ECLS-K BASE YEAR PUBLIC-USE DATA FILES AND

ELECTRONIC CODEBOOK

Prepared by:

Westat

Rockville, Maryland

Educational Testing Service

Princeton, New Jersey

University of Michigan

School of Education

Ann Arbor, Michigan

Karen Tourangeau

John Burke

Thanh Le

Siu Wan

Margaret Weant

Eugene Brown

Nancy Vaden-Kiernan

Elizabeth Rinker

Richard Dulaney

Kirsten Ellingsen

Brandon Barrett

Ismael Flores-Cervantes

Nicholas Zill

Judith Pollack

Donald Rock

Sally Atkins-Burnett

Samuel Meisels

The National Center for

Education Statistics

U.S. Department of Education

Washington, DC

 Jonaki Bose

 Jerry West

Education Statistics Services

 Institute

Washington, DC

Kristen Denton

 Amy Rathbun

 Jill Walston
iii

TABLE OF CONTENTS

Chapter Page

1 INTRODUCTION ........................................................................................... 1-1

1.1 Background......................................................................................... 1-2

1.2 Conceptual Model............................................................................... 1-3

1.3 Study Components.............................................................................. 1-4

1.4 Contents of Manual............................................................................. 1-5

1.5 Differences Between the ECLS-K Restricted-Use Base Year

Files and the ECLS-K Base Year Public-Use Files ............................ 1-6

2 DESCRIPTION OF DATA COLLECTION INSTRUMENTS ...................... 2-1

2.1 The Direct Child Assessments............................................................ 2-1

2.2 Parent Interview.................................................................................. 2-9

2.3 Teacher Questionnaires....................................................................... 2-14

2.3.1 Content of the ARS ............................................................. 2-14

2.3.2 Teacher SRS........................................................................ 2-16

2.4 Special Education Teacher Questionnaires......................................... 2-17

2.5 Adaptive Behavior Scale .................................................................... 2-20

2.6 School Administrator Questionnaire .................................................. 2-21

2.7 School Facilities Checklist.................................................................. 2-21

2.8 School Records Abstract Form........................................................... 2-21

2.9 Salary and Benefits Questionnaire...................................................... 2-23

3 ASSESSMENT AND RATING SCALE SCORES USED IN THE

ECLS-K ........................................................................................................... 3-1

3.1 Direct Cognitive Assessment.............................................................. 3-1

3.1.1 Number-Right Scores.......................................................... 3-1

3.1.2 IRT Scale Scores................................................................. 3-2

3.1.3 Standardized Scores (T-Scores) .......................................... 3-6

3.1.4 Proficiency Scores............................................................... 3-7

3.1.5 Choosing the Appropriate Score for Analysis..................... 3-11

3.1.6 Reliabilities ......................................................................... 3-13

3.2 Indirect Cognitive Assessment (ARS)................................................ 3-15

3.2.1 Comparison to Direct Cognitive Assessment...................... 3-15

3.3 IRT Scores Available for the ARS ..................................................... 3-15

3.4 The Oral Language Development Scale (OLDS) ............................... 3-17
iv

TABLE OF CONTENTS (continued)

Chapter Page

3.5 Psychomotor Assessment ................................................................... 3-18

3.6 SRS ..................................................................................................... 3-19

3.6.1 Teacher SRS........................................................................ 3-20

3.6.2 Parent SRS .......................................................................... 3-21

4 SAMPLE DESIGN AND IMPLEMENTATION............................................ 4-1

4.1 Selecting the Area Sample.................................................................. 4-1

4.2 Selecting the School Sample............................................................... 4-2

4.2.1 Frame Construction............................................................. 4-4

4.2.2 School Measure of Size, Stratification, and Sample

Selection.............................................................................. 4-5

4.2.3 Freshening the School Sample ............................................ 4-6

4.2.4 Sampling Children, Parents, and Teachers within Schools. 4-8

4.3 Calculation and Use of Sample Weights ............................................ 4-10

4.3.1 Types of Sample Weights ................................................... 4-11

4.3.2 Weighting Procedures ......................................................... 4-18

4.3.3 Computation of School and Teacher Weights..................... 4-20

4.3.4 Computation of Child Weights ........................................... 4-21

4.3.5 Replicate Weights ............................................................... 4-24

4.3.6 Characteristics of Sample Weights ..................................... 4-24

4.4 Variance Estimation............................................................................ 4-25

4.4.1 Paired Jackknife Replication Method ................................. 4-26

4.4.2 Taylor Series Method.......................................................... 4-28

4.4.3 Specifications for Computing Standard Errors ................... 4-28

4.5 Design Effects..................................................................................... 4-31

4.5.1 Use of Design Effects.......................................................... 4-32

4.5.2 Average Design Effects for the ECLS-K ............................ 4-33

5 DATA COLLECTION METHODS AND RESPONSE RATES.................... 5-1

5.1 Data Collection Methods .................................................................... 5-1

5.2 Study Endorsements and School Recruitment.................................... 5-1

5.3 Field Staff Training............................................................................. 5-4
v

TABLE OF CONTENTS (continued)

Chapter Page

5.4 Fall-Kindergarten Data Collection...................................................... 5-7

5.4.1 Advance Mailings ............................................................... 5-7

5.4.2 Preassessment Visits ........................................................... 5-8

5.4.3 Conducting the Direct Child Assessment............................ 5-12

5.4.4 Conducting the Parent Interview......................................... 5-14

5.4.5 Teacher Data Collection...................................................... 5-15

5.5 Fall-Kindergarten Response Rates...................................................... 5-15

5.6 Spring-Kindergarten Data Collection ................................................. 5-22

5.6.1 Advance Contact with Respondents.................................... 5-22

5.6.2 Preassessment Contact ........................................................ 5-22

5.6.3 Conducting Direct Child Assessments................................ 5-26

5.6.4 Conducting the Parent Interview......................................... 5-27

5.6.5 Teacher and School Data Collection................................... 5-28

5.6.6 Conducting Data Collection on Children Who

Withdrew from Their Original School................................ 5-29

5.6.7 Collection of Head Start Data ............................................. 5-30

5.7 Data Collection Quality Control......................................................... 5-31

5.7.1 Child Assessment Observations.......................................... 5-32

5.7.2 Parent Validations ............................................................... 5-32

5.7.3 School Validations .............................................................. 5-33

5.7.4 Quality Control of the OLDS Scoring................................. 5-34

5.7.5 Assessor Effects .................................................................. 5-34

5.8 Spring-Kindergarten Response Rates ................................................. 5-35

5.9 Base Year Response Rates.................................................................. 5-46

6 DATA PREPARATION.................................................................................. 6-1

6.1 Coding and Editing Specifications for CATI/CAPI ........................... 6-1

6.1.1 Range Specifications........................................................... 6-1

6.1.2 Consistency Checks (Logical Edits) ................................... 6-2

6.1.3 Coding................................................................................. 6-2

6.2 Coding and Editing Specifications for Hard Copy Questionnaires .... 6-6

6.2.1 Receipt Control ................................................................... 6-6

6.2.2 Coding................................................................................. 6-6
vi

TABLE OF CONTENTS (continued)

Chapter Page

6.2.3 Data Entry ........................................................................... 6-8

6.2.4 Data Editing ........................................................................ 6-9

7 DATA FILE CONTENT AND COMPOSITE VARIABLES......................... 7-1

7.1 Identification Variables....................................................................... 7-1

7.2 Missing Values ................................................................................... 7-2

7.3 Variable Naming Conventions............................................................ 7-4

7.4 Composite Variables........................................................................... 7-6

7.4.1 Child Variables ................................................................... 7-6

7.4.2 Family and Household Variables ........................................ 7-8

7.4.3 Teacher Variables................................................................ 7-12

7.4.4 School and Class Characteristics ........................................ 7-13

7.4.5 Parent Identifiers and Household Composition .................. 7-17

7.4.6 Changes in Household Composition between

Fall-Kindergarten and Spring-Kindergarten ....................... 7-21

7.4.7 The Supplemental Section (SPQ) in the

Spring-Kindergarten Parent Interview ................................ 7-21

7.4.8 Industry and Occupation Codes Used in ECLS-K .............. 7-22

7.5 Using Teacher Data Collected Either in Fall-kindergarten or

Spring-kindergarten ............................................................................ 7-25

7.6 The Relationship Among Composite Variables on the Child,

Teacher, and School Catalogs............................................................. 7-26

7.7 Creating a Child File for Classroom Level Analysis.......................... 7-31

7.8 Creating a Classroom Level File Using Teacher Data........................ 7-31

7.9 Masked Variables ............................................................................... 7-72

8. GUIDE TO THE ECLS-K BASE YEAR PUBLIC-USE ELECTRONIC

CODE BOOK .................................................................................................. 8-1

8.1 Introduction......................................................................................... 8-1

8.1.1 Hardware/Software Requirements ...................................... 8-2

8.1.2 ECLS-K Base Year Public-Use ECB Features ................... 8-2

8.2 Installing, Starting, and Exiting the ECLS-K Base Year

Public-Use ECB.................................................................................. 8-3

8.2.1 Installing the ECLS-K Base Year Public-Use ECB

Program on Your PC........................................................... 8-3

8.2.2 Starting the ECLS-K Base Year Public-Use ECB .............. 8-8
vii

TABLE OF CONTENTS (continued)

Chapter Page

8.2.3 Exiting the ECLS-K Base Year Public-Use ECB ............... 8-11

8.2.4 Removing the ECB Program From Your PC ...................... 8-11

8.2.5 The Title Bar ....................................................................... 8-12

8.2.6 The Menu Bar ..................................................................... 8-12

8.2.7 Using Shortcut Keys to Navigate........................................ 8-13

8.3 The Variable List ................................................................................ 8-14

8.3.1 Searching the Code Book for Variables.............................. 8-16

8.4 The Working Taglist........................................................................... 8-22

8.4.1 Opening A Taglist............................................................... 8-23

8.4.2 Adding Variables to the Working Taglist ........................... 8-23

8.4.3 Removing Variables from the Working Taglist.................. 8-25

8.4.4 Saving Taglists.................................................................... 8-26

8.4.5 Using Predefined Taglists ................................................... 8-28

8.4.6 Deleting Taglists ................................................................. 8-29

8.4.7 Viewing Code Book and Variable Information .................. 8-30

8.5 Extracting Data from the ECB............................................................ 8-37

8.5.1 Reviewing the Extract Specifications ................................. 8-44

8.5.2 Repairing and Compacting the Database ............................ 8-45

8.6 The Child Catalog............................................................................... 8-45

8.6.1 Child Catalog Topical Variable Groupings......................... 8-47

8.6.2 Child Catalog Predefined Taglists ...................................... 8-56

8.6.3 Child Catalog Limiting Fields............................................. 8-63

8.7 The Teacher Catalog........................................................................... 8-64

8.7.1 Teacher Catalog Topical Variable Groupings..................... 8-65

8.7.2 Teacher Catalog Predefined Taglists................................... 8-68

8.7.3 Teacher Catalog Limiting Fields......................................... 8-68

8.8 The School Catalog............................................................................. 8-69

8.8.1 School Catalog Topical Variable Groupings ...................... 8-71

8.8.2 School Catalog Predefined Taglists .................................... 8-73

8.8.3 School Catalog Limiting Fields .......................................... 8-73
viii

TABLE OF CONTENTS (continued)

Chapter Page

8.9 Switching Catalogs ............................................................................. 8-74

8.10 Menu Bar Descriptions ....................................................................... 8-76

List of Tables

Tables

2-1 Instruments used in the fall and spring ECLS-K ............................................. 2-1

2-2 Flow of the fall direct child assessment........................................................... 2-4

2-3 Direct child assessment.................................................................................... 2-8

2-4 Flow of the spring direct child assessment ...................................................... 2-9

2-5 ECLS-K parent interview by major content topics and point of data

collection.......................................................................................................... 2-12

2-6 ARS response scale.......................................................................................... 2-15

2-7 Teacher questionnaire...................................................................................... 2-18

2-8 Special education teacher questionnaire1

......................................................... 2-20

2-9 School administrator questionnaire1 ................................................................ 2-22

3-1 Direct cognitive assessment: types of scores, variable names, descriptions,

ranges, means, and standard deviations1

.......................................................... 3-3

3-2 Reliability of IRT-based scores and routing test number correct .................... 3-14

3-3 Split half reliability of item-cluster-based scores (proficiency level scores)... 3-14
ix

TABLE OF CONTENTS (continued)

List of Tables (continued)

Table Page

3-4 Reliability for the Rasch-based score .............................................................. 3-16

3-5 ARS: variable names, descriptions, ranges, means, and standard

deviations......................................................................................................... 3-17

3-6 OLDS: variable names, descriptions, ranges, means, and standard

deviations......................................................................................................... 3-18

3-7 Psychomotor scales: variable names, descriptions, ranges, means, and

standard deviations .......................................................................................... 3-19

3-8 SRS response scale .......................................................................................... 3-19

3-9 Split half reliability for the teacher SRS scale scores ...................................... 3-20

3-10 Split half reliability for the parent SRS scale scores........................................ 3-20

3-11 Teacher social rating scores: variable names, descriptions, ranges, means,

and standard deviations.................................................................................... 3-21

3-12 Parent social rating scores: variable names, descriptions, ranges, means,

and standard deviations.................................................................................... 3-22

4-1 Stratum definitions for the 38 non-self-representing strata ............................. 4-3

4-2 Distribution of the ECLS-K PSU sample by SR status, MSA status, and

census region ................................................................................................... 4-4

4-3 Characteristics of the ECLS-K original school sample ................................... 4-9

4-4 The ECLS-K: fall-kindergarten cross-sectional weights ................................. 4-12

4-5 The ECLS-K: spring-kindergarten cross-sectional weights............................. 4-13

4-6 The ECLS-K: base year longitudinal weights ................................................. 4-17

4-7 Distribution of schools by number of cases (children) with nonzero weights. 4-18

4-8 Number of children who were not assessed due to special situations ............. 4-22
x

TABLE OF CONTENTS (continued)

List of Tables (continued)

Table Page

4-9 Characteristics of teacher- and school-level weights....................................... 4-24

4-10 Characteristics of child-level weights.............................................................. 4-25

4-11 ECLS-K Taylor Series stratum and first-stage unit identifiers........................ 4-29

4-12 Specifications for computing standard errors .................................................. 4-30

4-13 ECLS-K, fall-kindergarten: standard errors and design effects for the

full sample – child level................................................................................... 4-35

4-14 ECLS-K, spring-kindergarten: standard errors and design effects for the

full sample – child level................................................................................... 4-36

4-15 ECLS-K, panel: standard errors and design effects for the full

sample – child level ......................................................................................... 4-37

4-16 ECLS-K: median design effects for subgroups – child level........................... 4-38

4-17 ECLS-K, fall-kindergarten: standard errors and design effects for the

full sample – teacher level ............................................................................... 4-40

4-18 ECLS-K, spring-kindergarten: standard errors and design effects for the

full sample – teacher level ............................................................................... 4-41

4-19 ECLS-K: median design effects for subgroups – teacher level ....................... 4-42

4-20 ECLS-K, spring-kindergarten: standard errors and design effects for the

full sample – school level ................................................................................ 4-43

4-21 ECLS-K: median design effects for subgroups – school level ........................ 4-44

5-1 OLDS routing results in fall-kindergarten ....................................................... 5-13

5-2 Number of children excluded from or accommodated in the fall-kindergarten

assessment........................................................................................................ 5-13

5-3 Number of children excluded from parts of the psychomotor assessment ...... 5-13

5-4 The ECLS-K, fall-kindergarten: number of cooperating schools and beforesubstitution school-level response rates........................................................... 5-16
xi

TABLE OF CONTENTS (continued)

List of Tables (continued)

Table Page

5-5 The ECLS-K fall-kindergarten: number of completed child-level cases and

child-level completion rates, by school characteristic ..................................... 5-18

5-6 The ECLS-K, fall-kindergarten: number of completed child-level cases and

child-level completion rates, by child characteristic........................................ 5-20

5-7 The ECLS-K, fall-kindergarten: number of completed teacher questionnaires

and teacher-level completion rates................................................................... 5-23

5-8 The ECLS-K, fall-kindergarten: number of completed child-level cases and

overall child-level response rates, by school characteristic ............................. 5-24

5-9 The ECLS-K, fall-kindergarten: number of completed child-level cases and

overall child-level response rates, by school characteristic ............................. 5-25

5-10 OLDS routing patterns in spring-kindergarten for fall-kindergarten

respondents ...................................................................................................... 5-26

5-11 OLDS routing results in spring-kindergarten .................................................. 5-27

5-12 Number of children excluded from or accommodated in the

spring-kindergarten assessment ....................................................................... 5-27

5-13 Number of children who moved by category and assessment result ............... 5-30

5-14 The ECLS-K, spring-kindergarten: number of cooperating schools and

before-substitution school-level response rates ............................................... 5-36

5-15 The ECLS-K, spring-kindergarten: number of completed child-level cases

and child-level completion rates, by school characteristic .............................. 5-37

5-16 The ECLS-K, spring-kindergarten: number of completed child-level cases

and child-level completion rates, by child characteristic................................. 5-40

5-17 The ECLS-K, spring-kindergarten: number of completed teacher

questionnaires and teacher-level completion rates........................................... 5-42

5-18 The ECLS-K, spring-kindergarten: number of completed school

administrator questionnaire and school-level completion rates....................... 5-43
xii

TABLE OF CONTENTS (continued)

List of Tables (continued)

Table Page

5-19 The ECLS-K, spring-kindergarten: child-level sample sizes and overall

response rates, by school characteristic ........................................................... 5-44

5-20 The ECLS-K, spring-kindergarten: number of completed teacher

questionnaires and teacher-level response rates .............................................. 5-47

5-21 The ECLS-K, spring-kindergarten: number of completed instruments

and child-level completion rates for additional data collected ........................ 5-48

5-22 The ECLS-K: number sampled and number and percent cooperating

during fall- or spring-kindergarten, with percent of fall respondents who

also responded in the spring............................................................................. 5-48

7-1 Missing data for SES variables........................................................................ 7-9

7-2 Preliminary Census poverty thresholds for 19981

............................................ 7-12

7-3 Pointers to parent figure questions................................................................... 7-19

7-4 SPQ item matches to fall-kindergarten items .................................................. 7-23

7-5 Relationship among composite variables on the three ECB catalogs.............. 7-27

7-6 Composite variables......................................................................................... 7-34

7-7 Masked variables in the child catalog.............................................................. 7-74

7-8 Masked variables in the teacher catalog .......................................................... 7-83

7-9 Masked variables in the school catalog ........................................................... 7-89

8-1 Add variables buttons ...................................................................................... 8-24

8-2 Remove variables buttons................................................................................ 8-25

8-3 Code book information .................................................................................... 8-30

8-4 Navigation buttons........................................................................................... 8-32

8-5 Child catalog required variables ...................................................................... 8-46
xiii

TABLE OF CONTENTS (continued)

List of Tables (continued)

Table Page

8-6 Child catalog blocks......................................................................................... 8-48

8-7 Child catalog topical variable groupings ......................................................... 8-50

8-8 Fall-kindergarten predefined taglist - child catalog ......................................... 8-56

8-9 Spring-kindergarten predefined taglist – child catalog.................................... 8-58

8-10 Kindergarten base year predefined taglist – child catalog ............................... 8-60

8-11 Teacher catalog required variables .................................................................. 8-64

8-12 Teacher catalog blocks..................................................................................... 8-66

