 math theta T score (restricted) 

F1TXMTI1 F1 math theta T score—multiple imputation value 1 of 5 

F1TXMTI2 F1 math theta T score—multiple imputation value 2 of 5 

F1TXMTI3 F1 math theta T score—multiple imputation value 3 of 5 

F1TXMTI4 F1 math theta T score—multiple imputation value 4 of 5 

F1TXMTI5 F1 math theta T score—multiple imputation value 5 of 5 

F1RESZIP F1 residential zip code for student/family 

F1TRSZIP F1 zip code of the spring 2004 destination schools of transfer students 

 F1QXDATR Date completed interview 

F1HISPIM Imputation flag—F1HISPAN 

F1ASNIM Imputation flag—F1ASIAN 

BYS16 Student’s Hispanic subgroup 

BYS17A Student is White 

BYS17B Student is Black/African American 

BYS17C Student is Asian 

BYS17D Student is Native Hawaiian/Pacific Islander 

BYS17E Student is American Indian/Alaska Native 

BYS18 Student’s Asian subgroup 

BYS25CAA 1st friend is White 

BYS25CAB 1st friend is Black/African American 

BYS25CAC 1st friend is Asian 

See note at end of table. 

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Appendix D: 

Public-Use Marked/Suppressed Variables on Restricted Files 

Table D-1. Restricted-use unique variables in base-year to first follow-up student-level and 

school-level megafiles: 2004—Continued 

Student-level restricted-use only variables—Continued 

BYS25CAD 1st friend is Native Hawaiian/Pacific Islander 

BYS25CAE 1st friend is American Indian/Alaska Native 

BYS25CBA 2nd friend is White 

BYS25CBB 2nd friend is Black/African American 

BYS25CBC 2nd friend is Asian 

BYS25CBD 2nd friend is Native Hawaiian/Pacific Islander 

BYS25CBE 2nd friend is American Indian/Alaska Native 

BYS25CCA 3rd friend is White 

BYS25CCB 3rd friend is Black/African American 

BYS25CCC 3rd friend is Asian 

BYS25CCD 3rd friend is Native Hawaiian/Pacific Islander 

BYS25CCE 3rd friend is American Indian/Alaska Native 

BYS63 Occupation expects to have after high school—verbatim 

BYS64 Occupation expects to have at age 30—verbatim 

BYS68 Student’s native language 

BYS81A Mother/female guardian’s occupation—verbatim 

BYS81B Mother/female guardian’s main job duties—verbatim 

BYS82A Father/male guardian’s occupation—verbatim 

BYS82B Father/male guardian’s main job duties—verbatim 

 F1N14A Mother/female guardian’s occupation—verbatim 

F1N14B Mother/female guardian’s main job duties—verbatim

 F1N15A Father/male guardian’s occupation—-verbatim 

F1N15B Father/male guardian’s main job duties—verbatim 

F1S51A 1st postsecondary school applied to 

F1S51B City of 1st postsecondary school applied to 

F1S51D 2nd postsecondary school applied to 

F1S51E City of 2nd postsecondary school applied to 

F1S56 Occupation expects to have after high school—verbatim 

F1S57 Occupation expects to have at age 30—verbatim 

F1T16EA Other reasons for transferring 

F1E24A Other way in which GED was earned (EG) 

F1E50 Current/most recent job or occupation (EG) 

F1D42 Program in which GED was earned (DO) 

F1D42A Other way in which GED was earned (DO) 

F1D60 Current/most recent job or occupation (DO) 

F1D66 Occupation expects to have at age 30—verbatim (DO) 

BYP14 Parent’s Hispanic subgroup 

BYP15A Parent is White 

BYP15B Parent is Black or African American 

BYP15C Parent is Asian 

BYP15D Parent is Native Hawaiian/Pacific Islander 

BYP15E Parent is American Indian/Alaska Native 

BYP16 Parent’s Asian subgroup 

BYP19A Mother’s occupation before coming to US 

BYP19B Mother’s main job duties outside US 

BYP22A Father’s occupation before coming to US 

BYP22B Father’s job main duties outside US 

BYP29 Native language of parent respondent 

See note at end of table. 

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Appendix D: 

Public-Use Marked/Suppressed Variables on Restricted Files 

Table D-1. Restricted-use unique variables in base-year to first follow-up student-level and 

school-level megafiles: 2004—Continued 

Student-level restricted-use only variables—Continued 

BYP39A Parent’s current/most recent job for pay in US 

BYP39B Parent’s main job duties 

BYP43A Spouse/partner’s current/most recent job for pay in US 

BYP43B Spouse/partner’s main job duties 

BYTE24A Teacher is White (English) 

BYTE24B Teacher is Black/African American (English) 

BYTE24C Teacher is Asian (English) 

BYTE24D Teacher is Native Hawaiian/Pacific Islander (English) 

BYTE24E Teacher is American Indian/Alaska Native (English) 

BYTM24A Teacher is White (math) 

BYTM24B Teacher is Black/African American (math) 

BYTM24C Teacher is Asian (math) 

BYTM24D Teacher is Native Hawaiian/Pacific Islander (math) 

BYTM24E Teacher is American Indian/Alaska Native (math) 

School-level restricted-use only variables 

BYSCMDST Base-year library media center questionnaire status 

BYG10ER Grade 10 enrollment—2001–02 school roster 

BYCENDIV Census division of school locale 

BYSTATE State code for school locale 

BYCOUNTY County code for school locale 

BYSCHZIP School zip code 

BYNCESDI NCES school district ID number 

BYNCESSI School identification number from CCD or PSS 

BYA01 Total student enrollment as of October 2001 

BYA02A School has prekindergarten 

BYA02B School has kindergarten 

BYA02C School has 1st grade 

BYA02D School has 2nd grade 

BYA02E School has 3rd grade 

BYA02F School has 4th grade 

BYA02G School has 5th grade 

BYA02H School has 6th grade 

BYA02I School has 7th grade 

BYA02J School has 8th grade 

BYA02K School has 9th grade 

BYA02L School has 10th grade 

BYA02M School has 11th grade 

BYA02N School has 12th grade 

BYA02O School has 13th grade or higher 

BYA03A Comprehensive public school 

BYA03B Public magnet school 

BYA03C Public magnet school with theme 

BYA03D Public school of choice 

BYA03E Year-round school 

BYA03F Area vocational school/center 

BYA03G Full-time technical/vocational school 

BYA03H Other technical or vocational school 

BYA03I Catholic diocesan school 

See note at end of table. 

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Appendix D: 

Public-Use Marked/Suppressed Variables on Restricted Files 

Table D-1. Restricted-use unique variables in base-year to first follow-up student-level and 

school-level megafiles: 2004—Continued 

School-level restricted-use only variables—Continued 

BYA03J Catholic parish 

BYA03K Catholic religious order 

BYA03L Catholic independent school 

BYA03M Other private school with religious affiliation 

BYA03N Private school without religious affiliation 

BYA03O Boarding school 

BYA03P Indian reservation school 

BYA03Q Military academy 

BYA03R Alternative/dropout prevention/continuation school 

BYA03S Charter school 

BYA21 % 10th-graders receive free/reduced-price lunch 

BYA22A # of full-time teachers 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, 2002” and “First Follow-up, 2004.” 

D-7 

Appendix E 

Glossary of Terms 

E-1 

Appendix E: 

Glossary of Terms 

Appendix E 

Glossary of Terms 

Accommodations (testing): In ELS:2002, certain accommodations were offered to students 

with barriers to participation, such as students with disabilities or English-language learners with 

limited English proficiency. An accommodation is a change in how a test is presented, in how a 

test is administered, or in how the test taker is allowed to respond. This term generally refers to 

changes that do not substantially alter what the test measures. The proper use of 

accommodations does not substantially change academic level or performance criteria. 

Appropriate accommodations are made to provide equal opportunity to demonstrate knowledge. 

Examples of test accommodations include allowing extra time, use of a large-print version of a 

test, or conveying instructions in sign language. Cases in which accommodations were 

implemented in ELS:2002 are specially flagged (the indicators are BYTXACC and F1TXACC). 

Adaptive testing: In the ELS:2002 base year, multiple test forms of varying levels of difficulty 

were assigned based on the examinee’s score on a routing test. Thus, the specific sequence of 

questions that each student answered was tailored to that student’s ability level. An advantage of 

adaptive tests is that reliability per unit of testing time is greater than in a nonadaptive test. 

Adaptive procedures help to minimize floor and ceiling effects (see “Ceiling effect” and “Floor 

effect”). ELS:2002 adaptive testing relies on Item Response Theory (see “IRT”) assumptions to 

place students who have taken different test forms on the same vertical score scale. In the first 

follow-up, each student’s test form was assigned on the basis of base-year test performance. 

American Indian or Alaska Native: An American Indian or Alaska Native is a person who has 

origins in any of the original peoples of North and South America (including Central America) 

and who maintains tribal affiliation or community attachment. 

Asian: An Asian is a person having origins in any of the original peoples of the Far East, 

Southeast Asia, or the Indian subcontinent, including, for example, Cambodia, China, India, 

Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam. 

Base weights: See “Design weights.” 

Bias: Bias is the difference between the reported value and the true value. Thus, the bias of an 

estimate is the difference between the expected value of a sample estimate and the corresponding 

true value for the population. Response bias is the difference between respondent reports and 

their behavior or characteristics. Nonresponse bias is the difference that occurs when 

respondents differ as a group from nonrespondents on a characteristic being studied. Sample 

bias is the unequal selection or the omission of members of the population, without appropriate 

weighting. Relatedly, undercoverage bias arises because some portion of the potential sampling 

frame is missed or excluded, or there are duplicate units. For example, if the school list from 

which a school sample is drawn is incomplete or inaccurate (owing, for example, to the birth of 

new schools subsequent to the time the list was drawn up), school undercoverage may occur. 

(See also “Nonresponse bias” and “Bias analysis.”) 

Bias analysis: Nonresponse bias analysis compares the characteristics of respondents and 

nonrespondents. Both unit nonresponse (school, student) and item nonresponse on 

E-3 
Appendix E: 

Glossary of Terms 

questionnaires were subject to bias analyses in ELS:2002. For example, certain key data items 

were obtained for both responding and nonresponding schools, so that a school nonresponse 

analysis could be conducted and bias in school-level estimates quantified. 

Black or African American: A person having origins in any of the Black racial groups of 

Africa. 

Burden: Formally, burden is the aggregate hours realistically required for data providers to 

participate in a data collection. Burden also has a subjective or psychological dimension: the 

degree to which providing information is regarded as onerous may depend on the salience to the 

respondent of the questions that are being posed and on other factors, such as competing time 

demands. 

Carnegie unit: A standard of measurement used for secondary education that represents the 

completion of a course that meets one period per day for 1 year. 

CAPI: Computer-assisted personal interviewing, in which the questionnaire is loaded into a 

field interviewer’s laptop computer. 

CATI: Computer-assisted telephone interviewing. 

CCD: Common Core of Data. Data annually collected from all public schools in the United 

States by NCES. Data from the CCD supplied the public school sampling frame for the 

ELS:2002 base year. 

CD-ROM: ELS:2002 data are distributed primarily in an optical laser disc medium, 

specifically, CD-ROM (Compact Disc Read-Only Memory). A CD-ROM is a computer storage 

disc in the same physical form as an audio CD; it can store approximately 650 megabytes of 

digital data. 

Ceiling effect: The result of a test having insufficient numbers of the more difficult items. In a 

longitudinal study, ceiling effects in the follow-up can cause change scores to be artificially 

constrained for high-ability examinees. The measurement problems related to floor and ceiling 

effects in combination with regression effects found at the extreme score ranges seriously 

hamper the accuracy of change measures in longitudinal studies. More information (i.e., smaller 

error of measurement) is obtained with respect to ability level if high-ability individuals receive 

relatively harder items (and if low-ability individuals receive proportionately easier items). The 

matching of item difficulty to a person’s ability level yields increased reliability at the extremes 

of the score distribution, where it is most needed for studies of longitudinal change. A strategy 

employed in ELS:2002 to minimize ceiling (and floor) effects is to employ test forms that are 

“adaptive” to the ability level of the examinee. Multilevel tests—with second stage test 

assignment that is based on the first stage (routing test) performance work—minimize the 

possibility that ceiling effects might bias the estimates of the score gains. (See also “Floor 

effect” and “Adaptive testing.”) 

Classical test theory: Classical test theory postulates that a test score can be decomposed into 

two parts—a true score and an error component; that the error component is random with a mean 

E-4 
Appendix E: 

Glossary of Terms 

of zero and is uncorrelated with true scores; and that true scores, observed scores, and error 

components are linearly related. 

Closed-ended: A type of question in which the data provider’s responses are limited to given 

alternatives (as opposed to an open-ended question). (See also “Open-ended.”) 

Clustering: A sample selection method in which small geographical areas such as schools (as is 

the case in ELS:2002), school districts, counties, or residential blocks are selected as an initial 

stage, with individuals selected in a subsequent step. (See also “Primary sampling unit.”) 

Cluster size: The number of ELS:2002 sample members attending a particular high school. 

Codebook: Documentation of each variable being measured, including variable name, columns 

occupied by each variable in the data matrix, values used to define each variable, unweighted 

frequencies, unweighted percents, and weighted valid percents. (See “Electronic codebook.”) 

Coefficient of variation: The ratio of the standard deviation of an estimate to the value of the 

estimate. 

Cognitive test battery: One of the two parts of the student survey (the second part being the 

student questionnaire). Two achievement areas (mathematics and reading) were measured in the 

base year. Mathematics achievement will be measured again in the first follow-up. 

Cohort: A group of individuals who have a statistical factor in common—for example, year of 

birth, grade in school, or year of high school graduation. ELS:2002 is a sophomore-grade cohort 

based on the spring term of the 2001–02 school year. It also contains, however, a nationally 

representative sample of high school seniors in the spring term of the 2003–04 school year (see 

“Freshening”). In contrast, the Program for International Student Assessment (PISA) is an age 

cohort, based on students who were 15.25 years of age in April of 2000 or 2003. 

Composite variable: A composite variable is one that is either constructed through the 

combination of two or more variables (socioeconomic status, for example, combines mother’s 

education, father’s education, mother’s occupation, father’s occupation, and family income) or 

calculated through the application of a mathematical function or transformation to a variable 

(e.g., conversion of raw test scores to percentile ranks). Also called a derived variable, created 

variable, or constructed variable. 

Concordance: Concordance is a weaker form of test linkage than equating in that the link is 

based on population distributions rather than the equivalence of interchangeable scores. 

Implementation of PISA scale scores in ELS:2002 was through a method of concordance. (See 

also “Equating” and “Equated test score.”) 

Confidence interval: A sample-based estimate expressed as an interval or range of values 

within which the true population value is expected to be located (with a specified degree of 

confidence). 

Confidentiality protections: NCES is required by law to protect individually identifiable data 

from unauthorized disclosure. To this end, the ELS:2002 data have been subject to a disclosure 

E-5 
Appendix E: 

Glossary of Terms 

risk analysis to determine which records require masking to produce the public-use data file from 

the restricted-use data file. Disclosure coarsening techniques (such as recoding of continuous 

variables into categorical, top and bottom coding, and so on) and data perturbation techniques 

(e.g., data swapping) have been used to provide disclosure protection to the ELS:2002 data. (See 

also “Data swapping” and “Disclosure risk analysis.”) 

Consent, active (explicit): One variety of informed consent is called active or explicit consent. 

Typically, in active consent, a signed agreement to participate in a study must be obtained. In 

ELS:2002, permission of parents was required before students could be surveyed. Some schools 

required active parental consent (i.e., that a signed permission form be obtained). 

Consent, passive (implied): Another variety of informed consent is called passive or implied 

consent. In passive consent, a permission form is sent to the relevant party (in ELS:2002, 

normally the parent or guardian of the sampled student), who has the opportunity to return the 

form to indicate denial of permission. If the form is not returned, it is assumed that the 

individual has no objection to survey participation. In ELS:2002, most schools allowed passive 

parental consent for their child’s participation in the study. 

Constructed response item: In the ELS:2002 assessment battery in the base year, a nonmultiple-choice item that required some type of written response. 

Contextual data: In ELS:2002, the primary unit of analysis is the student, and information from 

the other study components, referred to as contextual data, should be viewed as extensions of the 

student data. For example, observations made in school administrator, teacher, librarian, and 

parent reports on the student’s school learning environment or home situation would be 

considered contextual data. 

Coverage rate: In ELS:2002 base-year contextual samples, the proportion of the responding 

student sample with a report from a given contextual source (e.g., the parent survey, the teacher 

survey, or the school administrator survey). For the teacher survey, the student coverage rate can 

be calculated as either the percentage of participating students with two teacher reports or the 

percentage with at least one teacher report. The teacher and parent surveys in ELS:2002 are 

purely contextual. The base-year school-level surveys (school administrator, library media 

center, facilities checklist) can be used contextually (with the student as the unit of analysis) or in 

standalone fashion (with the school as the unit of analysis). (See “Response rate.”) Finally, test 

completions (reading assessments, mathematics assessments) are also calculated on a base of the 

student questionnaire completers (in the first follow-up, for the in-school student sample only), 

rather than on the entire sample, and thus express a coverage rate. “Coverage” can also refer to 

the issue of missed target population units on the sampling frame (undercoverage), or duplicated 

or erroneously enumerated units (overcoverage) (see “Bias” for discussion of undercoverage 

bias). 

Cross-sectional analysis: A cross-sectional design represents events and statuses at a single 

point in time. For example, a cross-sectional survey may measure the cumulative educational 

attainment (achievements, attitudes, statuses) of students at a particular stage of schooling, such 

as 10th or 12th grade. In contrast, a longitudinal survey (or repeated measurement of the same 

sample units) measures the change or growth in educational attainment that occurs over a 

E-6 
Appendix E: 

Glossary of Terms 

particular period of schooling. The longitudinal design of ELS:2002 generates two 

representative cross sections (high school sophomores in 2002 and, through sample freshening, 

seniors in 2004). It also permits analysis of individual-level change over time through 

longitudinal analysis and of group-level and intercohort change through the cross-sectional 

comparisons to past studies of similarly defined grade cohorts. (See also “Longitudinal or panel 

survey” and “Cross-cohort analysis.”) 

Cross-cohort (or intercohort) analysis: The ELS:2002 base-year and first follow-up surveys 

contained many data elements that were comparable to items from prior studies. These repeated 

items will supply a basis for comparison with earlier sophomore cohorts (such as 1980 

sophomores in the High School and Beyond [HS&B] longitudinal study and 1990 sophomores in 

the National Education Longitudinal Study of 1988 [NELS:88]). With a freshened senior 

sample, the ELS:2002 first follow-up supports comparisons to 1972 (National Longitudinal 

Study of the High School Class of 1972 [NLS-72]), 1980 (HS&B), and 1992 (NELS:88). The 

first follow-up academic transcript component will offer a further opportunity for cross-cohort 

comparisons with the high school transcript studies of HS&B, NELS:88, and the National 

Assessment of Educational Progress (NAEP). With three or more timepoints, trend analyses are 

possible. With ELS:2002, this condition has now been met for both the sophomore and senior 

cohorts. Essentially, three kinds of intercohort comparison are possible. First, cohorts can be 

compared on an intergenerational or cross-cohort time-lag basis. Both cross-sectional and 

longitudinal time-lag comparisons may be made. An example of a cross-sectional time-lag 

comparison would be looking at the status of HS&B (1980), NELS:88 (1990), and ELS:2002 

(2002) sophomores to see how the situation of sophomores has changed over time. An example 

of longitudinal time-lag comparison would be an examination of the magnitude and correlates of 

achievement gain of HS&B, NELS:88, and ELS:2002 sophomores over the last 2 years of high 

school. Second, fixed-time comparisons are also possible, in which groups within each study are 

compared at different ages but the same point in time (e.g., the NLS-72, HS&B senior, and 

HS&B sophomore cohorts all could be looked at in 1986, some 14, 6, and 4 years after each 

respective cohort graduated from high school). Such a perspective would permit one to compare, 

for example, employment rates for 22-, 24-, and 32-year-old high school graduates. Finally, 

longitudinal comparative analysis of the cohorts can be performed by modeling the history of the 

grade cohorts. 

Data element: The most basic unit of information. In data processing, it is the fundamental 

data structure. It is defined by its size (in characters) and data type (e.g., alphanumeric, numeric 

only, true/false, date) and may include a specific set of values or range of values. 

Data swapping: Data swapping is defined in the NCES Statistical Standards (Seastrom 2003) 

as a perturbation disclosure limitation technique that results in a confidentiality edit. An 

example of data swapping would be to assume a data file has two potential individual identifying 

variables, for example, sex and age. If a sample case needs disclosure protection, it is paired 

with another sampled case so that each element of the pair has the same age, but different sexes. 

The data on these two records are then swapped. After the swapping, anyone thinking they have 

identified either one of the paired cases gets the data of the other case, so they have not made an 

accurate match and the data have been protected. (See also “Confidentiality protections.”) 

E-7 
Appendix E: 

Glossary of Terms 

Design effect: A measure of sample efficiency. The design effect (DEFF) is the variance of an 

estimate divided by the variance of the estimate that would have occurred if a sample of the same 

size had been selected using simple random sampling. Sometimes it is more useful to work with 

standard errors than with variances. The root design effect (DEFT) expresses the relation 

between the actual standard error of an estimate and the standard error of the corresponding 

estimates from a simple random sample. (See also “Effective sample size.”) 

Design weights: Design weights compensate for unequal probabilities of selection. More 

specifically, the design weight is the inverse of the probability of selection. Design weights are 

also called raw weights, base weights, unadjusted weights, or sampling weights. Design weights 

may be contrasted to adjusted weights (adjusted to compensate for nonresponse, and also called 

final weights or analysis weights). Roughly, the design weight is calculated as the inverse of the 

probability of selection, taking into account all stages of the sample selection process. More 

precisely, design weights are the inverses of the expected frequencies with which population 

units appear in conceptually repeated samples selected using the sampling design developed for 

the study. Unlike the final weights, design weights are generated for all sample members, 

respondents and nonrespondents alike. Design weights do not appear on the ELS:2002 publicuse files. (See also “Final weights” and “Sampling weights.”) 

Differential Item Functioning (DIF): DIF exists when examinees of equal ability differ on an 

item solely because of their membership in a particular group (e.g., if an item favors males over 

females, or one racial or ethnic group over another, and cannot be explained by relevant factors 

such as differential coursetaking). DIF for ELS:2002 items was examined in the base-year and 

first follow-up field tests. Items with DIF problems were revised or deleted. 

Disability: A disability is a physical or mental impairment that substantially limits one or more 

of the major life activities (Title 42 U.S.C. Section 12102). 

Disclosure risk analysis: Investigation of study data to evaluate and minimize the risk of 

identification of individual sample units to preserve the confidentiality of the data. ELS:2002 

data have been subjected to a disclosure risk analysis to protect confidential information about 

individual respondents (see “Public-use data file”). For a more detailed account of disclosure 

risk analysis, and of means of altering data (including masking, data perturbation, and data 

swapping) to prevent disclosure, see the NCES Statistical Standards (Seastrom 2003). 

Domain: A domain refers to a defined universe of knowledge, skills, abilities, attitudes, 

interests, or other human characteristics. 

Dropouts: A dropout was defined as a sophomore cohort member who, during spring term 

2004, had not been in school for 4 consecutive weeks or more and was not absent due to accident 

or illness. Also surveyed as a dropout were students who, at the time of their school’s survey 

day, had been back in school less than 2 weeks after a period in which the student had missed 

school for 4 or more consecutive weeks not due to accident or illness. (See also “Not currently 

in school questionnaire [NCSQ].”) 

E-8 
Appendix E: 

Glossary of Terms 

Early graduate questionnaire (EGQ): This first follow-up questionnaire was administered to 

individuals who had graduated or received high school equivalency certification (e.g., the GED) 

prior to March 15, 2004. 

Effective sample size: Effective sample size may be defined as the ratio of the raw sample size 

divided by the design effect. (For example, the sampling variance of a mean standard score is 

equal to the reciprocal of the effective sample size, not the reciprocal of the raw sample size.) In 

essence, then, effective sample size is the sample size under a simple random sample design that 

is equivalent to the actual sample under the complex sample design, wherein the actual sample 

size is determined by multiplying the effective sample size by the anticipated design effect. (See 

also “Design effect.”) 

Electronic codebook (ECB): While hardcopy codebooks with item stems, response categories, 

associated response frequency distributions, unweighted percents, and weighted valid percents 

are contained within the ELS:2002 base-year user’s manual, ELS:2002 data are also available on 

CD-ROM in an electronic codebook (ECB) format. Electronic codebooks are menu-driven 

systems that allow users to perform functions such as the following: (a) search a list of database 

variables based upon key words or variable names/labels, (b) display unweighted percentages for 

each variable in the database, (c) display question text for each variable in the database, (d) select 

or tag variables for subsequent analysis, (e) generate SAS-PC or SPSS-PC+ program 

code/command statements for subsequently constructing a system file of the selected variables, 

and (f) generate a codebook of the selected variables. 

Equating: Equating of two tests is established when examinees of every ability level and from 

every population group can be indifferent about which of two tests they take. Not only should 

they have the same expected mean score on each test, but they should also have the same errors 

of measurement. In contrast, test linkage results from placing two or more tests on the same 

scale, so that scores can be used interchangeably. (See also “Equated test score” and 

“Concordance.”) 

Equated test score: Test equating takes place in two distinct contexts in ELS:2002. One 

context is vertical equating of forms for use in successive grades, such that the achievement 

growth of individual ELS:2002 sample members over time can be accurately measured. Another 

context is cross-sectional equating and linking, as to other tests (e.g., placing ELS:2002 

sophomores and HS&B or NELS:88 sophomores on an equivalent scale). 

ETS: Educational Testing Service. RTI’s subcontractor for ELS:2002 cognitive test 

development, scoring, and scaling. 

Expanded sample: Although no sophomores were excluded from ELS:2002, those who could 

not validly be assessed or could not validly complete the student questionnaire (e.g., students 

with a severe disability or limitation in their knowledge of the English language) were not 

eligible for these components. Contextual data (parent, teacher, school administrator) reports 

were collected for this group. Later in the study, their transcripts will be collected. The 

base-year expanded sample comprises all ELS:2002 sophomores, that is, both those who were 

eligible to complete the student questionnaire and test and those who were not. The first 

follow-up expanded sample also includes freshened cases. Some students who were eligible for 

E-9 
Appendix E: 

Glossary of Terms 

questionnaire completion in 2002 suffered an impairment that led to their reclassification as 

ineligible in 2004. With greater frequency, some 2002 sophomores who were not capable of 

questionnaire completion became eligible in 2004, as their status changed. The expanded sample 

comprises all sample members regardless of eligibility for questionnaire completion. 

Facilities checklist: Completed by the RTI survey administrator in the base year of the study, 

the facilities checklist is designed to extend the information available about the school by 

providing data on the school buildings and grounds that will help researchers understand the 

adequacy and appearance of the school’s physical plant, its safety and security features, and its 

role as a constituent of the school’s general environment. 

File: Refers to a data file containing a set of related computerized records. 

Final weights: Final weights are sometimes called nonresponse-adjusted weights, adjusted 

weights, or analysis weights. Building on the design (raw) weight, they compensate for 

nonresponse. (See “Design weights.”) 

Floor effect: The result of a cognitive test being too difficult for a large number of the 

examinees, causing the low-ability examinees to receive chance scores on the first testing, and on 

subsequent testings if the test remains too difficult. Floor effects result in an inability to 

discriminate among low-ability individuals at time one or time two and, thus, no reliable 

discrimination among examinees with respect to amounts of change. A possible solution, used in 

ELS:2002, is to develop test forms that are “adaptive” to the ability level of the examinee, which 

tends to minimize the possibility of floor effects biasing the estimates of the score gains. (See 

also “Ceiling effect” and “Adaptive testing.”) 