8-13 Teacher catalog topical variable groupings ..................................................... 8-67

8-14 School catalog required variables .................................................................... 8-69

8-15 School catalog blocks ...................................................................................... 8-71

8-16 School catalog topical variable groupings ....................................................... 8-72

List of Figures

Figure

1-1 ECLS-K conceptual model .............................................................................. 1-4

8-1 Windows Run screen ....................................................................................... 8-4

8-4 Choose Destination Location........................................................................... 8-6

8-5 Setup Status ..................................................................................................... 8-6

8-7a. Desktop icon .................................................................................................... 8-8

8-7b Desktop screen - click start.............................................................................. 8-8

8-8 First time user dialog box ................................................................................ 8-9
xiv

TABLE OF CONTENTS (continued)

List of Figures (continued)

Figures Page

8-9 The ECLS-K Base Year ECB splash screen.................................................... 8-9

8-10 Select Catalog screen....................................................................................... 8-10

8-11 Main ECB screen............................................................................................. 8-10

8-12 Exit screen ....................................................................................................... 8-11

8-13 Save Working Taglist dialog box .................................................................... 8-11

8-14 Title Bar ........................................................................................................... 8-12

8-15 Menu Bar ......................................................................................................... 8-13

8-16 The ECLS-K Base Year ECB main screen...................................................... 8-14]

8-17 The Variable List ............................................................................................. 8-15

8-18 The “Go” button............................................................................................... 8-17

8-19 Narrow Text Search dialog box ....................................................................... 8-18

8-20 No matches found message.............................................................................. 8-19

8-21 Example of narrowing a search........................................................................ 8-20

8-22 Expand Text Search dialog box ....................................................................... 8-21

8-23 No matches found message.............................................................................. 8-21

8-24 The ECB Working Taglist ............................................................................... 8-22

8-25 Open Taglist dialog box................................................................................... 8-23

8-26 Add Taglist dialog box .................................................................................... 8-25

8-27 Save Taglist as dialog box ............................................................................... 8-27

8-28 Save Taglist as dialog box (#2)........................................................................ 8-28

8-29 Delete Taglist Selection screen........................................................................ 8-29
xv

TABLE OF CONTENTS (continued)

List of Figures (continued)

Figures Page

8-30 Delete Taglist confirmation window ............................................................... 8-29

8-31 ECB view......................................................................................................... 8-31

8-32 Printing Status screen....................................................................................... 8-32

8-33 Export Taglist selection screen ........................................................................ 8-33

8-34 Variable Quick View ....................................................................................... 8-34

8-35 Add All Variables dialog box .......................................................................... 8-35

8-36 View of the entire code book........................................................................... 8-36

8-37 Printer Dialog box............................................................................................ 8-36

8-38 Teacher catalog limiting fields dialog box....................................................... 8-38

8-39 Save SAS program file dialog box................................................................... 8-39

8-40 Save SAS program file location browse screen............................................... 8-39

8-41 Save SAS data file dialog box ......................................................................... 8-40

8-42 Save SPSS program file dialog box................................................................. 8-41

8-43 Save SPSS program file location browse screen ............................................. 8-41

8-44 Save SPSS data file dialog box........................................................................ 8-42

8-45 Save STATA program file dialog box............................................................. 8-43

8-46 Save STATA program file location browse screen ......................................... 8-43

8-47 Save STATA data file dialog box.................................................................... 8-44

8-48 Repair database completed screen ................................................................... 8-45

8-49 Child catalog limiting fields ............................................................................ 8-63

8-50 Teacher catalog limiting fields ........................................................................ 8-68
xvi

TABLE OF CONTENTS (continued)

List of Figures (continued)

Figures Page

8-51 School catalog limiting fields .......................................................................... 8-73

8-52 Save changes.................................................................................................... 8-74

8-53 Select Catalog screen....................................................................................... 8-75

List of Exhibits

Exhibit

5-1 Timeline of base year data collection .............................................................. 5-2

List of Appendixes

Appendix

A PDF FILES CONTAINING BASE YEAR INSTRUMENTS

B RECORD LAYOUT OF SCHOOL, TEACHER, AND CHILD DATA FILES
1-1

1. INTRODUCTION

The Early Childhood Longitudinal Study-Kindergarten Class of 1998-99 (ECLS-K) is a

multisource, multimethod study that focuses on children’s early school experiences beginning with

kindergarten. The ECLS-K has been developed under the sponsorship of the U.S. Department of

Education, National Center for Education Statistics (NCES). Westat is conducting this study with

assistance provided by the Survey Research Center and the School of Education at the University of

Michigan and Educational Testing Services (ETS).

The ECLS-K is following a nationally representative cohort of children from kindergarten

through fifth grade. A total of 21,260 children throughout the country participated by having a child

assessment or parent interview in the fall and/or spring of kindergarten. The children were in kindergarten

when sampled. Base year data were collected in the fall of 1998 and spring of 1999. Four waves of data

collection are planned beyond kindergarten: fall and spring first grade, and spring third and fifth grades.

All data collection will be completed in the spring of 2004 when most of the children will be in fifth

grade.

The ECLS-K has several major objectives and numerous potential applications. The

ECLS-K combines elements of (1) a study of achievement in the elementary years; (2) an assessment of

the developmental status of children in the United States at the start of their formal schooling and at key

points during the elementary school years; (3) a cross-sectional study of the nature and quality of

kindergarten programs in the United States; and (4) a study of the relationship of family, preschool, and

school experiences to children’s developmental status at school entry and their progress during the

kindergarten and early elementary school years.

The ECLS-K is part of a longitudinal studies program comprising two cohorts—a

kindergarten cohort and a birth cohort. The birth cohort (ECLS-B) will follow a national sample of

children, born in the year 2001, from birth through first grade. ECLS-B will focus on the characteristics of

children and their families that influence children’s first experiences with the demands of formal school,

as well as children’s early health care and in- and out-of-home experiences. Together these cohorts will

provide the range and breadth of data required to more fully describe and understand children’s health,

early learning, development, and education experiences.
1-2

The ECLS-K has both descriptive and analytic purposes. It will provide descriptive data on

children’s status at entry into school, children’s transition into school, and their progress through fifth

grade. The ECLS-K also will provide a rich data set that will enable researchers to analyze how a wide

range of family, school, community, and individual variables affect children’s early success in school;

explore school readiness and the relationship between the kindergarten experience and later elementary

school performance; and record children’s cognitive and academic growth as they move through

elementary school.

1.1 Background

National policymakers and the public at large have increasingly recognized that the

prosperity of the United States depends on the successful functioning of the American education system.

There is also growing awareness that school reform efforts cannot focus solely on the secondary and

postsecondary years but must pay attention to the elementary and preschool years as well. Increased

policy interest in the early grades is reflected in an intensified recent national policy aimed at ensuring

that children are capable of reading by the third grade, providing college student and adult volunteer

tutors for children who are having difficulty learning to read, and increasing the number of children

served by Head Start to 1 million by the year 2002.

Efforts to expand and improve early education will benefit from insights gained through

analyses of data from the large-scale, nationally representative, longitudinal ECLS-K database. The

ECLS-K database contains information about the types of preschool and elementary programs in which

children participate, the services they receive, and repeated measures of the children’s cognitive skills and

knowledge. The ECLS-K database also contains measures of children’s physical health and growth, social

development, and emotional well being, along with information on family background and the

educational quality of their home environments.

As a study of early achievement, the ECLS-K allows researchers to examine how children’s

progress is affected by such factors as placement in high or low ability groups, receipt of special services

or remedial instruction, grade retention, and frequent changes in schools attended because of family

moves. Data on these early school experiences are collected as they occur. This produces a more accurate

measurement of these antecedent factors and enables stronger causal inferences to be made about their

relationship to later academic progress.
1-3

The ECLS-K enables educational policy analysts to use an ecological perspective on early

childhood education, using techniques such as multilevel modeling to study how school and classroom

factors affect the progress of individual children. The data collected will enable analysts to examine how

children’s status at school entry and performance in school are jointly determined by an interaction of

child characteristics and family and school environments.

Data collected during the kindergarten year can serve as baseline measures to examine how

schooling shapes later individual development. The longitudinal nature of the study enables researchers to

study children’s cognitive, social, and emotional growth and to relate trajectories of change to variations

in children’s school experiences in kindergarten and the early grades.

A goal of the kindergarten data collection has been to describe accurately the diversity of

kindergarten children and the programs they attend. For instance, national data are available for the first

time on public and private kindergarten programs and the children who attend them. The ECLS-K sample

includes substantial numbers of children from various minority groups. Thus, the ECLS-K data present

many possibilities for studying cultural and ethnic differences in the educational preferences and

approaches of families, the developmental patterns and learning styles of children, and the educational

resources and opportunities that different groups are afforded in the United States.

1.2 Conceptual Model

The design of the ECLS-K has been guided by a framework of children’s development and

schooling that emphasizes the interaction between the child and family, the child and school, the family

and school, and the family, school, and community. The ECLS-K recognizes the importance of factors

that represent the child’s health status, socio-emotional and intellectual development and incorporates

factors from the child’s family, community, and school-classroom environments. The conceptual model is

presented in figure 1-1. The study has paid particular attention to the role that parents and families play in

helping children adjust to formal school and in supporting their education through the primary grades. It

has also gathered information on how schools prepare for and respond to the diverse backgrounds and

experiences of the children and families they serve.
1-4

Child

Characteristics

Child and Family 

Health

Parent 

Characteristics

Parent-Child 

Interactions

Community 

Structure/

Social Support

Early Childhood

NonParental Care/

Education Characteristics

Kindergarten

Outcomes

Elementary School

Characteristics

Elementary School

Outcomes

Figure 1-1. ECLS-K conceptual model

1.3 Study Components

The emphasis that is being placed on the whole of the child’s environments and development

has critical implications for the design of the ECLS-K. The design of the study allows for the collection of

data from the child, the child’s parents/guardians, teachers, and schools.

 Children are asked to participate in various activities to measure the extent to which

they exhibit those abilities and skills deemed important to success in school. They are

asked to participate in activities designed to measure important cognitive (e.g., general

knowledge, literacy, and quantitative skills) and noncognitive (e.g., fine motor and

gross motor coordination, socio-emotional) skills and knowledge. All measures of a

child’s cognitive skills are obtained through an untimed one-on-one assessment of the

child. During later years of the ECLS-K, children will report on their own experiences

in and out of school.

 Parents/guardians are an important source of information about the families of the

children selected for the study and about themselves. They are asked to provide key

information about their children, especially during the first years of the study. Parents

are one of the important sources of information about children’s development at

school entry and their experiences both with family members and others. Information
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is collected from parents each time children are assessed using computer-assisted

telephone interviewing (CATI) [or computer-assisted personal interviewing (CAPI) if

they do not have a telephone].

 Teachers, like parents, represent a valuable source of information on themselves, the

children in their classrooms, and the children’s learning environment, i.e., the

classroom. Teachers are not only asked to provide information about their own

backgrounds, teaching practices, and experience, they are also called upon to provide

information on the classroom setting for the sampled children they teach and to

evaluate each sampled child on a number of critical cognitive and noncognitive

dimensions. Teachers complete self-administered questionnaires each time children

are assessed, with the exception of the fall first grade data collection.

 School administrators are asked to complete self-administered questionnaires during

the spring data collection. They are asked to provide information on the physical,

organizational, and fiscal characteristics of their schools, and on the schools’ learning

environment and programs. Special attention is paid to the instructional philosophy of

the school and its expectations for students.

1.4 Contents of Manual

This manual provides documentation and guidance for users of the three public-use data files

of the ECLS-K: the child file, teacher file, and the school file. The manual contains information about the

data collection instruments (chapter 2) and the psychometric properties of these instruments (chapter 3).

The manual describes the ECLS-K sample design (chapter 4); data collection procedures and response

rates (chapter 5); and data processing procedures (chapter 6). In addition, the manual shows how the

public-use data file is structured, provides definitions of composite variables (chapter 7), and explains

how to use the Electronic Code Book (chapter 8). The Electronic Code Book contains unweighted

frequencies for all variables.

Analysts who wish to obtain descriptive information about U.S. kindergarten students or

their families, or who want to examine relationships involving children and families, children and

teachers, or children and schools, should make use of the child file. Analysts wishing to obtain descriptive

information about the population of kindergarten teachers in the United States, or to study relationships

involving teachers as the principal focus of attention, should employ the teacher file. Analysts who want

to obtain descriptive information about public and private schools that contain kindergarten classes, or

who want to examine relationships among school characteristics, should make use of the school file.
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1.5 Differences Between the ECLS-K Restricted-Use Base Year Files and the ECLS-K Base

Year Public-Use Files

In preparing the ECLS-K Base Year Public-Use data files the National Center for Education

Statistics (NCES) has taken steps to minimize the likelihood that an individual school, teacher, parent or

child participating in the study can be identified. This is in compliance with the Privacy Act of 1974 and

the National Education Statistics Act of 1994, both of which mandate the protection of the confidentiality

of respondents. The process began with the ECLS-K Restricted-Use Base Year data files, which

underwent a formal disclosure risk analysis. Variables identified as posing the greatest disclosure risk

were altered, and in some instances entirely suppressed, and in this way the ECLS-K Base Year PublicUse data files were created. Every effort has been made to alter the files as little as possible, consistent

with the requirement for confidentiality protection. After altering the variables the disclosure risk analysis

was repeated to verify that the disclosure risk had been reduced to acceptable levels.

The following data modifications account for the differences between the base year publicuse and restricted-use data files:

 Outlier values were top- or bottom- coded;

 Individual cases for which a particular variable posed an especially high risk for

disclosure had the value of that variable altered (usually by no more than 5 to 10

percent) to reduce the risk;

 Some continuous variables were modified into categorical variables, and certain

categorical variables had their categories collapsed; and

 Certain variables with too few cases and a sparse distribution were suppressed

altogether, rather than modified.

A comprehensive list of the variables that have been altered or suppressed can be found in

section 7.9.

Both the public- and restricted-use files provide data at the individual child, teacher, and

school levels. The modifications that were implemented to avoid the identification of schools, teachers,

and children do not affect the overall data quality and most researchers should be able to find all that they

need in the public-use files. While very few of the variables have been suppressed, there are a few users

who might require the restricted files. Those researchers examining certain rare subpopulations such as

the disabled, or children with specific non-English home languages or countries of birth and those
1-7

interested in examining the type and number of hours of kindergarten programs offered in schools will

find that the restricted-use files contain a few more variables. However, in many instances even though

the detailed information on the restricted-use files may be of interest, the sample sizes will be too small to

support these analyses. NCES recommends that researchers uncertain of which data release to use, first

examine the public-use files to ascertain whether their specific analytic objectives can be met using those

data files.
2-1

2. DESCRIPTION OF DATA COLLECTION INSTRUMENTS

This chapter describes the instruments used to collect base year data in the fall of 1998 and

spring of 1999 for the Early Childhood Longitudinal Study-Kindergarten Class of 1998-99 (ECLS-K). In

the fall, data were collected by teacher questionnaires, parent computer-assisted interviews (CAIs), and

direct child assessments. Most of the fall instruments were repeated for the spring of 1999 data collection,

although the parent and teacher measures varied by content between the two data collection points to ease

respondent burden. This chapter also describes the Head Start verification study, where part of the data

was collected in the fall parent interview and student record abstract. Table 2-1 below lists all of the

instruments used in each of the two rounds of data collection.

Table 2-1. Instruments used in the fall and spring ECLS-K

Fall-Kindergarten Spring-Kindergarten

Parent Interview Parent Interview

Child Assessment Child Assessment

Teacher Questionnaire-Part A Teacher Questionnaire-Part A

Teacher Questionnaire-Part B Teacher Questionnaire-Part B

Teacher Questionnaire-Part C Teacher Questionnaire-Part C

Special Education Teacher Questionnaire-Part A

Special Education Teacher Questionnaire-Part B

Adaptive Behavior Scale

School Administrator Questionnaire

Student Record Abstracts

School Facilities Checklist

Salary and Benefits Questionnaire

Appendix A contains a copy of the base year instruments, except for the direct child

assessment, the social rating scale1

 in the parent interview and teacher questionnaire, and the adaptive

 

1

 Adapted with permission from Elementary Scale A (“How Often?”), Frank M. Gresham and Stephen N. Elliott ©1990, American Guidance

Service, Inc.
2-2

behavior scale.2

 These latter measures contain copyright protected materials and agreements with the test

publishers that restrict their distribution.

2.1 The Direct Child Assessments

Fall Child Assessments. In the fall of the base year, one-on-one child assessments were

conducted with the sampled children. This assessment included cognitive, psychomotor, and physical

components. The assessment took approximately 50-70 minutes and was designed to provide data on the

developmental status of children in the United States at the start of their formal schooling. The ECLS-K

cognitive assessment battery consisted of questions in three subject areas: language and literacy,

mathematical thinking, and general knowledge. Psychomotor assessments were also included in the fall,

along with assessments of the child’s height and weight.

The assessment began by verifying the child’s name and administering a set of warm-up

exercises similar in form to the items used to administer the cognitive component. Prior to administering

the cognitive assessment battery, a language-screening assessment was administered to those children

identified from their school records (or by their teacher, if no school records were available) as coming

from a language minority background (meaning that their primary home language was not English). This

screening test was used to determine if a child was able to understand and respond to the cognitive

assessment items in English.

The Language Screener. Efforts were made to include children who spoke a language other

than English in the ECLS-K assessment. Field supervisors either checked the school records to determine

children’s home language or, if records were not available, requested this information directly from

children’s teachers. (See chapter 5 for a complete description of this process.) A brief language screener,

the Oral Language Development Scale (OLDS), was given to those children who had a non-English

language background. The screener determined if children understood English well enough to receive the

direct child assessment in English. Children who passed the language screener received the full ECLS-K

direct assessment battery. Children who did not pass an established cut score on the language screener

received a reduced version of the ECLS-K assessments.

 

2

 Lambert, Nadine, Nihira, Kazuo, and Leland, Henry, Adaptive Behavior Scale-Second Edition, ©1993, The American Association on Mental

Retardation.
2-3

The OLDS was also used to capture baseline information on children whose primary

language was not English. The baseline data for these children can be used by researchers to examine

English language acquisition over time.

The OLDS measured children’s listening comprehension, vocabulary, and ability to

understand and produce language. The OLDS consisted of three parts extrapolated from the preLAS 2000

(Duncan, S.E. and De Avila, E.A., 19983

). For the OLDS, children participated in Simon Says, Art Show,

and Let’s Tell Stories. Part one, Simon Says, measured listening comprehension of basic English

instructions (i.e., asking a child to do things such as touch ear, pick up paper, or knock on table). Part two,

Art Show, was a picture vocabulary assessment where children were asked to name pictures they were

shown. The Art Show served as an assessment of a child’s ability to produce language and measured the

child’s command of expressive language. The final part of the OLDS, Let’s Tell Stories, was used to

obtain a sample of a child’s natural speech by asking a child to retell a story read by the assessor. The

child was read two different stories and asked to tell what happened using pictures as prompts. The

assessor recorded on paper exactly what the child said and scored the story using the established preLAS

2000 scoring rules. The scores assigned were based on the complexity of the child’s sentence structure

and vocabulary in his or her retelling of the story. These scores provide researchers with a direct measure

of oral language performance.

Children who passed the language screener received the full English direct assessment.