Frame: A list of all the sampling units that represent the population. The Common Core of 

Data (CCD) and Private School Survey (PSS) were drawn upon for the ELS:2002 school frame. 

For an implicit list of the nation’s high school sophomores as of spring term 2002, school rosters 

from participating schools listing their sophomore class were relied on. 

Frame population: The set of elements (e.g., schools) that can be enumerated prior to the 

selection of a survey sample. 

Freshening: A freshened sample includes cases from the longitudinal sample of a dataset, plus 

new cases added to produce cross-sectional estimates of the population at the time of a 

subsequent wave of a longitudinal data collection. In the ELS:2002 first follow-up, freshening 

was the means by which high school seniors were added in who had not been in the 10th grade in 

the United States 2 years before. A similar freshening procedure was implemented in NELS:88. 

(See also “Half-open interval.”) 

Half-open interval: A technique used to increase coverage. It is usually applied to a new list 

that includes cases that were covered in a previous frame, as well as new in-scope units not 

included in the previous frame. In this technique, new in-scope units between unit A on the 

previous frame up to, but not including, unit B (the next unit on the previous frame) are 

associated with unit A. These new units have the same selection probability as do unit As. This 

process is repeated for every unit on the previous frame. The new units associated with the 

actual sample cases are now included in the sample with their respective selection probabilities 

E-10 
 

Appendix E: 

Glossary of Terms 

(freshening). Student sample freshening in the NELS:88 first and second follow-ups, and the 

freshening conducted in the ELS:2002 first follow-up, relied on such a procedure. The half-open 

interval procedure was also used for ELS:2002 base-year sample updating prior to survey day. 

(See also “Freshening” and “Sample updating or refreshing.”) 

Hispanic or Latino: A person of Cuban, Mexican, Puerto Rican, South or Central American, or 

other Spanish culture or origin, regardless of race. The term “Spanish origin” can be used in 

addition to “Hispanic or Latino.” 

Homeschool student questionnaire (HSQ): In the first follow-up, this questionnaire was 

administered to sophomore cohort members who were in a homeschool situation as of the spring 

term of the 2003–04 school year. 

HS&B: High School and Beyond. The second in the series of longitudinal high school cohort 

studies sponsored by NCES. The HS&B base-year study surveyed sophomore and senior 

students in 1980. The sophomore cohort was last interviewed in 1992 and their postsecondary 

transcripts collected in 1993. The senior cohort was last interviewed in 1986. 

Imputation: Imputation involves substituting values for missing or inconsistent data in a 

dataset. Prediction of a missing value is typically based on a procedure that uses a mathematical 

model in combination with available information. Missing data for key items in ELS:2002 have 

been imputed. 

Individualized Education Program (IEP): A written statement or plan for each individual 

with a disability that is developed, reviewed, and revised in accordance with Title 42 U.S.C. 

Section 1414(d). 

Individually identifiable data: Data from any record, response form, completed survey, or 

aggregation about an individual or individuals from which information about particular 

individuals may be revealed. 

Instrument: An evaluative device that includes tests, scales, and inventories to measure a 

domain using standardized procedures. 

IRT: Item Response Theory. A method of estimating achievement level by considering the 

pattern of right, wrong, and omitted responses on all items administered to an individual student. 

IRT postulates that the probability of correct responses to a set of test questions is a function of 

true proficiency and of one or more parameters specific to each test question. Rather than 

merely counting right and wrong responses, the IRT procedure also considers characteristics of 

each of the test items, such as their difficulty and the likelihood that they could be guessed 

correctly by low-ability individuals. IRT scores are less likely than simple number-right or 

formula scores to be distorted by correct guesses on difficult items if a student’s response vector 

also contains incorrect answers to easier questions. Another attribute of IRT that makes it useful 

for ELS:2002 is the calibration of item parameters for all items administered to all students. This 

makes it possible to obtain scores on the same scale for students who took harder or easier forms 

of the test. IRT also was used to vertically scale across ELS:2002 rounds, that is, between the 

two grade levels (10th grade in 2002, 12th grade in 2004). (See, in contrast, “Classical test 

theory.”) 

E-11 
Appendix E: 

Glossary of Terms 

Item nonresponse: The amount of missing information when a valid response to an item or 

variable was expected. (See also “Unit nonresponse” and “Bias analysis.”) 

LEP: Limited English proficient. A concept developed to assist in identifying those languageminority students (individuals from non-English language backgrounds) who need language 

assistance services, in their own language or in English, in the schools. (See also “NEP” and 

“LM.”) An LEP student is one who meets one or more of the following conditions: 

a. the student was born outside of the United States or the student’s native language is not 

English, 

b. the student comes from an environment in which a language other than English is 

dominant, or 

c. the student is an American Indian or Alaska Native and comes from an environment in 

which a language other than English has had a significant impact on his/her level of 

English language proficiency, 

and who has such difficulty speaking, reading, writing, or understanding the English language as 

to deny him or her the opportunity to learn successfully in English-only classrooms. 

LM: Language Minority. A non-, limited-, or fully English-proficient student in whose home a 

non-English language is typically spoken. 

Library media center questionnaire: This base-year instrument supplies information about 

library/media center organization and staffing, technology resources, extent of library and media 

holdings, student access to and use of the library/media center, and its role in supporting the 

school’s curriculum. 

Longitudinal or panel survey: In a longitudinal design, similar measurements—of the same 

sample of individuals, institutions, households, or of some other defined unit—are taken at 

multiple timepoints. ELS:2002 employs a longitudinal design that follows the same individuals 

over time and permits the analysis of individual-level change. (See also “Cross-sectional 

analysis.”) 

Machine editing: Also called forced data cleaning or logical editing. Uses computerized 

instructions (including logical or deductive imputation) in the data cleaning program that ensure 

common sense consistency within and across the responses from a data provider. 

Microdata (microrecords): Observations of individual sample members, such as those 

contained on the ELS:2002 data files. 

MPR Associates: An RTI subcontractor for the ELS:2002 base-year and first follow-up studies. 

NAEP: The National Assessment of Educational Progress. NAEP is a cross-sectional 

assessment program that measures achievement at the group level for students in 4th, 8th, and 

12th grades and provides a time series for measuring trends in academic progress of 9-, 13-, and 

17-year-olds. ELS:2002 tests differ from but complement those of NAEP by providing a basis 

E-12 
Appendix E: 

Glossary of Terms 

for measuring individual-level achievement growth between 10th and 12th grades in 

mathematics and relating cognitive gains in this subject to the individual, school, and family 

factors and processes that are measured in the various ELS:2002 questionnaires and school 

records (transcript) studies. 

Native Hawaiian or Other Pacific Islander: Any person having origins in any of the original 

peoples of Hawaii, Guam, Samoa, or other Pacific Islands. 

NCES: The National Center for Education Statistics, Institute of Education Sciences, U.S. 

Department of Education. This governmental agency is the sponsor of ELS:2002 and is also the 

sponsoring agency for (among other studies) the National Assessment of Educational Progress 

(NAEP), the U.S. component of the Program for International Student Assessment (PISA), the 

National Education Longitudinal Study of 1988 (NELS:88), the High School and Beyond 

(HS&B) longitudinal study, and the National Longitudinal Study of the High School Class of 

1972 (NLS-72). 

NELS:88: The National Education Longitudinal Study of 1988. Third in the series of 

longitudinal high school cohort studies sponsored by NCES. The study represents three cohorts: 

the eighth-grade class of 1988, the sophomore class of 1990, and the senior class of 1992. The 

study collected questionnaire and test data in 1988, 1990, and 1992 on students’ school 

experiences, as well as background information from school administrators, teachers, parents (in 

the base year and second follow-up only), and school records. Data on postsecondary and outof-school experiences were collected in interviews conducted in 1994 and 2000 and through a 

postsecondary education transcripts study in 2000–01. 

NEP: No English proficiency. A student who does not speak English. (See also “LEP.”) 

New participant student questionnaire (NPSQ): This first follow-up questionnaire was 

administered to students in the base-year schools 2 years later. The NPSQ elicited responses 

from two distinct groups: sophomore cohort members who had been base-year nonparticipants, 

and students brought in through sample freshening. (A small number of students whose 

eligibility status had changed between rounds completed a NPSQ.) The questionnaire comprised 

both base-year items (the standard classification variables) and first follow-up items pertaining to 

students’ current school experience. 

New participant supplement (NPS): Base-year nonrespondents who responded in the first 

follow-up but were not enrolled in the base-year schools (e.g., transfers, dropouts, early 

graduates) completed this supplement in addition to an appropriate questionnaire. The 

supplement consists wholly of items from the base year, so that the standard classification 

variables could be captured for all sample members. 

Noncoverage: Units of the target population that are missing from the frame population. 

Includes the problems of incomplete frames and missing units. 

Nonresponse: See “Item nonresponse,” “Unit nonresponse,” “Bias analysis,” and “Nonresponse 

bias.” 

E-13 
Appendix E: 

Glossary of Terms 

Nonresponse bias: Nonresponse bias may occur as a result of not obtaining 100 percent 

response from the selected cases. More specifically, nonresponse bias occurs when the expected 

observed value deviates from the population parameter. The potential magnitude of nonresponse 

bias is estimated as the product of the nonresponse rate and the difference in values of a 

characteristic between respondents and nonrespondents. (See also “Bias” and “Bias analysis.”) 

NLS-72: The National Longitudinal Study of the High School Class of 1972. This project was 

the first in the series of longitudinal high school cohort studies sponsored by NCES. The final 

round of data collection took place in 1986. 

Nonsampling error: An error in sample estimates that cannot be attributed to sampling 

fluctuations. Such errors may arise from many sources, including imperfect implementation of 

sampling procedures, differential unit or item nonresponse across subgroups, bias in estimation, 

or errors in observation and recording. 

Not currently in school questionnaire (NCSQ): This first follow-up questionnaire was 

administered to sophomore cohort dropouts. It includes questions both on present circumstances 

and retrospective items on schooling experience and school disengagement behaviors. (See also 

“Dropouts.”) 

OMB: The Office of Management and Budget, U.S. Executive Branch. OMB is a federal 

agency with the responsibility for reviewing all studies funded by executive branch agencies. 

OMB reviewed, commented on, and approved the ELS:2002 questionnaires, as indicated by their 

approval number and its expiration date in the top right corner of the questionnaire covers. 

Open-ended: A type of question in which the data provider’s responses are not limited to given 

alternatives. 

Optical disc: A disc that is read optically (e.g., by laser technology), rather than magnetically. 

(See also “CD-ROM.”) 

Optical scanning: A system of recording responses that transfers responses into machinereadable data through optical mark reading. Data from base-year and first follow-up in-school 

survey sessions (and indeed all non-CATI operations across components) were optically 

scanned. 

Oversampling: Deliberately sampling a portion of the population at a higher rate than the 

remainder of the population. For example, in ELS:2002, private schools have been oversampled. 

Within schools, Asians have been oversampled. 

Parent/guardian questionnaire: The ELS:2002 base-year parent component sought to collect 

information from parents of all base-year student sample members. The parent or guardian who 

knew most about his or her child’s educational experience was asked to complete the 

questionnaire. 

PISA: The Program for International Student Assessment. PISA assesses 15-year-olds in 

reading, mathematics, and science. In 2000, the primary focus of the assessment was reading. 

The United States and 31 other nations participated, under the aegis of the Organization for 

E-14 
Appendix E: 

Glossary of Terms 

Economic Cooperation and Development (OECD). In 2003, the primary focus was mathematics, 

and in 2006, the primary focus will be science. A crosswalk (or concordance) has been 

developed between the ELS:2002 reading test and the PISA reading test, so that the PISA scale 

can be implemented in ELS:2002. A similar scale linkage will be effected between the 

ELS:2002 mathematics test (2002) and the PISA math test (2003). 

Population: All individuals in the group to which conclusions from a data collection activity are 

to be applied. Weighted results of ELS:2002 data provide estimates for populations and 

subgroups. 

Population variance: A measure of dispersion defined as the average of the squared deviations 

between the observed values of the elements of a population or sample and the population mean 

of those values. 

Postsecondary education: The provision of formal instructional programs with a curriculum 

designed primarily for students who have completed the requirements for a high school diploma 

or equivalent. This includes programs of an academic, vocational, and continuing professional 

education purpose and excludes vocational and adult basic education programs. 

Poststratification adjustment: A weight adjustment that forces survey estimates to match 

independent population totals within selected poststrata (adjustment cells). 

Practical significance: With large sample sizes, as in ELS:2002 and its predecessor studies, 

even tiny differences, of little or no substantive or practical import, can be statistically 

significant. Therefore, measures of practical significance, such as the effect size (expressed in 

standard deviation units), are sometimes also used. (See the NCES Statistical Standards, 

Seastrom 2003, Guideline 5-1-4F). (Compare “Statistical significance.”) 

Precision: The difference between a sample-based estimate and its expected value. Precision is 

measured in terms of the sampling error (or standard error) of an estimate. 

Primary sampling unit (PSU): Unit chosen at the first stage of a cluster sample. In ELS:2002, 

the PSU is the school; in other studies, geographical units such as a county or metropolitan 

statistical area (MSA) may serve as the PSU. 

Probability sample: A sample selected by a method such that each unit has a fixed and 

determined probability of selection—that is, each population unit has a known, nonzero chance 

of being included. 

Proficiency score: Proficiency scores (or criterion-referenced mastery scores) are based on 

clusters of items within each test that are of similar content and difficulty. Both normative (e.g., 

achievement quartiles) and proficiency scores are available from the ELS:2002 database. 

PSS: Private School Survey. An NCES universe survey encompassing the nation’s private 

schools. PSS was the private school sampling frame for the ELS:2002 base year. 

Public-use data file: A public-use file that includes a subset of data that have been coded, 

aggregated, or otherwise altered to mask individually identifiable information; it thus is available 

E-15 
Appendix E: 

Glossary of Terms 

to all external users. Unique identifiers, geographic detail, and other variables that cannot be 

suitably altered are not included in public-use data files. Public-use edits are based on an 

assumption that external users have access to both individual respondent records and secondary 

data sources that include data that could be used to identify respondents. For this reason, the 

editing process is relatively extensive. When determining an appropriate masking process, the 

public-use edit takes into account and guards against matches on common variables from all 

known files that could be matched to the public-use file. The analysis used to determine which 

records require masking is called a disclosure risk analysis. 

Range check: A determination of whether responses fall within a predetermined set of 

acceptable values. 

Record format: The layout of the information contained in a data record (includes the name, 

type, and size of each field in the record). 

Records: A logical grouping of data elements within a file upon which a computer program 

acts. 

Refreshed student: See “Sample updating or refreshing.” 

Relative bias: Relative bias is the bias of the estimate divided by the estimate. It provides an 

indication of the order of magnitude of the bias with respect to the estimate. 

Reliability: The consistency in results of a test or measurement including the tendency of the 

test or measurement to produce the same results when applied twice to some entity or attribute 

believed not to have changed in the interval between measurements. 

Reserve code (or reserved code): Certain codes have been reserved to represent various 

situations in which missing data occur in response frequencies. In ELS:2002, the reserve code 

conventions are as follows: -1 = “Don’t know;” -2 = “Refuse;” -3 = “Legitimate skip/NA;” 

-4 = “Nonrespondent;” -5 = “Out of range;” -6 = “Multiple response;” -7 = “Partial interview— 

breakoff;” -8 = “Item not applicable to sample member;” and -9 = “Missing.” 

Response rate: In general, unit response rates are calculated as the ratio of the weighted number 

of completed instruments to the weighted number of in-scope cases, using the sample base 

weight (the inverse of the probability of selection). In multistage samples, such as the base year 

of ELS:2002, overall response is the product of both stages (though for many purposes, the 

stages are reported separately). Item response rates are calculated as the ratio of the number of 

respondents for whom an in-scope response was obtained to the number of respondents who are 

asked to answer a given item. Calculation of unit and item response rates can be a complex 

matter, and additional considerations arise in reporting in follow-up waves of longitudinal 

studies, for composite (constructed) variables, and for other cases. More detailed information 

can be found by consulting NCES Standard 1-3 in the NCES 2002 Statistical Standards 

document (available at http://nces.ed.gov/statprog/2002/stdtoc.asp). Bias analyses conducted 

when response rates are below targets help to assess any possible limitations to the 

generalizability of survey estimates. (See “Bias analysis.”) 

E-16 
Appendix E: 

Glossary of Terms 

Restricted-use data file: A restricted-use file includes individually identifiable information that 

is confidential and protected by law. The file contains all public-use data, as well as additional 

data. Use of the restricted data requires the researcher to obtain a special license from NCES. 

RTI International (RTI): A nonprofit university-affiliated research organization with 

headquarters at Research Triangle Park, North Carolina, that conducted the base year and first 

follow-up of ELS:2002 and is currently conducting the second follow-up of the study on behalf 

of NCES. RTI International is a trade name of Research Triangle Institute. 

Sample: Subgroup selected, by a probability method, from the entire population, in order to 

represent it. 

Sample updating or refreshing: Because students can transfer into or out of a school after 

sampling, the base-year student sample in ELS:2002 (as in HS&B and NELS:88) was updated to 

remove students who had transferred out and to give sophomores who had transferred in since 

sampling a chance of selection. The half-open interval procedure was employed for sample 

updating prior to survey day, using the school 10th-grade enrollment lists. 

Sampling error: The part of the difference between a value for an entire population and an 

estimate of that value derived from a probability sample that results from observing only a 

sample of values. 

Sampling frame. See “Frame” or “Frame population.” 

Sampling variance: A measure of dispersion of values of a statistic that would occur if the 

survey were repeated a large number of times using the same sample design, instrument, and 

data collection methodology. The square root of the sampling variance is the standard error. 

Sampling weight: A multiplicative factor equal to the reciprocal of the probability of a 

respondent being selected for the study, with adjustment for nonresponse. The sum of the 

weights provides an estimate of the number of persons in the population represented by a 

respondent in the sample. 

Scaling: Scaling refers to the process of assigning a scale score based on the pattern of 

responses. (See also “Equated test score” and “IRT.”) 

School administrator questionnaire: This questionnaire was administered in both the base 

year and, with changes, the first follow-up. The questionnaires sought basic information about 

school policies, curriculum and program offerings, and student and teacher characteristics. 

School climate: The social system and ethos or culture of the school, including the 

organizational structure of the school and values and expectations within it. 

School coordinator: A person designated in each school to act as a contact person between the 

school and RTI. This person assisted with establishing a survey day in the school and preparing 

for the survey. 

E-17 
 

Appendix E: 

Glossary of Terms 

Selection probability: The chance that a particular sampling unit has of being selected in the 

sample. 

Simple random sampling (SRS): SRS uses equal probability sampling with no strata or clusters. 

The ELS:2002 sample is stratified and clustered. Most statistical analysis software assumes SRS 

and independently distributed errors. For studies such as ELS:2002, special variance estimation 

software (such as SUDAAN, WesVar, AM, or Stata) is required to compute the standard error of 

estimates. 

Standard deviation: The most widely used measure of dispersion of a frequency distribution. 

It is equal to the positive square root of the population variance. 

Standard error: The positive square root of the sampling variance. It is a measure of the 

dispersion of the sampling distribution of a statistic. Standard errors are used to establish 

confidence intervals for the statistics being analyzed. 

Statistical significance: The finding (based on a derived probability, rather than a certitude) 

that two or more estimates are truly different from one another and not a merely apparent 

difference reflecting chance variation. (See also “Practical significance.”) 

Stratification: The division of a population into parts, or strata. In a stratified sample, the total 

population is divided into strata or subgroups. Strata are created by partitioning the frame and 

are generally defined to include relatively homogeneous units within strata. Stratification is used 

to reduce sampling error. In ELS:2002, the sampling frame was sorted to create strata or 

subgroups of schools, and schools were selected independently within each stratum. Schools 

were stratified by superstrata (combinations of school type or sector and geographic region) and 

substrata (urban, suburban, rural). 

Student questionnaire: One of the two parts of the ELS:2002 base-year and first follow-up 

student survey (the other part being the assessment). In both rounds, this instrument contained a 

locator section for tracing sample members for future waves of ELS:2002 and a series of 

questions about school and home environments, time use, attitudes, values, and aspirations. In 

the first follow-up, this questionnaire was administered only to participating base-year students 

who remained in the same school 2 years later. In some instances, an abbreviated version of the 

student questionnaire was administered (usually in CATI, but sometimes in a hardcopy version). 

Survey administrator: A member of RTI’s field staff in charge of conducting in-school data 

collection sessions (see “Survey day”). The individual in this role was called a team leader in 

NELS:88 and a survey representative in HS&B. 

Survey day: A day chosen by the school during the data collection period when an RTI survey 

administrator and assistant administered the survey to the school’s sample of students. The 

survey day session lasted about 2 hours in the base year and 90 minutes in the first follow-up. 

Two make-up days were normally offered for students who missed the survey day. 

Target population: The finite set of observable or measurable elements that will be studied, or 

the conceptual population of analytic units for which data are collected and estimates are made. 

E-18 
 

 

Appendix E: 

Glossary of Terms 

In the ELS:2002 base year, the target population was spring term 2002 sophomores in all regular 

public and private schools with 10th grades in the 50 states and the District of Columbia. 

Teacher questionnaire: In the base year, mathematics and English teachers of ELS:2002 

sophomore participants were asked to complete a teacher questionnaire, which collected data on 

school and teacher characteristics (including teacher qualifications and experience) and 

evaluations of student performance. 

Teacher sample: In the ELS:2002 base year, two teacher reports were sought for each student: 

one from the student’s mathematics teacher and one from the student’s English teacher. 

Technical review panel (TRP): A TRP is a specially appointed, independent group of 

substantive, methodological, and technical experts who offer advice to the study’s contractor on 

issues of study design and content. TRP members are nominated by the contractor and approved 

by NCES. Typically, TRPs are convened at least once a year within the life of a contract. 

Transfer student questionnaire (TSQ): This first follow-up questionnaire was administered to 

students who moved from their base-year school to a new school between spring 2002 and spring 

2004. It collected data both on students’ school experience and their reason for transferring to a 

new school. 

Trimming: A process by which extreme weights are reduced (trimmed) to diminish the effect 

of extreme values on estimates and estimated variances. 

Unit nonresponse: Failure of a survey unit (e.g., at the institutional level, a school, or at the 

individual level, a respondent, such as a student or a teacher) to cooperate or complete a survey 

instrument. Overall unit nonresponse reflects a combination of unit nonresponse across two or 

more levels of data collection, where participation at the second stage of data collection is 

conditional upon participation in the first stage of data collection. In ELS:2002, overall 

nonresponse is the product of school-level nonresponse times student nonresponse. Total 

nonresponse reflects a combination of the overall unit nonresponse and item nonresponse. (See 

also “Item nonresponse” and “Nonresponse bias.”) 

Urbanicity (or metropolitan status): The ELS:2002 school sample was stratified by 

metropolitan status or urbanicity, in accordance with the following three locale codes: 

(1) Urban: the school is in a large or mid-size central city; (2) Suburban: the school is in a large 

or small town or is on the urban fringe of a large or mid-size city; and (3) Rural: the school is in 

a rural area. Locale indicators were taken from the Common Core of Data (CCD) for public 

schools and the Private School Survey (PSS) for private schools. 

Validity: The capacity of an item or instrument to measure what it was designed to measure, 

stated most often in terms of the correlation between scores in the instrument and measures of 

performance on some external criterion. It is the extent to which a test or set of operations 

measures what it is supposed to measure. Reliability, on the other hand, refers to consistency of 

measurement over time. (See “Reliability.”) 

E-19 
 

 

Appendix E: 

Glossary of Terms 

Variance: The average of the squared deviations of a random variable from the expected value 

of the variable. The variance of an estimate is the squared standard error of the estimate. (See 

also “Population variance” and “Sampling variance.”) 

Wave: A wave is a round of data collection in a longitudinal survey (e.g., the base year and each 

successive follow-up are each waves of data collection). 

Weighted response rates: Unit response rates are calculated as the ratio of the weighted 

number of completed interviews to the weighted number of in-scope sample cases. Unit 

response rates are calculated using the sample base weights (inverse of the probability of 

selection). 

Weighted estimates: Weighted estimates (as in the ELS:2002 codebook) are survey estimates 

in which the sample data are statistically weighted (multiplied) by factors reflecting the sample 

design. The general purpose of weighting is to compensate for unequal probabilities of selection 

into the sample and to adjust for the fact that not all schools or individuals selected into the 

sample actually participated. The design weights (also known as base weights, and typically 

equal to the reciprocals of the overall selection probabilities) are multiplied by a nonresponse or 

poststratification adjustment for a final weight. Thus, for example, in ELS:2002, the 752 

participating schools in the base year represent a national population of 24,795 schools. 

Individual schools may “represent” anywhere from a minimum of 1 school to a maximum of 96 

schools. To take an ELS:2002 base-year student-level example, 7,613 base-year questionnaire 

respondents reported themselves to be male, and 7,688 reported themselves to be female. When 

these cases are multiplied by the nonresponse-adjusted student weights to yield a weighted 

percent that reflects the national population of high school sophomores, the estimate for males is 

50.5 percent of the 2002 tenth-grade cohort, while females are estimated to comprise 49.5 

percent of the nation’s 2002 tenth-graders. 

White: A person having origins in any of the original peoples of Europe, the Middle East, or 

North Africa. 

Appendix E Reference 

Seastrom, M. (2003). NCES Statistical Standards (NCES 2003–601). U.S. Department of 

Education, National Center for Education Statistics. Washington, DC: U.S. Government 

Printing Office. Available: http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2003601. 

E-20 
Appendix F 

Student Questionnaire Critical Items 

F-1 

 

Appendix F: 

Student Questionnaire Critical Items 

Appendix F 

Student Questionnaire Critical Items 

Critical items are data elements deemed to be of special importance (for future locating of 

the respondent, for research, or as a data quality check on whether skip patterns are being 

followed correctly). These items were therefore subject to edit and retrieval in the course of the 

in-school survey session (see tables F-1 and F-2). 

Table F-1. ELS:2002 first follow-up student questionnaire critical items: 2004 

Variable Variable description 

F1S01 Name, address, phone number1 

1 F1S02 Mother’s name 

F1S03 Is her address and telephone number the same as respondent’s?1 

F1S04 Mother’s address and home telephone number1 

F1S05 Mother’s work telephone number1 

1 F1S06 Father’s name 

F1S07 Is his address and telephone number same as respondent’s?1 

F1S08 Father’s address and home telephone number1 

F1S09 Father’s work telephone number1 

F1S10 Name, address, and telephone number of relative or close friend1 

F1S12 Social security number1 

F1S13 Interview date1 

F1S14 Grade level 

F1S15 Expected graduation/certification status 

F1S45 Educational plans immediately after high school 

F1S47 Educational plans for the future 

F1S53 Plan to work right after high school 

1 Variable not included in any release file. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

F-3 
 

Appendix F: 

Student Questionnaire Critical Items 

Table F-2. ELS:2002 first follow-up new participant student questionnaire additional critical items 

(base-year classification variables): 2004 

Variable Variable description 

F1N01 Date of birth 

F1N02 Sex 

F1N03 Hispanic ethnicity, yes or no 

F1N04 Hispanic subgroup 

F1N05 Race 

F1N06 Asian subgroup 

F1N07 Native language = English, yes or no 

F1N08 Native language 

F1N09 English language competency 

F1N10 In 10th grade in spring term 2002, yes or no 

F1N11 Ever held back a grade 

F1N12 Grade repeated 

F1N13 Household composition 

F1N14 Mother’s occupation 

F1N15 Father’s occupation 

F1N16(A-B) Mother’s and father’s educational attainment 

F1N17(A-J) Household items 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

F-4 
Appendix G 

Base-Year to First Follow-up 

Electronic Codebook 

G-1 

Appendix G: 

Base-Year to First Follow-up Electronic Codebook 

Appendix G 

Base-Year to First Follow-up Electronic Codebook 

A web-published version of the base-year to first follow-up electronic codebook is 

available as a PDF file at http://nces.ed.gov/surveys/els2002/. 

G-3 

Appendix H 

Cross-Cohort Comparisons 

H-1 

 

Appendix H: 

Cross-Cohort Comparisons 

Appendix H 

Cross-Cohort Comparisons 

H.1 Cross-Cohort Comparison Crosswalks 

The Education Longitudinal Study of 2002 (ELS:2002) first follow-up (2004) data can be 

used in cross-cohort (intercohort) comparisons with the senior cohorts of the National 

Longitudinal Study of the High School Class of 1972 (NLS-72), the High School and Beyond 

(HS&B) longitudinal study in 1980, and the National Education Longitudinal Study of 1988 

(NELS:88) in 1992. The ELS:2002 first follow-up data can also be used in comparisons to 

HS&B and NELS:88 of the sophomore cohort “2 years later”—including comparisons of 

sophomore cohort dropouts. This appendix contains crosswalks designed to identify ELS:2002 

variables that also appear on the NLS-72 (1972), HS&B (1980) senior cohort,1 or NELS:88 

(1992) datasets. Some items identified in the crosswalks are only approximate matches, and for 

these, analysts should judge whether they are sufficiently comparable for the analysis at hand. In 

other cases, question stems and response options correspond exactly across questionnaires. All 

NLS-72 1972 and HS&B senior cohort 1980 participants are by definition 12th-graders. 

However, for NELS:88 and ELS:2002, the subset of participants who were seniors at the time 

must be invoked through use of the senior cohort flag. 

Although the four studies have been designed to produce comparable results, there are 

also differences between them that may affect the comparability as well as the precision of 

estimates. Analysts should be aware of and take account of these several factors. In particular, 

there are differences in sample eligibility and sampling rates, differences in response rates, and 

some differences in key classification variables, such as race/Hispanic ethnicity. Other 

differences (and possible threats to comparability) are imputation of missing data, differences in 

test content and reliabilities, differences in questionnaire content, potential mode effects in data 

collection, and possible questionnaire context and order effects. 

H.1.1 Eligibility 

Quite similar definitions were used in deciding issues of school eligibility across the 

studies. Differences in student sampling eligibility, however, are more problematic. Although 

the target population is highly similar2

 across the studies (all seniors who can validly be assessed 

or at minimum meaningfully respond to the questionnaire), exclusion rules and their 

1 There were two cohorts in HS&B: a senior cohort and a sophomore cohort. In 1982, most members of the 

sophomore cohort were seniors. However, the sophomore cohort sample was not freshened in 1982. This means 

that the sophomore cohort in 1982 does not fully represent high school seniors of that year, since no 1982 seniors 

who were not 1980 sophomores are included. Therefore, the 1982 seniors should not be compared with the 1972, 

1980, 1992, and 2004 seniors, unless some adjustment is made for the “missing” seniors. Although some of the 

“missing” seniors were out of the country in 1980, most were held back a year or more, making them a very different 

group from the sophomore cohort members who remained in modal grade sequence. By and large, these missing 

cases would more closely resemble the HS&B sophomore cohort members who fell behind their classmates and did 

not become 1982 seniors. 

2

 “Similar” seems a more accurate description than “the same” because of differences in emphasis, such as between 

the importance of test completion and the importance of questionnaire completion. HS&B, for example, regarded 

impediments to assessment as of overriding importance for determining eligibility, whereas ELS:2002 included 

students who could not be tested but could complete the questionnaire (in either self- or interviewer-administered 

interviews). 

H-3 
 

 

Appendix H: 

Cross-Cohort Comparisons 

implementation have varied somewhat, and exclusion rates are known to differ where they are 

known at all. 

Not all students are able to meaningfully respond to research instruments such as the 

assessments and questionnaires administered in the four studies. Some English language 

learners are too limited in their English proficiency to do so, whereas others may be precluded 

from participation by a severe physical or mental disability. HS&B excluded as ineligible 

students with such barriers to participation, although an overall exclusion rate has not been 

documented. In NELS:88, 5.3 percent of the base-year 8th-grade sample was excluded for such 

reasons (this figure is similar to the exclusion rate for 8th grade in the National Assessment of 

Educational Progress [NAEP] in similar subjects in the same period). However, a sample of the 

NELS:88 ineligible students was followed over time, and some students whose status changed 

were incorporated into the first and second follow-ups, from which the NELS:88 sophomore and 

senior cohorts are drawn. In ELS:2002, no students were classified as ineligible as such, 

although some were exempted from completing the questionnaire (and others also a test); still 

others were tested under circumstances in which they were provided with special 

accommodations. The overall rate of instrument-exempted sophomores in ELS:2002 is quite 

low, below 1 percent in the ELS:2002 base year. Base-year students incapable of completing a 

questionnaire were reevaluated in the first follow-up. Although not all were seniors, and the 

eligibility status of many remained unchanged, others became capable of questionnaire 

completion, particularly students who had been excluded for language reasons. (Note that the 

questionnaire-incapable students are considered to be part of the study but do not appear on the 

ELS:2002 public-use file.) 

The fact that a larger proportion of the student population was included in ELS:2002 (99 

percent of the potential cohort in ELS:2002 as contrasted to 95 percent in NELS:88) may affect 

cross-cohort estimates of change. This is the case because the excluded students in NELS:88 

tended to be quite different from the included students.3

 At the same time, there are ways to 

make the samples somewhat more comparable. Thus, while for optimal cross-sectional 

estimation, all the ELS:2002 cases might be used for comparison of achievement results across 

cohorts, the ELS:2002 cases that reflect testing accommodations should be dropped.4 

H.1.2 Sample Design Differences 

Differences in sampling rates, sample sizes, and design effects across the studies also 

affect precision of estimation and comparability. Asian students, for example, were oversampled 

in NELS:88 and ELS:2002, but not in NLS-72 or HS&B, where their numbers were quite small. 

Also, although Catholic schools were oversampled in three of the four studies, HS&B had few 

(only 38) private non-Catholic schools, and NLS-72 had few nonpublic schools of any kind. The 

base-year (1980) participating sample in HS&B numbered 30,030 sophomores. In contrast, 

3

 For example (Ingels 1996), though just 5 percent of the population, inclusion of the ineligible students changes the 

cohort dropout rate between 1988 and 1990 from 6 percent to 7 percent. Only 62 percent of the base-year ineligibles 

were still in high school 4 years later, compared with 83 percent of the total sample. Of this 62 percent, 58 percent 

were in modal grade sequence, and 42 percent were not (80 percent of the overall in-school sample was in modal 

grade sequence, i.e., seniors 4 years later). 

4 In the same way, adjustments are commonly made to render the HS&B and NELS:88 transcript studies comparable 

to the NAEP high school transcripts. Specifically, only the subset of the HS&B or NELS:88 senior cohort that in fact 

graduated is included, while graduates on the NAEP file with special education diplomas are excluded from analysis. 

H-4 
 

Appendix H: 

Cross-Cohort Comparisons 

15,362 sophomores participated in the base year of ELS:2002. Cluster sizes within school were 

much larger for HS&B (on average, 30 sophomores per school) than for ELS:2002 (just over 20 

sophomores per school; larger cluster sizes are better for school effects research but carry a 

penalty in greater sample inefficiency). Mean design effect (a measure of sample efficiency5

) 

also is quite variable across the studies: for example, for 10th grade, 2.9 for HS&B and 3.9 for 

NELS:88 (reflecting high subsampling after the 8th-grade base year), with the most favorable 

design effect, 2.4, for the ELS:2002 base year. Other possible sources of difference between the 

cohorts that may impair change measurement are different levels of sample attrition over time 

and changes in the population of nonrespondents. 

H.1.3 Participation Rates 

Response rates also differ somewhat across the studies, although nonresponse-adjusted 

weights were generated for each of the cohorts. At the school level, response rates were 

somewhat higher in HS&B and NELS:88 (unweighted, around 70 percent) than in ELS:2002 

(unweighted, 62 percent). School nonresponse bias analyses were performed for each study and 

may be found in the study documentation. At the student level, there is even more variation in 

response rates. In HS&B, 80.7 percent of 1980 senior cohort members completed a 

questionnaire (Zahs et al. 1995, p. 67). In the NELS:88 second follow-up, 92.5 percent of 

students participated (Ingels et al. 1994), and in ELS:2002, 93.6 percent of the in-school sample 

was surveyed in the first follow-up (all response rates are unweighted). 

H.1.4 Changing Race Definitions 

In some cases, federal race definitions or preferences for the means by which ethnicity 

and race data are to be collected have changed. In HS&B and NELS:88, students were asked to 

mark one race only. Based on revised race-reporting guidelines issued by the Office of 

Management and Budget (OMB), ELS:2002 added a new race category, and, more important, 

students are now allowed to mark all that apply, thus generating a further category, 

Multiracial/More than one race. 

The new race category is Native Hawaiian or Other Pacific Islander. For purposes of 

cross-cohort comparisons, cases identified in ELS:2002 as Native Hawaiian or Other Pacific 

Islander should be combined with the Asian category to achieve comparability with earlier 

studies. 

However, for students who considered themselves to be multiracial and marked more 

than one race, there is no ready means to map them back into a one-race scheme. With 5 race 

categories and with values based on a single race reported, none reported, the 10 possible 

combinations of 2 races, the 10 possible combinations of 3 races, the 5 possible combinations of 

4 races, and the possibility of a combination of all 5 races, there are 32 separate race categories. 

When race is crossed by ethnicity (race by Hispanic or not Hispanic), there are 64 possible 

race/Hispanic ethnicity combinations. It is impossible to know, for example, whether a student 

who marked White and Black in ELS:2002 would have marked White or Black in NELS:88, in 

5

 Effective sample size can be quite different from the nominal sample size; effective sample size is more meaningful 

than raw sample size in terms of statistical analysis—for example, the sampling variance of a mean standard score is 

equal to the reciprocal of the effective sample size, not the reciprocal of the raw sample size. Effective sample size 

may be defined as the raw sample size divided by the design effect. 

H-5 
Appendix H: 

Cross-Cohort Comparisons 

which only one race was allowed. There are over 700 non-Hispanic multiracial sophomores 

recorded in the ELS:2002 base-year dataset, but the distorting effect on cross-cohort estimation 

is likely to be greatest for small population subgroups with many claimants to multiple race, such 

as the American Indian category. Analysts should be cautious, then, about conclusions 

concerning racial subgroup trends between the seniors of 1972, 1980, 1992, and 2004. 

H.1.5 Other Classification Variables 

Other key classification variables have been constructed to the extent possible in the same 

way in ELS:2002 as in the prior studies, although in many cases (in ELS:2002 only) there are 

imputed versions of the variable as well as the original version with the various types of missing 

data categorized by reserve code. The socioeconomic status (SES) variable offers a good 

example of the subtle differences that may exist between the same variable in different studies, 

despite efforts to maximize cross-cohort consistency of measures. Continuities and differences 

in SES constituents and construction in the three prior studies are summarized below in 

table H-1. Table H-2 summarizes the elements comprising the SES measure in ELS:2002. 

Table H-1. Elements of the socioeconomic composite, by study: Selected years, 1972–2002 

NLS-72, HS&B NELS:88 NELS:88 student 

(student reported) (parent reported) survey substitutions 

Father’s occupation Father’s occupation Father’s occupation 

Mother’s occupation Mother’s occupation 

Father’s education Father’s education Father’s education 

Mother’s education Mother’s education Mother’s education 

Family income Family income Household items 

Household items — — 

— Not available. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002); National Longitudinal Study of the High School Class of 1972 (NLS-72); High School and Beyond 

(HS&B) Longitudinal Study (1980); and National Education Longitudinal Study of 1988 (NELS:88). 

Table H-2. Elements of socioeconomic composite, by source: 2002 

Preferred source Student report substitution if 

(parent reported) missing from parent Imputed if still missing 

Father’s occupation Father’s occupation Father’s occupation 

Mother’s occupation Mother’s occupation Mother’s occupation 

Father’s education Father’s education Father’s education 

Mother’s education Mother’s education Mother’s education 

Family income — Family income 

— Not available. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002). 

ELS:2002 largely follows the NELS:88 model above. In both studies, the composite is 

based on five equally weighted, standardized components: father’s education, mother’s 

education, family income, father’s occupation, and mother’s occupation. Parent data are used to 

construct this variable. Student data are substituted where parent data are missing. However, for 

parent education and occupation, where both parent and student reports are missing, ELS:2002 

H-6 
 

Appendix H: 

Cross-Cohort Comparisons 

education and occupation values are imputed. Family income was not asked of students. While 

in NELS:88 a student-provided household item index, which served as an income proxy, was 

substituted when income data were missing, a different procedure was followed in ELS:2002. 

When parent data on income were missing, income was statistically imputed. 

Some differences across the studies are based on differences in design. The studies had 

different starting points. NLS-72 student respondents were high school seniors, HS&B base-year 

respondents were sophomores or seniors, and NELS:88 base-year respondents were 8th-graders. 

ELS:2002 base-year respondents were sophomores. A parent interview was sought for all 

NELS:88 and ELS:2002 base-year student respondents. HS&B had a parent survey, but it only 

encompassed a subsample of student respondents. NLS-72 had no parent survey at all. Because 

the quality of reporting on parental occupation and education increases with student age or grade, 

it may be of concern whether reports were gathered at grade 8, 10, or 12. However, since parent 

reports are markedly superior to student reports in these matters, it may be of concern that only 

in NELS:88 and ELS:2002 are the data primarily parent reported. Likewise, students are poor 

reporters of family income, but the income question was asked of students in NLS-72 and HS&B 

and of parents alone in NELS:88 and ELS:2002. 

Some differences reflect changing social circumstances over time. For example, many 

fewer mothers worked in 1972 than in recent years. The importance of gathering information 

about maternal occupation increased with the passage of time and the increasing labor market 

participation of American females. The household items list has been revised for each survey. 

For NLS-72, owning a color television discriminated between people of various income levels. 

by the time of HS&B, 8 years later, this was no longer so. By 2002, HS&B items such as 

ownership of a typewriter had ceased to function as good proxies for family income, while other 

items, such as access to the Internet or having a digital video disc player, did.6

 Although items 

differ across the index over time, in each case the items are those that are needed to provide a 

measure that has a reasonable correlation with income. Another area where change over time is 

possible is in occupations and their relative prestige. To accommodate this factor, two sets of 

prestige scores were drawn upon in NELS:88: the 1961 Duncan socioeconomic indicator 

measure that had been employed in NLS-72 and HS&B, as well as a 1989 revision by Nakao and 

Treas (1992). The same strategy has been employed in ELS:2002. 

H.1.6 Imputation of Missing Data for ELS:2002 Key Variables 

One difference between the SES variable in ELS:2002 and in prior studies arises from the 

use of imputation in ELS:2002. Because all the constituents of SES are subject to imputation, it 

has been possible to create an SES composite with no missing data for ELS:2002. For the 

HS&B sophomores, SES was missing for around 9 percent of the participants, and for NELS:88 

(in 1990) just under 10 percent. The availability of imputed variables (including both key 

classification variables and achievement test scores) also poses a novel question for analysts 

interested in intercohort comparisons. Because imputed values are flagged, it is the analyst’s 

choice whether or not to employ them. If the imputed variables are used, they should have the 

effect of improving cross-sectional estimation. On the other hand, since imputation was not used 

in the prior studies, it is also possible that use of ELS:2002 imputed values might decrease 

6 The household items were asked in ELS:2002, but the index was not used in the creation of SES, since missing 

income data were imputed. 

H-7 
Appendix H: 

Cross-Cohort Comparisons 

comparability of results across studies. To explore the issue of the magnitude of the effect of 

imputation on comparative bivariate and multivariate analysis, appendix C compares imputed 

and unimputed ELS:2002 estimates, including estimates based on an SES composite using the 

household items index substitution and an SES composite based on parent data with missings 

imputed. 

H.1.7 Differences of Test Content and Reliabilities 

The test battery has evolved over time. Only one school subject—mathematics—has 

been tested at all timepoints, and the early mathematics tests were limited to quantitative 

comparison items. The NLS-72 and HS&B 1980 senior tests also were administered in 

vocabulary and in reading, as well as in a number of ability domains not closely linked to the 

school curriculum (a picture number test gauged associative memory, a mosaic comparisons test 

measured perceptual speed and accuracy, and another test measured visualization in three 

dimensions). The HS&B sophomore tests—because they were to be repeated after 2 years of 

additional schooling—took a different tack. Arguably more curriculum sensitive, they measured 

knowledge in six areas: vocabulary, reading, mathematics, science, writing, and civics. The test 

battery in NELS:88 comprised assessments in reading, mathematics, science, and social studies 

(history, geography, and civics). In ELS:2002, reading and mathematics assessments were 

administered in the base year, and mathematics again in the first follow-up. 

Although different tests have been equated, the linkage does not carry through 

uninterruptedly from NLS-72 to ELS:2002. The NLS-72 and HS&B senior tests were equated 

(Rock et al. 1985), and the NELS:88 and ELS:2002 12th-grade tests have been equated (as 

documented in this report). (For sophomores, a link has been effected from the HS&B 

sophomore cohort in 1980 to the NELS:88 scale in 1990 and the ELS:2002 in 2002 [Ingels et al. 

2004]). However, certain kinds of test score analyses, using effect sizes, are possible across the 

various senior cohorts (see Green, Dugoni, and Ingels 1995). 

In addition, starting in NELS:88, the tests were made at least moderately adaptive (in 

1990, 1992, and 2004, through using the prior round’s ability estimate to assign a specific test 

form; in 2002, through a two-stage test in which performance on a routing test determined 

assignment of the second-stage form). In consequence, test reliabilities are higher for the later 

assessment batteries (for example, in mathematics, 0.85–0.86 for NLS-72 and HS&B; 0.92–0.94 

for NELS:88 and ELS:2002). 

H.1.8 Differences of Questionnaire Content 

Readers are referred to the crosswalk in section H.2 to identify comparable items. 

H.1.9 Mode Effects in Data Collection, Context Effects 

Survey responses can be influenced by the mode of questionnaire administration 

(Tourangeau, Rips, and Rasinski 2000). There are some mode of administration differences 

across the studies (such differences will grow greater with future rounds—for example, 

ELS:2002 will collect 2006 data via self-administration on the Web, as well as computer-assisted 

telephone interviews and computer-assisted personal interviews, as contrasted to paper-andpencil mail surveys in the NLS-72 and HS&B era). Order and context effects are also possible 

(questions have been added, dropped, and reordered, over time). Though possible threats to 

H-8 
Appendix H: 

Cross-Cohort Comparisons 

comparability of data over time, little methodological work has been done on mode or context 

effects within this longitudinal studies series. 

The crosswalk in section H.2 links ELS:2002 base-year student questionnaire items with 

similar items from three previous NCES high school senior cohort questionnaires: the NELS:88 

second follow-up questionnaire (1992), the HS&B base-year senior cohort questionnaire (1980), 

and the NLS-72 base-year questionnaire (1972). This crosswalk will facilitate analyses of trends 

among high school seniors, spanning a 32-year period. Linked questions may be identical in 

content and format or may differ in one or more ways: the question, item, or response wording; 

the order in which response options were presented; the manner in which the data were collected 

(e.g., categorical response option versus open-ended response fields, instructions to mark one 

versus mark all that apply); and the population to which the question applies. Therefore, it is 

strongly recommended that analysts review documentation (including facsimiles of the 

questionnaires) to determine if linked questions are appropriate for their purpose. 

H.2 Cross-Cohort Item Crosswalk 

Table H-3 lists the contents of the ELS:2002 first follow-up questionnaires, with the 

exception of the locator section, which has not been made a part of the data release. In the first 

column, an abbreviated stem is provided for each item. In the second column, the item’s 

ELS:2002 status is indicated, that is, the variable name for each ELS:2002 first follow-up 

questionnaire (for brevity, the prefix “F1” has been dropped). For example, math coursework 

carries an entry for all five first follow-up questionnaires (student, transfer student, 

homeschooled student, early graduate, dropout). In the third column, the corresponding 

NELS:88 second follow-up item (if any) is indicated. The fourth and fifth columns supply 

linkage from the NELS:88 second follow-up item (if any) to the relevant HS&B senior 

questionnaire variable, and the sixth column to the base year of the NLS-72. 

H.3 Appendix H References 

Green, P.J., Dugoni, B., and Ingels, S.J. (1995). Trends Among High School Seniors, 1972-1992 

(NCES 95–380). U.S. Department of Education. Washington, DC: National Center for 

Education Statistics. 

Ingels, S.J., Dowd, K.L., Baldridge, J.D., Stipe, J.L., Bartot, V.H., and Frankel, M.R. (1994). 

NELS:88 Second Follow-up: Student Component Data File User’s Manual (NCES 94– 

374). U.S. Department of Education. Washington, DC: National Center for Education 

Statistics. 

Ingels, S.J. (1996). Sample Exclusion in NELS:88—Characteristics of Base Year Ineligible 

Students; Changes in Eligibility Status After Four Years (NCES 96–723). U.S. 

Department of Education. Washington, DC: National Center for Education Statistics. 

Ingels, S.J., Pratt, D.J., Rogers, J., Siegel, P.H., and Stutts, E.S. (2004). Education Longitudinal 

Study of 2002: Base Year Data File User’s Manual (NCES 2004–405). U.S. 

Department of Education. Washington, DC: National Center for Education Statistics. 

Available: http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2004405. 

H-9 
Appendix H: 

Cross-Cohort Comparisons 

Nakao, K., and Treas, J. (1992). The 1989 Socioeconomic Index of Occupations: Construction 

from the 1989 Occupational Prestige Scores (General Social Survey Methodological 

Report No. 74). Chicago: National Opinion Research Center. 

Rock, D.A., Ekstrom, R.B., Goertz, M.E., Hilton, T.L., and Pollack, J.M. (1985). Factors 

Associated With Decline of Test Scores of High School Seniors, 1972 to 1980. U.S. 

Department of Education. Washington, DC: National Center for Education Statistics. 

Tourangeau, R., Rips, L.J., and Rasinski, K. (2000). The Psychology of Survey Response. New 

York: Cambridge University Press. 

Zahs, D., Pedlow, S., Morrissey, M., Marnell, P., and Nichols, B. (1995). High School and 

Beyond Fourth Follow-up Methodology Report (NCES 95–426). U.S. Department of 

Education, National Center for Education Statistics. Washington, DC: U.S. Government 

Printing Office. 

H-10 
Table H-3. Cross-cohort item crosswalk for longitudinal studies, by item: Selected years, 1972–2002 

ELS:2002 first follow-up NELS:88 second follow- HS&B 1982 HS&

Question questionnaires up questionnaires seniors 

Grade level S14, T18, H14 S6A 

Diploma or certificate most likely to receive S15, T19, H15, E19 S6B 

Science coursework S16, T20, H16, E29, D27 Math coursework S17, T21, H17, E30, D28 Confidence in math S18 Calculators/computers in math S19 S19B 

Computer use in math classes S20 College entrance tests S21, T22, H18 S44 8 

How studied for college tests S22 S45 

Participated in college preparation program 

 for disadvantaged S23 S14A 11cd 

Yrs participated in Talent Search, etc. S24 S14B 

Victimization S25 S8 Extracurricular activities S26, T23, H20, E31 S30A, S30B 38 

Hours/week spent on extracurricular activities S27, T24, H21, E32 S31 

School has library media/resource center S28, T25 How often uses school library S29, T26 How often uses public library S30, T27, H22, E33, D49 

Hours/week spent on homework both in and 

out of school S31, T28, H23 S25f 15 

Hours on math homework S32 S25a 

Hours/week spent reading outside of school S33, T29, H24, E34, D50 S32 60b 

Hours watching television S34, T30, H25, E35, D51 S35 61 

Hours playing video games S35, T31, H26, E36, D52 S34 

Computer use for schoolwork/other 

S36, T32, H27, E37, D53 

Computer use at various locations S37, T33, H28, E38, D54 Computer use for fun, school, learn things S38 Activities outside of school S39, T34, H29, E39, D55 S33, D35 60 

Life values S40, T35, H30, E40, D56 S40, D36 73 

How will spend summer S41 S46 

H-11 

See notes at end of table. 
Table H-3. Cross-cohort item crosswalk for longitudinal studies, by item: Selected years, 1972–2002—Continued 

ELS:2002 first follow-up NELS:88 second follow- HS&B 1982 HS&

Question questionnaires up questionnaires seniors 

How far in school respondent thinks will get S42, T36, H31, E41, D57 S43, D38 80 

How far mother and father wants to go S43, T37, H32, E42, D58 S42, D37 81 Most important thing right after high school S44, T38, H33 S41 63 Plans to go on to school right after high school 

S45, T39, H34 S49 87h 

Reasons decided not to go right after high school 

S46, T40, H35 S50 

Plans to continue education some time in future 

S47, T41, H36, E44 S56 122 

Where went for info on college entrance S48 Type of school plans to attend S49, T42, H37, E45 S61 115 

Number of school applied to S50, T43, H38, E46 S60A 124 

Importance of school characteristics S52, T45, H40, E48 S59 123 

Plans to work right after high school S53, T46, H41 S51 87a 

Has regular full-time job lined up S54, T47, H42 S52 88 

Who helped select jobs S55 S53 Occupation expects to have after high  school—verbatim (restricted) S56, T48, H43 S64 77a 

Occupation expects to have at age 30—  verbatim (restricted) S57, T49, H44, E56, D66 S64, D40A 77a 

How much education respondent thinks will 

be needed for job at age 30 S58, T50, H45, E57, D67 S65 

Ever worked for pay not around house S59, T51, H46 S86A 24 

How many hours usually works a week during school year S60, T52, H47 S88 25 

How many hours works on the weekend during school year S61 S89 Performed unpaid volunteer/community service work S62, T53, H48, E58, D68 S37 

Types of volunteer organizations S63 S39 

How often discuss with parents S64, T54, H49 S99 

Friends’ plans for after high school S65, T55, H50, E59, D69 S69, D59 

H-12 

See notes at end of table. 
Table H-3. Cross-cohort item crosswalk for longitudinal studies, by item: Selected years, 1972–2002—Continued 

ELS:2002 first follow-up NELS:88 second follow- HS&B 1982 HS&

Question questionnaires up questionnaires seniors 

When began going to transfer school T15 Reasons for transferring T16 Agreement w/ statements re school/teachers T17 S7 66, 67 

Month and year last attended school E20, D19 D6 

Grade when last attended school E21, D20 D7 

How earned GED E24, D42 Why decided to complete GED E25, D43 State where GED/equivalency was earned— restricted E26, D44 Month and year graduated/received equivalency from high school E27, D45 E114, D32 G1 

Why decided to graduate/complete early E28 E115 G2 

Enrolled in an educational institution since  high school E43 E127A, D23 G13A2 

Number of jobs held since left high school E49, D59 D44A 

Current/most recent job or occupation— E121A, E121B, D45A, restricted 

E50, D60 D45B G10.1 

Month and year started working at this job E51, D61 E122, D45E G10.5 

Still have this job E52, D62 E123, D45F G10.6 

Month and year left most recent job E53, D63 E123, D45G G10.6 

Current/most recent pay per hour E54, D64 D45K 

Number of hours/week usually worked at this job E55, D65 D45L 

Whether passed last grade attended D21 D8 

Left school for more than a month before last left D22 D10A 

Month and year first left school for more than a 

month D23 D10B 

Month and year returned to school D24 D11 Attended school during 2002–03 school year D25 D14A 

H-13 

See notes at end of table. 
Table H-3. Cross-cohort item crosswalk for longitudinal studies, by item: Selected years, 1972–2002—Continued 

ELS:2002 first follow-up NELS:88 second follow- HS&B 1982 HS&

Question questionnaires up questionnaires seniors 

H-14 

Number of school days missed during 2002– 03 school year D26 D14B Reasons for leaving school E22, D29 D9A 

Feels that leaving school was a good decision 

E23, D30 D17A 

What people at school did D31 D21 

What parents did D32 D22 Things that happened in past 2 years D33 D24 

Participated in an alternative program D34 D25 Month and year entered most recent  alternative program D35 D26A Still enrolled in alternative program D36 D26B Month and year left/completed most recent alternative program D37 D26C 

Who referred to alternative program D38 D27 Services received from alternative program D39 D29 

Number of alternative programs participated in D40 D30 

Plan to get GED or high school diploma D41 D31 Currently taking class to prepare for GED  examination D46 D33A Plan to go back to high school/take GED class 

D47 D33B 

Month and year expects to receive high  school diploma/GED D48 D34 Sex N2 N2 Student is Hispanic N3 N17 Student’s Hispanic subdivision N4 N19 

Race N5 N17 Student’s Asian subdivision N6 N18 English is student’s native language N7 S107 Student’s native language (restricted) N8 N20 English skills N9 S109 

See notes at end of table. 
Table H-3. Cross-cohort item crosswalk for longitudinal studies, by item: Selected years, 1972–2002—Continued 

ELS:2002 first follow-up NELS:88 second follow- HS&B 1982 HS&

Question questionnaires up questionnaires seniors 

Ever held back a grade N11 N16 

Grades repeated N12 N16 Lives in household at least half of time N13 Mother/female guardian’s work N14 N5 

Father/male guardian’s work N15 N7 

Parents’ education N16 N8 Family has items in home N17 N12 

NOTE: This crosswalk was constructed by linking ELS:2002 first follow-up items with the NELS:88 second follow-up items from the Inte

Questionnaire Crosswalk in appendix E of the NELS:88 Second Follow-up: Student Component Data File User’s Manual (94–374). S = 

H = Homeschool, E = Early Graduate, D = Dropout (Not Currently in School), N = New Participant Supplement. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002); Nof the High School Class of 1972 (NLS-72); High School and Beyond (HS&B) Longitudinal Study (1980); and National Education Longitu(NELS:88). 