Certain components of the direct child assessment could also be conducted in Spanish. If a child did not

pass the language screener but spoke Spanish, he or she was administered a Spanish translated form of the

mathematics assessment and an alternate form of the language screener, the Spanish version of the Oral

Language Development Scale (Spanish OLDS), as well as a psychomotor assessment that was conducted

in Spanish. The Spanish OLDS that was administered is similar in content to the English OLDS and

measures the same constructs.

A variety of steps were undertaken to confirm that the scores obtained from the Spanish

mathematics assessment would be comparable to those for the English version. After the test items were

translated into Spanish, a back-translation was carried out, followed by a review of the assessment

instrument by two Spanish-speaking math experts. Psychometric analyses were also performed to

compare the English and Spanish mathematics test results. Differential Item Functioning (DIF)

 

3

 Duncan, S.E. and De Avila, E. A., preLAS 2000 Cue Picture Book English Form C, CTB/McGraw-Hill Companies, Inc., 1998
2-4

procedures were carried out to determine whether the relative difficulty of each of the mathematics items

was comparable in the English and Spanish version. Of the 31 mathematics items with sufficient numbers

of observations for analysis, 16 appeared to be relatively easier for the Spanish speaking children and 15

for the English version. Of these 31 DIF statistics, 28 showed differences that were slight and/or not

statistically significant. The three items marked as having “C” level DIF (sizeable difference in

performance, and statistical significance) were split between the groups, with one item found to be

relatively easier on the Spanish test and two relatively easier in English. A finding of differential item

functioning does not necessarily indicate bias in the test; it may simply be an indication that achievement

differences among groups may be more pronounced for certain skills than for others. A review of the “C”

level test items by developmental and Spanish-language experts found no evidence of bias in either the

content or the translation of the items. When Item Response Theory (IRT) procedures were carried out to

calculate scaled scores, plots showing the fit of data to item parameters were examined for the Spanish

compared to the English mathematics tests. The results were very similar to the DIF findings: there was

an essentially identical fit for almost all of the test items; there were small differences in one direction or

the other for a few items; and there was no evidence of systematic bias. All of these analyses support the

conclusion that the language of administration had little or no impact on the scores obtained.

Children who did not pass the established cut score on the language screener and whose

native language was not Spanish were excluded from the assessment; however, assessors collected

physical measurements of these children’s height and weight. Table 2-2 shows the paths of the direct

child assessment by home language and scores on the English OLDS.

Table 2-2. Flow of the fall direct child assessment

Home

Language English OLDS

Spanish

OLDS

Warm-up

Booklet Reading Math

PsychomotorGeneral

Knowledge

Height/

Weight

English 9 999 9 9

Other

9 Score at or

Above Cutpoint

9 999 9 9

9 Score Below

Cutpoint

Speaks Spanish

9 9

Spanish

9

Spanish

9

Spanish

9

9 Score Below

Cutpoint

Doesn’t Speak

Spanish

9
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Two-Stage Assessment Design

The direct cognitive assessment consisted of a set of two-stage assessments: a first-stage

routing section for each of the three subject areas, followed by several alternative second-stage forms.

The same reading, mathematics, and general knowledge routing sections, consisting of 12 to 20 items

with a broad range of difficulty, were administered to all children. A child’s performance on the routing

section determined the second-stage form that was administered. The reading and mathematics

assessments had low, middle, and high difficulty second-stage options, while the general knowledge

assessment had two second-stage alternatives. The purpose of this adaptive assessment design was to

maximize accuracy of measurement and minimize administration time.

The second-stage forms varied by level of difficulty so that a child would be administered

questions appropriate to his or her current level of ability for each cognitive domain. Administering

assessment items that are too hard for a particular child not only causes frustration and distress but also

provides very little information on the precise level of the child’s ability. Because most of the items are

likely to be answered incorrectly, all that can be concluded is that the child’s ability level is below the

difficulty level of the questions, but there is no information on how much below. Similarly, giving a larger

number of very easy items to a child of high ability may be boring and, again, they are not very useful in

pinpointing the child’s achievement level. The assessment items that provide the best information are

those that are slightly too easy or slightly too hard for an individual. The pattern of right and wrong

responses on such items makes it possible to estimate ability within a narrow range. The number of

questions included in this assessment was limited in order to minimize the time and burden on the

children. Consequently, it was important to match the difficulty of the questions to the ability level of the

children, to the extent that this was possible with preselected sets of items. The routing section provided a

rough estimate of each child’s achievement level, so that a second-stage form with items of the

appropriate difficulty for maximizing measurement accuracy could be selected.

The cognitive assessment included both multiple choice and open-ended items. For ease of

administration, questions of similar format were grouped together in order of increasing difficulty within

each group. When the question format changed, practice items were used to introduce children to the new

format. Assessments were shortened or discontinued if the administrator perceived that the child was

uncomfortable or distressed about responding to the assessment items. When a child did not respond to a

question, the assessor repeated the question. If there was still no response, or the child did not know the
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answer, the assessor entered a code for “don’t know” and moved on to the next question, while

periodically reminding the child to try. For a child who did not respond to several questions in a row, the

assessor still waited ten seconds after reading each question and entered a code for “don’t know,” but did

not continue to repeat each question. If no response was given to ten questions in a row, assessors entered

a “refuse” code into the computer for the remainder of the items in that subject area, without reading the

questions, until reaching the next subject area, where he or she resumed reading the questions. This

procedure was used to give children who did not want to respond to questions in one subject area

(e.g., reading) a chance to respond to questions in another subject area (e.g., math). Scores in each subject

area were computed only if at least ten questions were answered in the combined first and second stages.

Cognitive Components. The cognitive assessment focused on three general areas of

competence: (1) language use and literacy (reading); (2) mathematics; and (3) knowledge of the social

and physical world, referred to as “general knowledge.” The assessment did not ask the children to write

anything or to explain their reasoning; rather, they used pointing or verbal responses to complete the

tasks. The data were collected using a computer-assisted interviewing methodology. The assessment

included the use of a small easel with pictures, letters of the alphabet, words, short sentences, numbers, or

number problems. A brief description of the three components of the cognitive assessment follows.

Language and Literacy. The language and literacy (reading) assessment included questions

designed to measure basic skills (print familiarity, letter recognition, beginning and ending sounds,

rhyming sounds, word recognition), vocabulary (receptive vocabulary), and comprehension (listening

comprehension, words in context). Comprehension items were targeted to measure skills in initial

understanding, developing interpretation, personal reflection, and demonstrating critical stance.

The reading assessment contained five proficiency levels. These five levels reflected a

progression of skills and knowledge; if a child had mastered one of the higher levels, he or she was very

likely to have passed the items that comprised the earlier levels as well. These five levels were:

(1) identifying upper- and lower-case letters of the alphabet by name; (2) associating letters with sounds

at the beginning of words; (3) associating letters with sounds at the end of words; (4) recognizing

common words by sight; and (5) reading word in context.

Mathematical Thinking. The mathematics assessment items were designed to measure

skills in conceptual knowledge, procedural knowledge, and problem solving. Approximately one-half of

the mathematics assessment consisted of questions on number sense and number properties and
2-7

operations. The remainder of the assessment included questions in measurement; geometry and spatial

sense; data analysis, statistics, and probability; and patterns, algebra, and functions. Each of the

mathematics assessment forms contained several items for which manipulatives were available for

children to use in solving the problems. Paper and pencil were also offered to the children to use for the

appropriate parts of the assessment.

The items in the mathematics assessment could also be grouped into five-level proficiency

levels or progression of skills, though the math clusters were less homogeneous in content than the

reading clusters. The clusters of math items included: (1) identifying some one-digit numerals,

recognizing geometric shapes, and one-to-one counting up to ten objects; (2) reading all one-digit

numerals, counting beyond ten, recognizing a sequence of patterns, and using nonstandard units of length

to compare objects; (3) reading two-digit numerals, recognizing the next number in a sequence,

identifying the ordinal position of an object, and solving a simple word problem; (4) solving simple

addition and subtraction problems; and (5) solving simple multiplication and division problems and

recognizing more complex number patterns.

General Knowledge. The general knowledge assessment consisted of science and social

studies material. The science items measure two broad classes of science competencies: a) conceptual

understanding of scientific facts, and b) skills and abilities to form questions about the natural world, to

try to answer them on the basis of the tools and the evidence collected, and to communicate answer and

how the answers were obtained. Social studies material included questions relating to history/government,

culture, geography, and economics. The assessment items drew on children’s experiences with their

environment, and many questions related to more than one of the categories.

The subject matter of the assessment of general knowledge was too diverse and

insufficiently ranked or graded to permit formation of a set of proficiency levels. A score was calculated

to represent each child’s breadth and depth of understanding of the world around them. This assessment

captured information on children’s conception and understanding of the social, physical, and natural

world and of their ability to draw inferences and comprehend implications. The skills children need to

establish relationships between and among objects, events, or people and to make inferences and to

comprehend the implications of verbal and pictorial concepts were also measured.

Physical Components. There were two parts to the physical component of the child

assessment, psychomotor and antropometric. The assessor administered a psychomotor measure during
2-8

the baseline data collection in the fall of kindergarten and recorded each child’s height and weight. The

physical component included a spiral bound psychomotor booklet, psychomotor instructions and score

sheet, a height and weight recording form, the Shorr Board (for measuring height), and a digital bathroom

scale. Materials used for measuring fine motor skills included ten wood blocks, a pencil, and two pieces

of plain white paper. The psychomotor assessment was divided into measuring fine and gross motor

skills. Fine motor skills were assessed by having each child use building blocks to replicate a model, copy

forms (e.g., an asterisk, a square) on paper, and draw a person. The child was asked to skip, hop on one

foot, walk backward, and stand on one foot to assess gross or large motor skills. To measure physical

growth and development, children’s height and weight were measured. Table 2-3 displays the major

domains measured during the child direct assessments in the fall and spring of kindergarten.

Table 2-3. Direct child assessment

Direct child assessment

FallKindergartenSpringKindergarten

Language screener (OLDS) X /

Reading (language and literacy) X X

General knowledge (science and social studies) X X

Mathematical thinking X X

Psychomotor X

Height and weight X X

Note: The columns to the right of each construct correspond to the waves of questionnaire administration. Waves that included the construct are

marked with an “X.” A “/” indicates that the OLDS was given to students new in the spring, or who did not pass the cut score in the

English version during the fall OLDS administration.

Child Assessment Spring

In spring of the base year, the children who participated in the fall data collection were

assessed a second time. In addition, children who were not assessed in the fall were assessed for the first

time. These children included those sampled in converted schools (see chapter 5, section 5.2 for more

detail). Assessments began at least 12 weeks before the end of the school year. Table 2-4 displays the
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Table 2-4. Flow of the spring direct child assessment

Home

Language English OLDS

Spanish

OLDS

Warm-up

Booklet Reading Math

General

Knowledge

Height/

Weight

English 999 9 9

Other

Score at or Above

Cutpoint in fallkindergarten

999 9 9

9 Score at or Above

Cutpoint 999 9 9

9 Score Below

Cutpoint

Speaks Spanish

9 9

Spanish

9

Spanish 9

9 Score Below

Cutpoint

Doesn’t Speak

Spanish

9

of the spring direct child assessments. The same assessment materials used in the fall were used with the

following exceptions:

1. The psychomotor assessment was not administered in the spring-kindergarten

assessment and beyond.

2. If the sampled child passed the OLDS in fall-kindergarten, he or she was

automatically routed by the computer program to take the assessment in English and

did not have to retake the OLDS. If the child did not score above the cut point for the

OLDS in fall-kindergarten, he or she took the OLDS again in spring-kindergarten and

was routed according to the new spring-kindergarten OLDS score.

3. If the child did not take the assessment at all in fall-kindergarten or was a newly

sampled child in spring-kindergarten, he or she was routed according to his or her

home language, just as the children were in fall-kindergarten.

2.2 Parent Interview

The majority of parents participating in the base year data collection were interviewed in the

fall of 1998 and again in the spring of 1999. Because more children were added to build school response

rates, a group of parents completed their first interview in the spring of 1999. (see chapter 5, section 5.2).

Parents or guardians were asked to provide important information about the sampled child, the home
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environment, parent behavior (e.g., interactions with the child’s teacher, activities with the sampled

child), and family characteristics using a computer-assisted telephone interview (CATI) or computerassisted personal interview (CAPI) for families without a telephone (see chapter 5, sections 5.4.4 and

5.6.4 for more details). Questions regarding family structure, child care use, household income, and child

rearing practices were also included in the parent interview.

Fall Parent Interview

Typically the respondent for the parent interview was the mother of the child; however, the

respondent could be a father, stepparent, adoptive parent, foster parent, grandparent, another relative, or

nonrelative guardian. The respondent had to be knowledgeable about the child’s care and education, be 18

years of age or older, and be living in the household with the child. In fall-kindergarten, respondents for

the parent interview were selected according to the following order of preference:

1. The child’s mother;

2. Another parent or guardian; and

3. Another household member.

The parent interview was conducted primarily in English, but provisions were made to

interview parents who spoke other languages. The questionnaire was translated into Spanish, which was

then printed on hardcopy. Bilingual interviewers were trained to conduct the parent interview in either

English or Spanish. If the interview was conducted in Spanish, the interviewer used the hardcopy

questionnaire and then entered respondents’ answers into the CAI program. The parent interview was also

translated into Chinese, Lakota, and Hmong languages and administered using the same data collection

procedures as were used with Spanish speaking parents.

Topics addressed in the fall parent interview included a roster of current household

members, family socio-demographic characteristics, languages spoken in the child’s home, child care

arrangements (currently and previously used), child’s physical functioning, home activities, parent

education and employment, and receipt of public assistance.
2-11

Parents were also asked to tell how often a student exhibited certain social skills and

behaviors. The social rating scale (SRS) has five scales: approaches to learning, self-control, social

interaction, impulsive/overactive, and sad/lonely.

See chapter 3, section 3.6.2 for variable names, ranges, means, and standard deviations for

these scales.

 The Approaches to Learning scale (Parent SRS) includes six items that rate how

often a child shows eagerness to learn, interest in a variety of things, creativity,

persistence, concentration, and responsibility.

 The Self-Control scale (Parent SRS) has five items that indicate children’s ability to

control their behavior. It includes items that are worded positively as well as negative

behaviors that are reverse coded (e.g., frequency with which a child fights, argues,

throws tantrums, or gets angry).

 The Social Interaction scale (Parent SRS) asks about children’s interactions with

peers and adults. The three items address children’s ease in joining in play, ability to

make and keep friends, and positively interacting (comforting, helping) with peers.

 The Impulsive/Overactive scale (Parent SRS) has two items that ask about children’s

impulsivity and activity level.

 The Sad/Lonely scale (Parent SRS) has four items that ask parents about children’s

problems with being accepted and liked by others, sadness, loneliness, and low selfesteem.

Table 2-5 shows the broad content areas addressed in the parent interview and the point of

data collection.

Spring Parent Interview

The content of the parent interview in spring-kindergarten differed from the fall interview.

To avoid redundancy and increased respondent burden, many questionnaire topics were split between fall

and spring data collection points. In spring-kindergarten, the parent interview included updating the

household roster, parent’s participation in activities in the child’s school, parent’s attitudes toward childrearing, parent’s psychological well-being and health, and the household’s food situation. In addition,

items from the SRS scales were repeated in the spring parent interview.
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Table 2-5 shows the overall structure of the interview and distribution of topics across the

two base year data collection points.

Table 2-5. ECLS-K parent interview by major content topics and point of data collection

Parent Questionnaire

FallKindergartenSpringKindergarten

Family structure X X

Demographics X X

Household roster X X

Marital status X X

Immigration status X

Primary language(s) spoken in home X

Parent’s involvement with child’s school X

Child care X

Arrangements with relatives X

Arrangements with nonrelatives X

Head Start attendance year before kindergarten X

Year before kindergarten child care arrangements X

Child’s health and well-being X X

Birth weight X

Physical functioning X

Services for children with special needs X

Social skills rating X X

Home environment and cognitive activities X X

Frequency of literacy activities X X

Computer use X

Television viewing X

Parental educational expectations for child X

Neighborhood safety X

Parent education X

Parent employment X

Parent income X
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Table 2-5. ECLS-K parent interview by major content topics and point of data collection (continued)

Parent Questionnaire

FallKindergartenSpringKindergarten

Welfare and other public assistance use X X

Parent/child interaction X

Parent discipline X

Parent health and emotional well-being X

Relationships and social support X X

Marital satisfaction X

Background data for fall-kindergarten nonresponding parents X

Child’s physical functioning/birth weight of child /

Home language /

WIC benefits /

Parent education/mother’s employment history /

Prekindergarten Head Start attendance

Services for children with special needs

/

/

Note: The columns to the right of each construct correspond to the waves of questionnaire administration. Waves that included the construct are

marked with an “X.” Content areas asked in spring only to new parent participants are marked with a “/.”

In the spring parent interview, households were routed to one of two questionnaire paths,

either as a new household or as a continuing household that participated in fall data collection. Parents

who completed the fall-kindergarten parent interview were classified as continuing households. Parents

who were selected to participate in fall-kindergarten, but either refused, did not finish the interview, could

not be located, or were unavailable during the field period were also classified as new households in the

spring. Parents of children in schools converted in 1999 were classified as new households. The group of

parents entering the study in spring 1999 were asked the same questions in the spring interview as those

who participated in the fall. These respondents were also asked an additional set of questions to replicate

some of the items included in the fall interview.

The rules for respondent selection varied by questionnaire path. Respondents in continuing

households were selected according to the following order of preference:

1. The fall-kindergarten respondent;

2. The child’s mother;
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3. Another parent or guardian; and

4. Another household member.

Respondents in new households were selected according to the following order of

preference:

1. The child’s mother;

2. Another parent or guardian; and

3. Another household member.

2.3 Teacher Questionnaires

Each kindergarten teacher received a self-administered questionnaire consisting of three

distinct parts. The first section, part A, asked about the teacher’s class and classroom characteristics. It

was designed to collect data about the composition and demographics of the children in the class. Part B

addressed more specific questions on class organization, typical class activities, and evaluation methods,

as well as teacher views on kindergarten readiness, school environment, and overall school climate.

Background questions about the teacher were also included in this section.

Part C asked teachers to report about the sampled children in their classrooms. Teachers

were asked to respond to 20 questions about the child’s academic performance. The academic rating scale

(ARS) gathered data on each sampled child’s skills in areas of language and literacy, general knowledge,

and mathematical thinking. For example, some questions asked if the child used complex sentence

structure, demonstrated early writing behaviors, formed explanations based on observations, or solved

problems involving numbers. Part C also included questions from the SRS that collected data on five

areas of children’s social skills.

2.3.1 Content of the ARS

There are three scales of the ARS: language and literacy, general knowledge, and

mathematics. Each of these is described below. The areas measured in the ARS overlap and augment

what is measured in the direct cognitive assessment. The items were designed to ascertain the current skill
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levels, knowledge, and behaviors of the child based on the teacher’s past observation and experience with

the child. Unless otherwise noted, the fall and spring ARS have the same number of items for each

concept.

 The Language and Literacy section of the ARS asks teachers to rate each child’s

proficiency in speaking (1 item), listening (1 item), early reading (3 items fall, 4 items

spring), and writing (1 item fall, 2 items spring). In addition, teachers rate the child’s

computer literacy (1 item).

 The General Knowledge section of the ARS asks teachers to rate each child’s

proficiency in social studies (1 item fall, 2 items spring) and science (3 items).

 In the Mathematics section, teachers rate each child on one item on each of five

skills: concept of numbers, solving number problems, use of math strategies, data

analysis (graphing), and measurement.