H-15 

Appendix I 

Standard Errors and Design Effects 

I-1 

Table I-1. Student design effects, by item using first follow-up questionnaire weight—All: 2004 

I-3 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.8 0.47 0.28 

Most likely to receive a GED F1S15 = 5 2.1 0.15 0.12 

Already took the SAT or ACT F1S21C = 3 63.9 0.77 0.41 

Already took an AP test F1S21D = 3 14.7 0.56 0.31 

Had something stolen at school at least once F1S25A = 2,3 25.8 0.56 0.43 

Was offered drugs at school at least once F1S25B = 2,3 20.8 0.58 0.40 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.5 0.39 0.30 Participated in school band (not as an officer/leader/captain) F1S26C = 2 15.0 0.46 0.30 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.5 0.60 0.43 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 55.5 0.56 0.41 

Uses the computer at home once or twice a week F1S37A = 4 25.0 0.49 0.35 

Rarely or never performs community service F1S39C = 1 60.9 0.57 0.40 

Being successful in line of work is very important F1S40A = 3 90.7 0.32 0.24 Marrying the right person is very important F1S40B = 3 80.3 0.44 0.33 

Having lots of money is very important F1S40C = 3 35.7 0.57 0.39 Expects to earn a 4-year degree, nothing more F1S42 = 6 31.4 0.48 0.38 

Mother expects student to graduate from college, nothing more F1S43A = 6 38.1 0.53 0.40 

Plans to continue education right after high school F1S47 = 2 77.7 0.55 0.35 

Plans to hold a part-time job right after school F1S53 = 2 49.6 0.57 0.43 

Volunteered with a youth organization F1S63A = 1 27.8 0.80 0.55 

Often discusses grades with parents F1S64D = 3 52.5 0.56 0.43 Lives with mother only F1FCOMP = 5 18.7 0.44 0.32 

Native language is Spanish F1HOMLNG = 2 8.4 0.62 0.23 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.9 0.17 0.14 

At age 30 expects to be in the military F1OCC30 = 7 1.0 0.11 0.08 

At age 30 expects to be an operative F1OCC30 = 8 0.6 0.09 0.07 

At age 30 expects to be a professional (group b) F1OCC30 = 10 13.6 0.36 0.28 

At age 30 expects to be in a technical field F1OCC30 = 16 4.9 0.23 0.18 

At age 30 doesn't know what expects to be F1OCC30 = -1 30.1 0.46 0.38 

Mathematics test score F1TXM1IR = 0-85 48.3 0.28 0.13 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-2. Student design effects, by item using first follow-up questionnaire weight—Male: 2004 

I-4 

Design 

standard 

Simple random 

sample 

Survey item (or composite variable) Variable Estimate error standard error Most likely to receive an honors diploma from high school F1S15 = 2 10.6 0.53 0.37 

Most likely to receive a GED F1S15 = 5 2.5 0.25 0.18 

Already took the SAT or ACT F1S21C = 3 59.1 0.92 0.60 Already took an AP test F1S21D = 3 14.1 0.66 0.43 

Had something stolen at school at least once F1S25A = 2,3 29.7 0.82 0.64 

Was offered drugs at school at least once F1S25B = 2,3 27.3 0.86 0.62 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 18.0 0.60 0.46 Participated in school band (not as an officer/leader/captain) F1S26C = 2 10.9 0.51 0.37 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.1 0.82 0.61 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 56.9 0.76 0.58 

Uses the computer at home once or twice a week F1S37A = 4 23.0 0.65 0.49 Rarely or never performs community service F1S39C = 1 66.5 0.73 0.55 

Being successful in line of work is very important F1S40A = 3 89.2 0.48 0.36 

Marrying the right person is very important F1S40B = 3 79.5 0.60 0.47 

Having lots of money is very important F1S40C = 3 43.3 0.75 0.58 

Expects to earn a 4-year degree, nothing more F1S42 = 6 31.9 0.68 0.54 

Mother expects student to graduate from college, nothing more F1S43A = 6 40.1 0.70 0.58 

Plans to continue education right after high school F1S47 = 2 72.3 0.78 0.54 Plans to hold a part-time job right after school F1S53 = 2 46.3 0.81 0.61 

Volunteered with a youth organization F1S63A = 1 33.7 1.25 0.88 Often discusses grades with parents F1S64D = 3 48.0 0.85 0.61 

Lives with mother only F1FCOMP = 5 17.9 0.57 0.44 

Native language is Spanish F1HOMLNG = 2 7.8 0.60 0.31 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.3 0.24 0.21 At age 30 expects to be in the military F1OCC30 = 7 1.7 0.20 0.15 

At age 30 expects to be an operative F1OCC30 = 8 1.2 0.17 0.13 

At age 30 expects to be a professional (group b) F1OCC30 = 10 9.4 0.45 0.34 

At age 30 expects to be in a technical field F1OCC30 = 16 6.0 0.37 0.28 At age 30 doesn't know what expects to be F1OCC30 = -1 32.8 0.69 0.55 

Mathematics test score F1TXM1IR = 0-85 49.3 0.32 0.19 

Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-3. Student design effects, by item using first follow-up questionnaire weight—Female: 2004 

I-5 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 14.9 0.64 0.42 

Most likely to receive a GED F1S15 = 5 1.7 0.20 0.15 

Already took the SAT or ACT F1S21C = 3 68.7 0.91 0.56 

Already took an AP test F1S21D = 3 15.4 0.71 0.44 

Had something stolen at school at least once F1S25A = 2,3 21.9 0.73 0.57 

Was offered drugs at school at least once F1S25B = 2,3 14.5 0.67 0.49 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 11.1 0.46 0.37 Participated in school band (not as an officer/leader/captain) F1S26C = 2 19.2 0.66 0.47 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.9 0.83 0.61 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 54.0 0.66 0.58 

Uses the computer at home once or twice a week F1S37A = 4 27.0 0.68 0.51 

Rarely or never performs community service F1S39C = 1 55.2 0.79 0.58 

Being successful in line of work is very important F1S40A = 3 92.1 0.42 0.31 Marrying the right person is very important F1S40B = 3 81.1 0.65 0.45 

Having lots of money is very important F1S40C = 3 28.1 0.69 0.52 

Expects to earn a 4-year degree, nothing more F1S42 = 6 31.0 0.64 0.54 

Mother expects student to graduate from college, nothing more F1S43A = 6 36.0 0.73 0.56 

Plans to continue education right after high school F1S47 = 2 83.1 0.61 0.45 

Plans to hold a part-time job right after school F1S53 = 2 53.0 0.75 0.60 

Volunteered with a youth organization F1S63A = 1 23.4 0.92 0.69 

Often discusses grades with parents F1S64D = 3 57.0 0.70 0.60 

Lives with mother only F1FCOMP = 5 19.5 0.62 0.46 Native language is Spanish F1HOMLNG = 2 9.0 0.79 0.33 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.6 0.22 0.19 

At age 30 expects to be in the military F1OCC30 = 7 0.4 0.08 0.07 

At age 30 expects to be an operative F1OCC30 = 8 0.1 0.06 0.04 

At age 30 expects to be a professional (group b) F1OCC30 = 10 17.8 0.56 0.45 

At age 30 expects to be in a technical field F1OCC30 = 16 3.8 0.26 0.22 

At age 30 doesn't know what expects to be F1OCC30 = -1 27.5 0.61 0.52 

Mathematics test score F1TXM1IR = 0-85 47.3 0.32 0.18 

Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-4. Student design effects, by item using first follow-up questionnaire weight—American Indian or Alaska N

Simple random 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 4.4 2.05 1.88 

Most likely to receive a GED F1S15 = 5 5.5 2.63 2.10 

Already took the SAT or ACT F1S21C = 3 38.4 6.06 4.68 

Already took an AP test F1S21D = 3 7.6 3.35 2.55 

Had something stolen at school at least once F1S25A = 2,3 33.9 6.59 5.47 

Was offered drugs at school at least once F1S25B = 2,3 31.1 6.59 5.31 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 13.8 4.30 3.22 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 12.6 3.93 3.11 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 28.2 8.81 5.50 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 69.3 4.37 4.17 

Uses the computer at home once or twice a week F1S37A = 4 28.7 5.33 4.11 

Rarely or never performs community service F1S39C = 1 69.2 5.29 4.22 

Being successful in line of work is very important F1S40A = 3 90.4 3.63 2.68 Marrying the right person is very important F1S40B = 3 69.9 6.73 4.17 

Having lots of money is very important F1S40C = 3 40.3 4.98 4.46 

Expects to earn a 4-year degree, nothing more F1S42 = 6 26.3 5.50 4.00 

Mother expects student to graduate from college, nothing more F1S43A = 6 34.2 4.46 4.40 Plans to continue education right after high school F1S47 = 2 61.4 5.63 4.58 

Plans to hold a part-time job right after school F1S53 = 2 52.7 4.95 4.76 

Volunteered with a youth organization F1S63A = 1 39.8 8.31 8.94 

Often discusses grades with parents F1S64D = 3 48.0 6.75 4.79 

Lives with mother only F1FCOMP = 5 24.2 3.70 3.84 

Native language is Spanish F1HOMLNG = 2 # # # 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.9 1.43 1.56 

At age 30 expects to be in the military F1OCC30 = 7 2.0 1.50 1.30 

At age 30 expects to be an operative F1OCC30 = 8 0.8 0.76 0.80 

At age 30 expects to be a professional (group b) F1OCC30 = 10 9.5 3.38 2.71 

At age 30 expects to be in a technical field F1OCC30 = 16 4.1 2.30 1.83 

At age 30 doesn't know what expects to be F1OCC30 = -1 41.3 5.15 4.55 

Mathematics test score F1TXM1IR = 0-85 41.3 1.66 1.23 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

I-6 

† Not applicable. # Rounds to zero. 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-5. Student design effects, by item using first follow-up questionnaire weight—Asian: 2004 

Simple random 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.9 1.26 0.87 

Most likely to receive a GED F1S15 = 5 0.7 0.21 0.22 

Already took the SAT or ACT F1S21C = 3 75.9 1.92 1.13 Already took an AP test F1S21D = 3 29.4 2.54 1.22 

Had something stolen at school at least once F1S25A = 2,3 27.4 2.10 1.40 

Was offered drugs at school at least once F1S25B = 2,3 13.3 1.80 1.06 Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 13.3 1.27 0.89 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 13.7 1.34 0.90 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 29.4 1.88 1.45 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 51.2 1.95 1.29 

Uses the computer at home once or twice a week F1S37A = 4 19.1 1.48 1.01 

Rarely or never performs community service F1S39C = 1 51.8 1.93 1.29 

Being successful in line of work is very important F1S40A = 3 88.6 1.01 0.82 Marrying the right person is very important F1S40B = 3 79.7 1.31 1.03 

Having lots of money is very important F1S40C = 3 42.6 1.85 1.27 

Expects to earn a 4-year degree, nothing more F1S42 = 6 31.9 1.56 1.20 

Mother expects student to graduate from college, nothing more F1S43A = 6 30.9 1.49 1.21 

Plans to continue education right after high school F1S47 = 2 89.6 1.28 0.81 

Plans to hold a part-time job right after school F1S53 = 2 52.1 1.89 1.32 

Volunteered with a youth organization F1S63A = 1 17.6 1.71 1.43 

Often discusses grades with parents F1S64D = 3 45.9 1.91 1.33 

Lives with mother only F1FCOMP = 5 9.9 0.89 0.77 Native language is Spanish F1HOMLNG = 2 0.4 0.28 0.16 

At age 30 expects to be a manager, administrator F1OCC30 = 6 4.0 0.94 0.51 

At age 30 expects to be in the military F1OCC30 = 7 0.5 0.20 0.19 At age 30 expects to be an operative F1OCC30 = 8 # 0.04 0.05 At age 30 expects to be a professional (group b) F1OCC30 = 10 20.4 1.43 1.05 

At age 30 expects to be in a technical field F1OCC30 = 16 5.1 0.82 0.57 

At age 30 doesn't know what expects to be F1OCC30 = -1 36.6 1.73 1.25 

Mathematics test score F1TXM1IR = 0-85 54.1 0.90 0.42 

Summary statistics 

Mean 

Minimum 

Median Maximum 

Standard deviation 

I-7 

# Rounds to zero. NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-6. Student design effects, by item using first follow-up questionnaire weight—Black or African American: 2

I-8 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 9.42 0.93 0.68 

Most likely to receive a GED F1S15 = 5 2.94 0.44 0.40 Already took the SAT or ACT F1S21C = 3 55.23 1.50 1.20 

Already took an AP test F1S21D = 3 5.15 0.59 0.54 Had something stolen at school at least once F1S25A = 2,3 32.83 1.61 1.36 

Was offered drugs at school at least once F1S25B = 2,3 20.58 1.48 1.17 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 17.78 0.98 0.90 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 17.28 1.23 0.89 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 25.19 1.41 1.33 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 73.27 1.20 1.01 

Uses the computer at home once or twice a week F1S37A = 4 26.94 0.97 1.00 

Rarely or never performs community service F1S39C = 1 64.49 1.36 1.09 Being successful in line of work is very important F1S40A = 3 93.77 0.64 0.55 

Marrying the right person is very important F1S40B = 3 75.59 1.15 0.97 

Having lots of money is very important F1S40C = 3 55.70 1.30 1.12 Expects to earn a 4-year degree, nothing more F1S42 = 6 29.67 1.21 1.03 

Mother expects student to graduate from college, nothing more F1S43A = 6 32.80 1.17 1.07 

Plans to continue education right after high school F1S47 = 2 78.34 1.24 0.99 

Plans to hold a part-time job right after school F1S53 = 2 46.48 1.39 1.20 

Volunteered with a youth organization F1S63A = 1 25.38 2.17 1.66 Often discusses grades with parents F1S64D = 3 59.47 1.55 1.20 

Lives with mother only F1FCOMP = 5 37.81 1.32 1.09 

Native language is Spanish F1HOMLNG = 2 0.59 0.22 0.18 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.96 0.45 0.39 

At age 30 expects to be in the military F1OCC30 = 7 0.69 0.26 0.19 At age 30 expects to be an operative F1OCC30 = 8 0.84 0.26 0.21 

At age 30 expects to be a professional (group b) F1OCC30 = 10 15.21 0.93 0.82 

At age 30 expects to be in a technical field F1OCC30 = 16 5.39 0.68 0.52 

At age 30 doesn't know what expects to be F1OCC30 = -1 26.13 1.27 1.01 

Mathematics test score F1TXM1IR = 0-85 38.79 0.45 0.29 

Summary statistics 

Mean Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-7. Student design effects, by item using first follow-up questionnaire weight—Hispanic or Latino: 2004 

Simple random 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 8.5 0.85 0.62 

Most likely to receive a GED F1S15 = 5 3.2 0.48 0.39 Already took the SAT or ACT F1S21C = 3 44.2 1.66 1.14 

Already took an AP test F1S21D = 3 13.3 1.07 0.79 

Had something stolen at school at least once F1S25A = 2,3 26.7 1.60 1.23 

Was offered drugs at school at least once F1S25B = 2,3 23.3 1.51 1.17 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 12.5 0.81 0.74 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 11.1 0.78 0.70 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.1 1.48 1.24 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 60.6 1.26 1.06 

Uses the computer at home once or twice a week F1S37A = 4 28.4 1.28 0.96 

Rarely or never performs community service F1S39C = 1 69.8 1.08 0.98 

Being successful in line of work is very important F1S40A = 3 89.3 0.94 0.66 

Marrying the right person is very important F1S40B = 3 77.3 1.14 0.89 

Having lots of money is very important F1S40C = 3 41.2 1.26 1.05 

Expects to earn a 4-year degree, nothing more F1S42 = 6 26.2 1.23 0.94 

Mother expects student to graduate from college, nothing more F1S43A = 6 33.0 1.03 1.02 

Plans to continue education right after high school F1S47 = 2 72.3 1.44 1.02 

Plans to hold a part-time job right after school F1S53 = 2 55.7 1.36 1.14 

Volunteered with a youth organization F1S63A = 1 21.9 1.84 1.55 

Often discusses grades with parents F1S64D = 3 54.2 1.24 1.15 Lives with mother only F1FCOMP = 5 19.9 1.14 0.85 

Native language is Spanish F1HOMLNG = 2 50.8 1.99 1.08 At age 30 expects to be a manager, administrator F1OCC30 = 6 2.4 0.36 0.34 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.26 0.22 

At age 30 expects to be an operative F1OCC30 = 8 0.5 0.19 0.15 

At age 30 expects to be a professional (group b) F1OCC30 = 10 12.9 0.81 0.73 

At age 30 expects to be in a technical field F1OCC30 = 16 5.4 0.56 0.49 

At age 30 doesn't know what expects to be F1OCC30 = -1 32.1 1.02 1.01 Mathematics test score F1TXM1IR = 0-85 41.2 0.47 0.31 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

I-9 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-8. Student design effects, by item using first follow-up questionnaire weight—More than one race: 2004 

I-10 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 10.4 1.45 1.21 

Most likely to receive a GED F1S15 = 5 2.7 0.99 0.64 

Already took the SAT or ACT F1S21C = 3 60.3 2.67 1.99 

Already took an AP test F1S21D = 3 13.2 1.81 1.39 

Had something stolen at school at least once F1S25A = 2,3 31.6 2.83 2.17 

Was offered drugs at school at least once F1S25B = 2,3 23.4 2.81 1.98 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.2 1.76 1.39 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 18.0 1.86 1.53 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 22.0 2.64 1.99 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 59.9 2.55 1.90 

Uses the computer at home once or twice a week F1S37A = 4 24.3 2.17 1.65 

Rarely or never performs community service F1S39C = 1 59.8 2.46 1.90 

Being successful in line of work is very important F1S40A = 3 87.3 1.72 1.29 

Marrying the right person is very important F1S40B = 3 79.1 1.88 1.57 

Having lots of money is very important F1S40C = 3 36.2 2.37 1.85 

Expects to earn a 4-year degree, nothing more F1S42 = 6 34.1 2.31 1.83 

Mother expects student to graduate from college, nothing more F1S43A = 6 38.4 2.55 1.92 

Plans to continue education right after high school F1S47 = 2 71.8 2.56 1.81 

Plans to hold a part-time job right after school F1S53 = 2 47.0 2.53 2.02 

Volunteered with a youth organization F1S63A = 1 29.5 3.75 2.62 

Often discusses grades with parents F1S64D = 3 54.9 2.81 2.03 

Lives with mother only F1FCOMP = 5 21.9 1.91 1.59 

Native language is Spanish F1HOMLNG = 2 0.7 0.42 0.33 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.77 0.64 At age 30 expects to be in the military F1OCC30 = 7 2.2 0.87 0.57 At age 30 expects to be an operative F1OCC30 = 8 0.2 0.18 0.16 At age 30 expects to be a professional (group b) F1OCC30 = 10 10.3 1.39 1.18 

At age 30 expects to be in a technical field F1OCC30 = 16 4.4 1.00 0.80 

At age 30 doesn't know what expects to be F1OCC30 = -1 30.2 2.35 1.78 

Mathematics test score F1TXM1IR = 0-85 47.5 0.81 0.60 

Summary statistics 

Mean 

Minimum 

Median Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-9. Student design effects, by item using first follow-up questionnaire weight—White: 2004 

Simple random 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 14.9 0.63 0.39 

Most likely to receive a GED F1S15 = 5 1.6 0.18 0.14 

Already took the SAT or ACT F1S21C = 3 70.5 0.84 0.52 

Already took an AP test F1S21D = 3 16.3 0.69 0.42 Had something stolen at school at least once F1S25A = 2,3 23.7 0.67 0.53 

Was offered drugs at school at least once F1S25B = 2,3 20.6 0.70 0.51 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.5 0.50 0.39 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 15.5 0.59 0.40 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.3 0.75 0.55 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 49.8 0.69 0.55 

Uses the computer at home once or twice a week F1S37A = 4 24.0 0.63 0.47 

Rarely or never performs community service F1S39C = 1 58.2 0.75 0.54 Being successful in line of work is very important F1S40A = 3 90.7 0.41 0.32 Marrying the right person is very important F1S40B = 3 82.5 0.53 0.41 

Having lots of money is very important F1S40C = 3 28.9 0.66 0.49 

Expects to earn a 4-year degree, nothing more F1S42 = 6 33.2 0.60 0.51 

Mother expects student to graduate from college, nothing more F1S43A = 6 41.2 0.69 0.54 

Plans to continue education right after high school F1S47 = 2 78.7 0.65 0.46 

Plans to hold a part-time job right after school F1S53 = 2 48.7 0.72 0.56 Volunteered with a youth organization F1S63A = 1 29.6 1.03 0.70 

Often discusses grades with parents F1S64D = 3 51.0 0.68 0.56 Lives with mother only F1FCOMP = 5 14.1 0.50 0.38 Native language is Spanish F1HOMLNG = 2 0.1 0.05 0.04 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.0 0.21 0.19 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.14 0.11 At age 30 expects to be an operative F1OCC30 = 8 0.7 0.11 0.09 At age 30 expects to be a professional (group b) F1OCC30 = 10 13.3 0.46 0.37 

At age 30 expects to be in a technical field F1OCC30 = 16 4.7 0.31 0.23 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.9 0.61 0.50 

Mathematics test score F1TXM1IR = 0-85 51.9 0.28 0.16 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

I-11 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-10. Student design effects, by item using first follow-up questionnaire weight—Public: 2004 

I-12 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.7 0.50 0.32 

Most likely to receive a GED F1S15 = 5 2.2 0.17 0.14 

Already took the SAT or ACT F1S21C = 3 61.6 0.83 0.48 

Already took an AP test F1S21D = 3 14.0 0.59 0.34 

Had something stolen at school at least once F1S25A = 2,3 25.8 0.60 0.49 

Was offered drugs at school at least once F1S25B = 2,3 21.5 0.63 0.46 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 13.9 0.41 0.33 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 14.6 0.48 0.34 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 23.5 0.63 0.49 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 56.2 0.59 0.46 

Uses the computer at home once or twice a week F1S37A = 4 25.5 0.52 0.40 

Rarely or never performs community service F1S39C = 1 62.1 0.60 0.45 

Being successful in line of work is very important F1S40A = 3 90.7 0.34 0.27 

Marrying the right person is very important F1S40B = 3 79.7 0.48 0.37 

Having lots of money is very important F1S40C = 3 36.4 0.60 0.45 Expects to earn a 4-year degree, nothing more F1S42 = 6 31.1 0.52 0.43 

Mother expects student to graduate from college, nothing more F1S43A = 6 37.7 0.57 0.46 

Plans to continue education right after high school F1S47 = 2 76.4 0.60 0.41 

Plans to hold a part-time job right after school F1S53 = 2 49.5 0.61 0.49 

Volunteered with a youth organization F1S63A = 1 27.7 0.89 0.66 

Often discusses grades with parents F1S64D = 3 52.3 0.60 0.49 

Lives with mother only F1FCOMP = 5 19.2 0.47 0.36 

Native language is Spanish F1HOMLNG = 2 8.8 0.67 0.27 At age 30 expects to be a manager, administrator F1OCC30 = 6 2.9 0.18 0.16 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.11 0.10 At age 30 expects to be an operative F1OCC30 = 8 0.7 0.10 0.08 At age 30 expects to be a professional (group b) F1OCC30 = 10 13.1 0.38 0.32 At age 30 expects to be in a technical field F1OCC30 = 16 5.1 0.25 0.21 At age 30 doesn't know what expects to be F1OCC30 = -1 30.0 0.49 0.43 

Mathematics test score F1TXM1IR = 0-85 47.6 0.30 0.15 

Summary statistics 

Mean Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-11. Student design effects, by item using first follow-up questionnaire weight—Catholic: 2004 

Simple random 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.5 1.38 0.76 

Most likely to receive a GED F1S15 = 5 0.5 0.16 0.16 

Already took the SAT or ACT F1S21C = 3 91.4 1.15 0.65 

Already took an AP test F1S21D = 3 22.4 2.05 0.99 

Had something stolen at school at least once F1S25A = 2,3 26.0 1.79 1.12 

Was offered drugs at school at least once F1S25B = 2,3 17.7 1.35 0.97 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 22.1 1.66 0.96 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 15.3 1.34 0.83 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 36.0 1.98 1.24 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 52.4 2.10 1.15 

Uses the computer at home once or twice a week F1S37A = 4 16.8 1.06 0.86 Rarely or never performs community service F1S39C = 1 43.2 1.80 1.14 

Being successful in line of work is very important F1S40A = 3 90.9 0.66 0.66 

Marrying the right person is very important F1S40B = 3 87.1 0.93 0.77 

Having lots of money is very important F1S40C = 3 29.2 1.62 1.05 

Expects to earn a 4-year degree, nothing more F1S42 = 6 37.9 1.34 1.12 

Mother expects student to graduate from college, nothing more F1S43A = 6 42.6 1.62 1.15 Plans to continue education right after high school F1S47 = 2 94.0 0.77 0.55 

Plans to hold a part-time job right after school F1S53 = 2 51.2 1.69 1.15 

Volunteered with a youth organization F1S63A = 1 31.8 1.88 1.30 

Often discusses grades with parents F1S64D = 3 56.7 1.28 1.15 

Lives with mother only F1FCOMP = 5 12.4 0.75 0.76 Native language is Spanish F1HOMLNG = 2 3.0 0.95 0.39 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.3 0.35 0.41 

At age 30 expects to be in the military F1OCC30 = 7 0.8 0.26 0.20 

At age 30 expects to be an operative F1OCC30 = 8 # # # 

At age 30 expects to be a professional (group b) F1OCC30 = 10 17.4 1.23 0.88 

At age 30 expects to be in a technical field F1OCC30 = 16 3.6 0.51 0.43 

At age 30 doesn't know what expects to be F1OCC30 = -1 30.9 1.13 1.07 

Mathematics test score F1TXM1IR = 0-85 55.9 0.61 0.29 

Summary statistics 

Mean Minimum 

Median 

Maximum Standard deviation 

I-13 

† Not applicable. # Rounds to zero. NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-12. Student design effects, by item using first follow-up questionnaire weight—Other private: 2004 

I-14 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 15.1 1.66 0.98 Most likely to receive a GED F1S15 = 5 0.6 0.16 0.20 

Already took the SAT or ACT F1S21C = 3 85.7 2.47 0.97 

Already took an AP test F1S21D = 3 24.7 3.11 1.22 

Had something stolen at school at least once F1S25A = 2,3 24.2 2.20 1.35 Was offered drugs at school at least once F1S25B = 2,3 7.9 1.05 0.85 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 20.9 2.40 1.11 Participated in school band (not as an officer/leader/captain) F1S26C = 2 26.7 3.24 1.22 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 32.7 2.27 1.51 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 39.4 2.37 1.33 

Uses the computer at home once or twice a week F1S37A = 4 20.3 2.21 1.09 

Rarely or never performs community service F1S39C = 1 50.2 2.82 1.36 

Being successful in line of work is very important F1S40A = 3 89.5 1.06 0.83 Marrying the right person is very important F1S40B = 3 86.8 1.04 0.92 Having lots of money is very important F1S40C = 3 26.4 1.68 1.19 Expects to earn a 4-year degree, nothing more F1S42 = 6 31.7 1.72 1.26 

Mother expects student to graduate from college, nothing more F1S43A = 6 41.4 1.77 1.35 Plans to continue education right after high school F1S47 = 2 90.5 1.58 0.81 

Plans to hold a part-time job right after school F1S53 = 2 49.6 2.18 1.38 Volunteered with a youth organization F1S63A = 1 22.6 1.59 1.50 

Often discusses grades with parents F1S64D = 3 53.0 1.90 1.38 

Lives with mother only F1FCOMP = 5 12.9 1.27 0.91 

Native language is Spanish F1HOMLNG = 2 2.8 1.29 0.45 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.0 0.65 0.47 

At age 30 expects to be in the military F1OCC30 = 7 0.8 0.26 0.24 

At age 30 expects to be an operative F1OCC30 = 8 0.2 0.18 0.12 

At age 30 expects to be a professional (group b) F1OCC30 = 10 21.7 1.70 1.13 At age 30 expects to be in a technical field F1OCC30 = 16 2.8 0.48 0.45 At age 30 doesn't know what expects to be F1OCC30 = -1 33.9 1.69 1.30 Mathematics test score F1TXM1IR = 0-85 57.3 0.92 0.38 

Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-13. Student design effects, by item using first follow-up questionnaire weight—Low socioeconomic status 

I-15 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 7.0 0.55 0.46 

Most likely to receive a GED F1S15 = 5 3.4 0.40 0.33 Already took the SAT or ACT F1S21C = 3 41.6 1.28 0.91 

Already took an AP test F1S21D = 3 7.3 0.67 0.49 Had something stolen at school at least once F1S25A = 2,3 26.5 1.17 0.97 

Was offered drugs at school at least once F1S25B = 2,3 19.0 1.11 0.86 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 12.5 0.74 0.60 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 13.6 0.72 0.62 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 21.1 1.18 0.93 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 62.9 1.03 0.83 

Uses the computer at home once or twice a week F1S37A = 4 25.5 0.90 0.74 

Rarely or never performs community service F1S39C = 1 70.2 0.91 0.78 

Being successful in line of work is very important F1S40A = 3 89.1 0.64 0.53 

Marrying the right person is very important F1S40B = 3 77.3 0.90 0.71 

Having lots of money is very important F1S40C = 3 41.5 0.97 0.84 

Expects to earn a 4-year degree, nothing more F1S42 = 6 25.6 0.89 0.74 

Mother expects student to graduate from college, nothing more F1S43A = 6 32.8 0.93 0.81 Plans to continue education right after high school F1S47 = 2 66.1 1.15 0.87 

Plans to hold a part-time job right after school F1S53 = 2 48.1 1.14 0.92 

Volunteered with a youth organization F1S63A = 1 23.8 1.62 1.35 Often discusses grades with parents F1S64D = 3 51.7 1.09 0.93 

Lives with mother only F1FCOMP = 5 26.8 1.00 0.75 

Native language is Spanish F1HOMLNG = 2 22.3 1.71 0.71 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.3 0.27 0.26 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.20 0.18 At age 30 expects to be an operative F1OCC30 = 8 1.4 0.27 0.20 

At age 30 expects to be a professional (group b) F1OCC30 = 10 11.5 0.61 0.55 

At age 30 expects to be in a technical field F1OCC30 = 16 5.4 0.44 0.39 

At age 30 doesn't know what expects to be F1OCC30 = -1 31.8 0.94 0.80 

Mathematics test score F1TXM1IR = 0-85 40.1 0.36 0.24 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-14. Student design effects, by item using first follow-up questionnaire weight—Middle socioeconomic stat

I-16 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 11.3 0.54 0.38 

Most likely to receive a GED F1S15 = 5 2.0 0.21 0.17 Already took the SAT or ACT F1S21C = 3 61.9 0.88 0.60 Already took an AP test F1S21D = 3 11.1 0.54 0.39 

Had something stolen at school at least once F1S25A = 2,3 25.9 0.71 0.62 

Was offered drugs at school at least once F1S25B = 2,3 22.2 0.83 0.59 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 15.0 0.53 0.43 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 14.9 0.60 0.43 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 22.8 0.82 0.61 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 56.7 0.76 0.59 Uses the computer at home once or twice a week F1S37A = 4 27.0 0.63 0.53 Rarely or never performs community service F1S39C = 1 63.4 0.74 0.57 

Being successful in line of work is very important F1S40A = 3 91.1 0.45 0.34 

Marrying the right person is very important F1S40B = 3 80.3 0.63 0.47 

Having lots of money is very important F1S40C = 3 35.8 0.77 0.57 

Expects to earn a 4-year degree, nothing more F1S42 = 6 33.4 0.64 0.56 

Mother expects student to graduate from college, nothing more F1S43A = 6 39.2 0.75 0.59 Plans to continue education right after high school F1S47 = 2 76.4 0.69 0.52 

Plans to hold a part-time job right after school F1S53 = 2 51.0 0.76 0.62 Volunteered with a youth organization F1S63A = 1 27.5 1.09 0.81 Often discusses grades with parents F1S64D = 3 53.0 0.79 0.62 

Lives with mother only F1FCOMP = 5 19.0 0.58 0.46 

Native language is Spanish F1HOMLNG = 2 4.7 0.35 0.25 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.23 0.20 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.16 0.12 At age 30 expects to be an operative F1OCC30 = 8 0.6 0.11 0.09 At age 30 expects to be a professional (group b) F1OCC30 = 10 11.7 0.43 0.38 

At age 30 expects to be in a technical field F1OCC30 = 16 5.5 0.33 0.27 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.6 0.62 0.55 

Mathematics test score F1TXM1IR = 0-85 47.2 0.27 0.18 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-15. Student design effects, by item using first follow-up questionnaire weight—High socioeconomic status 

I-17 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 20.5 0.94 0.62 

Most likely to receive a GED F1S15 = 5 1.1 0.23 0.16 

Already took the SAT or ACT F1S21C = 3 86.3 0.78 0.54 

Already took an AP test F1S21D = 3 27.8 1.20 0.71 

Had something stolen at school at least once F1S25A = 2,3 24.9 1.06 0.76 

Was offered drugs at school at least once F1S25B = 2,3 19.7 0.94 0.70 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 15.4 0.74 0.56 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 16.6 0.82 0.57 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 29.9 1.05 0.81 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 45.7 1.08 0.77 

Uses the computer at home once or twice a week F1S37A = 4 20.5 0.94 0.62 

Rarely or never performs community service F1S39C = 1 46.7 0.95 0.77 

Being successful in line of work is very important F1S40A = 3 91.3 0.51 0.43 

Marrying the right person is very important F1S40B = 3 83.3 0.75 0.57 

Having lots of money is very important F1S40C = 3 29.9 0.98 0.70 

Expects to earn a 4-year degree, nothing more F1S42 = 6 33.3 0.94 0.72 

Mother expects student to graduate from college, nothing more F1S43A = 6 41.0 0.96 0.76 

Plans to continue education right after high school F1S47 = 2 90.1 0.67 0.46 

Plans to hold a part-time job right after school F1S53 = 2 48.3 0.97 0.78 

Volunteered with a youth organization F1S63A = 1 29.8 1.30 0.89 

Often discusses grades with parents F1S64D = 3 52.4 1.09 0.78 

Lives with mother only F1FCOMP = 5 9.8 0.61 0.45 

Native language is Spanish F1HOMLNG = 2 2.1 0.31 0.22 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.8 0.40 0.29 At age 30 expects to be in the military F1OCC30 = 7 0.9 0.20 0.15 At age 30 expects to be an operative F1OCC30 = 8 0.1 0.05 0.04 At age 30 expects to be a professional (group b) F1OCC30 = 10 19.4 0.75 0.61 At age 30 expects to be in a technical field F1OCC30 = 16 3.3 0.36 0.28 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.7 0.95 0.70 

Mathematics test score F1TXM1IR = 0-85 57.2 0.35 0.21 Summary statistics 

Mean Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-16. Student design effects, by item using first follow-up questionnaire weight—Urban: 2004 

I-18 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.5 0.89 0.48 Most likely to receive a GED F1S15 = 5 2.3 0.30 0.22 

Already took the SAT or ACT F1S21C = 3 63.7 1.60 0.72 

Already took an AP test F1S21D = 3 18.2 1.24 0.58 

Had something stolen at school at least once F1S25A = 2,3 27.5 1.10 0.77 Was offered drugs at school at least once F1S25B = 2,3 21.9 1.13 0.72 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.0 0.76 0.51 Participated in school band (not as an officer/leader/captain) F1S26C = 2 14.7 0.84 0.52 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 27.7 1.17 0.79 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 59.2 1.06 0.70 

Uses the computer at home once or twice a week F1S37A = 4 24.0 0.96 0.60 

Rarely or never performs community service F1S39C = 1 61.1 1.10 0.69 Being successful in line of work is very important F1S40A = 3 90.5 0.60 0.41 

Marrying the right person is very important F1S40B = 3 78.6 0.85 0.58 

Having lots of money is very important F1S40C = 3 39.1 1.01 0.69 

Expects to earn a 4-year degree, nothing more F1S42 = 6 30.7 0.93 0.65 

Mother expects student to graduate from college, nothing more F1S43A = 6 36.0 0.91 0.69 

Plans to continue education right after high school F1S47 = 2 79.0 0.94 0.60 Plans to hold a part-time job right after school F1S53 = 2 50.9 1.10 0.74 Volunteered with a youth organization F1S63A = 1 26.6 1.62 0.91 

Often discusses grades with parents F1S64D = 3 54.8 1.03 0.74 

Lives with mother only F1FCOMP = 5 22.6 0.97 0.59 Native language is Spanish F1HOMLNG = 2 14.4 1.57 0.50 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.30 0.23 

At age 30 expects to be in the military F1OCC30 = 7 0.9 0.16 0.13 

At age 30 expects to be an operative F1OCC30 = 8 0.5 0.19 0.10 

At age 30 expects to be a professional (group b) F1OCC30 = 10 16.3 0.70 0.53 

At age 30 expects to be in a technical field F1OCC30 = 16 4.7 0.45 0.30 

At age 30 doesn't know what expects to be F1OCC30 = -1 30.1 0.78 0.66 Mathematics test score F1TXM1IR = 0-85 46.8 0.62 0.23 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-17. Student design effects, by item using first follow-up questionnaire weight—Suburban: 2004 

I-19 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.4 0.66 0.40 Most likely to receive a GED F1S15 = 5 2.0 0.21 0.17 

Already took the SAT or ACT F1S21C = 3 65.0 1.04 0.59 

Already took an AP test F1S21D = 3 14.1 0.77 0.44 

Had something stolen at school at least once F1S25A = 2,3 25.6 0.80 0.61 Was offered drugs at school at least once F1S25B = 2,3 21.3 0.81 0.57 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 15.3 0.54 0.44 Participated in school band (not as an officer/leader/captain) F1S26C = 2 14.6 0.61 0.43 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.2 0.81 0.62 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 53.7 0.77 0.59 

Uses the computer at home once or twice a week F1S37A = 4 24.9 0.67 0.51 

Rarely or never performs community service F1S39C = 1 60.7 0.74 0.58 Being successful in line of work is very important F1S40A = 3 90.8 0.42 0.34 

Marrying the right person is very important F1S40B = 3 81.1 0.62 0.46 

Having lots of money is very important F1S40C = 3 35.1 0.84 0.56 

Expects to earn a 4-year degree, nothing more F1S42 = 6 32.2 0.69 0.55 

Mother expects student to graduate from college, nothing more F1S43A = 6 39.1 0.77 0.58 

Plans to continue education right after high school F1S47 = 2 78.0 0.79 0.51 Plans to hold a part-time job right after school F1S53 = 2 49.3 0.79 0.61 Volunteered with a youth organization F1S63A = 1 27.8 1.04 0.79 

Often discusses grades with parents F1S64D = 3 51.7 0.80 0.62 

Lives with mother only F1FCOMP = 5 17.1 0.56 0.44 Native language is Spanish F1HOMLNG = 2 6.7 0.73 0.30 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.1 0.24 0.21 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.17 0.13 

At age 30 expects to be an operative F1OCC30 = 8 0.6 0.11 0.09 

At age 30 expects to be a professional (group b) F1OCC30 = 10 12.6 0.50 0.40 

At age 30 expects to be in a technical field F1OCC30 = 16 4.8 0.32 0.26 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.7 0.70 0.55 Mathematics test score F1TXM1IR = 0-85 49.1 0.39 0.19 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-18. Student design effects, by item using first follow-up questionnaire weight—Rural: 2004 

I-20 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 14.1 0.97 0.68 

Most likely to receive a GED F1S15 = 5 2.2 0.33 0.29 

Already took the SAT or ACT F1S21C = 3 61.5 1.57 0.98 

Already took an AP test F1S21D = 3 11.3 0.90 0.65 

Had something stolen at school at least once F1S25A = 2,3 23.8 1.12 0.97 

Was offered drugs at school at least once F1S25B = 2,3 18.3 1.24 0.88 Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 13.5 0.84 0.67 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 16.7 1.20 0.74 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 21.2 1.36 0.96 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 54.3 1.20 0.96 

Uses the computer at home once or twice a week F1S37A = 4 26.7 1.08 0.85 

Rarely or never performs community service F1S39C = 1 61.0 1.45 0.94 Being successful in line of work is very important F1S40A = 3 90.5 0.82 0.56 Marrying the right person is very important F1S40B = 3 80.9 0.96 0.75 

Having lots of money is very important F1S40C = 3 32.2 1.12 0.90 

Expects to earn a 4-year degree, nothing more F1S42 = 6 30.6 0.94 0.88 

Mother expects student to graduate from college, nothing more F1S43A = 6 38.5 1.19 0.95 

Plans to continue education right after high school F1S47 = 2 75.1 1.34 0.86 Plans to hold a part-time job right after school F1S53 = 2 48.6 1.26 1.00 Volunteered with a youth organization F1S63A = 1 29.4 1.80 1.35 

Often discusses grades with parents F1S64D = 3 51.3 1.13 1.01 

Lives with mother only F1FCOMP = 5 16.7 0.95 0.71 Native language is Spanish F1HOMLNG = 2 3.5 0.38 0.36 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.36 0.32 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.23 0.20 

At age 30 expects to be an operative F1OCC30 = 8 0.9 0.22 0.19 

At age 30 expects to be a professional (group b) F1OCC30 = 10 11.9 0.74 0.63 

At age 30 expects to be in a technical field F1OCC30 = 16 5.5 0.48 0.44 

At age 30 doesn't know what expects to be F1OCC30 = -1 31.2 0.92 0.90 Mathematics test score F1TXM1IR = 0-85 48.4 0.45 0.29 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-19. Student design effects, by item using base-year to first follow-up panel weight—All: 2004 

I-21 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.9 0.47 0.28 

Most likely to receive a GED F1S15 = 5 2.1 0.16 0.12 Already took the SAT or ACT F1S21C = 3 64.6 0.76 0.41 Already took an AP test F1S21D = 3 14.9 0.57 0.31 

Had something stolen at school at least once F1S25A = 2,3 25.7 0.56 0.43 

Was offered drugs at school at least once F1S25B = 2,3 20.8 0.58 0.40 Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.7 0.40 0.30 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 15.0 0.47 0.30 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.5 0.60 0.43 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 55.5 0.56 0.41 Uses the computer at home once or twice a week F1S37A = 4 25.1 0.50 0.36 Rarely or never performs community service F1S39C = 1 60.8 0.58 0.41 

Being successful in line of work is very important F1S40A = 3 90.8 0.32 0.24 

Marrying the right person is very important F1S40B = 3 80.3 0.45 0.33 

Having lots of money is very important F1S40C = 3 35.5 0.57 0.40 

Expects to earn a 4-year degree, nothing more F1S42 = 6 31.6 0.49 0.38 

Mother expects student to graduate from college, nothing more F1S43A = 6 38.4 0.54 0.41 Plans to continue education right after high school F1S47 = 2 77.9 0.55 0.36 

Plans to hold a part-time job right after school F1S53 = 2 49.8 0.57 0.43 Volunteered with a youth organization F1S63A = 1 27.8 0.80 0.55 Often discusses grades with parents F1S64D = 3 52.6 0.56 0.43 

Lives with mother only F1FCOMP = 5 18.7 0.45 0.32 

Native language is Spanish F1HOMLNG = 2 8.0 0.58 0.23 At age 30 expects to be a manager, administrator F1OCC30 = 6 3.0 0.17 0.14 

At age 30 expects to be in the military F1OCC30 = 7 1.0 0.11 0.08 At age 30 expects to be an operative F1OCC30 = 8 0.6 0.09 0.07 At age 30 expects to be a professional (group b) F1OCC30 = 10 13.7 0.36 0.29 

At age 30 expects to be in a technical field F1OCC30 = 16 4.9 0.23 0.18 At age 30 doesn't know what expects to be F1OCC30 = -1 29.9 0.46 0.38 

Mathematics test score F1TXM1IR = 0-85 48.5 0.28 0.13 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-20. Student design effects, by item using base-year to first follow-up panel weight—Male: 2004 

I-22 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 10.8 0.54 0.37 

Most likely to receive a GED F1S15 = 5 2.5 0.25 0.19 Already took the SAT or ACT F1S21C = 3 59.9 0.92 0.60 

Already took an AP test F1S21D = 3 14.2 0.67 0.44 

Had something stolen at school at least once F1S25A = 2,3 29.6 0.82 0.64 

Was offered drugs at school at least once F1S25B = 2,3 27.3 0.86 0.62 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 18.2 0.62 0.47 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 10.9 0.51 0.38 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.2 0.81 0.62 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 57.2 0.78 0.58 

Uses the computer at home once or twice a week F1S37A = 4 23.1 0.66 0.49 Rarely or never performs community service F1S39C = 1 66.5 0.75 0.56 

Being successful in line of work is very important F1S40A = 3 89.3 0.49 0.36 

Marrying the right person is very important F1S40B = 3 79.6 0.62 0.47 

Having lots of money is very important F1S40C = 3 43.1 0.76 0.58 

Expects to earn a 4-year degree, nothing more F1S42 = 6 32.2 0.71 0.55 

Mother expects student to graduate from college, nothing more F1S43A = 6 40.7 0.71 0.59 Plans to continue education right after high school F1S47 = 2 72.6 0.78 0.54 

Plans to hold a part-time job right after school F1S53 = 2 46.5 0.81 0.61 

Volunteered with a youth organization F1S63A = 1 33.7 1.24 0.88 

Often discusses grades with parents F1S64D = 3 48.1 0.86 0.62 

Lives with mother only F1FCOMP = 5 17.9 0.59 0.45 Native language is Spanish F1HOMLNG = 2 7.3 0.56 0.31 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.3 0.24 0.21 

At age 30 expects to be in the military F1OCC30 = 7 1.7 0.19 0.15 

At age 30 expects to be an operative F1OCC30 = 8 1.2 0.17 0.13 

At age 30 expects to be a professional (group b) F1OCC30 = 10 9.4 0.46 0.35 

At age 30 expects to be in a technical field F1OCC30 = 16 5.9 0.37 0.28 

At age 30 doesn't know what expects to be F1OCC30 = -1 32.8 0.68 0.56 

Mathematics test score F1TXM1IR = 0-85 49.5 0.32 0.19 

Summary statistics 

Mean Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-21. Student design effects, by item using base-year to first follow-up panel weight—Female: 2004 

I-23 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 15.1 0.65 0.43 Most likely to receive a GED F1S15 = 5 1.7 0.20 0.15 Already took the SAT or ACT F1S21C = 3 69.3 0.90 0.56 

Already took an AP test F1S21D = 3 15.6 0.72 0.45 Had something stolen at school at least once F1S25A = 2,3 22.0 0.73 0.57 

Was offered drugs at school at least once F1S25B = 2,3 14.4 0.67 0.49 Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 11.2 0.47 0.38 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 19.2 0.66 0.47 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.8 0.83 0.61 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 53.8 0.66 0.59 

Uses the computer at home once or twice a week F1S37A = 4 27.1 0.70 0.52 

Rarely or never performs community service F1S39C = 1 55.1 0.80 0.58 Being successful in line of work is very important F1S40A = 3 92.3 0.41 0.31 

Marrying the right person is very important F1S40B = 3 81.1 0.65 0.46 Having lots of money is very important F1S40C = 3 27.8 0.68 0.52 Expects to earn a 4-year degree, nothing more F1S42 = 6 31.1 0.65 0.54 

Mother expects student to graduate from college, nothing more F1S43A = 6 36.2 0.74 0.57 

Plans to continue education right after high school F1S47 = 2 83.2 0.61 0.45 Plans to hold a part-time job right after school F1S53 = 2 53.1 0.76 0.61 

Volunteered with a youth organization F1S63A = 1 23.5 0.92 0.69 

Often discusses grades with parents F1S64D = 3 57.0 0.71 0.60 

Lives with mother only F1FCOMP = 5 19.6 0.63 0.46 

Native language is Spanish F1HOMLNG = 2 8.7 0.76 0.33 At age 30 expects to be a manager, administrator F1OCC30 = 6 2.6 0.23 0.19 

At age 30 expects to be in the military F1OCC30 = 7 0.4 0.08 0.07 At age 30 expects to be an operative F1OCC30 = 8 0.1 0.06 0.04 At age 30 expects to be a professional (group b) F1OCC30 = 10 17.9 0.57 0.45 At age 30 expects to be in a technical field F1OCC30 = 16 3.9 0.27 0.23 At age 30 doesn't know what expects to be F1OCC30 = -1 27.0 0.61 0.52 Mathematics test score F1TXM1IR = 0-85 47.5 0.33 0.18 

Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-22. Student design effects, by item using base-year to first follow-up panel weight—American Indian or Ala

I-24 

Simple random 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 4.4 2.04 1.91 

Most likely to receive a GED F1S15 = 5 6.0 2.83 2.21 

Already took the SAT or ACT F1S21C = 3 39.4 6.15 4.77 

Already took an AP test F1S21D = 3 7.7 3.41 2.62 

Had something stolen at school at least once F1S25A = 2,3 33.7 6.54 5.46 

Was offered drugs at school at least once F1S25B = 2,3 31.0 6.53 5.30 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.0 4.32 3.28 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 12.0 3.91 3.09 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 28.2 8.79 5.50 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 69.4 4.37 4.22 

Uses the computer at home once or twice a week F1S37A = 4 28.0 5.25 4.12 Rarely or never performs community service F1S39C = 1 68.8 5.29 4.26 Being successful in line of work is very important F1S40A = 3 90.1 3.70 2.74 

Marrying the right person is very important F1S40B = 3 71.2 6.74 4.15 

Having lots of money is very important F1S40C = 3 39.3 4.94 4.48 Expects to earn a 4-year degree, nothing more F1S42 = 6 26.7 5.56 4.06 

Mother expects student to graduate from college, nothing more F1S43A = 6 34.4 4.48 4.47 

Plans to continue education right after high school F1S47 = 2 61.6 5.52 4.64 Plans to hold a part-time job right after school F1S53 = 2 52.9 4.91 4.83 Volunteered with a youth organization F1S63A = 1 39.4 8.28 8.92 

Often discusses grades with parents F1S64D = 3 47.4 6.75 4.85 

Lives with mother only F1FCOMP = 5 23.7 3.75 3.87 

Native language is Spanish F1HOMLNG = 2 # # # 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.0 1.48 1.61 

At age 30 expects to be in the military F1OCC30 = 7 2.1 1.57 1.35 At age 30 expects to be an operative F1OCC30 = 8 0.8 0.79 0.82 At age 30 expects to be a professional (group b) F1OCC30 = 10 9.6 3.42 2.77 At age 30 expects to be in a technical field F1OCC30 = 16 4.2 2.34 1.87 At age 30 doesn't know what expects to be F1OCC30 = -1 41.1 5.18 4.61 Mathematics test score F1TXM1IR = 0-85 41.5 1.74 1.24 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

† Not applicable.# Rounds to zero. 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-23. Student design effects, by item using base-year to first follow-up panel weight—Asian: 2004 

I-25 

Simple random 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 13.0 1.28 0.89 

Most likely to receive a GED F1S15 = 5 0.7 0.21 0.22 

Already took the SAT or ACT F1S21C = 3 77.3 1.95 1.13 

Already took an AP test F1S21D = 3 30.2 2.64 1.25 

Had something stolen at school at least once F1S25A = 2,3 27.3 2.10 1.40 Was offered drugs at school at least once F1S25B = 2,3 13.3 1.82 1.06 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 13.5 1.29 0.91 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 13.9 1.39 0.92 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 29.4 1.89 1.45 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 51.3 1.95 1.31 Uses the computer at home once or twice a week F1S37A = 4 19.5 1.54 1.04 Rarely or never performs community service F1S39C = 1 51.4 1.96 1.31 

Being successful in line of work is very important F1S40A = 3 89.1 1.05 0.81 Marrying the right person is very important F1S40B = 3 79.9 1.30 1.05 

Having lots of money is very important F1S40C = 3 41.9 1.85 1.29 

Expects to earn a 4-year degree, nothing more F1S42 = 6 31.5 1.59 1.22 

Mother expects student to graduate from college, nothing more F1S43A = 6 31.1 1.58 1.23 

Plans to continue education right after high school F1S47 = 2 90.1 1.24 0.80 

Plans to hold a part-time job right after school F1S53 = 2 52.7 1.92 1.35 

Volunteered with a youth organization F1S63A = 1 17.6 1.72 1.43 

Often discusses grades with parents F1S64D = 3 45.4 1.95 1.35 

Lives with mother only F1FCOMP = 5 10.1 0.91 0.79 

Native language is Spanish F1HOMLNG = 2 0.4 0.29 0.17 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.9 0.99 0.51 