In all sections, the teacher rated the child’s skills, knowledge and behaviors on a scale from

“Not Yet” to “Proficient” (see table 2-6). If a skill, knowledge, or behavior had not been introduced into

the classroom, the teacher coded that item as N/A (Not applicable).

See chapter 3, section 3.3 for scale scores, value ranges, means, and standard deviations for

the ARS.

Table 2-6. ARS response scale

Not yet: Child has not yet demonstrated skill, knowledge, or behavior.

Beginning: Child is just beginning to demonstrate skill, knowledge, or behavior but does so very

inconsistently.

In progress: Child demonstrates skill, knowledge, or behavior with some regularity but varies in

level of competence.

Intermediate: Child demonstrates skill, knowledge, or behavior with increasing regularity and

average competence but is not completely proficient.

Proficient: Child demonstrates skill, knowledge, or behavior competently and consistently.

N/A: Not applicable: Skill, knowledge, or behavior has not been introduced in classroom

setting.
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2.3.2 Teacher SRS

Teachers rated individual students as part of a self-administered questionnaire. These items

are intended to measure approaches to learning, self-control, and interpersonal skills. The items were

rated on a scale of one (Never) to four (Very often). See chapter 3, section 3.6.1 for variable names,

ranges, means, and standard deviations for these scales.

 The Approaches to Learning Scale (Teacher SRS) measures behaviors that affect the

ease with which children can benefit from the learning environment. It includes six

items that rate the child’s attentiveness, task persistence, eagerness to learn, learning

independence, flexibility, and organization.

 The Self-Control (Teacher SRS) Scale has four items that indicate the child’s ability

to control behavior by respecting the property rights of others, controlling temper,

accepting peer ideas for group activities, and responding appropriately to pressure

from peers.

 The five Interpersonal Skills (Teacher SRS) items rate the child’s skill in forming

and maintaining friendships, getting along with people who are different, comforting

or helping other children, expressing feelings, ideas and opinions in positive ways,

and showing sensitivity to the feelings of others.

The two problem behavior scales reflect behaviors that may interfere with the learning

process and with the child’s ability to interact positively in the classroom.

 Externalizing Problem Behaviors (Teacher SRS) include acting out behaviors. Five

items on this scale rate the frequency with which a child argues, fights, gets angry,

acts impulsively, and disturbs ongoing activities.

 The Internalizing Problem Behavior (Teacher SRS) Scale asks about the apparent

presence of anxiety, loneliness, low self-esteem, and sadness. This scale comprises

four items.

This measure is adapted with permission from the instrument Elementary Scale A (“How

Often?”) (Gresham, F. M. and Elliott, S.N., 1990).

Fall-Kindergarten Teacher Questionnaires

Part A of the fall-kindergarten teacher questionnaire was given to all kindergarten teachers,

regardless of whether they taught a sampled child. It included items characterizing the classroom and
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students. Part B included questions regarding class organization and evaluation, typical class activities,

and views on kindergarten readiness. The part C questionnaire asked teachers to complete an ARS and

SRS rating for each sampled child in his or her classroom.

Spring-Kindergarten Teacher Questionnaires

Similar to the fall instrument, the spring questionnaire was also divided into three sections.

The content of the items varied between fall and spring instruments to ease respondent burden. Part A was

again completed by all kindergarten teachers in the school, regardless of whether they taught a sampled

child. This section asked about the characteristics of the kindergarten class and the children in the class.

The spring part B questionnaire included some, but not all, of the questions used in the part B fall

questionnaire. Part B of the teacher questionnaire was completed only by kindergarten teachers new to the

study. This questionnaire was similar to the part B questionnaire completed in fall. It was used to gather

information about teacher background, instructional practices, views on kindergarten readiness, and the

teaching climate of the school. The teacher part C questionnaire was completed by kindergarten teachers

who taught sampled children. This questionnaire, similar to the part C questionnaire completed in fall,

asked teachers to provide ratings of each sampled child in their classroom in academic and social areas.

The spring part C also included a section titled “student information” that asked for overall academic and

physical activity comparisons of the sampled child with the other children in the class and student support

service participation. Table 2-7 shows the overall structure of the teacher questionnaire and the

distribution of topics among the fall and spring data collection points.

2.4 Special Education Teacher Questionnaires

The special education teacher questionnaires were new in the spring. ECLS-K supervisors

reviewed accommodation and inclusion information for children who received special education services.

During the preassessment visit, the field supervisors specified primary special education teachers of

sampled children and listed special education staff working with each child (e.g., speech pathologists,

reading instructors, and audiologists). These questionnaires were given to special education teachers who

taught sampled children. If a child received special education services from more than one special

education teacher, a field supervisor determined the child’s primary special education teacher.
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Table 2-7. Teacher questionnaire

Teacher Questionnaire

FallKindergartenSpringKindergarten

Part A

Description of class—age, race, and gender distribution X

Class organization X1 X

Children with special needs X

Classroom aides X

Class assignment and grouping X

Types of activity/ interest areas X

Class activities X1 X

Instructional time in different subjects X

Types of materials and activities X

Child vs. teacher initiated activities X1

Parent involvement X

Professional development X

Part B

Evaluation and grading practices of students X /

Sharing progress information with parents /

Teachers’ views on school readiness X /

Perceptions about school climate X /

Perception of personal influence on policies and classroom planning X

Teacher demographic information X /

Teacher experience and education X /

Job satisfaction X /

Transition to school activities X /
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Table 2-7. Teacher questionnaire (continued)

Teacher Questionnaire

FallKindergartenSpringKindergarten

Part C

Indirect child cognitive evaluation by teacher X X

Language and literacy, mathematics, general knowledge X X

Teacher evaluation of target child’s social skills X X

Sampled child additional information X

Participation in special services and programs X

Target child’s overall academic skills and physical activity X

Reading group participation X

1 Collected in part B for fall-kindergarten.

Note: The columns to the right of each construct correspond to the waves of questionnaire administration. Waves that included the construct are

marked with an “X.” Content areas asked in spring only to new teacher participants are marked with a “/.”

The primary special education teacher was defined as:

 The teacher who managed the child’s Individualized Education Plan (IEP), or

 The teacher who spent the most amount of time providing special education services

to the child, or

 The teacher who is most knowledgeable about the child’s special needs and

equipment.

Items in the special education teacher questionnaires addressed topics such as the child’s disability, IEP

goals, the amount and type of services used by sampled students, and communication with parents and

general education teachers.

Part A of the special education teacher questionnaire was designed to collect information

about the special education teacher’s professional background and experience. Part B asked about the

special education services provided to the child and the nature of the child’s special education curriculum.

Table 2-8 provides a summary of the content areas addressed in the special education teacher

questionnaire.
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Table 2-8. Special education teacher questionnaire1

Teachers of Sampled Students with IEPs Questionnaire

SpringKindergarten

Part A

Teacher’s gender X

Teacher’s age X

Teacher’s race-ethnicity X

Teaching experience X

Educational background X

Special education teacher background X

Location of service provision X

Student load/week X

Part B

Disability category X

IEP goals for the school year X

Extent of services X

Types of services provided for the year X

Primary placement X

Teaching practices, methods, and materials X

Assistive technologies used by child X

General education goals, expectations and assessments X

Collaboration/communication with child’s general education teacher X

Frequency of communicating with child’s parents X

Receipt of formal evaluations in the past year X

1 Data collected only in the spring.

2.5 Adaptive Behavior Scale

The Adaptive Behavior Scale was completed for all sampled children who were identified in

spring-kindergarten as excluded from the direct child assessment. A child was excluded from the

assessment if he or she needed the assessment administered in Braille, enlarged print, or sign language, or

if the child’s IEP specifically prohibited the child from taking standardized assessments. This

questionnaire was completed by the child’s primary special education teacher and asked the teacher to

provide ratings of the sampled child in three domains: independent functioning, language development,

and numbers and time.
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2.6 School Administrator Questionnaire

The school principal, administrator, or headmaster was asked to complete the school

administrator questionnaire in the spring of 1999 (shown in table 2-9). This self-administered

questionnaire was intended to gather information about the school, student body, teachers, school

policies, and administrator characteristics. The questionnaire was divided into nine sections. The first

seven sections requested mainly factual information about each school and the programs offered at the

school. These sections could be completed by either a principal or a designee who was able to provide the

requested information. The school’s principal was asked to complete the remaining two sections

concerning his or her background and evaluations of the school climate. This questionnaire was

administered only in the spring of the base year data collection.

2.7 School Facilities Checklist

ECLS-K supervisors completed the facilities checklist. The facilities checklist collects

information about the (1) availability and condition of the selected schools, (2) presence and adequacy of

security measures, (3) presence of environmental factors that may affect the learning environment, and

(4) overall learning climate of the school. (See chapter 5, section 5.6.5 for more detail on the collection of

these data.)

2.8 School Records Abstract Form

School staff completed the student records abstract form. This instrument was used to obtain

information about the child’s attendance record, report card, and use of an individualized education plan.

Information about the type of language or English proficiency screening that the school used and whether

the child participated in Head Start prior to kindergarten was also retrieved from the students’ records.

(See chapter 5, section 5.6.5 for more detail on the collection of these forms.)
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Table 2-9. School administrator questionnaire1

School Questionnaire

SpringKindergarten

School characteristics X

Type of school X

Admission requirements X

School size X

Student characteristics X

Race-ethnicity of students X

Children eligible for special services X

Types of kindergarten programs X

School facilities and resources X

Equipment X

Community characteristics and school safety X

Teaching and other school staff characteristics X

Range of salary paid to teachers X

Race-ethnicity of staff X

School policies and programs X

Assessments, testing, and retention X

School-family-community connections X

Programs and activities for families X

Parent involvement and participation X

Programs for special populations X

ESL and bilingual education X

Special education X

Gifted and talented X

Principal characteristics X

Gender, race-ethnicity, age of principal X

Experience and education X

School governance and climate X

Goals and objectives for kindergarten teachers X

School functioning and decisionmaking X

1 Collected only in the spring.
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2.9 Salary and Benefits Questionnaire

The salary and benefits questionnaire collects information on the base salary, merit pay, and

benefit pay of teachers and principals. The salary and benefits questionnaire was completed at the school

or district level, generally by the school or district business administrator or by a private school

administrator or headmaster. The teacher salary and benefits questionnaire can be used to develop childlevel school resource variables that can be linked to child outcomes. The interest in payroll information

stems from the fact that salaries and benefits constitute approximately 80 percent of all current

expenditures in school budgets. Although instructional expenditures are 61.8 percent of total current

expenditures, salaries and benefits for instruction alone constitute 56 percent of total current expenditures,

and 91.6 percent of all instructional expenditures.4

 These data provide an opportunity to learn more about

how resources are allocated and used in schools and how those spending decisions impact children’s

achievement.

2.10 Head Start Verification

The goal of this part of the study is to verify that parent and school reports of children’s

Head Start participation. Respondents to the fall-kindergarten parent interview were asked in a series of

questions about childcare outside the home whether or not the sampled child had ever attended Head

Start. If the response was “Yes,” the respondent was asked whether or not the sampled child attended

Head Start in the year before kindergarten. Information on the name and location of the Head Start facility

was matched by the interviewer against a database of Head Start centers. Similar data were collected in

the spring-kindergarten student record abstract. In the student record abstract, one item asked whether or

not the sampled child had attended a Head Start center before entering kindergarten. If the answer was

“Yes,” then the school staff person was asked to record the name, address, and telephone number of the

Head Start center and the name of the Head Start director. The next step was to verify that the centers

reported by the respondents were Head Start centers and that the child did attend the center in the 1997-98

school year. Chapter 5, section 5.6.7, describes how the data were collected.

 

4

 U.S. Department of Education, National Center for Education Statistics. Digest of Education Statistics, 1999, NCES 2000-031, Washington DC

2000. Pp. 185, table 168.
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3. ASSESSMENT AND RATING SCALE SCORES USED IN THE ECLS-K

Several types of scores are used in the Early Childhood Longitudinal Study-Kindergarten

Class of 1998-99 (ECLS-K) to describe children’s cognitive and social development during their

kindergarten year. These scores are for the direct cognitive assessment, the academic rating scale (ARS),

the psychomotor assessment, and the social rating scale (SRS). Descriptions of the scores for each

assessment or scale are provided below, along with variable names, variable descriptions, and value

ranges found in the ECLS-K data files. Guidelines for when to use each cognitive test score are also

provided in this chapter.

3.1 Direct Cognitive Assessment

The direct cognitive assessment contained items on reading, mathematics, and general

knowledge in the fall and spring of kindergarten. In each subject area, children received a 12 to 20 item

routing test. Performance on the routing items guided the selection of one of several alternative secondstage forms. The second-stage form contained items of appropriate difficulty for the level of ability

indicated by the routing items. There are five different types of scores that can be used to describe

children’s performance on the direct cognitive assessment: (1) number right scores and (2) item response

theory (IRT) scores, which measure children’s performance on a set of test questions with a broad range

of difficulty; (3) standardized scores, which report children’s performance relative to their peers;

(4) criterion-referenced proficiency level and (5) proficiency probability scores, which evaluate children’s

performance with respect to subsets of test items that mark specific skills. See chapter 2, section 2.1 for a

description of the ECLS-K assessment battery.

Table 3-1 shows the types of scores, variable names, descriptions, ranges means, and

standard deviations for the direct cognitive assessment.

3.1.1 Number-Right Scores

Number-right scores are counts of the raw number of items a child answered correctly.

These scores are useful for descriptive purposes only for tests that are the same for all children. However,
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when these scores are for tests that vary in average difficulty, they are not comparable to each other. For

example, a student who took the middle difficulty mathematics second-stage form would probably have

gotten more questions correct if he or she had taken the easier low form and fewer correct if the more

difficult high form had been administered. For this reason, raw number right scores are reported in the

database only for the first stage (routing) tests, which were the same for all children. Each routing test

consisted of sets of items spanning a wide range of skills. For example, the reading routing test had four

questions each on letter recognition, recognizing beginning sounds, recognizing ending sounds, reading

simple sight words, and selecting words in the context of a sentence. An analyst might use the routing test

number right scores to report actual performance on this particular set of tasks.

See table 3-1 for the variable names, ranges, means, and standard deviations for the number

right scores.

3.1.2 IRT Scale Scores

Scores based on the full set of test items were calculated using IRT procedures. IRT made it

possible to calculate scores that could be compared regardless of which second-stage form a child took.

IRT uses the pattern of right, wrong, and omitted responses to the items actually administered in a test and

the difficulty, discriminating ability, and “guess-ability” of each item to place each child on a continuous

ability scale. The items in the routing test, plus a core set of items shared among the different secondstage forms, made it possible to establish a common scale. It is then possible to estimate the score the

child would have achieved if all of the items in all of the test forms had been administered.

IRT has several other 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 low

ability student guessing several hard items correctly. If answers on several easy items are wrong, a correct

difficult item is, in effect, assumed 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 scoring, which, in effect, 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 possible longitudinal measurement of gain in

achievement over time, even though the tests administered are not identical at each point. The common
Table 3-1. Direct cognitive assessment: types of scores, variable names, descriptions, ranges, means, and standard dev

Type of Score Variable Description Range of V

Number Right C1RROUNR C1Reading Routing Test, - Number Right 0 - 20 

C1MROUNR C1 Mathematics Routing Test, - Number Right 0 - 16 C1GROUNR C1 Genl Knowledge Routing Test, - # Right 0 - 12 C2RROUNR C2Reading Routing Test, - Number Right 0 - 20 C2MROUNR C2 Mathematics Routing Test, - Number Right 0 - 16 C2GROUNR C2 Genl Knowledge Routing Test, - # Right 0 - 12 

IRT C1RSCALE C1 Reading IRT Scale Score 0.0 - 72

C1MSCALE C1 Math IRT Scale Score 0.0 - 64C1GSCALE C1 General Knowledge IRT Scale Score 0.0 - 5C2RSCALE C2 Reading IRT Scale Score 0.0 - 72C2MSCALE C2 Math IRT Scale Score 0.0 - 64C2GSCALE C2 General Knowledge IRT Scale Score 0.0 - 5

Standardized Score C1RTSCOR C1 Reading T-Score 0.0 - 90

C1MTSCOR C1 Math T-Score 0.0 - 90C1GTSCOR C1 General Knowledge T-Score 0.0 - 90C2RTSCOR C2 Reading T-Score 0.0 - 90C2MTSCOR C2 Math T-Score 0.0 - 90C2GTSCOR C2 General Knowledge T-Score 0.0 - 90

Proficiency Level Score C1RPROF1 C1 Prof 1 - Letter Recognition 0 - 1 

C1RPROF2 C1 Prof 2 - Beginning Sounds 0 - 1 C1RPROF3 C1 Prof 3 - Ending Sounds 0 - 1 C1RPROF4 C1 Prof 4 - Sight Words 0 - 1 

1 See chapter 7, section 7.3 for variable naming conventions.

3-3
Table 3-1. Direct cognitive assessment: types of scores, variable names, descriptions, ranges, means, and standard dev

Type of Score Variable Description Range of V

C1RPROF5 C1 Prof 5 - Word in Context 0 - 1 C1MPROF1 C1 Prof 1 - Count, Number, Shape 0 - 1 C1MPROF2 C1 Prof 2- Relative size 0 - 1 C1MPROF3 C1 Prof 3 - Ordinality, sequence 0 - 1 C1MPROF4 C1 Prof 4 -Add/Subtract 0 - 1 C1MPROF5 C1 Prof 5 - Multiply/Divide 0 - 1 C2RPROF1 C2 Prof 1 - Letter Recognition 0 - 1 C2RPROF2 C2 Prof 2 - Beginning Sounds 0 - 1 C2RPROF3 C2 Prof 3 - Ending Sounds 0 - 1 C2RPROF4 C2 Prof 4 - Sight Words 0 - 1 C2RPROF5 C2 Prof 5 - Word in Context 0 - 1 C2MPROF1 C2 Prof 1- Count, Number, Shape 0 - 1 C2MPROF2 C2 Prof 2 - Relative Size 0 - 1 C2MPROF3 C2 Prof 3 - Ordinality, Sequence 0 - 1 C2MPROF4 C2 Prof 4 - Add/Subtract 0 - 1 C2MPROF5 C2 Prof 5 - Multiply/Divide 0 - 1 

Proficiency Probability Score C1RPROB1 C1 Prob 1 - Letter Recognition 0.0 - 1

C1RPROB2 C1 Prob 2 - Beginning Sounds 0.0 - 1C1RPROB3 C1 Prob 3 - Ending Sounds 0.0 - 1C1RPROB4 C1 Prob 4 - Sight Words 0.0 - 1C1RPROB5 C1 Prob 5 - Word in Context 0.0 - 1C1MPROB1 C1 Prob 1 - Count, Number, Shape 0.0 - 1C1MPROB2 C1 Prob 2 - Relative Size 0.0 - 1C1MPROB3 C1 Prob 3 - Ordinality, Sequence 0.0 - 1C1MPROB4 C1 Prob 4 - Add/Subtract 0.0 - 1

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Table 3-1. Direct cognitive assessment: types of scores, variable names, descriptions, ranges, means, and standard dev

Type of Score Variable Description Range of V

C1MPROB5 C1 Prob 5 - Multiply/Divide 0.0 - 1C2RPROB1 C2 Prob 1 - Letter Recognition 0.0 - 1C2RPROB2 C2 Prob 2 - Beginning Sounds 0.0 - 1C2RPROB3 C2 Prob 3 - Ending Sounds 0.0 - 1C2RPROB4 C2 Prob 4 - Sight Words 0.0 - 1C2RPROB5 C2 Prob 5 - Word in Context 0.0 - 1C2MPROB1 C2 Prob 1 - Count, Number, Shape 0.0 - 1C2MPROB2 C2 Prob 2 - Relative Size 0.0 - 1C2MPROB3 C2 Prob 3 - Ordinality, Sequence 0.0 - 1C2MPROB4 C2 Prob 4 - Add/Subtract 0.0 - 1C2MPROB5 C2 Prob 5 - Multiply/Divide 0.0 - 1C1RPRINT C1 Print Familiarity 0 - 3 C2RPRINT C2 Print Familiarity 0 - 3 

3-5
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items present in the routing test and in overlapping second-stage forms allow the test scores to be placed

on the same scale, even as the two-stage test design adapts to children’s growth over time.