At age 30 expects to be in the military F1OCC30 = 7 0.5 0.20 0.19 

At age 30 expects to be an operative F1OCC30 = 8 # 0.04 0.05 

At age 30 expects to be a professional (group b) F1OCC30 = 10 20.7 1.47 1.07 

At age 30 expects to be in a technical field F1OCC30 = 16 5.3 0.85 0.59 

At age 30 doesn't know what expects to be F1OCC30 = -1 36.2 1.77 1.27 Mathematics test score F1TXM1IR = 0-85 54.3 0.91 0.42 

Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

# Rounds to zero. 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-24. Student design effects, by item using base-year to first follow-up panel weight—Black or African Ameri

I-26 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 9.40 0.92 0.69 

Most likely to receive a GED F1S15 = 5 2.94 0.45 0.40 

Already took the SAT or ACT F1S21C = 3 55.73 1.51 1.21 Already took an AP test F1S21D = 3 5.17 0.60 0.55 

Had something stolen at school at least once F1S25A = 2,3 32.95 1.63 1.36 Was offered drugs at school at least once F1S25B = 2,3 20.64 1.50 1.17 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 17.97 1.00 0.91 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 17.29 1.21 0.90 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.92 1.46 1.32 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 73.43 1.16 1.02 Uses the computer at home once or twice a week F1S37A = 4 27.05 0.98 1.01 Rarely or never performs community service F1S39C = 1 64.72 1.38 1.09 

Being successful in line of work is very important F1S40A = 3 93.80 0.65 0.55 

Marrying the right person is very important F1S40B = 3 75.51 1.17 0.98 Having lots of money is very important F1S40C = 3 55.42 1.30 1.13 

Expects to earn a 4-year degree, nothing more F1S42 = 6 29.96 1.21 1.04 

Mother expects student to graduate from college, nothing more F1S43A = 6 33.22 1.18 1.09 Plans to continue education right after high school F1S47 = 2 78.15 1.26 1.00 Plans to hold a part-time job right after school F1S53 = 2 46.40 1.43 1.21 

Volunteered with a youth organization F1S63A = 1 25.52 2.23 1.67 Often discusses grades with parents F1S64D = 3 59.43 1.54 1.21 

Lives with mother only F1FCOMP = 5 38.09 1.33 1.10 

Native language is Spanish F1HOMLNG = 2 0.59 0.22 0.18 At age 30 expects to be a manager, administrator F1OCC30 = 6 3.02 0.46 0.40 

At age 30 expects to be in the military F1OCC30 = 7 0.71 0.26 0.19 At age 30 expects to be an operative F1OCC30 = 8 0.81 0.26 0.21 At age 30 expects to be a professional (group b) F1OCC30 = 10 15.19 0.94 0.83 At age 30 expects to be in a technical field F1OCC30 = 16 5.50 0.69 0.53 At age 30 doesn't know what expects to be F1OCC30 = -1 25.69 1.28 1.01 Mathematics test score F1TXM1IR = 0-85 38.86 0.47 0.30 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-25. Student design effects, by item using base-year to first follow-up panel weight—Hispanic or Latino: 20

I-27 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 8.7 0.88 0.64 

Most likely to receive a GED F1S15 = 5 3.1 0.51 0.39 

Already took the SAT or ACT F1S21C = 3 45.4 1.67 1.17 

Already took an AP test F1S21D = 3 13.9 1.11 0.82 Had something stolen at school at least once F1S25A = 2,3 26.2 1.58 1.22 

Was offered drugs at school at least once F1S25B = 2,3 23.1 1.50 1.17 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 12.9 0.84 0.76 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 11.0 0.78 0.71 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.1 1.49 1.24 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 60.5 1.30 1.07 

Uses the computer at home once or twice a week F1S37A = 4 28.3 1.32 0.98 Rarely or never performs community service F1S39C = 1 69.4 1.11 1.00 Being successful in line of work is very important F1S40A = 3 89.4 0.95 0.67 

Marrying the right person is very important F1S40B = 3 77.3 1.19 0.91 

Having lots of money is very important F1S40C = 3 40.9 1.33 1.07 Expects to earn a 4-year degree, nothing more F1S42 = 6 26.3 1.26 0.95 

Mother expects student to graduate from college, nothing more F1S43A = 6 33.6 1.06 1.04 Plans to continue education right after high school F1S47 = 2 72.7 1.46 1.03 Plans to hold a part-time job right after school F1S53 = 2 56.1 1.38 1.16 

Volunteered with a youth organization F1S63A = 1 21.7 1.81 1.54 Often discusses grades with parents F1S64D = 3 53.9 1.25 1.17 

Lives with mother only F1FCOMP = 5 20.2 1.18 0.87 

Native language is Spanish F1HOMLNG = 2 49.4 1.95 1.10 At age 30 expects to be a manager, administrator F1OCC30 = 6 2.5 0.38 0.34 

At age 30 expects to be in the military F1OCC30 = 7 1.0 0.26 0.22 At age 30 expects to be an operative F1OCC30 = 8 0.5 0.19 0.16 At age 30 expects to be a professional (group b) F1OCC30 = 10 12.8 0.78 0.74 At age 30 expects to be in a technical field F1OCC30 = 16 5.3 0.54 0.49 At age 30 doesn't know what expects to be F1OCC30 = -1 32.0 1.05 1.03 Mathematics test score F1TXM1IR = 0-85 41.4 0.48 0.32 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-26. Student design effects, by item using base-year to first follow-up panel weight—More than one race: 2

I-28 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 10.5 1.47 1.23 

Most likely to receive a GED F1S15 = 5 2.0 0.84 0.56 

Already took the SAT or ACT F1S21C = 3 61.1 2.68 2.00 

Already took an AP test F1S21D = 3 13.5 1.86 1.41 

Had something stolen at school at least once F1S25A = 2,3 31.6 2.83 2.17 

Was offered drugs at school at least once F1S25B = 2,3 23.3 2.81 1.97 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.2 1.79 1.40 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 17.8 1.83 1.53 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 21.9 2.64 1.98 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 60.2 2.50 1.91 

Uses the computer at home once or twice a week F1S37A = 4 24.6 2.21 1.67 

Rarely or never performs community service F1S39C = 1 59.8 2.50 1.91 

Being successful in line of work is very important F1S40A = 3 87.3 1.73 1.29 Marrying the right person is very important F1S40B = 3 79.3 1.88 1.57 

Having lots of money is very important F1S40C = 3 35.6 2.37 1.86 Expects to earn a 4-year degree, nothing more F1S42 = 6 33.9 2.28 1.83 

Mother expects student to graduate from college, nothing more F1S43A = 6 38.7 2.55 1.93 

Plans to continue education right after high school F1S47 = 2 71.9 2.54 1.82 Plans to hold a part-time job right after school F1S53 = 2 46.9 2.56 2.04 

Volunteered with a youth organization F1S63A = 1 29.8 3.78 2.62 

Often discusses grades with parents F1S64D = 3 55.1 2.82 2.04 Lives with mother only F1FCOMP = 5 22.0 1.95 1.60 

Native language is Spanish F1HOMLNG = 2 0.7 0.42 0.33 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.78 0.65 At age 30 expects to be in the military F1OCC30 = 7 2.2 0.85 0.57 At age 30 expects to be an operative F1OCC30 = 8 0.2 0.18 0.17 At age 30 expects to be a professional (group b) F1OCC30 = 10 10.5 1.42 1.20 

At age 30 expects to be in a technical field F1OCC30 = 16 4.5 1.02 0.81 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.9 2.35 1.79 Mathematics test score F1TXM1IR = 0-85 47.9 0.79 0.60 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-27. Student design effects, by item using base-year to first follow-up panel weight—White: 2004 

I-29 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 15.1 0.63 0.40 

Most likely to receive a GED F1S15 = 5 1.6 0.18 0.14 

Already took the SAT or ACT F1S21C = 3 70.9 0.83 0.52 

Already took an AP test F1S21D = 3 16.4 0.70 0.43 Had something stolen at school at least once F1S25A = 2,3 23.7 0.67 0.54 Was offered drugs at school at least once F1S25B = 2,3 20.5 0.70 0.51 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.5 0.52 0.39 Participated in school band (not as an officer/leader/captain) F1S26C = 2 15.5 0.59 0.40 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.3 0.75 0.55 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 49.9 0.69 0.55 

Uses the computer at home once or twice a week F1S37A = 4 24.1 0.64 0.47 Rarely or never performs community service F1S39C = 1 58.2 0.76 0.54 Being successful in line of work is very important F1S40A = 3 90.8 0.41 0.32 

Marrying the right person is very important F1S40B = 3 82.5 0.54 0.42 

Having lots of money is very important F1S40C = 3 28.8 0.65 0.50 Expects to earn a 4-year degree, nothing more F1S42 = 6 33.4 0.61 0.52 

Mother expects student to graduate from college, nothing more F1S43A = 6 41.5 0.70 0.55 

Plans to continue education right after high school F1S47 = 2 78.9 0.64 0.46 Plans to hold a part-time job right after school F1S53 = 2 48.9 0.71 0.57 

Volunteered with a youth organization F1S63A = 1 29.6 1.03 0.70 

Often discusses grades with parents F1S64D = 3 51.2 0.68 0.57 

Lives with mother only F1FCOMP = 5 14.0 0.50 0.38 

Native language is Spanish F1HOMLNG = 2 0.1 0.05 0.04 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.0 0.22 0.19 At age 30 expects to be in the military F1OCC30 = 7 1.0 0.13 0.11 At age 30 expects to be an operative F1OCC30 = 8 0.7 0.11 0.09 At age 30 expects to be a professional (group b) F1OCC30 = 10 13.4 0.46 0.38 

At age 30 expects to be in a technical field F1OCC30 = 16 4.7 0.30 0.23 At age 30 doesn't know what expects to be F1OCC30 = -1 29.7 0.62 0.50 Mathematics test score F1TXM1IR = 0-85 52.0 0.28 0.16 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-28. Student design effects, by item using base-year to first follow-up panel weight—Public: 2004 

I-30 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.9 0.51 0.32 Most likely to receive a GED F1S15 = 5 2.2 0.17 0.14 

Already took the SAT or ACT F1S21C = 3 62.3 0.82 0.48 

Already took an AP test F1S21D = 3 14.1 0.60 0.35 Had something stolen at school at least once F1S25A = 2,3 25.8 0.60 0.49 

Was offered drugs at school at least once F1S25B = 2,3 21.5 0.63 0.46 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.1 0.42 0.34 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 14.6 0.49 0.34 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 23.5 0.63 0.49 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 56.3 0.59 0.47 

Uses the computer at home once or twice a week F1S37A = 4 25.7 0.53 0.41 Rarely or never performs community service F1S39C = 1 62.0 0.61 0.46 

Being successful in line of work is very important F1S40A = 3 90.8 0.34 0.27 

Marrying the right person is very important F1S40B = 3 79.8 0.48 0.38 

Having lots of money is very important F1S40C = 3 36.1 0.60 0.45 Expects to earn a 4-year degree, nothing more F1S42 = 6 31.4 0.52 0.43 

Mother expects student to graduate from college, nothing more F1S43A = 6 38.1 0.58 0.46 Plans to continue education right after high school F1S47 = 2 76.6 0.60 0.42 Plans to hold a part-time job right after school F1S53 = 2 49.7 0.61 0.49 

Volunteered with a youth organization F1S63A = 1 27.8 0.89 0.66 Often discusses grades with parents F1S64D = 3 52.4 0.60 0.50 

Lives with mother only F1FCOMP = 5 19.2 0.48 0.37 

Native language is Spanish F1HOMLNG = 2 8.4 0.62 0.26 At age 30 expects to be a manager, administrator F1OCC30 = 6 3.0 0.18 0.16 

At age 30 expects to be in the military F1OCC30 = 7 1.0 0.11 0.10 At age 30 expects to be an operative F1OCC30 = 8 0.7 0.10 0.08 At age 30 expects to be a professional (group b) F1OCC30 = 10 13.2 0.38 0.32 At age 30 expects to be in a technical field F1OCC30 = 16 5.0 0.25 0.21 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.7 0.50 0.43 

Mathematics test score F1TXM1IR = 0-85 47.8 0.30 0.15 

Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-29. Student design effects, by item using base-year to first follow-up panel weight—Catholic: 2004 

Design 

Simple random 

sample 

Survey item (or composite variable) Variable Estimate standard error standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.5 1.38 0.76 Most likely to receive a GED F1S15 = 5 0.5 0.16 0.16 

Already took the SAT or ACT F1S21C = 3 91.4 1.15 0.65 

Already took an AP test F1S21D = 3 22.5 2.05 0.99 

Had something stolen at school at least once F1S25A = 2,3 26.0 1.79 1.12 

Was offered drugs at school at least once F1S25B = 2,3 17.7 1.35 0.97 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 22.1 1.66 0.96 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 15.3 1.34 0.83 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 36.0 1.98 1.24 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 52.3 2.11 1.15 

Uses the computer at home once or twice a week F1S37A = 4 16.8 1.06 0.86 

Rarely or never performs community service F1S39C = 1 43.2 1.79 1.14 

Being successful in line of work is very important F1S40A = 3 90.9 0.66 0.66 

Marrying the right person is very important F1S40B = 3 87.1 0.93 0.77 Having lots of money is very important F1S40C = 3 29.2 1.63 1.05 Expects to earn a 4-year degree, nothing more F1S42 = 6 37.9 1.34 1.12 

Mother expects student to graduate from college, nothing more F1S43A = 6 42.6 1.62 1.15 Plans to continue education right after high school F1S47 = 2 93.9 0.77 0.55 Plans to hold a part-time job right after school F1S53 = 2 51.2 1.69 1.15 

Volunteered with a youth organization F1S63A = 1 31.8 1.88 1.30 

Often discusses grades with parents F1S64D = 3 56.6 1.28 1.15 

Lives with mother only F1FCOMP = 5 12.4 0.75 0.76 

Native language is Spanish F1HOMLNG = 2 3.0 0.94 0.39 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.2 0.34 0.41 At age 30 expects to be in the military F1OCC30 = 7 0.8 0.26 0.20 At age 30 expects to be an operative F1OCC30 = 8 # # # 

At age 30 expects to be a professional (group b) F1OCC30 = 10 17.5 1.23 0.88 At age 30 expects to be in a technical field F1OCC30 = 16 3.6 0.52 0.43 

At age 30 doesn't know what expects to be F1OCC30 = -1 31.0 1.13 1.07 

Mathematics test score F1TXM1IR = 0-85 55.9 0.60 0.29 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

I-31 

† Not applicable.# Rounds to zero. 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-30. Student design effects, by item using base-year to first follow-up panel weight—Other private: 2004 

I-32 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 15.5 1.70 1.00 

Most likely to receive a GED F1S15 = 5 0.6 0.16 0.21 

Already took the SAT or ACT F1S21C = 3 86.2 2.48 0.97 

Already took an AP test F1S21D = 3 25.1 3.11 1.24 Had something stolen at school at least once F1S25A = 2,3 24.2 2.19 1.35 

Was offered drugs at school at least once F1S25B = 2,3 7.9 1.04 0.85 Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 20.7 2.48 1.12 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 26.3 3.11 1.22 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 32.6 2.28 1.51 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 39.3 2.38 1.34 

Uses the computer at home once or twice a week F1S37A = 4 20.3 2.18 1.10 

Rarely or never performs community service F1S39C = 1 50.1 2.94 1.37 Being successful in line of work is very important F1S40A = 3 89.3 1.10 0.84 

Marrying the right person is very important F1S40B = 3 86.8 1.04 0.93 Having lots of money is very important F1S40C = 3 26.4 1.69 1.21 Expects to earn a 4-year degree, nothing more F1S42 = 6 31.7 1.72 1.27 

Mother expects student to graduate from college, nothing more F1S43A = 6 41.6 1.79 1.37 

Plans to continue education right after high school F1S47 = 2 90.4 1.61 0.82 Plans to hold a part-time job right after school F1S53 = 2 49.8 2.15 1.40 

Volunteered with a youth organization F1S63A = 1 22.7 1.63 1.50 

Often discusses grades with parents F1S64D = 3 52.5 1.89 1.40 Lives with mother only F1FCOMP = 5 13.3 1.28 0.93 

Native language is Spanish F1HOMLNG = 2 2.5 1.22 0.43 At age 30 expects to be a manager, administrator F1OCC30 = 6 3.1 0.68 0.48 

At age 30 expects to be in the military F1OCC30 = 7 0.8 0.27 0.24 At age 30 expects to be an operative F1OCC30 = 8 0.2 0.19 0.12 At age 30 expects to be a professional (group b) F1OCC30 = 10 22.0 1.73 1.15 At age 30 expects to be in a technical field F1OCC30 = 16 2.7 0.48 0.45 

At age 30 doesn't know what expects to be F1OCC30 = -1 34.3 1.63 1.31 

Mathematics test score F1TXM1IR = 0-85 57.5 0.96 0.38 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-31. Student design effects, by item using base-year to first follow-up panel weight—Low socioeconomic st

I-33 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 7.3 0.57 0.47 

Most likely to receive a GED F1S15 = 5 3.4 0.42 0.33 

Already took the SAT or ACT F1S21C = 3 42.7 1.31 0.93 

Already took an AP test F1S21D = 3 7.6 0.69 0.50 Had something stolen at school at least once F1S25A = 2,3 26.4 1.17 0.97 Was offered drugs at school at least once F1S25B = 2,3 19.0 1.11 0.86 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 12.8 0.75 0.61 Participated in school band (not as an officer/leader/captain) F1S26C = 2 13.6 0.72 0.63 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 21.1 1.18 0.93 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 63.3 1.05 0.84 

Uses the computer at home once or twice a week F1S37A = 4 25.8 0.92 0.75 

Rarely or never performs community service F1S39C = 1 70.2 0.93 0.79 Being successful in line of work is very important F1S40A = 3 89.3 0.64 0.53 

Marrying the right person is very important F1S40B = 3 77.4 0.92 0.72 

Having lots of money is very important F1S40C = 3 41.2 0.99 0.85 Expects to earn a 4-year degree, nothing more F1S42 = 6 25.8 0.92 0.75 

Mother expects student to graduate from college, nothing more F1S43A = 6 33.2 0.95 0.83 

Plans to continue education right after high school F1S47 = 2 66.4 1.15 0.88 Plans to hold a part-time job right after school F1S53 = 2 48.2 1.14 0.93 

Volunteered with a youth organization F1S63A = 1 23.8 1.62 1.35 

Often discusses grades with parents F1S64D = 3 51.6 1.11 0.94 Lives with mother only F1FCOMP = 5 27.0 1.00 0.76 

Native language is Spanish F1HOMLNG = 2 21.4 1.60 0.72 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.3 0.28 0.26 

At age 30 expects to be in the military F1OCC30 = 7 0.9 0.18 0.17 At age 30 expects to be an operative F1OCC30 = 8 1.4 0.27 0.20 At age 30 expects to be a professional (group b) F1OCC30 = 10 11.6 0.62 0.56 

At age 30 expects to be in a technical field F1OCC30 = 16 5.5 0.45 0.40 

At age 30 doesn't know what expects to be F1OCC30 = -1 31.5 0.95 0.81 

Mathematics test score F1TXM1IR = 0-85 40.2 0.36 0.24 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-32. Student design effects, by item using base-year to first follow-up panel weight—Middle socioeconomic 

I-34 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 11.4 0.55 0.39 

Most likely to receive a GED F1S15 = 5 2.0 0.22 0.17 

Already took the SAT or ACT F1S21C = 3 62.4 0.87 0.60 

Already took an AP test F1S21D = 3 11.1 0.54 0.40 

Had something stolen at school at least once F1S25A = 2,3 25.9 0.71 0.62 

Was offered drugs at school at least once F1S25B = 2,3 22.2 0.83 0.59 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 15.1 0.54 0.44 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 15.0 0.61 0.44 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 22.8 0.81 0.61 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 56.6 0.77 0.59 

Uses the computer at home once or twice a week F1S37A = 4 27.1 0.63 0.53 

Rarely or never performs community service F1S39C = 1 63.3 0.76 0.58 

Being successful in line of work is very important F1S40A = 3 91.2 0.45 0.34 

Marrying the right person is very important F1S40B = 3 80.2 0.63 0.47 Having lots of money is very important F1S40C = 3 35.7 0.76 0.57 Expects to earn a 4-year degree, nothing more F1S42 = 6 33.6 0.64 0.56 

Mother expects student to graduate from college, nothing more F1S43A = 6 39.5 0.76 0.59 

Plans to continue education right after high school F1S47 = 2 76.6 0.68 0.52 Plans to hold a part-time job right after school F1S53 = 2 51.3 0.76 0.62 Volunteered with a youth organization F1S63A = 1 27.6 1.09 0.81 

Often discusses grades with parents F1S64D = 3 53.0 0.80 0.62 Lives with mother only F1FCOMP = 5 19.1 0.59 0.47 

Native language is Spanish F1HOMLNG = 2 4.5 0.34 0.25 At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.23 0.20 At age 30 expects to be in the military F1OCC30 = 7 1.1 0.16 0.13 At age 30 expects to be an operative F1OCC30 = 8 0.6 0.12 0.09 At age 30 expects to be a professional (group b) F1OCC30 = 10 11.8 0.44 0.39 

At age 30 expects to be in a technical field F1OCC30 = 16 5.5 0.32 0.27 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.4 0.62 0.55 

Mathematics test score F1TXM1IR = 0-85 47.5 0.27 0.18 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-33. Student design effects, by item using base-year to first follow-up panel weight—High socioeconomic s

I-35 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 20.7 0.96 0.63 

Most likely to receive a GED F1S15 = 5 1.1 0.23 0.16 

Already took the SAT or ACT F1S21C = 3 86.8 0.78 0.53 

Already took an AP test F1S21D = 3 28.1 1.21 0.72 Had something stolen at school at least once F1S25A = 2,3 24.9 1.07 0.76 

Was offered drugs at school at least once F1S25B = 2,3 19.7 0.95 0.70 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 15.4 0.76 0.56 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 16.3 0.81 0.57 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 29.8 1.07 0.81 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 45.8 1.07 0.77 

Uses the computer at home once or twice a week F1S37A = 4 20.4 0.95 0.62 

Rarely or never performs community service F1S39C = 1 46.8 0.98 0.77 Being successful in line of work is very important F1S40A = 3 91.5 0.51 0.43 

Marrying the right person is very important F1S40B = 3 83.3 0.77 0.57 Having lots of money is very important F1S40C = 3 29.5 0.95 0.70 

Expects to earn a 4-year degree, nothing more F1S42 = 6 33.5 0.94 0.73 

Mother expects student to graduate from college, nothing more F1S43A = 6 41.4 0.98 0.77 

Plans to continue education right after high school F1S47 = 2 90.2 0.69 0.46 

Plans to hold a part-time job right after school F1S53 = 2 48.4 0.97 0.78 

Volunteered with a youth organization F1S63A = 1 29.8 1.30 0.89 

Often discusses grades with parents F1S64D = 3 52.6 1.08 0.78 

Lives with mother only F1FCOMP = 5 9.8 0.62 0.46 

Native language is Spanish F1HOMLNG = 2 2.0 0.31 0.22 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.8 0.41 0.30 

At age 30 expects to be in the military F1OCC30 = 7 0.9 0.20 0.15 At age 30 expects to be an operative F1OCC30 = 8 0.1 0.05 0.04 

At age 30 expects to be a professional (group b) F1OCC30 = 10 19.5 0.74 0.62 

At age 30 expects to be in a technical field F1OCC30 = 16 3.2 0.35 0.27 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.4 1.00 0.71 

Mathematics test score F1TXM1IR = 0-85 57.3 0.35 0.21 

Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-34. Student design effects, by item using base-year to first follow-up panel weight—Urban: 2004 

I-36 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.8 0.91 0.49 

Most likely to receive a GED F1S15 = 5 2.3 0.31 0.22 

Already took the SAT or ACT F1S21C = 3 65.0 1.56 0.72 

Already took an AP test F1S21D = 3 18.5 1.28 0.59 

Had something stolen at school at least once F1S25A = 2,3 27.4 1.11 0.77 

Was offered drugs at school at least once F1S25B = 2,3 21.9 1.12 0.72 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 14.1 0.79 0.51 Participated in school band (not as an officer/leader/captain) F1S26C = 2 14.6 0.86 0.52 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 27.6 1.18 0.79 

Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 59.4 1.06 0.71 

Uses the computer at home once or twice a week F1S37A = 4 24.1 0.98 0.61 

Rarely or never performs community service F1S39C = 1 61.0 1.10 0.70 Being successful in line of work is very important F1S40A = 3 90.7 0.57 0.41 

Marrying the right person is very important F1S40B = 3 78.6 0.87 0.58 

Having lots of money is very important F1S40C = 3 39.0 0.99 0.70 

Expects to earn a 4-year degree, nothing more F1S42 = 6 31.0 0.95 0.66 

Mother expects student to graduate from college, nothing more F1S43A = 6 36.5 0.92 0.70 

Plans to continue education right after high school F1S47 = 2 79.4 0.91 0.60 Plans to hold a part-time job right after school F1S53 = 2 51.2 1.10 0.75 Volunteered with a youth organization F1S63A = 1 26.6 1.62 0.92 

Often discusses grades with parents F1S64D = 3 55.0 1.05 0.75 

Lives with mother only F1FCOMP = 5 23.0 1.01 0.60 

Native language is Spanish F1HOMLNG = 2 13.8 1.43 0.50 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.31 0.24 

At age 30 expects to be in the military F1OCC30 = 7 0.8 0.15 0.13 

At age 30 expects to be an operative F1OCC30 = 8 0.5 0.19 0.10 

At age 30 expects to be a professional (group b) F1OCC30 = 10 16.4 0.71 0.53 

At age 30 expects to be in a technical field F1OCC30 = 16 4.7 0.46 0.31 

At age 30 doesn't know what expects to be F1OCC30 = -1 29.8 0.80 0.66 

Mathematics test score F1TXM1IR = 0-85 47.0 0.62 0.23 Summary statistics 

Mean 

Minimum 

Median 

Maximum 

Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-35. Student design effects, by item using base-year to first follow-up panel weight—Suburban: 2004 

I-37 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 12.5 0.67 0.40 

Most likely to receive a GED F1S15 = 5 1.9 0.22 0.17 

Already took the SAT or ACT F1S21C = 3 65.4 1.05 0.59 

Already took an AP test F1S21D = 3 14.2 0.78 0.44 

Had something stolen at school at least once F1S25A = 2,3 25.6 0.80 0.61 

Was offered drugs at school at least once F1S25B = 2,3 21.2 0.81 0.57 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 15.4 0.55 0.44 

Participated in school band (not as an officer/leader/captain) F1S26C = 2 14.7 0.62 0.43 

Spends 1-3 hours a week on math homework outside of school F1S32B = 4 24.3 0.80 0.62 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 53.8 0.77 0.60 

Uses the computer at home once or twice a week F1S37A = 4 25.0 0.69 0.52 

Rarely or never performs community service F1S39C = 1 60.7 0.76 0.59 

Being successful in line of work is very important F1S40A = 3 91.0 0.43 0.34 

Marrying the right person is very important F1S40B = 3 81.1 0.64 0.47 Having lots of money is very important F1S40C = 3 34.8 0.84 0.57 

Expects to earn a 4-year degree, nothing more F1S42 = 6 32.3 0.71 0.56 

Mother expects student to graduate from college, nothing more F1S43A = 6 39.5 0.78 0.59 