The IRT scale scores in the database represent estimates of the number of items students

would have answered correctly if they had taken all of the 72 questions in the first- and second-stage

reading forms, the 64 questions in all of the mathematics forms, and the 51 general knowledge items.

These scores are not integers because they are probabilities of correct answers, summed over all items in

the pool. Gain scores may be obtained by subtracting the estimated number right at time 1 from the

estimated number right at time 2. (Note that scores for different subject areas are not comparable to each

other because they are based on different numbers of test questions; that is, it would not be correct to

assume that a child is doing better in reading than in mathematics because his or her IRT scale score in

reading is higher.)

See table 3-1 for variable names, ranges, means, and standard deviations for the IRT scale

scores.

3.1.3 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 T-score mean for a particular subgroup

indicates that the group’s performance is high in comparison to other groups. It does not mean that group

members have mastered a particular set of skills, only that their mastery level is greater than a comparison

group. Similarly, a change in T-score means over time reflects a change in the group’s status with respect

to other groups. In other words, they provide information on status compared to children’s peers, while

the IRT scale scores and proficiency 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 and how much this relative ranking changes

over time.

The standardized scores reported in the database are transformations of the IRT theta

(ability) estimates, rescaled to a mean of 50 and standard deviation of 10 using cross-sectional sample

weights for fall- and spring-kindergarten. For example, a T-score of 55 (C1RTSCOR) represents a
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reading achievement level that is one-half of a standard deviation higher than the mean for the fallkindergarten population represented by the tested sample of ECLS-K participants.

See table 3-1 for variable names, ranges, means, and standard deviations for the standardized

(T) scores.

3.1.4 Proficiency Scores

Proficiency scores provide a means of distinguishing status or gain in specific skills within a

content area from the overall achievement measured by the IRT scale scores and T-scores. Since the

ECLS-K direct cognitive child assessment was a two-stage design (where not all children were

administered all items), information on children’s specific proficiencies are presented in two ways:

proficiency scores (raw scores) and proficiency probability scores (IRT-based scores). In most situations,

analysts use the proficiency probability scores in analyzing children’s specific reading and mathematics

knowledge and skills. Clusters of assessment questions having similar content and difficulty were

included at several points along the score scale of the reading and mathematics assessments. No

proficiency scores were computed for the general knowledge test, because the questions did not follow a

hierarchical pattern. The following proficiencies were identified in the reading and mathematics

assessments.

Reading:

 Letter recognition: identifying upper- and lowercase letters by name;

 Beginning sounds: associating letters with sounds at the beginning of words;

 Ending sounds: associating letters with sounds at the end of words;

 Sight words: recognizing common words by sight; and

 Comprehension of words in context: reading words in context.

Mathematics:

 Number and Shape: identifying some one-digit numerals, recognizing geometric

shapes, and one-to-one counting of up to ten objects;

 Relative Size: reading all single-digit numerals, counting beyond ten, recognizing a

sequence of patterns, and using nonstandard units of length to compare objects;
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 Ordinality, Sequence: reading two-digit numerals, recognizing the next number in a

sequence, identifying the ordinal position of an object, and solving a simple word

problem;

 Addition/Subtraction: solving simple addition and subtraction problems; and

 Multiplication/Division: solving simple multiplication and division problems and

recognizing more complex number patterns.

Clusters of items provide a more reliable test of proficiency than do single items because of

the possibility of guessing; it is very unlikely that a student who has not mastered a particular skill would

be able to guess enough answers correctly to pass a four-item cluster. The proficiency levels were

assumed to follow a Guttman model; that is, a student passing a particular skill level was expected to have

mastered all lower levels; a failure should have indicated nonmastery at higher levels. Only a very small

percentage of students in fall- and spring-kindergarten had response patterns that did not follow the

Guttman model, that is, a failing score at a lower level followed by a pass on a more difficult item cluster.

Overall, including both fall- and spring-kindergarten, 5.5 percent of the children did not follow the model

for reading and 6.6 percent of the children did not follow the model for mathematics. This does not

necessarily indicate a different order or learning for these children - since most of the proficiency-level

items were multiple choice, many of these reversals are due to children guessing.

Proficiency level (dichotomous) scores and proficiency probability (continuous) level scores

are two types of proficiency scores used in the ECLS-K. The following is a description of these scores.

Proficiency Level Scores (Dichotomous)

The proficiency level scores reflect the children’s raw ECLS-K direct cognitive assessment

scores. These scores are intended for very distinct kinds of analysis. Most analysts should use the

proficiency probability scores.

For each proficiency level, a score of one was assigned to children who correctly answered

at least three of the four items in the cluster, and a score of zero was given if at least two items were

incorrect or don’t know. If children did not answer enough items for pass or fail to be determined for a

particular cluster, a pass/fail score was assigned only if the remaining proficiency scores indicated a level

that was unambiguous. That is, a “fail” could be inferred if there were easier cluster(s) that had been

failed and no higher cluster passed; and a “pass” was imputed if harder cluster(s) were passed and no
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easier one failed. In the case of ambiguous (e.g., pass, blank, fail, where the blank could be either a pass

or a fail) or contradictory (e.g., fail, blank, pass) patterns, missing cluster scores were left blank.

Averaging a population subgroup’s zero and one scores for a particular proficiency cluster

results in an estimate of the proportion of children in the subgroup who had mastered the material at that

level. The difference between this average at two points in time represents the proportion of children who

attained mastery during that time period. These scores are not designed to extrapolate to the entire

population of kindergarten children. These scores simply show - of the children who took the items

represented by the level, how many passed the level. For example, these scores would be used in an

analysis involving only the population of children that comprehend words in context. The analyst would

analyze data on the subset of children that received a value of 1 on reading proficiency level 5

(comprehension of words in context), and would base the analysis only on this group of children.

See table 3-1 for variable names, ranges, means, and standard deviations for the proficiency

level scores.

Proficiency Probability Scores (Continuous)

The proficiency probability scores are based on the same clusters of items as the proficiency

level scores but differ from them in several ways. They are continuous rather than dichotomous and can

take on any value between zero and one. They are estimates based on overall performance rather than

counts of actual item responses. They are also estimates for all children with scorable test data, not only

for the ones who were administered the test items in the cluster.

Due to the two-stage format of the cognitive assessment battery, not all children received all

items. An IRT model was employed to produce proficiency probability scores, which reflect the

probability that a child would have passed a proficiency level. The item clusters were treated as single

items for the purpose of IRT calibration, in order to estimate students’ probabilities of mastery of each set

of skills. The hierarchical nature of the skill sets justified the use of the IRT model in this way. Because

the proficiency probabilities were based on overall performance, they could be calculated for all children

who had scorable test data, not just those with relatively complete sets of responses to the necessary item

clusters.
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The proficiency probability scores can be averaged to produce estimates of mastery rates

within population subgroups. These continuous measures can provide a closer look at individuals’ status

and change over time. Gains in probability of mastery at each proficiency level allow researchers to study

not only the amount of gain in total scale score points but also where along the score scale different

children are making their largest gains in achievement during a particular time interval. Thus, students’

school experiences can be related to improvements in specific skills.

Proficiency level scores differ from proficiency probability scores. Proficiency level scores

are based on the items administered to each child. Since not all children received the same items because

of the two-stage assessment design, these scores only represent those children who were administered the

items. The use of proficiency level scores to estimate the total population of children mastering a specific

proficiency level is not recommended, because stopping rules within the test resulted in missing data for

the lower-achieving children. The proficiency probability scores are more suited for estimating the total

population of children mastering specific proficiency scores.

See table 3-1 for variable names, ranges, means, and standard deviations for the proficiency

probability scores.

Familiarity with Conventions of Print

Some items from the child assessment measured children’s familiarity with conventions of

print but were not part of the set of proficiency scores because they did not fit the hierarchical pattern.

The score for these questions was obtained by counting the number of correct answers (0-3) for the

following three items, administered while the child was looking at an illustrated story.

1. Indicating that reading goes from left to right;

2. Going to the beginning of the next line after a line ends; and

3. Finding the end of the story.

These items were part of the reading score calculations in the direct cognitive assessment but

were not part of the hierarchical set of proficiency and proficiency probability scores because they did not

fit the proficiency scoring pattern. The proficiency levels assume that mastery of a higher level usually

means that the child has mastered lower levels. This was not the case with conventions of print. Some
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children scored high on conventions of print but could not recognize letters, while others had the reverse

pattern. Thus, the score for familiarity with conventions of print is reported separately.

See table 3-1 for variable names, ranges, means, and standard deviations for the conventions

of print scores.

3.1.5 Choosing the Appropriate Score for Analysis

Each of the types of scores described above measures children’s achievement from a slightly

different perspective. The choice of the most appropriate score for analysis purposes should be driven by

the context in which it is to be used:

 A measure of overall achievement vs. achievement in specific skills;

 An indicator of status at a single point in time versus growth over time; and

 A criterion-referenced vs. norm-referenced interpretation.

IRT-Based Scores

The scores derived from the IRT model (IRT scale scores, T-scores, proficiency

probabilities) are based on all of the child’s responses to a subject area 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 IRT scale scores are overall, criterion-referenced measures of status at a point in

time. They are useful in identifying cross-sectional differences among subgroups in

overall achievement level and provide a summary measure of achievement useful for

correlational analysis with status variables such as demographics, school type, or

behavioral measures.

The IRT scale scores may also be used as longitudinal measures of growth, but it is

important to remember that gains made at different points on the score scale have

qualitatively different interpretations. For example, children who make gains in

recognizing letters and letter sounds are learning very different things from those who

are making the jump from reading words to reading sentences, although the gains in
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number of scale score points may be the same. Comparison of gains in scale score

points is most meaningful for groups that started with similar initial status. When

initial status is very different, comparisons of scale score gains may be misleading

because the skills being learned are qualitatively different, and comparisons of total

number of points gained may be difficult to interpret.

 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 children 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. Tscore means may be used longitudinally to illustrate the increase or decrease in gaps

in achievement among subgroups over time.

 Proficiency probability scores, although derived from the overall IRT model, are

criterion-referenced measures of proficiency in specific skills. Because each

proficiency score targets a particular narrow set of skills, they are ideal for studying

the details of achievement, rather than the single summary measure provided by the

scale scores and T-scores. They are useful as longitudinal measures of change

because they show not only the extent of gains, but also where on the achievement

scale the gains are taking place. Thus, they can provide information on differences in

skills being learned by different groups, as well as the relationships with processes,

both in and out of school, that correlate with learning specific skills. For example,

high SES kindergarten children show very little gain in the lowest reading proficiency

level, letter recognition, because they were already proficient in this skill at

kindergarten entry. At the same time, low SES children are making big gains in basic

skills, but most have not yet made major gains in reading words and sentences. The

proficiency level at which the largest change is taking place is likely to be different for

children with different initial status, background, and school setting. Changes in

proficiency probabilities over time may be used to identify the process variables that

are effective in promoting achievement gains in specific skills.

Non-IRT Based Scores

The routing test number right, proficiency level, and Conventions of Print scores do not

depend on the assumptions of the IRT model. They are counts of actual number correct for specific sets of

test items, rather than estimates based on patterns of overall performance.

 Routing test number right scores for the reading, math, and general knowledge

assessments are based on 20, 16, and 12 items respectively. They target specific sets

of skills and cover a broad range of difficulty. These scores may be of interest to

researchers because they are based on a specific set of test items, which was the same

for all children who took the test.
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 Proficiency level scores are based on the same sets of items as the proficiency

probability scores, but are dichotomous, rather than continuous, measures of

proficiency. They have a somewhat more intuitive interpretation than the probability

scores, since they simply report whether children were able to answer correctly on at

least three out of four actual test items in a cluster. Users of the proficiency level

scores should be aware of possible bias due to missing data. Stopping rules employed

in the administration of the tests to minimize stress on low-performing children results

in substantial numbers of missing scores for the higher proficiency levels. Estimates

based on variables with substantial amounts of missing data can be assumed to

generalize to the whole sample only if “missing-ness” is unrelated to what the variable

is measuring. This condition is called “MAR,” or Missing-At-Random. The missing

level four and five scores for low-achieving children are not missing-at-random, they

were not administered based on performance. Interpretations of results based on these

scores must take this into account. Similarly, missing data for the lowest math

proficiency level are due to items in this cluster having been taken from the low

second stage test, which was not taken by high-achieving children. Estimates based on

proficiency level scores, without adjustments for missing data, would overstate the

population performance at the high proficiency levels and understate performance at

math level one.

 Conventions of print scores, like the proficiency level scores, are based on a count of

the number correct for a particular set of items. Users may wish to relate this score to

process variables to get a perspective that is somewhat different from that of the

hierarchical levels of reading skills.

3.1.6 Reliabilities

Reliability statistics appropriate for each type of score were computed for each subject area,

for fall- and spring-kindergarten assessments. For the IRT-based scores, the reliability of the overall

ability estimate, theta, is based on the variance of repeated estimates of theta. These reliabilities, ranging

from 0.88 to 0.95, apply to all of the scores derived from the theta estimate, namely, the IRT scale scores,

T-scores, and proficiency probabilities. Alpha coefficients for the routing test number correct ranged from

0.78 to 0.88. Split half reliabilities were computed for the item clusters that made up the dichotomous

proficiency level scores and the conventions of print cluster. These reliabilities were higher for the

reading clusters (0.60 to 0.83) than for the math levels (0.27 to 0.66). The difference in internal

consistency statistics is due to the reading items being essentially replications of the same task, while the

math items had a greater diversity of content.

Note that the split half reliabilities for the low level item clusters decreased from fall- to

spring-kindergarten, while the reliabilities for the clusters at the upper end increased. This is a
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consequence of changes in the variance of the cluster scores as children progressed in their development

of skills. By spring-kindergarten, the vast majority of children had mastered the lowest proficiency levels,

so the sample variance was low, resulting in lower reliability than for the previous fall. Conversely, the

sample variance for the difficult tasks was very low in the fall-kindergarten, when most children had not

mastered these skills, and the reliability rose as some children attained high-level proficiency by the

spring, increasing the total variance. This effect is more pronounced for the math than for the reading

clusters for two reasons. First, the math item clusters were more heterogeneous than the reading, in terms

of content and difficulty. Second, the reading item clusters were based entirely on items from the routing

test, which was taken by all children, while the lowest math cluster employed items from the low level

second stage test as well. By spring-kindergarten, fewer than half of the test takers were routed to the low

form, and this constrained variance in ability resulted in a lower reliability for children who had this

score. Tables 3-2 and 3-3 present the reliability statistics for all of the test scores.

Table 3-2. Reliability of IRT-based scores and routing test number correct

IRT-based Scores

(Reliability of Theta)

Routing Test Number Correct

(Alpha Coefficient)

Fall-k Spring-k Fall-k Spring-k

Reading 0.93 0.95 0.86 0.88

Math 0.92 0.94 0.78 0.81

General Knowledge 0.88 0.89 0.79 0.79

Table 3-3. Split half reliability of item-cluster-based scores (proficiency level scores)

Fall-k Spring-k

Reading Level 1 0.83 0.79

Reading Level 2 0.76 0.76

Reading Level 3 0.72 0.76

Reading Level 4 0.78 0.77

Reading Level 5 0.60 0.69

Conventions of Print 0.70 0.68

Math Level 1 0.41 0.27

Math Level 2 0.58 0.49

Math Level 3 0.63 0.66

Math Level 4 0.54 0.63

Math Level 5 0.46 0.53
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3.2 Indirect Cognitive Assessment (ARS)

The ARS was developed for the ECLS-K to measure teachers’ evaluations of students’

academic achievement in the three domains that are also directly assessed in the cognitive battery:

language and literacy (reading), general knowledge (science and social studies), and mathematical

thinking. The difference between the direct and indirect cognitive assessments, and the scores available,

are described below. For a discussion of the content areas and response scales of the ARS, see chapter 2,

section 2.3.1.

3.2.1 Comparison to Direct Cognitive Assessment

The ARS was designed both to overlap and to augment the information gathered through the

direct cognitive assessment battery. Although the direct and indirect instruments measure children’s skills

and behaviors within the same broad curricular domains with some intended overlap, several of the

constructs they were designed to measure differ in significant ways. Most importantly, the ARS includes

items designed to measure both the process and products of children’s learning in school, whereas the

direct cognitive battery measures only the products of children’s achievement. Because of time and space

limitations, the direct cognitive battery is less able to measure the process of children’s thinking including

the strategies they use to read, solve math problems, or investigate a scientific phenomenon.

Another major difference between the ARS and direct cognitive assessment is that the skills,

knowledge, and behaviors on the ARS reflect a broader sampling of the most recent national curriculum

standards and guidelines from early childhood professionals and researchers. The ARS items were not

limited by the constraints of a standardized testing format as were the direct cognitive items. Therefore,

the scope of curricular content represented in the indirect measures is broader than the content represented

on the direct assessment battery.

3.3 IRT Scores Available for the ARS

IRT was employed to calculate scores for the ARS in order to compare performance of

students from fall to spring and to be able to compare students who were not rated on all items. The Rasch
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Rating Scale Model uses the pattern of ratings on the items actually administered to determine an estimate

of the difficulty of each item and to place each student on a continuous ability scale.

A Rasch analysis was performed on the spring ARS data. In the fall-kindergarten, a large

percentage of the teachers had not introduced at least some of the items to the classroom setting, resulting

in a large number of missing ratings for all but four items.

By the spring-kindergarten, teachers had introduced the skills, knowledge, and behaviors

represented in the items to their classrooms. The NA category was used in less than three percent of the

cases on all items except “Composes simple stories” (7.52 percent NA); “Uses the computer” (15.83

percent NA); “Recognizes ways people rely on each other” (3.26 percent NA); and “Uses instruments for

measuring” (9.82 percent NA). On the majority of the items, the use of the NA category was less than one

percent.

The item difficulties from the spring analysis were applied to the fall items and ability

estimates were computed for each of the children based on the difficulty estimates of the items and the

pattern of ratings children received on those items. The Rasch analysis of the spring data showed that the

reliability of the estimates of child ability was very high for all domains in both spring and fall (see

table 3-4).

Table 3-4. Reliability for the Rasch-based score

Fall-Kindergarten Spring-Kindergarten

ARS Language and Literacy 0.87 0.91

ARS Mathematical Thinking 0.92 0.93

ARS General Knowledge 0.92 0.94

The ARS Scores were rescaled to have a low of one and a high of five to correspond to the

five-point rating scale that teachers used in rating children on these items. The ARS scores in the database

represent estimates of the rating students would have received in that domain if they had been rated on all

of the items in the ARS. Gain scores may be obtained by subtracting the estimated rating at fall (time 1)

from the estimated rating at spring (time 2).

The variable names, descriptions, value ranges, means, and standard deviations for the fall

(T1) and spring (T2) kindergarten ARS scores are shown in table 3-5.
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Table 3-5. ARS: variable names, descriptions, ranges, means, and standard deviations1

Variable Name Description Range of Values Mean

Standard

Deviation

T1ARSLIT T1 Literacy ARS Score 1 - 5 2.6 0.8

T1ARSMAT T1 Math ARS Score 1 - 5 2.5 0.8

T1ARSGEN T1 General Knowledge ARS Score 1 - 5 2.6 1.0

T2ARSLIT T2 Literacy ARS Score 1 - 5 3.3 0.8

T2ARSMAT T2 Math ARS Score 1 - 5 3.5 0.9

T2ARSGEN T2 General Knowledge ARS Score 1 - 5 3.6 1.0

1 See chapter 7, section 7.3 for variable naming conventions.

The majority of teachers rated more than one student on the ARS. The number of students

rated by each teacher ranged from 1 to more than 20.

3.4 The Oral Language Development Scale (OLDS)

The language assessment scores (OLDS scores) for language minority children are located

with the other child scores on the file. There are a total of 16 scores – 4 English and 4 Spanish for each of

the two rounds, i.e., fall-kindergarten and spring-kindergarten. Children in households speaking languages

other than English were first given the English OLDS. Of that group, those scoring below the cut point of

the English OLDS were administered the Spanish OLDS if the child’s home language was noted as

Spanish by the school. (See chapter 2, section 2.1 for more detail on the content of the OLDS items).

The variable names, descriptions, value ranges, means, and standard deviations for the

OLDS are shown in table 3-6.
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Table 3-6. OLDS: variable names, descriptions, ranges, means, and standard deviations1

Variable Description

Range of

Values Mean

Standard

Deviation

C1SCTOT C1 AIQ400 Child’s Total OLDS Score 1-60 34.0 16.0

C1SCORD C1 AIQ400 Simon Says Child Score 1-10 8.0 2.3

C1SCART C1 AIQ400 Art Show Child Score 1-10 7.3 2.7

C1SCSTO C1 AIQ400 Tell Stories Child Score 4-40 22.4 9.0

C1SSCTOT C1 SAIQ400 Spanish Total OLDS Scores 2-44 22.2 6.5

C1SSCORD C1 SAIQ400 Spanish Simon Says Child Score 1-10 9.7 1.1

C1SSCART C1 SAIQ400 Spanish Art Show Child Score 1-10 8.0 1.8

C1SSCSTO C1 SAIQ400 Spanish Tell Stories Score 4-24 10.6 5.3

C2SCTOT C2 AIQ400 Child’s Total OLDS Score 1-60 30.9 15.2

C2SCORD C2 AIQ400 Simon Says Child Score 1-10 7.6 2.4

C2SCART C2 AIQ400 Art Show Child Score 1-10 6.8 2.6

C2SCSTO C2 AIQ400 Tell Stories Child Score 4-40 20.7 8.7

C2SSCTOT C2 SAIQ400 Spanish Total OLDS Scores 2-44 23.7 6.4

C2SSCORD C2 SAIQ400 Spanish Simon Says Child Score 1-10 9.8 0.6

C2SSCART C2 SAIQ400 Spanish Art Show Child Score 1-10 8.5 1.6

C2SSCSTO C2 SAIQ400 Spanish Tell Stories Score 4-24 10.0 5.0

1 See chapter 7, section 7.3 for variable naming conventions.

3.5 Psychomotor Assessment

The psychomotor assessment includes two scales, one measuring visual motor skills (eyehand coordination) and the other measuring gross motor skills (balance and motor planning). The visual

motor skills score is the sum of the points for seven tasks: build a gate, draw a person, and copy five

simple figures. Children could receive up to two points for each of the first two tasks and one point for

each of the figures. Gross motor skills consisted of balancing, hopping, skipping and walking backward—

children could receive up to two points for each skill. Confirmatory factor analysis during the ECLS-K

design phase (using LISREL) confirmed the two scales. The internal consistency of the scales was

constrained by the limited number of items in each scale combined with the variety of motor skills

measured and the limited variance in item scores (maximum score on items was 1-2). Alpha coefficients

(reliabilities) were 0.57 for fine motor skills, 0.51 for gross motor skills, and 0.61 for the composite motor
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skills. Variable names, descriptions, value ranges, means, and standard deviations for the three scales are

shown in table 3-7 below.

Table 3-7. Psychomotor scales: variable names, descriptions, ranges, means, and standard deviations1

Variable Description

Range of

Values Mean

Standard

Deviation

C1FMOTOR C1 Fine Motor Skills 0 - 9 5.7 2.1

C1GMOTOR C1 Gross Motor Skills 0 - 8 6.3 1.9

C1CMOTOR C1 Composite Motor Skills 0 - 17 12.1 3.1

1 See chapter 7, section 7.3 for variable naming conventions.

3.6 SRS

The SRS asked both teachers and parents to tell how often a student exhibited certain social

skills and behaviors. Teachers and parents used a frequency scale (see table 3-8) to report on how often

the student demonstrated the behavior described. See chapter 2, section 2.2 and 2.3.2 for additional

information on the parent and teacher SRS instruments. The scale scores on all SRS scales are the mean

rating on the items included in the scale. Scores were computed only if the student was rated on at least

two-thirds of the items in that scale. The same items were administered in the fall and in the spring so

change scores may be computed by subtracting time 1 (fall) from time 2 (spring). The reliability for the

teacher SRS scales is high (see table 3-9). The reliability is lower for the parent scales (see table 3-10).

Table 3-8. SRS response scale

1. Never Student never exhibits this behavior.

2. Sometimes Student exhibits this behavior occasionally or sometimes.

3. Often Student exhibits this behavior regularly but not all the time.

4. Very Often Student exhibits this behavior most of the time.

N/O. No Opportunity No opportunity to observe this behavior.
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Table 3-9. Split half reliability for the teacher SRS scale scores

FallKindergartenSpringKindergarten

Approaches to Learning 0.89 0.89

Self-Control 0.79 0.80

Interpersonal 0.89 0.89

Externalizing Problem Behaviors 0.90 0.90

Internalizing Problem Behaviors 0.80 0.78

Table 3-10. Split half reliability for the parent SRS scale scores

FallKindergartenSpringKindergarten

Approaches to Learning 0.68 0.69

Self-Control 0.74 0.75

Social Interaction 0.70 0.68

Impulsive/Overactive 0.46 0.47

Sad/Lonely 0.60 0.61

3.6.1 Teacher SRS

Teachers rated individual students as part of a self-administered questionnaire. The five

social skill teacher scales are: approaches to learning, self-control, interpersonal skills, externalizing

problem behaviors, and internalizing problem behaviors. (See chapter 2, section 2.3.2 for a description of

the teacher scales.)

Variable names for the teacher scores, descriptions, ranges, means, and standard deviations

for these scales are shown in table 3-11.
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Table 3-11. Teacher social rating scores: variable names, descriptions, ranges, means, and standard

deviations1

Variable Description

Range of

Values Mean

Standard

Deviation

T1LEARN T1 Approaches to Learning 1-4 3.0 0.7

T1CONTRO T1 Self-Control 1-4 3.1 0.6

T1INTERP T1 Interpersonal 1-4 3.0 0.6

T1EXTERN T1 Externalizing Problem Behaviors 1-4 1.6 0.6

T1INTERN T1 Internalizing Problem Behaviors 1-4 1.6 0.5

T2LEARN T2 Approaches to Learning 1-4 3.1 0.7

T2CONTRO T2 Self-Control 1-4 3.2 0.6

T2INTERP T2 Interpersonal 1-4 3.1 0.6

T2EXTERN T2 Externalizing Problem Behaviors 1-4 1.7 0.7

T2INTERN T2 Internalizing Problem Behaviors 1-4 1.6 0.5

1 See chapter 7, section 7.3 for variable naming conventions.

Care should be taken when entering these scales into the same analysis due to problems of

multicollinearity. The factor intercorrelations among the scales for social skills are high. The factor

intercorrelations with the internalizing problem behaviors are the lowest. The correlations between the

teacher SRS factors range from 0.25 to 0.78 in fall-kindergarten and from 0.30 to 0.80 in springkindergarten (absolute values).

3.6.2 Parent SRS

The items on the parent SRS were administered as part of a longer telephone or in-person

survey. (See chapter 2, section 2.2 for a description of the parent scales.) The factors on the parent SRS

are similar to the teacher SRS; however, the items in the parent SRS are geared to the home environment

and thus are not the same items. It is also important to keep in mind that parents and teachers observe the

children in very different environments. The five social skill parent scales are: approaches to learning,

self-control, social interaction, impulsive/overactive, and sad/lonely. The correlations between the parent

SRS factors were not as high as the teacher SRS factors. They ranged from 0.05 to 0.45 in fallkindergarten, and from 0.08 to 0.45 in spring-kindergarten (absolute values).
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Variable names for the parent scores, descriptions, ranges, means, and standard deviations

for these scales are shown in table 3-12.

Table 3-12. Parent social rating scores: variable names, descriptions, ranges, means, and standard

deviations1

Variable Description

Range of

Values Mean

Standard

Deviation

P1LEARN P1 Approaches to Learning 1-4 3.1 0.5

P1CONTRO P1 Self-Control 1-4 2.8 0.5

P1SOCIAL P1 Social Interaction 1-4 3.3 0.6

P1SADLON P1 Sad/Lonely 1-4 1.5 0.4

P1IMPULS P1 Impulsive/Overactive 1-4 2.0 0.7

P2LEARN P2 Approaches to Learning 1-4 3.1 0.5

P2CONTRO P2 Self-Control 1-4 2.9 0.5

P2SOCIAL P2 Social Interaction 1-4 3.4 0.5

P2SADLON P2 Sad/Lonely 1-4 1.6 0.4

P2IMPULS P2 Impulsive/Overactive 1-4 2.0 0.7

1 See chapter 7, section 7.3 for variable naming conventions.
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4. SAMPLE DESIGN AND IMPLEMENTATION

The Early Childhood Longitudinal Study-Kindergarten Class of 1998-99 (ECLS-K)

employed a multistage probability sample design to select a nationally representative sample of children

attending kindergarten in 1998-99. The primary sampling units (PSUs) were geographic areas consisting

of counties or groups of counties. The second-stage units were schools within sampled PSUs. The third

and final stage units were students within schools.

4.1 Selecting the Area Sample

The point of departure for the ECLS-K area sample frame development was an existing

multipurpose frame of PSUs created using 1990 county-level population data. This frame contained 1,404

PSUs that were counties or groups of contiguous counties. PSUs did not cut across census regional

boundaries but were allowed to cross state boundaries. Each 1990 Metropolitan Statistical Area (MSA)

constituted a single PSU except where an MSA crossed census regions, and it was split into two PSUs.

The minimum size of a PSU in the multipurpose frame was 15,000 persons.

Since the focus of the ECLS-K is kindergarten students, the existing PSU frame was updated

with 1994 population estimates of five-year-olds by race-ethnicity, the most up-to-date estimates

available from the U.S. Census Bureau at the time. The counts of five-year-olds by race-ethnicity were

used to revise PSU definitions relative to a different minimum PSU size and to construct a measure of

size that facilitated the oversampling of Asians and Pacific Islanders (APIs).

Each PSU in the frame that did not have at least 320 five-year-olds was collapsed with an

adjacent PSU. This minimum PSU size was developed based on assumptions concerning anticipated

school response rates, the average number of schools that would be selected per PSU, and the target

number of students to be sampled per school. After this collapsing, the final ECLS-K PSU frame

contained 1,335 records.
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The measure of size used for selecting PSUs took into account the amount of oversampling

of APIs required to meet the ECLS-K precision goals. The weighted measure of size was calculated as

follows:

API other MOS = 2.5× n + n

where 2.5 is the oversampling rate for APIs and nAPI and nother are the counts of five-yearold APIs and all others, respectively. The oversampling rate for APIs was calculated as the target number

of completed API cases divided by the expected number of completed API cases without oversampling.

In all, 100 PSUs were selected for the ECLS-K. The 24 PSUs with the largest measures of

size were designated as certainty selections or self-representing (SR) and were set aside. Once the SR

PSUs were removed, the remaining PSUs were partitioned into 38 strata of roughly equal measure of size.

The frame of non-SR PSUs was first sorted into eight superstrata by MSA/nonMSA status and by Census

region. Within the four MSA superstrata, the variables used for further stratification were race-ethnicity

(high concentration of API, Black, or Hispanic), size class (MOS >= 13,000 and MOS < 13,000) and

1988 per capita income. Within the four non-MSA superstrata, the stratification variables were raceethnicity and per capita income. Details of the stratification of non-self-representing strata are presented

in table 4-1.

Two PSUs were selected from each non-SR stratum using Durbin’s Method.1

 This method

selects two first-stage units per stratum without replacement, with probability proportional to size and a

known joint probability of inclusion. The Durbin method was used because it allows variances to be

estimated as if the units were selected with replacement. Table 4-2 summarizes the characteristics of the

ECLS-K PSU sample.

4.2 Selecting the School Sample

In the second stage of sampling, public and private schools offering kindergarten programs

were selected. For each ECLS-K PSU, a frame of public and private schools offering kindergarten

 

1 Durbin, J. (1967). Design of Multi-Stage Surveys for the Estimation of Sampling Errors. Journal of the Royal Statistical Society C, 16, 152-164.
4-3

Table 4-1. Stratum definitions for the 38 non-self-representing strata

Metro Census PSU size Per capita income

status region Race-ethnicity (%) (MOS) Range ($)

≥13,000 22,062 25,424

16,342 22,030

Northeast - 18,128 29,084

<13,000 16,697 18,032

12,279 16,616

≥13,000 17,277 18,150

16,103 17,092

Midwest - 16,552 24,009

<13,000 15,732 16,475

14,450 15,693

10,185 14,433

Hispanic ≥30 -- -

MSA Black ≥40 -- -

26≤ Black <40 - 14,743 18,731

- 10,892 14,573

South 16,435 16,601

≥13,000 14,586 16,337

Black <26 15,572 22,824

<13,000 14,194 15,432

11,262 13,979

API ≥15 -- -

API <15 - - -

West Hispanic ≥30 -- -

12≤ Hispanic <30 - - -

Hispanic <12 - 15,048 21,840

- 9,993 14,839

Northeast - - - -

14,124 17,446

Midwest - - 13,277 14,121

12,169 13,272

6,992 12,147

NonMSA Black ≥42 -- -

25≤ Black <42 - - -

South 12,727 20,059

Black <25 - 11,165 12,676

6,018 11,142

West - - 12,887 23,286

6,959 12,884
4-4

Table 4-2. Distribution of the ECLS-K PSU sample by SR status, MSA status, and census region

Census Region SR

status

MSA

status Northeast Midwest South West Total

SR MSA 6 5 6 7 24

Non-SR MSA 10 12 18 12 52

Non-SR Non-MSA 2 8 10 4 24

Total 18 25 34 23 100

programs was constructed using existing school universe files: the 1995-96 Common Core of Data2

(CCD) and the 1995-96 Private School Universe Survey3 (PSS). The school frame was freshened in the

spring of 1998 to include newly opened schools that were not included in the CCD and PSS and schools

that were in the CCD and PSS but did not offer kindergarten according to those sources. A school sample

supplement was selected from the freshened frame.

4.2.1 Frame Construction

The 1995-96 CCD Public School Universe File was the primary source for the ECLS-K

public school sampling frame. Most schools run by the Bureau of Indian Affairs (BIA) and the schools

run by the Department of Defense (DOD) are not included on the CCD. The 1995-96 Office of Indian

Education Programs Education Directory was consulted in order to complete the list of BIA schools in the

CCD file. For the DOD schools, a 1996 list of schools obtained directly from the DOD was used. The

1995-96 PSS Universe File was used as the primary source of the private school sampling frame.

The first step in frame construction involved subsetting the file to schools located in counties

that constituted the ECLS-K PSU sample. Further subsetting retained only those schools that offered

transitional kindergarten, kindergarten, or transitional first grade, or which were strictly ungraded, as

indicated by the school’s gradespan.

 

2 U.S. Department of Education, National Center for Education Statistics, Common Core of Data, Public School Universe Survey, 1995-96.

3

 U.S. Department of Education, National Center for Education Statistics. Private School Universe Survey, 1995-96, NCES 98-229, by Stephen P.

Broughman and Lenore A. Colaciello. Washington, DC: 1998.
4-5

The constructed ECLS-K school frame included 18,911 public-school records and 12,412

private-school records. The school frame was freshened in the spring of 1998 to include schools that

would be operational in fall 1998 but that were not included in the frame just described. The freshening

procedures are given later in this section.

4.2.2 School Measure of Size, Stratification, and Sample Selection

Within each PSU, schools with fewer than a predetermined minimum number of

kindergarten students were clustered together before sampling in order to obtain a sample that is closer to

self-weighting. The minimum was 24 kindergartners for public schools and 12 for private schools. For

simplicity’s sake, the term “schools” will be used in reference to both individual schools and clusters of

schools in the remainder of this discussion.

Schools were selected with probability proportional to size. As with the PSU sample, a

weighted measure of size was constructed taking into account the oversampling of APIs.

The target number of sampled schools per PSU was calculated separately for public and for

private schools and was adjusted upward to offset anticipated school response and eligibility rates. The

number of schools allocated to each PSU was set proportional to the weighted measure of size of the

PSU. A minimum of one school per PSU was imposed for any PSU so small that it would not otherwise

have been allocated a school.

Public and private schools constituted distinct sampling strata. Within each of these strata,

schools were sorted to ensure good sample representation across other characteristics.

The public school sample was selected using a traditional nested two-stage design of schools

within the 100 PSUs. Within each PSU, public schools were ranked by measure of size and partitioned

into three classes of roughly equal aggregate measure of size. Within each size class, schools were sorted

in a serpentine manner by the proportion of APIs.

The private school sample was nested within PSUs only for the 76 non-SR PSUs, where

schools were grouped within PSU by affiliation (religious vs. nonsectarian) and then sorted in a

serpentine manner by the measure of size. To better control the sample distribution of religious/
4-6

nonsectarian schools, schools in the 24 SR PSUs were treated as if they were sampled from a single PSU

and the sort was by affiliation and measure of size alone.

The selection of both public and private schools was systematic, with probability

proportional to the measure of size. A total of 1,280 schools were selected for the ECLS-K, of which 934

were public and 346 were private schools.

4.2.3 Freshening the School Sample

Each public school district having one or more schools sampled was sent a sampling framebased list of all schools offering kindergarten and was asked whether any school expected to offer

kindergarten in academic year 1998-1999 was missing from the list. For each such school identified,

school name, address, telephone number, grade span, and kindergarten enrollment were obtained. Also

contacted were districts that fell within the boundaries of the ECLS-K PSUs but for which the CCD file

listed no schools offering kindergarten, unless it was clear from their name that they were strictly

secondary school districts (e.g., Middlebury Union High School District). The information obtained from

the school districts was checked against the ECLS-K public school frame to confirm that these schools

were truly new or newly eligible. Bona fide new schools were given an appropriate chance of being

sampled. A new school’s chance of selection was conditioned on the school district’s probability of

selection, which had been calculated exactly, based on the sampling intervals used during the systematic

selection of the main school sample, and the positions in the frame and measures of size of all schools in

the frame. Overall 252 new public schools were identified, and 19 were selected using systematic

sampling with probability proportional to size.