Plans to continue education right after high school F1S47 = 2 78.2 0.79 0.51 Plans to hold a part-time job right after school F1S53 = 2 49.4 0.79 0.62 Volunteered with a youth organization F1S63A = 1 27.9 1.04 0.79 Often discusses grades with parents F1S64D = 3 51.6 0.79 0.62 Lives with mother only F1FCOMP = 5 17.0 0.56 0.45 

Native language is Spanish F1HOMLNG = 2 6.4 0.73 0.29 

At age 30 expects to be a manager, administrator F1OCC30 = 6 3.1 0.24 0.21 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.16 0.13 

At age 30 expects to be an operative F1OCC30 = 8 0.6 0.11 0.09 

At age 30 expects to be a professional (group b) F1OCC30 = 10 12.7 0.51 0.40 

At age 30 expects to be in a technical field F1OCC30 = 16 4.8 0.32 0.26 At age 30 doesn't know what expects to be F1OCC30 = -1 29.6 0.70 0.55 Mathematics test score F1TXM1IR = 0-85 49.3 0.38 0.19 Summary statistics 

Mean Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-36. Student design effects, by item using base-year to first follow-up panel weight—Rural: 2004 

I-38 

Survey item (or composite variable) Variable Estimate 

Design 

standard error 

Simple random 

sample 

standard error 

Most likely to receive an honors diploma from high school F1S15 = 2 14.3 0.98 0.69 

Most likely to receive a GED F1S15 = 5 2.2 0.34 0.29 Already took the SAT or ACT F1S21C = 3 62.0 1.58 0.98 Already took an AP test F1S21D = 3 11.5 0.92 0.65 

Had something stolen at school at least once F1S25A = 2,3 23.9 1.13 0.97 Was offered drugs at school at least once F1S25B = 2,3 18.3 1.24 0.88 

Participated in intramural sports at school (not as an officer/leader/captain) F1S26A = 2 13.6 0.84 0.68 Participated in school band (not as an officer/leader/captain) F1S26C = 2 16.6 1.17 0.74 Spends 1-3 hours a week on math homework outside of school F1S32B = 4 21.2 1.37 0.96 Watches TV/DVD 2-3 hours a day on weekdays F1S34A = 4,5,6 54.2 1.21 0.97 

Uses the computer at home once or twice a week F1S37A = 4 26.8 1.09 0.85 Rarely or never performs community service F1S39C = 1 60.8 1.47 0.94 

Being successful in line of work is very important F1S40A = 3 90.5 0.82 0.57 Marrying the right person is very important F1S40B = 3 81.0 0.95 0.76 

Having lots of money is very important F1S40C = 3 31.8 1.14 0.90 

Expects to earn a 4-year degree, nothing more F1S42 = 6 30.9 0.96 0.89 

Mother expects student to graduate from college, nothing more F1S43A = 6 38.8 1.21 0.96 

Plans to continue education right after high school F1S47 = 2 75.2 1.34 0.87 Plans to hold a part-time job right after school F1S53 = 2 48.8 1.22 1.01 Volunteered with a youth organization F1S63A = 1 29.4 1.81 1.36 

Often discusses grades with parents F1S64D = 3 51.6 1.16 1.01 Lives with mother only F1FCOMP = 5 16.6 0.97 0.72 

Native language is Spanish F1HOMLNG = 2 3.3 0.39 0.35 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.37 0.32 

At age 30 expects to be in the military F1OCC30 = 7 1.1 0.24 0.20 

At age 30 expects to be an operative F1OCC30 = 8 0.9 0.23 0.19 

At age 30 expects to be a professional (group b) F1OCC30 = 10 12.1 0.76 0.64 

At age 30 expects to be in a technical field F1OCC30 = 16 5.5 0.48 0.45 

At age 30 doesn't know what expects to be F1OCC30 = -1 31.0 0.92 0.90 

Mathematics test score F1TXM1IR = 0-85 48.5 0.45 0.29 

Summary statistics 

Mean Minimum 

Median 

Maximum Standard deviation 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-37. Dropout design effects, by item using first follow-up questionnaire weight—All: 2004 

Design 

Simple random 

sample 

Survey item (or composite variable) Variable Estimate standard error standard error 

11th grade was the last grade attended in school F1D20 = 2 49.3 2.13 1.94 Left school for a job F1D29A = 1 27.9 2.04 1.72 

Left school because they did not like it F1D29B = 1 36.4 2.21 1.85 

Left school because they could not get along with teachers F1D29C = 1 24.9 2.10 1.66 Left school because they were pregnant F1D29E = 1 28.6 2.93 2.63 

Left school because they did not feel safe F1D29I = 1 9.9 1.42 1.15 Left school because they were expelled F1D29K = 1 10.2 1.38 1.16 Left school because they had no feeling of belonging F1D29L = 1 19.6 1.87 1.52 

Left school because they were getting poor grades/failing F1D29N = 1 38.0 2.14 1.86 

Left school because getting a GED was easier F1D29T = 1 40.1 2.29 1.88 

Plans to get GED or high school diploma F1D41 = 2 87.0 1.47 1.29 

Currently taking class to prepare for the GED F1D46 = 1 20.5 1.92 1.67 

Watches TV/DVD 2-3 hours a day on weekdays F1D51A = 4 18.7 1.66 1.51 

Uses the computer at home once or twice a week F1D54A = 4 22.4 1.77 1.60 

Being successful in line of work is very important F1D56A = 3 84.0 1.59 1.41 

Marrying the right person is very important F1D56B = 3 75.1 1.98 1.66 

Having lots of money is very important F1D56C = 3 43.8 2.25 1.91 

Having strong friendships is very important F1D56D = 3 74.6 1.88 1.68 

Being able to find steady work is very important F1D56E = 3 87.3 1.36 1.28 

Lives with mother only F1FCOMP = 5 26.2 1.86 1.68 

Native language is Spanish F1HOMLNG = 2 16.1 2.06 1.44 At age 30 expects to be a farmer, farm manager F1OCC30 = 3 # # # 

At age 30 expects to be a homemaker F1OCC30 = 4 0.1 0.12 0.13 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.66 0.65 At age 30 expects to be in the military F1OCC30 = 7 0.3 0.19 0.22 

At age 30 expects to be an operative F1OCC30 = 8 1.7 0.55 0.50 

At age 30 expects to be a professional (group b) F1OCC30 = 10 5.9 1.07 0.92 

At age 30 expects to be a school teacher F1OCC30 = 14 0.6 0.31 0.29 

At age 30 expects to be in a technical field F1OCC30 = 16 3.6 0.85 0.72 At age 30 doesn't know what expects to be F1OCC30 = -1 36.8 2.27 1.88 Summary statistics 

Mean 

Minimum 

Median 

Maximum Standard deviation 

I-39 

† Not applicable. # Rounds to zero. NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Table I-38. Dropout design effects, by item using base-year to first follow-up panel weight—All: 2004 

I-40 

Design 

Simple random 

sample 

Survey item (or composite variable) Variable Estimate standard error standard error 

11th grade was the last grade attended in school F1D20 = 2 49.8 2.15 1.96 

Left school for a job F1D29A = 1 27.8 2.07 1.74 Left school because they did not like it F1D29B = 1 36.6 2.27 1.87 

Left school because they could not get along with teachers F1D29C = 1 25.0 2.15 1.68 Left school because they were pregnant F1D29E = 1 27.8 2.91 2.64 

Left school because they did not feel safe F1D29I = 1 10.0 1.45 1.17 

Left school because they were expelled F1D29K = 1 9.9 1.37 1.16 

Left school because they had no feeling of belonging F1D29L = 1 19.9 1.96 1.55 

Left school because they were getting poor grades/failing F1D29N = 1 38.0 2.16 1.89 

Left school because getting a GED was easier F1D29T = 1 40.5 2.33 1.91 Plans to get GED or high school diploma F1D41 = 2 86.8 1.51 1.32 

Currently taking class to prepare for the GED F1D46 = 1 20.7 1.96 1.70 Watches TV/DVD 2-3 hours a day on weekdays F1D51A = 4 18.1 1.66 1.51 

Uses the computer at home once or twice a week F1D54A = 4 21.9 1.74 1.61 Being successful in line of work is very important F1D56A = 3 84.1 1.61 1.42 

Marrying the right person is very important F1D56B = 3 75.2 2.01 1.68 

Having lots of money is very important F1D56C = 3 43.5 2.27 1.93 Having strong friendships is very important F1D56D = 3 74.5 1.92 1.70 

Being able to find steady work is very important F1D56E = 3 87.2 1.38 1.30 

Lives with mother only F1FCOMP = 5 26.2 1.89 1.70 

Native language is Spanish F1HOMLNG = 2 15.9 2.06 1.45 

At age 30 expects to be a farmer, farm manager F1OCC30 = 3 # # # 

At age 30 expects to be a homemaker F1OCC30 = 4 0.1 0.11 0.13 

At age 30 expects to be a manager, administrator F1OCC30 = 6 2.8 0.65 0.65 

At age 30 expects to be in the military F1OCC30 = 7 0.3 0.20 0.23 

At age 30 expects to be an operative F1OCC30 = 8 1.7 0.55 0.51 

At age 30 expects to be a professional (group b) F1OCC30 = 10 6.1 1.11 0.94 

At age 30 expects to be a school teacher F1OCC30 = 14 0.6 0.31 0.30 

At age 30 expects to be in a technical field F1OCC30 = 16 3.6 0.85 0.73 

At age 30 doesn't know what expects to be F1OCC30 = -1 36.7 2.31 1.90 Summary statistics 

Mean 

Minimum Median Maximum Standard deviation 

† Not applicable. # Rounds to zero. 

NOTE: DEFF = design effect; DEFT = root design effect; N = sample size. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), “First Follow-up, P
Appendix J 

Synopsis of the ELS:2002 First Follow-up 

Field Test (2003) 

J-1 

Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

Appendix J 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

J.1 Overview of the First Follow-up Field Test 

The overall purpose of the Education Longitudinal Study of 2002 (ELS:2002) first 

follow-up field test was to provide a trial and evaluation of the instruments, forms, sampling, 

data collection, and processing procedures to be used in the main study 1 year later. The field 

test also provided a basis for evaluating the adequacy of the study design as manifested in a 

follow-up round of data collection. A major product of the field test was recommendations for 

how main study instruments and procedures can be improved. Data generated in the field test 

have been used both to guide the final choice of test and questionnaire items and to support 

specific recommendations for the revision of questionnaire and test items and survey procedures. 

The overall design for the field test included testing the process of gaining cooperation 

once again from base-year field test schools (and associated districts) and implementing the five 

main data-gathering components of the study: 

• an in-school student survey and assessment; 

• a dropout survey; 

• an out-of-school survey of transfer and homeschooled students and early graduates; 

• a survey of school administrators; and 

• a school records component (collection of academic transcripts). 

Special procedures to be evaluated in the field test included the following: 

• examination of the impact of monetary incentives on in-school student participation; 

• freshening of the cohort to make it representative of high school seniors; 

• tracing of students who have left their base-year school; 

• identification of both regular and augmented dropouts; and 

• receipt of school, parent, and student permission for the transcript component. 

In addition, the field test served to evaluate the various survey instruments: the 

questionnaire and mathematics test for students in the core ELS school sample, the transfer 

student questionnaire, the early graduate questionnaire, the questionnaire for homeschooled 

students, the dropout questionnaire, and the school administrator questionnaire. 

Instruments were evaluated in a number of ways. For the questionnaires, analyses 

included evaluation of item nonresponse, test-retest reliabilities, scale reliabilities, and 

correlations between theoretically related measures. For the achievement tests in mathematics, 

item parameters were estimated and both classical and Item Response Theory (IRT) techniques 

employed to determine the most appropriate items for inclusion in the first follow-up math test. 

In addition, items were tested for differential item functioning (DIF) to see if they had different 

J-3 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

meaning for different subgroups such that a given item could put a subgroup at an unjustified 

disadvantage in terms of assessment results. 

The school sample for the field test comprised over 50 public and private schools in the 

five field test states. The states—New York, North Carolina, Texas, Illinois, and Florida—were 

chosen in the base year on the basis of their demographic heterogeneity and represent various 

regions of the United States. 

This synopsis of 2003 field test results focuses on several areas of key importance for 

planning and implementing the 2004 full-scale study. One such area is that of formulating and 

testing plans for sample freshening, to ensure that a nationally representative senior cohort could 

be identified and surveyed in the first follow-up. A second area of concern was designing and 

testing a program of student incentives that could be used to help achieve outstandingly high inschool response rates. A third area of critical importance was use of the field test to refine the 

design for the ELS:2002 mathematics assessment. 

J.2 Sample Freshening 

Because part of the target population consisted of those students who were enrolled in the 

12th grade in the 2002–03 school year (or for the main study, 2003–04), the first follow-up field 

test included students at the base-year sample school who were enrolled in the 12th grade but 

were not in the 10th grade in the United States during the 2000–01 school year, at the time of the 

base-year survey. During this time, such students may have been out of the country or enrolled 

in school in the United States in a grade other than 10th (either at the sampled school or at 

another school). In addition, some students may have reenrolled, although they were temporarily 

out of school during the 2000–01 school year because of illness, injury, being institutionalized, 

being homeschooled, or having dropped out of school. 

Student freshening was limited to the base-year sample schools because all sample 

students were identified at these schools regardless of their status and could be linked to potential 

freshened students. However, the freshening process was also performed at a handful of new 

schools that had effectively replaced base-year schools. These schools received base-year 

students in an en masse transfer because the base-year school had either closed or did not offer a 

12th grade. Some small amount of bias may arise from the fact that some students eligible for 

freshening did not have a chance of selection if they attended a new school (one that came into 

existence subsequent to the base year), since, owing to cost and logistical constraints, freshening 

was not conducted in schools to which base-year sample members transferred (other than in the 

case of en masse transfer). 

The freshening process differed somewhat from the procedures used in the National 

Education Longitudinal Study of 1988 (NELS:88). In both studies, students on the 12th-grade 

list following 10th-grade sample students were identified. In NELS:88, the school was asked 

about all of those identified students, but in the ELS:2002 field test, the school was asked only 

about those students not on the 10th-grade list. The latter method places less burden on the 

school and may identify more students eligible to be included in the sample. 

List collection was the basis for identification of the freshened sample. If both the 

original and new enrollment lists were electronic, they were sorted alphabetically within stratum 

(as the original list was sorted for sample selection) to facilitate the comparison of the original 

J-4 
 

 

Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

and new lists. If one of the lists was electronic and one was hard copy, then the electronic list 

was sorted alphabetically within stratum and printed for the freshening process. If both lists 

were hard copy, then they were used as is in the freshening process. 

The freshening process began by identifying the base-year sample students on the new 

list. If the student immediately following each sampled base-year student within the 

race/ethnicity strata on the new list was not on the original list, then that student was selected as 

a potential addition to the sample. Whenever a potential new sample student was identified, the 

next student on the list was examined to determine whether that student was on the original list. 

If this next student was not on the original list, then the student was also a potential addition to 

the sample. This process was continued until reaching a student who was on the original list. 

Then, this process was repeated with the next base-year sample student on the list.1 

Next, the school was contacted to determine the eligibility of the freshened students. Any 

student identified as eligible by the school was selected into the sample. Some 275 high school 

seniors were identified as potential candidates for the first follow-up freshening sample. Of 

these 275 students, 57 (22 percent) were found to be eligible for inclusion in the study, 205 

students were found to be ineligible, and 13 students’ eligibility was undetermined. The high 

ineligibility rate was expected since the freshening procedure selected 12th-grade students who 

were not on the 10th-grade list without information on their status in the 10th grade. Many of 

these sampled students were 10th-graders who transferred in from another school, which 

contributed to the high ineligibility rate. The expected number of freshened students was about 1 

per school. The actual number of freshened students was approximately 1.2 students per school 

(57 students out of 46 schools that sent 12th-grade enrollment lists). 

J.3 Maximizing In-School Response Rates Through the Use of 

Incentives 

A major concern for the first follow-up was achieving a high in-school student response 

rate, given that spring term of senior year is a time when many students are disengaging from 

high school, and response propensities are historically low, particularly for low-stakes/highburden assessments and surveys. An incentive experiment was therefore undertaken. 

J.3.1 Incentive Experiment 

To explore means to obtain the needed high response rates in the ELS:2002 first followup, a test of student-level incentives was implemented in the 2003 field test. The key hypothesis 

to be tested was that providing a $20 cash incentive would prove more effective than a token 

incentive in eliciting high levels of student participation. 

J.3.1.1 Incentives Experiment: Design 

Schools in the 2003 first follow-up field test were essentially the same schools that had 

participated in the base-year field test in 2001. A listing of the schools was sorted by school 

sector (private vs. public), state, region (urban, suburban, and rural), and consent type (active vs. 

passive). After sorting, systematic sampling was used to divide the field test schools into two 

1 This process is also known as the half-open interval rule. For further information on half-open interval procedures, 

see Kish (1965, p. 56) or Groves (1989, p. 127). 

J-5 
 

 

Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

groups: one receiving monetary incentives and one not receiving monetary incentives. In this 

example of systematic sampling (an analogue of random sampling), a sample selection flag 

(0 vs. 1) was assigned to each school alternating between 0 and 1 until all schools had an 

assignment. After the incentive assignments were made, distributions of the sorting variables 

were examined to check the distributions across the control variables. 

After sampling had been completed, coordinators at schools selected for the incentive 

treatment were contacted by telephone to advise them of the availability of cash incentives for 

participating students and to confirm that it was permissible to offer a cash incentive to the 

students. Some schools preferred a noncash monetary incentive (such as gift certificates); these 

and other arrangements were allowed (further detail appears below). In schools where incentives 

of any kind were approved, the type of incentive and amount were stated in the parent consent 

letter. A flyer mentioning the incentive was also included in the parent consent mailing for the 

parent to share with the selected student. The flyer invited the student to participate in the study 

and announced the incentive treatment that participating students at the school were to receive 

($20 cash, $20 gift certificate, or, in one case, a pizza party). Additionally, it was requested that 

the school coordinator reinforce awareness of the incentive by mentioning it to sampled students 

prior to the scheduled survey day. 

Survey administrators presented cash/gift certificates to each participating student 

immediately following completion of the questionnaire and test. At schools that were not 

selected for monetary incentives, the survey administrator presented each participating student 

with a token incentive of relatively small monetary value (a “Class of 2003” key ring) after 

completing the questionnaire and test. In both cases, participating students received the 

incentive whether they participated on Survey Day or a Makeup Day. 

J.3.1.2 Incentives Experiment: Results 

Results of the experiment were as follows. Of the 27 schools selected to receive 

monetary incentives, 16 allowed the students to be paid in cash, 9 allowed each participating 

student to be given an equivalent amount ($20) in a bookstore gift certificate, and 1 used the 

incentive money for a pizza party for the participating students. One school refused any 

incentive of any kind. This school and the pizza party school were not included in the analysis. 

Of the remaining schools, 19 were not offered incentives, and 4 schools, which were not 

statistically sampled and were not included in the experiment, were offered incentives on a 

special case basis.2 

To test the hypothesis that cash incentives would have a positive effect on participation, 

chi-squared tests were performed. A respondent was defined as an eligible student who 

participated in the in-school survey by completing at least the student questionnaire. 

2 These four schools were offered an incentive due to the extra burden of either not administering the survey during 

the regular school day or mailing parental consent forms for student participation. Of these four schools, two allowed 

cash incentives, one allowed a gift certificate, and one refused the incentive. Again, these schools were not included 

in the analysis of results of the experiment. 

J-6 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

As shown in table J-1, for both active and passive consent schools combined, there was a 

significant difference (p = 0.036) in the response rates for students who received a monetary 

incentive of either cash or a gift certificate and those students who did not receive any incentive. 

When the two incentive types were examined separately, students who received cash incentives 

were more likely to respond than those who did not receive any incentive (p = 0.032). However, 

when students were offered only gift certificates as incentives, there was no significant 

difference (p = 0.307) in student response rates. 

Table J-1. Response rate comparisons, by school consent type and incentive type: 2003 

Characteristic Total students Response rate (percent) P value 

Respondent status for all students 742 88.41 

Cash and gift certificate incentive 415 90.60 

Token incentive 327 85.63 0.036 

Cash incentive 285 91.23 

Token incentive 327 85.63 0.032 

Gift certificate incentive 130 89.23 

Token incentive 327 85.63 0.307 

Respondent status for students in passive schools 607 90.94 

Cash and gift certificate incentive 304 93.09 

Token incentive 303 88.78 0.064 

Cash incentive 174 95.98 

Token incentive 303 88.78 0.007 

Gift certificate incentive 130 89.23 

Token incentive 303 88.78 0.891 

Respondent status for students in active schools 135 77.04 

Cash and gift certificate incentive 111 83.78 

Token incentive 24 45.83 0.000 

Cash incentive 111 83.78 

Token incentive 24 45.83 0.000 

Gift certificate incentive 0 

Token incentive 24 45.83 † 

† Not applicable. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

J-7 
 

Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

In passive consent schools, the response rates were significantly different at .10 

(p = 0.064) among students who received either a cash incentive or gift certificate and those who 

did not. Similar results were found for those students receiving a cash incentive (p = 0.007). 

However, when students in passive consent schools were offered only gift certificates as 

incentives, there was no significant difference (p = 0.891) in student response rates. 

For the two active consent schools, one school received an incentive in the form of cash 

and one received no incentive. Therefore, only a significance test for differences in response 

rates based on cash incentive could be performed. Thus, for active consent schools, the data 

showed that students receiving cash incentives were more likely to respond (p = 0.000) than 

those students not receiving any incentive. 

In addition to the issue of participation, a further issue was quality or completeness of 

participation, that is, whether respondents completed both the questionnaire and the test. 

Overall, 94.2 percent of questionnaire completers were also test completers, with very little 

variation between treatment groups. 

Given the positive outcome of the incentives experiment, a cash incentive was adopted 

for the main study in-school survey. Of course, incentives to participate are an issue for the outof-school sample as well, and perhaps particularly for high school dropouts. Although no formal 

experiment took place with the out-of-school group, an incentive was also implemented for the 

full-scale out-of-school sample. 

As a postscript to the discussion of the 2003 field test experiment, it may be of interest to 

examine results of the 2004 main study, in terms of the possible effects of the incentives 

designed in the field test. There is no basis for conclusively attributing the success of the main 

study—a 91.2 percent unweighted or 89.0 percent weighted student in-school response rate,3

 a 

higher response rate than achieved for sophomores 2 years before—specifically to a cash 

incentive. Nevertheless, the results are at least consistent with such a relationship and are 

especially suggestive given the success of the incentives experiment in the field test. Table J-2 

provides a concise summary of main study ELS:2002 first follow-up completion rates by type of 

incentive received. 

3

 Although this response rate is predicated upon questionnaire completion, it should be noted that the assessment 

was was completed by 99.1 percent (weighted and unweighted) of the in-school questionnaire completers. By 

qualitative measures, such as number of omitted items or strength of coefficient alpha reliabilities, the tests were 

taken with seriousness by the test takers, as seriously at least as the low-stakes tests in prior studies, such as 

NELS:88, which did not give the test takers a cash incentive. 

J-8 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

Table J-2. ELS:2002 in-school unweighted completion rate, by school consent type and incentive 

type: Spring term 2004 

Characteristic 

 Total 

Number of students 

12,048 

Number of respondents 

11,276 

Response rate (percent)

93.59 

Active consent 

Cash 

Gift certificate 

Other 

941 

685 

209 

47 

804 

605 

157 

42 

85.44 

88.32 

75.12 

89.36 

Passive consent 

Cash 

Gift certificate 

Other 

None

11,107 

7,955 

2,356 

444 

352 

10,472 

7,605 

2,146 

401 

320 

94.28 

95.60 

91.09 

90.32 

90.91 

NOTE: Because this is a methodological table, it contains some cases that were not included in other ELS:2002 first 

follow-up tables; therefore, respondent totals may not wholly agree with other tables in this data file documentation 

(NCES 2006–344). For purposes of reporting the in-school incentive results, cases were included from so-called 

convenience schools (see chapter 4), as well as all freshening cases, regardless of whether they were included on 

the data file. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, 2004.” 

J.4 Assessment Design Issues and Recommendations 

The field test was designed to help provide information to successfully deal with a 

number of testing issues: 

• About 90 percent of base-year mathematics questions were presented in multiplechoice format. The 10 percent of items that were open ended were scored as right or 

wrong, with no partial credit awarded. Results of the 10th-grade testing were used to 

recast the selected open-ended items in multiple-choice format. This was intended 

not only to save time and expense in scoring but to increase scoring accuracy and 

reduce administration time. 

• The 2001 base-year field test results suggested that additional difficult mathematics 

questions would be required to avoid ceiling effects in the high-difficulty first followup test form. Several of the most difficult NELS:88 items were added to the 2003 

first follow-up field test forms. 

• Two test forms of approximately parallel difficulty and content were employed in the 

first follow-up field test, with a total of 63 test items. Booklet covers and answer 

sheets were color coded to avoid confusion. 

• Some first follow-up participants were not tested in mathematics in 10th grade and 

thus lacked the base-year ability estimate required for test form assignment. A 

broadband form was therefore developed for administration to freshened sample 

students and others who lacked base-year mathematics scores. This form was 

designed to provide an approximately rectangular distribution of item difficulties that 

would make it suitable for a wide range of achievement levels. 

J-9 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

Each of the 63 field test items was selected to serve a particular purpose: 

• to evaluate the performance of items that were reformatted from open-ended to 

multiple-choice presentation (10 items); 

• to obtain statistics on a set of items with higher difficulty levels than most of those 

used in 10th grade, to avoid a ceiling effect in the first follow-up (8 items); and 

• to provide a link to grade 10 main study score scales (45 items). 

The 45 grade 10 items used in the first follow-up field test were selected for one or more of the 

following reasons: 

• 15 items: Items that were administered to all 10th-grade main study participants are 

valuable for targeting the level of difficulty required for the first follow-up test forms. 

The same 15-item routing test was administered to all students in the base year. This 

routing test included the items that defined the middle three mathematics proficiency 

levels (levels 2, 3, and 4) in the NELS:88 survey. (One additional item, counted 

below, appeared in all three base-year second-stage forms.) 

• 8 items: The lowest and highest NELS:88 mathematics proficiency levels (levels 

1 and 5), consisting of 4 items each, appeared in the ELS:2002 base-year low and 

high second-stage forms, respectively. Percentage correct for students who received 

these items in the base year was compared with percentage correct for the low and 

high quartile of 2003 field test participants. The first follow-up main study plan 

called for selecting a test form for each student based on his or her performance in 

10th grade. Analysis of grade 10 main study versus grade 12 field test performance 

on these items supplemented the information available from the 15 routing test items 

for estimating growth trajectories for the low and high quartiles of the base-year 

sample. 

• 12 items: The first follow-up field test contained 12 of the items that showed the 

biggest grade 10 versus grade 12 differences in the base-year field test. These items 

were considered prime candidates for selection for first follow-up main study forms 

because they were likely to be strongly curriculum related. (They included the one 

item mentioned above that was used in all three grade 10 second-stage forms.) 

• 3 items: Some of the 10th-grade mathematics items consisted of several questions 

based on the same stem or premise and increasing in difficulty. Three of the field test 

items that might not otherwise have been selected were included because they were 

part of item sets, and statistics for other items in the sets might have been affected if 

the context had been changed. 

• 7 items: After the 38 items above had been selected for the reasons described, there 

were some large gaps in the estimated difficulty ranges of the field test forms. Seven 

additional items were selected to fill these gaps. 