The procedure for obtaining new school information from Catholic dioceses was exactly the

same as for public schools. Since a diocese may cut across county or even state lines, each school

identified by a diocese had to be associated with the correct county, and hence the correct PSU, before

checking to see whether it was truly new. Since dioceses may cross PSU boundaries, a new Catholic

school’s chance of being sampled had to be conditioned on the diocese probability of selection within the

PSU where the new school was located. There were 126 new Catholic schools identified, and 6 were

selected using systematic sampling with probability proportional to size.
4-7

The search for non-Catholic private schools was considerably more complicated. Three

classes of PSS schools that had previously not been given a chance of selection were reconsidered. Those

were schools that had an unknown grade span because they had not responded to the 1995-96 PSS, those

that responded but did not report offering kindergarten, and those that appeared for the first time on the

1997-98 PSS file. All told these accounted for 2,544 potential new non-Catholic private schools. Beyond

these additions from PSS, procedures similar to those used by the Census Bureau in the PSS area frame

search4

 were followed. These procedures included collecting lists of schools from different sources,

matching them against the PSS list frame to remove duplicates, and further screening by telephone to

verify new school status. The majority of new schools found by the Census Bureau for PSS came from

Yellow Pages listings. The Yellow Pages search was the main source of new non-Catholic private schools

in the ECLS-K as well, yielding an additional 8,861 possible new private schools. Since the number of

kindergartners enrolled in these schools was unknown, a minimum kindergarten enrollment was assumed

for sampling purposes (typically 24, unless the name was suggestive of daycare in which case 12 was

assumed). From the 11,405 schools, a sample of 279 schools was selected using systematic sampling with

a probability proportional to these imputed enrollments. The sampled schools were screened, during

which process it was ascertained whether each school was public or private; if it was private, whether it

would be open in academic year 1998-1999; and if it would be open in 1999-2000, whether it would offer

kindergarten. If the answer to the last question was yes and the school was not Catholic, the school was

released for data collection.

Local Education Agencies (LEAs) and local government offices were contacted for

information on non-Catholic private schools, only in the smallest ECLS-K PSUs, on the theory that if

these PSUs had coverage problems their large weights were likely to introduce a larger bias in the

estimates. All LEAs within these PSUs were contacted by telephone. For each city/town within the PSU,

a list of local government offices was compiled using the Blue Pages. Successive government offices

were called within a city or town until one was found that could provide information on private schools.

As with the Yellow Pages, new schools identified by LEAs and local government offices were

unduplicated against the PSS file before being added to the new school frame. Since kindergarten

enrollment was unknown, it was imputed as described in the previous paragraph and sampling was

performed using systematic sampling with probability proportional to size. The LEA search resulted in

the identification of 30 new private schools after unduplication, of which 14 were sampled. The local

government search yielded 19 new schools, of which 8 were sampled. Finally, three additional new

 

4

 ibid.
4-8

private schools were reported by field staff based on personal knowledge. Of these, two schools were

sampled. All told, there were 109 new non-Catholic private schools selected.

The characteristics of the ECLS-K original school sample are presented in table 4-3. Schools

that were discovered to be ineligible during recruitment have been omitted from the tabulation. For counts

of responding schools and a discussion of the limited use of school substitution within the ECLS-K, see

chapter 5, sections 5.5, Fall-Kindergarten Response Rates, and 5.8, Spring-Kindergarten Response Rates.

4.2.4 Sampling Children, Parents, and Teachers within Schools

The goal of the student sample design was to obtain an approximately self-weighting sample

of students and at the same time to achieve a minimum required sample size for each targeted

subpopulation. As mentioned earlier, APIs were the only subgroup that needed to be oversampled to meet

the sample size goals. For each sampled school, the field staff obtained a complete list of kindergartners

enrolled, taking special care that no child was excluded from the list because of disability or language

problems.

Two independent sampling strata were formed within each school, one containing API

students and the second, all other students. Within each stratum, students were selected using equal

probability systematic sampling with twins being sampled as a unit rather than as individuals, i.e. if one

twin was sampled, both were included. In general, the target number of children sampled at any one

school (not including the second twin) was 24. In some schools the oversampling goal for API students

could not be met. For example, in a school with 24 kindergartners enrolled, all students would be

sampled, which meant that API students could not be sampled at 2.5 times the rate of non-API students in

that school. To offset shortfalls of this kind, the oversampling rate for APIs was increased to three at this

stage of sampling.

Once the sampled children were identified, parent contact information was obtained from the

school. The information was used to locate a parent or guardian and gain parental consent for the child

assessment and for the parent interview.
4-9

Table 4-3. Characteristics of the ECLS-K original school sample

Public Private Total

Total 914 363 1,277

Region

Northeast

Midwest

South

West

161

210

306

237

82

88

112

81

243

298

418

318

Urbanicity

Large central city

Mid-size central city

Urban fringe of large city

Urban fringe of mid-size city

Large town

Small town

Rural

168

172

265

78

24

76

131

77

76

117

21

9

36

27

245

248

382

99

33

112

158

KG enrollment

1 – 24

25 – 49

50 – 99

100 – 149

150 – 169

170 +

51

95

402

226

49

91

187

110

59

7

0

0

238

205

461

233

49

91

Religious affiliation

Catholic

Other religious

Nonreligious, private

-

-

-

120

149

94

120

149

94

Free Lunch Program

Low (<=25% eligible students)

Medium low (>25% and <=50%)

Medium high (>50% and <=75%)

High (>75%)

Missing

268

157

129

114

246

-

-

-

-

-

268

157

129

114

246

During the fall 1998 data collection, a census of kindergarten teachers was taken at each

school. Each sampled child was linked to his or her kindergarten teacher. A child could be linked to only

one teacher. If a child was taught by more than one teacher, a “primary” teacher was identified for the

child. For the specifics of how this was done, see section 5.4.2, Fall-Kindergarten Data Collection,

Distribution of Teacher Questionnaires. In spring 1999, teacher-child linkages were reviewed and

updated. If new kindergarten teachers had joined the school, they were added to the census of
4-10

kindergarten teachers. Special education teachers who taught one or more sampled children were included

in the spring of kindergarten data collection. If a sampled child received special education services from

such a teacher, the teacher was linked to that child. As with regular teachers, a child could be linked to

only one special education teacher.

4.3 Calculation and Use of Sample Weights

The ECLS-K data were weighted to compensate for differential probabilities of selection at

each sampling stage and to adjust for the effects of nonresponse. In general, there are three types of

weights: child, teacher, and school-level weights. The use of these weights is essential to produce

estimates that are representative of the population of kindergarten children, kindergarten teachers, and

schools offering kindergarten programs. Data collected from different sources can be used to produce

estimates at these three levels. For example, data collected from parents are used to produce estimates of

characteristics of children as reported by parents.

Several sets of weights were computed for each of the two rounds of data collection (falland spring-kindergarten). Longitudinal weights were also computed for children with complete data from

both rounds of the study. Unlike surveys that have only one type of survey instrument aimed at one type

of sampling unit, the ECLS-K is a complex study with multiple types of sampling units, each having its

own survey instrument. Each type of unit was selected into the sample through a different mechanism:

children were sampled directly through a sample of schools; parents of the sampled children were

automatically included in the survey; all kindergarten teachers in the sampled schools were included;

special education teachers were in the sample if they taught any of the sampled children. Each sampled

unit had its own survey instrument: children were assessed directly using a series of cognitive and

physical assessments; parents were interviewed with a parent instrument; teachers filled out at least two

different types of questionnaires depending on the round of data collection and on whether they were

regular or special education teachers; school principals reported their school characteristics using the

school administrator questionnaire. The stages of sampling in conjunction with the different nonresponse

level at each stage and the diversity of survey instruments require that multiple sampling weights be

computed for use in analyzing the ECLS-K data.

This section describes the different types of sample weights computed for the ECLS-K, how

they were calculated, how they should be used, and their statistical characteristics.
4-11

4.3.1 Types of Sample Weights

Weighting was carried out in stages to produce child, teacher, and school weights. Several

sets of child-level weights were computed for each round of data collection and for children with

complete data from both rounds. While it is straightforward to use school- and teacher-level weights to

produce school- and teacher-level estimates, careful consideration should be given to the choice of a

child-level weight since it depends on the type of data analyzed. Each set of child-level weights is

appropriate for a different set of data or combination of sets of data. Teacher-level weights were

computed for each round of data collection, but there are no longitudinal teacher-level weights. School

level weights were computed for use with data collected in spring-kindergarten through the school

administrator questionnaires. These weights can also be used with any school-level data such as data from

the school facilities checklists.

Tables 4-4 and 4-5 summarize the different types of cross-sectional weights and how they

should be used. Cross-sectional weights provide an accurate estimate for the specific round of data

collection. Table 4-4 describes weights for fall-kindergarten estimates, and table 4-5 describes weights for

spring-kindergarten estimates, and table 4-6 describes weights for base-year or longitudinal estimates.

These tables are designed to help users choose appropriate weights for their analysis.

Answers to the following three questions can help in the selection of the correct weight.

1. Is the analysis concerned with one point in time or two?

2. What is the population of interest or unit of analysis (i.e. child, teacher or school)?

3. What instruments do the data to be used in the analysis come from?

1. Is the analysis concerned with one point in time or two? If the analysis pertains only to

fall kindergarten (single point in time) then table 4-4 guides the selection of weights, spring kindergarten

(single point in time) then go to table 4-5, and both fall- and spring-kindergarten (two points in time) then

go to table 4-6.
4-12

Table 4-4. The ECLS-K: fall-kindergarten cross-sectional weights

Fall-kindergarten cross-sectional weights

Child-level weight to be used for analysis of ...

C1CW0 fall-kindergarten direct child assessment data alone or in conjunction with

any combination of a) a limited set of child characteristics (e.g. age, sex,

race-ethnicity), b) any fall-kindergarten teacher questionnaire A, B or C

data, and c) data from the school administrator questionnaire or facilities

checklist

C1PW0 fall-kindergarten parent interview data alone or in combination with a) fall

child assessment data, b) fall-kindergarten teacher questionnaire A, B, or C

data, and c) data from the school administrator questionnaire or facilities

checklist.

Exception: If data from the parent AND child assessment AND teacher

questionnaire A or B (not C) are used then C1CPTW0 should be used.

C1CPTW0 fall-kindergarten direct child assessment data combined with fallkindergarten parent interview data AND fall-kindergarten teacher

questionnaire A or B (not C) data alone or conjunction with data from the

school administrator questionnaire or facilities checklist

Teacher-level weight to be used for analysis of ...

B1TW0 fall-kindergarten teacher data, questionnaire part A or B alone or with data

from the school administrator questionnaire or facilities checklist.

Exception: When using items that were in the spring-kindergarten teacher

questionnaire B (i.e. questions asked of teachers who were not present

during fall-kindergarten data collection) B2TW0 weight should be used.
4-13

Table 4-5. The ECLS-K: spring-kindergarten cross-sectional weights

Spring-kindergarten cross-sectional weights

Child-level weight to be used for analysis of ...

C2CW0 spring-kindergarten direct child assessment data, alone or in conjunction

with any combination of a) a limited set of child characteristics (e.g. age,

sex, race-ethnicity), b) any spring-kindergarten teacher questionnaire A, B

or C data, and c) data from the school administrator questionnaire or

facilities checklist

C2PW0 spring-kindergarten parent interview data alone or in combination with a)

spring child assessment data, b) spring-kindergarten teacher questionnaire

A, B, or C data, and c) data from the school administrator questionnaire or

facilities checklist.

Exception: If data from the parent AND child assessment AND teacher

questionnaire A or B (not C) are used then C2CPTW0 should be used

C2CPTW0 spring-kindergarten direct child assessment data combined with springkindergarten parent interview data AND spring-kindergarten teacher data

alone or in conjunction with data from the school administrator or

facilities checklist

Teacher-level weight to be used for analysis of ...

B2TW0 spring-kindergarten data from questionnaire part A;

fall- or spring-kindergarten data from questionnaire part B; or

combination of data from fall- and spring-kindergarten teacher

questionnaires A and/or B (there is no longitudinal teacher weight) alone

or in conjunction with data from the school administrator questionnaire or

facilities checklist

School administrator weight to be used for analysis of ...

S2SAQW0 school administrator data or facility checklist data
4-14

2. What is the population of interest or unit of analysis (i.e. child, teacher or school)? After

identifying the appropriate table based on the response to question 1, the next step involves whether the

analysis requires a child-, teacher- or school-level weight. If the population of inference is kindergarten

children, then the child-level weights will be appropriate. If generalizing to kindergarten teachers or

classrooms, then the teacher-level weights should be used, and if generalizing to the population of schools

with kindergartens, the school-level weight will be appropriate.

3. What instruments do the data to be used in the analysis come from? There are several

options when deciding on which child-level weights to use, and the source of the data affect which weight

to use. In each of the tables details under “to be used in the analysis of . . .” provide guidance based on

whether the data were collected through the child assessments, parent interviews or teacher

questionnaires.

Weight C1CW0 is used to estimate child-level characteristics or assessment scores for fallkindergarten, and C2CW0 is for spring-kindergarten. Examples of such estimates are the percent of

kindergarten children who are male, the percent of children who are API, the percent of children who are

6 when they enter kindergarten, the mean reading score of children, and the mean math score of children.

These weights are also used for estimates of child characteristics for language minority (LM)/not Spanish

children and children with disabilities. Some of these children were not assessed but their background

characteristics such as age, gender, race-ethnicity, and characteristics of parents, teachers, and classrooms

are available from the parent interview and the teacher questionnaires. The social rating scores (see

chapter 3, section 3.5) from parents and teachers are also available for LM/not Spanish children and

children with disabilities, regardless of whether they were assessed. In this chapter the terminology “LM/

not Spanish” refers to those children who spoke a non-English and non-Spanish language at home and did

not pass the cut score in the OLDS. For these children, only height and weight measurements were taken.

Similarly, in this chapter, the terminology “children with disabilities” refer to those children who were not

able to participate in the assessment due to reasons of disability as specified in their IEP. Both these

groups of children were assigned child weights even though they did not have cognitive assessment data.

When analyzing child data in conjunction with teacher data (e.g., teacher characteristics

from teacher questionnaires A or B or social rating scores reported by teachers from teacher questionnaire

C) and classroom data from teacher questionnaire A, weights C1CW0 (for fall-kindergarten) and C2CW0

(for spring-kindergarten) should be used. An example for the use of C1CW0 is in the analysis of the

relationship between children’s approaches to learning as rated by their teachers, the teacher’s type of
4-15

teaching certification, and the children’s cognitive skills and knowledge. Some data may be missing

because some teachers did not complete the questionnaire, but these are the most appropriate weights for

this type of analysis. However, different weights should be used for analysis of child data in conjunction

with both parent and teacher data (CPTW0).

C1PW0 (for fall-kindergarten) and C2PW0 (for spring-kindergarten) are used for child-level

estimates associated with data collected through the parent interview. Examples are the percent of

children whose mothers are currently employed, the percent of children who are in a particular type of

child care, and the percent of children who are read to at least every day. These weights should not be

used for estimates solely using direct child assessment data but should be used when analyzing parent and

child assessment data together, for example, when exploring the relationship between home literacy

behaviors and children’s reading skills.

C1CPTW0 (for fall-kindergarten) and C2CPTW0 (for spring-kindergarten) are used when

child direct assessment and teacher and parent data are combined in an analysis; for example, in the

analysis of the relationship between parent education, teacher education and children’s reading

knowledge and skills. These weights should be not be used for estimates using only direct child

assessment data or only parent interview data. Also, any analysis of the subgroup of children who moved

to a different school between fall- and spring-kindergarten should be done using C2CW0 or C2PW0

because these movers were treated separately in the nonresponse adjustment of C2CW0 and C2PW0.

B1TW0 (for fall-kindergarten) and B2TW0 (for spring-kindergarten) are used for teacherand classroom-level estimates. For example, these weights would be used to estimate the percent of

kindergarten teachers who teach a particular type of kindergarten program, the percent of teachers who

use a language other than English in their classroom, or the percent of teachers who do not have any

teacher certification. These weights would also be used in the estimation of classroom characteristics such

as the percent of kindergarten classrooms with computer areas. Weights for the corresponding round

should be used to produce round specific estimates; for example, B1TW0 should be used to estimate the

number of teachers teaching kindergarten programs in fall 1998; B2TW0 should be used to estimate the

number of kindergarten teachers in spring 1999. Teachers who were new to the study in the spring were

asked a subset of questions from the fall-kindergarten teacher questionnaires A and B (springkindergarten teacher questionnaire B). When analyzing data from these items B2TW0 should be used

(even though the variables start with the B1 preface, see chapter 7 for details). A panel weight was not

created as there were very few repeated measurements between fall- and spring-kindergarten. Most of the
4-16

data collected from the teachers about themselves and their classrooms were meant to pertain to the

school year.

S2SAQW0 is used in the estimation of school characteristics such as the percent of schools

that offer programs for children with special needs, or the percent of schools that use a standardized

achievement tests as a requirement for admission, or the percent of school administrators who believe that

their school computer resources are inadequate.

The longitudinal or panel weights (table 4-6) are used for estimates of differences at two

points in time. Examples of analysis using longitudinal weights include:

 Kindergarten fall-spring difference in mean child assessment scores (BYCW0);

 Kindergarten fall-spring difference in mean social skills as rated by children’s

teachers (BYCW0) (Data collected using the teacher questionnaire C are at a childlevel and are considered as part of the child assessment data.);

 Kindergarten fall-spring difference in mean social skills as rated by children’s parents

(BYPW0);

 The relationship between the gains children make in their reading knowledge and

skills, how often their parents read to them, how often their parents take them to the

library, teacher certification, and how much class time teachers spend on reading

(BYCPTW0) (This weight is used when the analysis includes data from all six

components- fall and spring child assessment, teacher and parent data.); and

 The relationship between the gains children make in their reading knowledge and

skills and parent and teacher beliefs on kindergarten readiness (BYCOMW0).

The difference between BYCPTW0 and BYCOMW0 is that BYCPTW0 is used for analysis

of both rounds of child direct assessment data in conjunction with both rounds of parent interview data

and both rounds of teacher data, while BYCOMW0 is used when analyzing a single round of parent

interview or teacher data in conjunction with both rounds of child direct assessment data.
4-17

Table 4-6. The ECLS-K: base year longitudinal weights

Base year longitudinal (panel) weights

Child-level weight to be used for analysis of ...

BYCW0 child direct assessment data and child characteristics from both fall- and

spring-kindergarten, alone or in conjunction with any combination of a) a

limited set of child characteristics (e.g. age, sex, race-ethnicity), b) falland/or spring- kindergarten teacher questionnaires A, B or C data, and c)

data from the school administrator questionnaire or facilities checklist

BYPW0 parent interview data from BOTH fall- and spring-kindergarten alone or in

combination with a) fall- and/or spring-kindergarten child assessment data,

b) fall- and/or spring-kindergarten teacher questionnaire A, B, or C data, and

c) data from the school administrator questionnaire or facilities checklist.

Exception: If data from the fall- AND spring-kindergarten parent, child

assessment, AND teacher questionnaire A or B (not C) are used then

BYCPTW0 should be used

BYCPTW0 fall- AND spring-kindergarten parent, child assessment, AND teacher

questionnaire A or B (not C) data

BYCOMW0 both rounds of child assessment data in conjunction with at least one or more

rounds (fall- and/or spring-kindergarten) of parent and/or teacher

questionnaires A and B (not C) data. This may or may not be in conjunction

with the school administrator questionnaire and facilities checklist data.