Tables J-3 and J-4 list the 63 first follow-up field test items: 32 in form A and 31 in 

form B. The column labeled “ELS:2002 grade 10 form” shows the item usage in the base year, 

if any. Form W is the routing test, and forms X, Y, and Z are the low, middle, and high secondstage forms, respectively. The reasons for selection are listed for each item. “Estimated B” is 

J-10 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

the item difficulty, in a metric corresponding to the estimates that were used for matching test 

forms to students’ ability. Difficulty estimates for items not previously used in ELS:2002 tests 

(the new NELS:88 items and the open-ended items reformatted to multiple choice) were derived 

from whatever information was available from other uses or other versions of the items. Other 

columns show the original source of each item (prior to any revisions that may have been 

implemented) and the content and process categories used for modeling the ELS:2002 test on 

NELS:88 test specifications. 

Table J-3. Field test items, form A, “Yellow Form,” by usage: 2003 

ELS:2002 

2003 field grade 10 Reason for Original 

test form, # form selection Est. B source Content Process 

A1 X Prof lev 1 -2.19 NELS Arithmetic Skill/knowledge 

A2 X Prof lev 1 -2.21 NELS Arithmetic Skill/knowledge 

A3 X Prof lev 1 -0.76 NELS Arithmetic Skill/knowledge 

A4 W Gr10 routing -0.33 NELS Algebra Understanding/comprehension 

A5 W Gr10 routing 0.12 NELS Geometry Understanding/comprehension 

A6 W Gr10 routing -0.19 NELS Algebra Skill/knowledge 

A7 W Gr10 routing -0.46 NELS Algebra Understanding/comprehension 

A8 X Fill gap -1.28 NELS Arithmetic Skill/knowledge 

A9 W Gr10 routing -1.19 NELS Data/probability Understanding/comprehension 

A10 XY Biggest gain -0.42 NELS Arithmetic Problem solving 

A11 Y Biggest gain 0.00 NAEP Geometry Understanding/comprehension 

A12 XY Reformat -0.30 PISA Arithmetic Understanding/comprehension 

A13 W Gr10 routing 1.26 NELS Algebra Problem solving 

A14 Y Fill gap 0.33 NELS Advanced topics Problem solving 

A15 Z Fill gap 2.01 NELS Arithmetic Problem solving 

A16 XY Biggest gain 0.00 PISA Data/probability Skill/knowledge 

A17 XY Part of set -1.48 PISA Data/probability Skill/knowledge 

A18 XY Part of set -1.18 PISA Data/probability Skill/knowledge 

A19 Y Biggest gain 1.30 PISA Data/probability Problem solving 

A20 YZ Biggest gain 0.08 NAEP Data/probability Understanding/comprehension 

A21 W Gr10 routing 0.85 NELS Algebra Problem solving 

A22 Z Biggest gain 2.11 NELS Geometry Problem solving 

A23 New NELS 1.41 NELS Geometry Problem solving 

A24 New NELS 1.15 NELS Advanced topics Skill/knowledge 

A25 New NELS 2.13 NELS Geometry Understanding/comprehension 

A26 New NELS 2.27 NELS Data/probability Understanding/comprehension 

A27 New NELS 2.78 NELS Geometry Problem solving 

A28 Z Reformat 2.60 PISA Geometry Problem solving 

A29 Z Part of set 0.00 PISA Geometry Problem solving 

A30 Z Reformat 2.30 PISA Geometry Problem solving 

A31abcd Z Biggest gain 2.70 PISA Geometry Problem solving 

A32 Z Prof lev 5 2.92 NELS Advanced topics Understanding/comprehension 

NOTE: NELS = National Education Longitudinal Study; NAEP = National Assessment of Educational Progress; 

PISA = Program for International Student Assessment. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

J-11 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

Table J-4. Field test items, form B, “Blue Form,” by usage: 2003 

ELS:2002 

2003 field grade 10 Reason for Original 

test form, # form selection Est. B source Content Process 

B1 X Fill gap -1.30 NELS Arithmetic Skill/knowledge 

B2 W Gr10 routing -0.54 NELS Arithmetic Skill/knowledge 

B3 W Gr10 routing -0.60 NELS Arithmetic Skill/knowledge 

B4 Y Biggest gain 0.05 NELS Arithmetic Understanding/comprehension 

B5 W Gr10 routing -0.26 NELS Arithmetic Skill/knowledge 

B6 XY Biggest gain -0.50 NAEP Algebra Skill/knowledge 

B7 X Prof lev 1 -2.26 NELS Arithmetic Understanding/comprehension 

B8 W Gr10 routing 0.06 NELS Algebra Understanding/comprehension 

B9 W Gr10 routing 0.02 NELS Arithmetic Understanding/comprehension 

B10 XY Reformat 0.00 NAEP Advanced topics Understanding/comprehension 

B11 YZ Reformat 0.00 PISA Advanced topics Understanding/comprehension 

B12 YZ Reformat 2.80 PISA Advanced topics Understanding/comprehension 

B13 YZ Reformat 1.60 PISA Advanced topics Understanding/comprehension 

B14 W Gr10 routing 1.02 NELS Geometry Problem solving 

B15 W Gr10 routing 0.14 NELS Geometry Problem solving 

B16ab XYZ Reformat 0.50 PISA Geometry Problem solving 

B17 XYZ Biggest gain 1.07 NELS Advanced topics Understanding/comprehension 

B18 YZ Biggest gain 1.29 NELS Geometry Understanding/comprehension 

B19 W Gr10 routing 1.02 NELS Algebra Understanding/comprehension 

B20 YZ Fill gap 0.40 NAEP Geometry Skill/knowledge 

B21 YZ Fill gap 1.60 NELS Algebra Understanding/comprehension 

B22 Y Fill gap 1.35 NELS Geometry Problem solving 

B23 Z Biggest gain 1.70 NAEP Algebra Understanding/comprehension 

B24 Z Reformat 1.80 NAEP Algebra Skill/knowledge 

B25 New NELS 2.42 NELS Data/probability Skill/knowledge 

B26 New NELS 2.26 NELS Algebra Understanding/comprehension 

B27 New NELS 1.27 NELS Algebra Understanding/comprehension 

B28 YZ Reformat 1.90 PISA Geometry Problem solving 

B29 Z Prof lev 5 2.67 NELS Data/probability Problem solving 

B30 Z Prof lev 5 2.78 NELS Geometry Problem solving 

B31 Z Prof lev 5 2.56 NELS Algebra Problem solving 

NOTE: NELS = National Education Longitudinal Study; NAEP = National Assessment of Educational Progress; 

PISA = Program for International Student Assessment. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

J.4.1 Field Test Assessment Sample 

Approximately 1,070 students in 52 schools took sets of mathematics items in the spring 

2003 field test (see table J-5). Students were randomly assigned to one of the two field test 

booklets. There were slightly more females than males, with enough participants that DIF could 

be evaluated by gender. Sample sizes and response rates for racial/ethnic minority groups 

allowed evaluation of DIF for Hispanic compared with White students for about half of the field 

test items, and for Black compared with White students for about one-quarter of the items. 

About two-thirds of test takers also participated in other field test activities; the remaining onethird were “test augmentation cases” added to the regular field test sample for the purpose of 

collecting enough test data for evaluation of items. The test augmentation cases were primarily 

J-12 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

12th-graders, with grades 9, 10, and 11 also represented, to reflect that not all main study 10thgraders progressed to 12th grade 2 years later. 

Table J-5. Field test sample counts, selected characteristics: 2003 

Characteristic Form A, “Yellow Form” Form B, “Blue Form” 

Total 543 523 

Male 265 250 

Female 279 273 

All other races/unknown 42 44 

Black or African American 119 108 

Hispanic or Latino 133 137 

White 250 234 

Public 520 497 

Catholic 12 12 

Other private 12 14 

Test augmentation cases 184 171 

Grade 9 6 3 

Grade 10 9 7 

Grade 11 49 49 

Grade 12 120 112 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

J.4.2 Assessment Timing and Completion Rates 

Completion rates indicated that the 26 minutes allotted for the first follow-up 

mathematics field tests were sufficient for most of the field test students (see table J-6). Not all 

students answered the last question, which could have been due to running out of time or 

discontinuing the test for some other reason. Nearly everyone got at least as far as question 24, 

about three-quarters of the way through the test form. On average, students answered all but one 

or two of the items in each form. The high proportion of students who answered most of the test 

questions, as well as the consistency of results (see later section on reliability), suggests that 

most of the students were motivated to take the test seriously. 

Table J-6. Test form, by timing, number of items, and completion rates: 2003 

Characteristic Form A, “Yellow Form” Form B, “Blue Form” 

Time (minutes) 26 26 

Number of questions 32 31 

Completion rates 

Average number of items answered 29.8 30.0 

Percentage reaching end of test 74% 85% 

Percentage reaching item 24 (3⁄4 of test) 95% 99% 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

Ten of the mathematics items were included in the first follow-up field test because they 

had been reformatted from open-ended presentation in the base year to multiple choice for the 

first follow-up. Although the primary reason for the reformatting was to increase scoring 

accuracy and reduce scoring complexity and expense, the change to multiple-choice format had a 

beneficial effect on response rates as well. 

J-13 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

Results observed in the ELS:2002 base year and in the National Assessment of 

Educational Progress (NAEP) suggest that, in a low-stakes test, students are more likely to omit 

open-ended than multiple-choice questions. Score statistics show that this is not necessarily due 

to their inability to answer the questions but is probably influenced by their unwillingness to 

extend the extra effort required to produce an open-ended response. This effect is noted not only 

for questions that require an extended response, such as solving a problem or writing an 

equation, but also for questions that simply require making a choice, such as picking one of 

several alternative diagrams and writing in a letter code. 

Table J-7 shows the percentage of omitted responses for the 10 questions that were open 

ended in the base-year field test and main study and for the same questions converted to multiple 

choice in the first follow-up field test. Omits are defined as unanswered questions followed by at 

least one question that was answered. The largest reductions in omit rates observed for the 

reformatted items tend to be for the most difficult questions. 

Table J-7. Percentage of omitted responses for reformatted items, by study stage: 2003 

Base-year field test Base-year main study First follow-up field test 

Item (open ended) (open ended) (multiple choice) 

A12 11 7 4 

A28 11 8 3 

A30 26 15 7 

B10 8 9 7 

B11 13 4-5 2 

B12 14 5-6 2 

B13 13 4-6 2 

B16 8 3-7 0 

B24 30 31-33 2 

B28 9 6 2 

NOTE: A range of percentages is reported in the table for items that appeared in more than one base-year secondstage form: omit rates were calculated separately by form. Omit rates are not reported when the item was the last 

item in the test, because failure to respond could be due to running out of time or discontinuing the test for some 

other reason. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “Base Year, Field Test, 2001,” “Base Year, Main Study, 2002,” and “First Follow-up, Field Test, 

2003.” 

J.4.3 Item and Test Performance 

This section describes the psychometric characteristics of the first follow-up mathematics 

field test item pool. The specific goals—reformatting open-ended items, adding difficult items 

to the pool to avoid a ceiling effect in grade 12, and establishing a basis for selection of items for 

grade 12 forms—are evaluated. Tables J-8 and J-9 present item statistics for the yellow and blue 

field test forms. 

Two different methodologies were used to evaluate item performance: classical item 

analysis and IRT estimation. The two methods reinforce each other in that both generate 

estimates of item difficulty and discrimination. In addition, each supplies a unique perspective 

on some aspect of the items that is not provided by the other tool. 

J-14 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

J.4.3.1 Classical Item Analysis 

Classical item analysis provides information on the total test, descriptive statistics for 

each test item, and the correlation of each item with the total test score. The number and 

percentage of test takers choosing each response option were computed, along with the average 

total test score for each of the response-option groups. The same statistics were computed for 

students who omitted each item but answered subsequent item(s) in the test and for those who 

omitted the item and did not answer any subsequent items (“not reached”). Item analysis tables 

also show “P+” (the percentage of correct responses) and R-biserials (adjusted correlations of 

item score with total test score). These statistics were reviewed to identify possible flaws in 

individual items, such as the following: 

• An incorrect response option that is selected by very few test takers may need to be 

replaced by a more plausible choice. 

• An item omitted by an unusually large number of test takers may have something 

unclear or offensive in the presentation. 

• For each item, the mean total test score for students choosing the correct response 

should be substantially higher than the score means for each of the incorrect groups. 

If this is not the case, it is possible that the question stem, the keyed correct response, 

or one or more of the incorrect response options may be ambiguous or incorrect. 

• Items that are much too easy (very high P+), with nearly all test takers able to answer 

correctly, may not be serving a useful purpose on the test. 

• Very difficult items (such as a four-choice item with a P+ of 0.25 or below, which 

could result from random guessing) may or may not be serving a useful purpose. 

Examination of the mean scores for those answering right and wrong can suggest 

whether a test item is helping to make distinctions among students at the highest 

achievement levels or is merely being guessed at random. 

The R-biserial statistic is a measure of discrimination, or how well each test item relates 

to the skill being measured by the test as a whole. Low R-biserials (below about 0.40) generally 

indicate items that are not strong measures of the overall construct. 

Table J-8 summarizes the classical item statistics for the field test forms. The difficulty 

of the items was appropriate for the field test sample. The distribution of number right on each 

form was approximately rectangular, with no perfect scores on either form and only a small 

percentage of below-chance scores. Only two items were so easy that more than 90 percent of 

the test takers got them right, whereas seven items were answered correctly by less than 25 

percent of students. When test forms were assembled for the first follow-up main study, 

additional easy items were needed for the easiest test form. These items were selected from 

among base-year items that were not field tested in 2003. 

R-biserials were generally high, falling below 0.40 for 11 of the 63 items. Two of the 

low R-biserial items were questions that had been reformatted from open ended to multiple 

choice; three others were difficult NELS:88 items that had not previously been used in ELS:2002 

test forms. 

J-15 
Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

Response options were reviewed for the 10 base-year items that had been converted to 

multiple-choice format. The response options had been selected to represent the most popular 

incorrect answers (as well as the correct answer) obtained when the items were administered in 

open-ended format in the base year. Ideally, students who do not know the correct answer to a 

test question should be able to do no better than guessing at random among the response options. 

If the question is strongly related to the construct being measured, this would result in similar 

mean total test scores for the group choosing each incorrect option and a much higher mean for 

those choosing the correct answer. Each incorrect option should be selected by a substantial 

number of test takers: there should be no “throwaway” options that virtually all test takers could 

eliminate from consideration. Review of the item statistics for the 10 reformatted items showed 

no need for revisions. That is, each incorrect response option was selected by a satisfactory 

number of test takers, and total score means for all incorrect options were substantially lower 

than the means for the correct response. 

Table J-8. Summary of classical item analysis statistics, by test form: 2003 

Test measures 

Perfect scores (Form A: 32; Form B: 31) 

More than 28 items correct 

Form A, “Yellow Form” 

0 

4% 

Form B, “Blue Form” 

0 

1% 

Chance scores (< 7 correct) 3% 5% 

Mean number right (standard deviation) 

Mean percentage correct (P+) for items 

Mean R-biserial 

17.2 (6.1) 

0.55 

0.57 

15.6 (6.2) 

0.51 

0.59 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

J.4.3.2 Item Response Theory (IRT) 

IRT provides an alternative way of measuring item difficulty and discrimination. The 

Parscale program uses a three-parameter IRT model to estimate item characteristics and 

test-taker ability. The IRT “a” parameter is an estimate of the discriminating ability of a test 

item, or how well it serves to distinguish between adjacent levels of ability. This is somewhat 

analogous to the R-biserial but applies to a certain point on the ability continuum rather than an 

overall correlation. Items with “a” parameters of about 1.0 or higher are doing a good job of 

discriminating levels of ability. The “b” parameter is a difficulty estimate, analogous to the 

percentage correct but compensating for the possibility of guessing. Items with a range of 

difficulty that matches the estimated ability range of the test takers will be selected. The 

guessing parameter, “c,” estimates the probability of a very low-skilled person answering the 

item correctly. It is important in obtaining estimates of probabilities of correct answers but was 

less important for the purpose of the field test, that is, for selecting items for the first follow-up 

main study forms. The Parscale program uses the scored-item responses to compute these item 

parameter estimates and ability estimates by iterating on the data until the system converges to 

within a predetermined tolerance. 

Table J-9 summarizes item and student performance in terms of the IRT metrics. IRT 

scaling was carried out for the two field test forms combined, so that parameter estimates could 

be evaluated on the same scale. 

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Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

Table J-9. Summary of Item Response Theory (IRT) estimates: 2003 

IRT measure Forms A and B combined 

Average item “a” parameter (discrimination) 1.16 

Average item “b” parameter (difficulty) -0.13 

Average theta (student ability) -0.42 

Standard deviation 1.01 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

The “b” parameters for the field test items represent the item difficulty, corresponding to 

the ability level at which 50 percent of students would answer an item correctly, after 

compensating for guessing. Satisfactory parameters were obtained for items ranging from about 

1.2 standard deviations below the mean ability of first follow-up participants to about 2 standard 

deviations above the mean. There were no large gaps in item difficulty, that is, no ability level 

within the expected first follow-up range that could not be matched to items of appropriate 

difficulty. As noted above, it was necessary to select base-year items that were not used in the 

field test for the main study first follow-up low difficulty test form. 

Twenty of the 63 field test items had “a” parameters below 1.0, meaning that the ability 

of the item to discriminate between closely adjacent levels of ability was somewhat weak. Only 

5 of the items had “a” parameters so low (below 0.70) that they were not likely to be selected for 

main study forms. The rest of the relatively weak items were chosen only if they were needed to 

fill difficulty gaps or to meet content specifications. 

The IRT system also provides for both statistical and graphical approaches to evaluating 

how well the IRT model is doing in representing the actual data. Graphs of item response 

functions were reviewed for each of the field test items to determine how well the estimates fit 

the field test data. The graphs also show whether the fit is satisfactory at all ability levels or only 

within a limited range. Fit statistics provide a numerical way to evaluate the success of the IRT 

model for estimating performance on each item. Fit of data to the IRT model was satisfactory 

for virtually all field test items. 

These two methodologies, classical item analysis and IRT, reinforce and complement 

each other by providing overlapping as well as unique information for evaluating item 

performance. Both offer measures of item difficulty and discrimination. In addition, classical 

item statistics supply information on performance of distractors (incorrect response options) and 

omit rates. IRT offers fit statistics and information on where along the ability continuum the 

item performs best. This was particularly useful in selecting items for the first follow-up main 

study test forms, where the ability range in which the item must perform was dictated by its 

assignment to a test form to be matched to each student’s expected achievement level. 

Combining information from the two methodologies provided a good idea of how well an item 

performed, whether any revisions were desirable, and whether the item was appropriate for all 

students or within a restricted range of ability. 

J.4.4 Reliability 

Reliabilities for the two mathematics forms were high (see table J-10). Coefficient alpha 

measures the internal consistency of the test, that is, the extent to which variance in performance 

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Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

on individual items is related to variance in performance on the whole test. The reliability of the 

IRT ability estimate is derived from a comparison of within-student variance to between-student 

variance. The field test reliability statistics are quite high for a test of 31 or 32 items. By 

coincidence alone, the alpha coefficients and reliability of the IRT ability estimate are identical 

for each test form. A consequence of the plan to select test forms to match students’ ability 

levels in the main study was to expect a low alpha coefficient for each form but raise the 

reliability of the IRT-based ability estimates. Restricting the ability range of the students taking 

each form means that the variance of total scores on the form was expected to be lower than the 

randomly assigned field tests, and thus the alpha coefficient would be smaller. Conversely, a 

better match of items to each student’s ability level than was the case for the broad range of 

items in the field test would result in better measurement for each student, that is, a higher 

reliability for the ability estimate. 

Table J-10. Reliabilities, by test form: 2003 

Reliability measure Form A, “Yellow Form” Form B, “Blue Form” 

Alpha coefficient 0.86 0.87 

Reliability of IRT theta (ability estimate) 0.86 0.87 

NOTE: IRT = Item Response Theory. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

J.4.5 Differential Item Functioning (DIF) 

Cognitive test items were checked for DIF for males compared with females, and for 

Black and Hispanic students compared with White students, to the extent that sample sizes 

permitted. It is not necessarily expected that different subgroups of students will have the same 

average performance on a set of items. But when students from different groups are matched on 

overall ability, performance on each test item for the matched groups should be about the same. 

There should be no relative advantage or disadvantage based on the student’s gender or 

racial/ethnic group alone. 

The DIF procedure carries out comparisons of subgroup performance for a focal group 

(e.g., females) compared with a reference group (e.g., males) matched on a criterion (e.g., 

number right on the whole test). It is based on the Mantel-Haenszel odds ratio and its associated 

chi-square. Items are classified as “A,” “B,” or “C” depending on the statistical significance of 

subgroup differences as well as effect sizes. Items identified as having “C”-level DIF have 

detectable differences that are both sizeable and statistically significant. A finding of differential 

functioning, however, does not automatically mean that the difference in performance is unfairly 

related to subgroup membership. A judgment that these items are unfair to particular population 

groups requires not only the measure of DIF but also a determination that the difference in 

performance is not related to the construct being measured. In other words, different population 

subgroups may have differential exposure or skill in solving test items relating to a topic that is 

to be measured. If so, the finding of differential performance may be an important and valid 

measure of the targeted skill. 

Analysis of the mathematics field test, using total number right score as the matching 

criterion, showed four items with C-level DIF, one favoring females and three favoring males. 

One of the items favoring males had already been identified in the base-year main study data and 

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Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

deleted from base-year scoring procedures. The remaining DIF items were reviewed and, if 

necessary, deleted from consideration for first follow-up forms. 

A minimum of 100 matched-ability students in each subgroup is required for the DIF 

procedure to be carried out for each test item. Small sample sizes may result in spurious findings 

of DIF where none exists. The numbers of Black and Hispanic students responding to each test 

question were sufficient for evaluation of DIF for only about one-quarter of the questions for the 

Black versus White contrast and about half of the questions for Hispanic versus White. 

J.4.6 Field Test Conclusions 

J.4.6.1 Reformatted Items 

Of the 10 items changed from open-ended to multiple-choice format, 7 had psychometric 

characteristics suitable for consideration for first follow-up main study forms. Table J-11 shows 

that the reformatting improved the R-biserial for the majority of the potentially useful items 

(relative to at least one of the base-year forms) and improved the IRT “a” parameter for all but 

one. The 2 items that had weak statistics in the multiple-choice versions had been weak in their 

original open-ended versions as well. In addition to maintaining or improving the psychometric 

characteristics of the items, the reformatting resulted in lower omit rates, as noted above, and 

was expected to reduce costs. 

Table J-11. Summary statistics for reformatted items, by item type: 2003 

R-biserial IRT “a” parameter 

Item 

Base year 

(open ended) 

Field test 

(multiple choice) 

Base year 

(open ended) 

Field test 

(multiple choice) Notes 

A12 0.54–0.66 0.62 0.98 1.01 

A28 0.46 0.34 0.71 0.91 

A30 0.44 0.41 0.69 0.51 Low “a,” R-biserial 

B10 0.50–0.64 0.68 0.98 1.02 

B11 0.46–0.55 0.65 1.07 1.17 

B12 0.43–0.55 0.45 0.82 1.50 

B13 0.30–0.51 0.60 0.92 1.08 DIF 

B16 0.36–0.41 0.35 0.41 0.32 Low “a,” R-biserial 

B24 0.61 0.55 1.28 1.08 

B28 0.50 0.63 0.66 0.92 

NOTE: A range of R-biserials is reported in the table for items that appeared in more than one base-year 

second-stage form. R-biserials were calculated separately by form. DIF = differential item functioning; IRT = Item 

Response Theory. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

J.4.6.2 Difficult Items 

Table J-12 shows summary statistics for the most difficult items in the test, sorted in 

ascending order of the IRT difficulty parameter, “b.” This is a more useful measure of difficulty 

than P+ (percentage correct), because the “b” parameter compensates for guessing while P+ does 

not. The table shows that there were sufficient numbers of items suitable for testing students 

whose ability level fell in the top quarter of field test participants (theta > 0.27, same metric as 

“b” parameter). Seven of the eight NELS items that were added to the ELS:2002 item pool have 

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Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

difficulty parameters in this high range, and two of these items were among the three most 

difficult items field tested. 

Table J-12. Summary statistics for difficult items: 2003 

IRT parameters Item statistics 

Item a b c P+ R-biserial 

A24 (new NELS item) 1.54 0.13 0.16 0.43 0.72 

B20 1.22 0.21 0.07 0.36 0.77 

A13 0.99 0.22 0.17 0.44 0.61 

B18 1.76 0.22 0.37 0.56 0.56 

A23 (new NELS item) 1.70 0.31 0.22 0.43 0.63 

B28 0.92 0.38 0.12 0.38 0.63 

A22 1.60 0.41 0.13 0.31 0.71 

B19 1.27 0.41 0.20 0.40 0.58 

B22 1.08 0.41 0.16 0.39 0.64 

A14 1.27 0.43 0.41 0.56 0.41 

B21 1.07 0.52 0.11 0.33 0.65 

B13 1.08 0.55 0.15 0.36 0.60 

B26 (new NELS item) 1.46 0.63 0.24 0.39 0.52 

A30 0.51 0.64 0.17 0.42 0.41 

A19 1.26 0.76 0.12 0.28 0.60 

A25 (new NELS item) 1.72 0.91 0.19 0.28 0.49 

B27 (new NELS item) 0.81 0.97 0.13 0.29 0.50 

A15 1.31 1.01 0.27 0.35 0.36 

A26 (new NELS item) 0.77 1.09 0.26 0.40 0.36 

B12 1.50 1.21 0.14 0.21 0.45 

B23 0.97 1.23 0.23 0.33 0.36 

B31 1.12 1.26 0.10 0.19 0.47 

A28 0.91 1.30 0.24 0.34 0.34 

A31 0.57 1.41 0.00 0.19 0.53 

B29 1.89 1.53 0.10 0.14 0.30 

B30 1.02 1.62 0.19 0.25 0.31 

B25 (new NELS item) 0.78 1.73 0.18 0.25 0.34 

A27 (new NELS item) 1.25 2.02 0.12 0.13 0.19 

A32 0.90 2.04 0.14 0.19 0.25 

NOTE: IRT = Item Response Theory; NELS = National Education Longitudinal Study; P+ = percentage correct. 

SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 

2002 (ELS:2002), “First Follow-up, Field Test, 2003.” 

Although two of the difficult items had “a” parameters that fell below the desired 

standard of 1.0, numerous high-quality items remained from which to select the first follow-up 

main study high form. For the most difficult items, the “a” parameter is a more useful measure 

of discrimination than the R-biserial, because the item may discriminate well only in the ability 

range close to its difficulty level but not at lower levels. Items with high “a” parameters and low 

R-biserials (such as A27) are suitable for a test form to be administered to high-ability students 

but not for easier forms. 

J.4.6.3 Timing 

The 26 minutes allotted was sufficient for most of the participants to complete the 31 or 

32 items in the field test forms. With first follow-up main study forms selected according to the 

anticipated achievement level of the test takers, it should be possible to administer 30 to 35 test 

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Appendix J: 

Synopsis of the ELS:2002 First Follow-up Field Test (2003) 

items in the same amount of time. This number of items are expected to result in a satisfactory 

level of reliability. 

J.4.6.4 Grade 12 Item Pool 

The 2003 mathematics field test resulted in a satisfactory item pool from which to 

assemble first follow-up main study test forms. Items of acceptable quality (high R-biserials and 

“a” parameters) were available for the full range of achievement levels encountered in the field 

test sample, without gaps in estimated difficulty. The item pool from which the main study 

forms were selected included all base-year main study items, with parameters calibrated on a 

common scale to facilitate comparisons. Review of item analysis statistics for response options 

showed no need for further revisions of items. 

J.5 Appendix J References 

Groves, R.M. (1989). Survey Errors and Survey Costs. New York: Wiley. 

Kish, L.A. (1965). Survey Sampling. New York: Wiley. 

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