Exception: Whenever BOTH rounds of parent data are used in the analysis

either BYPW0 or BYCPTW0 is used.

Minimal teacher data were collected repeatedly in both rounds that would allow analysis of

change over time for those data. In the absence of the teacher panel weights, the spring-kindergarten

teacher weights can be used to cross-tabulate the fall- and spring-kindergarten teacher data from

questionnaire part A (TQA).

Careful consideration should be given to which set of weights is appropriate for the desired

analysis. Using the wrong weights will result in more biased or inefficient estimates. For example, if

C1CPTW0 were used in an analysis of child and teacher/classroom data only, then the resulting estimates

will be inefficient compared to estimates using C1CW0. The lower parent response causes C1CPTW0 to

result in lower sample size with positive weights. There may be combinations of data from a different

source for which no weights were developed, but most analyses are possible from the weights provided.

For example, no parent-level weights were calculated but parents are linked one-to-one to children so that
4-18

the child-level weights can be used for parent-level analysis, e.g., education levels of parents of

kindergartners. No child-teacher weights were computed for analyzing child data in conjunction with

teacher data because the response rates for the teachers are high; for the analysis of child assessment data

in conjunction with teacher data, the child-level weights should be used. For further advice on which

weights to use when analyzing a complex combination of data, contact NCES at ECLS@ed.gov.

For each type of weight, table 4-7 gives the distribution of schools by number of sampled

students with nonzero weights and the mean number of sampled students with nonzero weights per

school. This is useful in analysis using hierarchical linear modeling. For spring-kindergarten, the increase

in the count of schools with one to five sampled students is due to students transferring to other schools

from the original sampled schools. For the longitudinal weights, schools are classified on the basis of the

number of students who did not transfer schools between rounds of data collection.

Table 4-7. Distribution of schools by number of cases (children) with nonzero weights

1 – 5 6 – 10 11 – 15 16 – 20 21 - 27

Mean cases

per school

Fall-kindergarten

C1CW0 32 42 52 187 636 20

C1PW0 39 45 78 301 486 19

C1CPTW0 41 53 109 328 401 18

Spring-kindergarten

C2CW0 398 45 63 348 520 15

C2PW0 439 51 114 421 387 13

C2CPTW0 85 59 163 428 300 17

Longitudinal

for base year

BYCW0 36 40 74 373 423 19

BYPW0 41 57 149 431 267 17

BYCPTW0 46 80 190 414 198 17

BYCOMW0 42 48 112 404 324 18

4.3.2 Weighting Procedures

In general, weights were computed in two stages. In the first stage, base weights were

computed. They are the inverse of the probability of selecting the unit—if units were sampled at a rate of

1 in 100, sampled units must be weighted by 100 to represent the entire population. In the second stage,
4-19

base weights were adjusted for nonresponse. Nonresponse leads to bias in the survey estimates when the

characteristics of the nonrespondents are very different from those of the respondents. Adjusting for

nonresponse is intended to reduce the bias.

Nonresponse adjustment cells were generated using variables with known values for both

respondents and nonrespondents. Analyses using the Chi-squared Automatic Interaction Detector

(CHAID) were conducted to identify variables most highly related to nonresponse. At the school level,

school characteristics used for constructing nonresponse cells were the type of school (public, Catholic

private, non-Catholic private, or nonsectarian private), the school locale (large city, mid-size city, suburb

of large city, suburb of mid-size city, large town, small town, or rural area), the region where the school is

located (Northeast, Midwest, South, or West), and the size classification of the school in terms of school

enrollment. At the child level, the variables used for constructing nonresponse cells were the type of

school, the locale and the geographic region where the school is located; the size classification of the

school; and child characteristics such as age group, gender and race-ethnicity, and whether the child

moved from the original sampled school (spring-kindergarten only). For the teachers, nonresponse cells

were constructed using the type of school, the school locale, region, and the school size classification.

Once the nonresponse cells were determined, the nonresponse adjustment factors are the

reciprocals of the response rates within the selected nonresponse cells. A detailed technical description of

the nonresponse adjustment procedure can be found in the ECLS-K methodology report (forthcoming).

Response rates are presented in chapter 5 for the different populations and different types of

instruments. A detailed analysis of response rates is available that includes a study of nonresponse bias. In

this study, the ECLS-K survey estimates are compared with estimates from the sampling frames; they are

also compared with estimates from other surveys such as the Current Population Survey (CPS) and the

National Household Education Survey (NHES). The study also includes comparison of estimates using

the nonresponse adjusted weights with estimates using unadjusted weights. Finally, a nonresponse

simulation study is also provided to estimate the potential nonresponse bias.
4-20

4.3.3 Computation of School and Teacher Weights

School Base Weights

School base weights were used in calculating teacher and child weights for teacher- and

child-level estimates and school administrator weights for school-level estimates. The base weight for

each school was the inverse of the probability of selecting the PSU (county or group of counties)

multiplied by the inverse of the probability of selecting the school within the PSU.

If schools were selected through the freshening procedure, as described in section 3.2.3, an

additional factor equal to the inverse of the selection probability of the district or diocese was included in

the base weight. This factor is necessary because new public and Catholic schools were identified through

the freshening procedure with the district/diocese, and their selection probability must be conditioned on

the probability of selecting that district/diocese within the stratum. This additional factor did not apply to

non-Catholic private schools; these were selected directly from lists, and the school base weights were the

simple inverse of the school selection probability.

School Administrator Weights (S2SAQW0)

School administrator weights were computed for schools sampled at the beginning of the

study (fall-kindergarten) that completed the school administrator questionnaire in spring-kindergarten.

The school administrator weight is the school base weight adjusted for school administrator nonresponse.

Teacher Weights (B1TW0 and B2TW0)

At each school sampled at the beginning of the study, all kindergarten teachers were

included in the study. Each of these schools was considered a respondent if it had at least one completed

teacher questionnaire, part B. The teacher weights were computed in two stages. First, the school base

weights were adjusted for school nonresponse. Then, the teacher weights were computed as the school

nonresponse adjusted weights adjusted for teacher nonresponse.
4-21

4.3.4 Computation of Child Weights

Child Weights

In general, child weights were computed in two stages. In the first stage, school base weights

were adjusted for school nonresponse and then multiplied by the poststratified within-school child

weights. In the second stage, the resulting weights were adjusted for child nonresponse. The poststratified,

within-school child weight is equal to the total number of children in the school divided by the number of

children sampled in the school. This is calculated separately for API and non-API children because

different sampling rates were used for these two groups of children. Within a school, all API children

have the same base weights and all non-API children have the same base weights.

A school was classified as responding using different criteria for fall-kindergarten, springkindergarten, and the base year. In fall-kindergarten, responding schools were eligible schools that agreed

to cooperate. A school was considered cooperating if it agreed to provide lists of students (for sampling)

and teachers (for distributing the teacher questionnaires) and certain information on students that would

be used to plan for the assessment. In spring-kindergarten, schools that satisfied at least one of the

following conditions were considered respondents: (1) have at least one child assessed in springkindergarten, or (2) have at least one sampled LM/not Spanish child who did not pass the Oral Language

Development Scale (OLDS) cut score, or (3) have at least one sampled child with disabilities who could

not be assessed according to the child’s IEP, or (4) have at least one parent interviewed in springkindergarten. For the base year, a responding school was one that satisfied at least one of the following

conditions: (1) have at least one child assessed in either round, or (2) have at least one sampled LM/not

Spanish child in either round, or (3) have at least one sampled child with disabilities in either round, or (4)

have at least one parent interviewed in either round. For each set of first-stage child weights, the

appropriate school base weights were adjusted for school nonresponse, and then used in the computation

of the final child weights.

Child Weights To Be Used with Direct Child Assessment Data (C1CW0, C2CW0,

BYCW0)

In fall-kindergarten, responding children for this type of weight were eligible children who

had fall-kindergarten scorable cognitive assessment data, or LM/not Spanish children who did not score at
4-22

or above the OLDS cut score, but height and weight measurements were collected from them, or children

with disabilities who according to specifications in their IEP could not participate in the assessments. A

child was eligible if he or she was in kindergarten during fall 1998. A child who transferred to

kindergarten in another school between sampling and assessment was considered to be a nonrespondent.

In contrast, children who moved to first grade between sampling and assessment were considered

ineligible. The fall-kindergarten child weights C1CW0 are the fall-kindergarten first-stage child weights

adjusted for fall-kindergarten child nonresponse.

In spring-kindergarten, responding children were classified using rules similar to those used

in fall-kindergarten. A child who transferred to another school between rounds and was not followed was

considered a nonrespondent; children who moved outside the country were considered ineligible. The

spring-kindergarten child weights C2CW0 are the spring-kindergarten first-stage child weights adjusted

for spring-kindergarten child nonresponse. The child longitudinal weights BYCW0 were computed as the

base year first-stage child weights adjusted for nonresponse. A respondent is defined as a child for whom

both C1CW0 and C2CW0 were nonzero.

Table 4-8 shows the number of children who were not assessed due to the following special

situations: children who were LM/not Spanish, children with disabilities, children who moved to another

school between fall- and spring-kindergarten and who could not be located or because the new school was

in a nonsampled county, and children who moved outside of the country or who were deceased. Only the

LM/not Spanish and children with disabilities had child weights.

Table 4-8. Number of children who were not assessed due to special situations

Number of children

Unweighted Weighted

Fall-kindergarten

With disabilities 88 18,106

LM/Not Spanish 415 39,148

Spring-kindergarten

With disabilities 70 13,693

LM/Not Spanish 229 20,211

Moved schools in spring 606 129,562

Became ineligible in spring 67 13,340
4-23

Child Weights To Be Used with Parent Data (C1PW0, C2PW0, BYPW0)

The child weights C1PW0 (fall-kindergarten) and C2PW0 (spring-kindergarten) to be used

with parent interview data are the corresponding first-stage child weights adjusted for nonresponse to the

parent interview. In both fall- and spring-kindergarten, a respondent was defined as a child for whom the

family structure section (FSQ) in that child’s parent interview for the corresponding round was

completed. The child longitudinal weights BYPW0 were computed as the base year first-stage child

weights adjusted for nonresponse. A respondent is defined as a child for whom both C1PW0 and C2PW0

are nonzero. Note that these weights are at the child level even though the data were collected from the

parents; they sum to all kindergarten children.

Child Weights To Be Used for Any Cross-Round Combination of Child Direct

Assessment Data and Parent Interview Data and Teacher Data (C1CPTW0,

C2CPTW0, BYCPTW0)

The child weights C1CPTW0 (fall-kindergarten) and C2CPTW0 (spring-kindergarten) to be

used for analysis involving child, parent, and teacher data are the corresponding first-stage child weights

adjusted for nonresponse. In both fall- and spring-kindergarten, a respondent for this type of weight was

defined as a child who had scorable cognitive assessment data for the corresponding round (or LM/not

Spanish children or children with disabilities), whose parent completed the FSQ section of the parent

interview for the corresponding round, and whose teacher completed part B of the teacher questionnaire.

The child longitudinal weights BYCPTW0 are the first-stage child weights for the base year adjusted for

nonresponse. A respondent is defined as a child for whom both C1CPTW0 and C2CPTW0 are nonzero.

Again, these weights are used to produce estimates of children even though the source of the data may be

parent or teacher.

Child Weights To Be Used With a Single Round of Parent Interview or Teacher Data

in Conjunction with Both Rounds of Child Direct Assessment Data (BYCOMW0)

These child longitudinal weights are the base year first-stage child weights adjusted for

nonresponse. For this type of weight, a respondent is defined as a child whose (a) fall- and springkindergarten cross-sectional weights C1CW0 and C2CW0 are nonzero, and (b) either fall- or spring-
4-24

kindergarten cross-sectional weight C1PW0 or C2PW0 is nonzero, and (c) either fall- or springkindergarten cross-sectional weight B1TW0 or B2TW0 is nonzero.

4.3.5 Replicate Weights

For each weight included in the data file, a set of replicate weights was calculated. Replicate

weights are used in the jackknife replication method to estimate the standard errors of survey estimates.

Any adjustments done to the full sample weights were repeated for the replicate weights. For each full

sample weight, there are 90 replicate weights with the same weight prefix. For example, the replicate

weights for C1CW0 are C1CW1 through C1CW90. The method used to compute the replicate weights

and how they can be used to compute the sampling errors of the estimates are described in the section on

variance estimation.

4.3.6 Characteristics of Sample Weights

The statistical characteristics of the sample weights are presented in table 4-9 (teacher- and

school-level weights) and in table 4-10 (child-level weights). For each type of weight, the number of

cases with nonzero weights is presented together with the mean weight, the standard deviation, the

coefficient variation (i.e., the standard deviation as a percentage of the mean weight), the minimum

weight, the maximum weight, the skewness, the kurtosis, and the sum of weights.

Table 4-9. Characteristics of teacher- and school-level weights

Number of

cases

Mean Standard

deviation

CV

(× 100)

Minimum Maximum Skewness Kurtosis Sum

Teacher

Fall-kindergarten

B1TW0 3,047 62.47 44.04 70.50 1.61 506.40 2.59 11.13 190,337

Spring-kindergarten

B2TW0 3,243 58.64 39.67 67.64 1.60 453.44 2.43 10.09 190,166

School

Spring-kindergarten

S2SAQW0 866 83.44 53.07 63.60 6.42 484.64 2.24 8.32 72,260
4-25

Table 4-10. Characteristics of child-level weights

Number of

cases

Mean Standard

deviation

CV

(× 100) Minimum Maximum Skewness Kurtosis Sum

Fall-kindergarten

C1CW0 19,173 201.63 91.94 45.60 1.64 755.65 1.35 4.85 3,865,946

C1PW0 18,097 213.62 96.19 45.03 2.03 832.40 1.47 5.71 3,865,946

C1CPTW0 17,124 225.76 104.57 46.32 2.17 1,018.25 1.45 5.49 3,865,946

Spring-kindergarten

C2CW0 19,967 193.49 104.72 54.12 1.60 900.00 2.16 8.20 3,863,512

C2PW0 18,950 203.88 98.75 48.44 1.98 900.00 1.62 5.91 3,863,512

C2CPTW0 17,454 221.35 107.58 48.60 2.17 918.89 1.47 5.43 3,863,512

Longitudinal for base year

BYCW0 18,211 212.14 119.54 56.35 1.59 900.00 2.45 9.82 3,863,204

BYPW0 16,906 228.51 109.75 48.03 2.22 900.00 1.62 5.63 3,863,204

BYCPTW0 15,420 250.53 121.33 48.43 2.54 1,146.11 1.58 6.07 3,863,204

BYCOMW0 17,060 226.45 126.48 55.85 1.59 900.00 2.33 8.82 3,863,204

The difference in the estimate of the population of teachers or students (sum of weights)

between rounds of data collection is due to a combination of factors, among them: (1) the increase in the

number of responding schools in spring-kindergarten that resulted from the refusal conversion efforts, and

(2) the number of teachers and students who became ineligible after fall-kindergarten. The population of

inference for all child-level weights is always the population of kindergartners in the school year 1998-99.

4.4 Variance Estimation

The precision of the sample estimates derived from a survey can be evaluated by estimating

the variances of these estimates. For a complex sample design such as the one employed in the ECLS-K,

replication and Taylor Series methods have been developed. These methods take into account the

clustered, multistaged characteristics of sampling and the use of differential sampling rates to oversample

targeted subpopulations. For the ECLS-K, in which the first-stage self-representing sampling units were

selected with certainty and the first-stage non-self-representing sampling units were selected with two

units per stratum, the paired jackknife replication method (JK2) is recommended. This section describes

the JK2 and the Taylor Series estimation methods.
4-26

4.4.1 Paired Jackknife Replication Method

In this method, a survey estimate of interest is calculated from the full sample. Subsamples

of the full sample are then selected to calculate subsample estimates of the same parameter. The

subsamples are called replicates, and the subsample estimates are called replicate estimates. The

variability of the replicate estimates about the full sample estimate is used to estimate the variance of the

full sample estimate. The variance estimator is computed as the sum of the squared deviations of the

replicate estimates from the full sample estimate:

() ( )

2

1

( ) ˆ ∑ ˆ ˆ

=

= − G

g

g v θ θ θ

where θ is the survey estimate of interest,

$

θ is the estimate of θ based on the full sample,

G is the number of replicates formed, and

( ) ˆθ g is the g

th replicate estimate of θ based on the observations included in the gth

replicate.

The variance estimates of selected survey items presented in section 3.5 were produced

using WesVar and the paired jackknife replication method.

Replicate Weights

Replicate weights were created to be used in the calculation of replicate estimates. Each

replicate weight was calculated using the same adjustment steps as the full sample weight but using only

the subsample of cases that constitute each replicate. For the ECLS-K, replicate weights were created

taking into account the Durbin method of PSU selection.5

 As mentioned in section 4.1, the Durbin method

selects two first-stage units per stratum without replacement, with probability proportional to size and a

known joint probability of inclusion.

 

5 Durbin, J. (1967). Design of Multi-Stage Surveys for the Estimation of Sampling Errors. Journal of the Royal Statistical Society C, 16, 152-164.
4-27

In the ECLS-K PSU sample design, there were 24 SR strata and 38 non-self-representing

(NSR) strata. Among the 38 NSR strata, 11 strata were identified as Durbin strata and were treated as SR

strata for variance estimation. The purpose of the Durbin strata is to allow variances to be estimated as if

the first-stage units were selected with replacement. This brings the number of SR PSUs to 46 (24

original SR PSUs and 22 Durbin PSUs from the 11 Durbin strata). The remaining 54 NSR PSUs are in 27

NSR strata; thus 27 replicates were formed, each corresponding to one NSR stratum. For the SR strata, 63

replicates were formed. The 90 replicates will yield about 76 degrees of freedom for calculating

confidence intervals for many survey estimates.

As stated above, the sample of PSUs was divided into 90 replicates or variance strata. The

27 NSR strata formed 27 variance strata of two PSUs each; each PSU formed a variance unit within a

variance stratum. All schools within an NSR PSU were assigned to the same variance unit and variance

stratum. Sampled schools in the 46 SR PSUs were grouped into 63 variance strata. In the SR PSUs,

schools were directly sampled and constituted PSUs. Public schools were sampled from within PSU while

private schools were pooled into one sampling stratum and selected systematically (except in the SR

PSUs identified through the Durbin method where private schools were treated as if they were sampled

from within PSU). Schools were sorted by sampling stratum, type of school (from the original sample or

newly selected as part of freshening), type of frame (for new schools only), and their original order of

selection (within stratum). From this sorted list, they were grouped into pairs within each sampling

stratum; the last pair in the stratum may be a triplet if the number of schools in the stratum is odd. This

operation resulted in a number of ordered preliminary variance strata of two or three units each. The first

ordered 63 strata were then numbered sequentially from 1 to 63; the next ordered 63 strata were also

numbered sequentially from 1 to 63, and so on until the list was exhausted, thus forming the desired 63

variance strata.

In strata with two units, a unit being a PSU in the case of NSR PSUs and a school in the case

of SR PSUs, the base weight of the first unit was doubled to form the replicate weight, while the base

weight of the second unit was multiplied by zero. In strata with three units, two variance strata were

created: in the first variance stratum, the base weight of two of the three units was multiplied by 1.5 to

form the replicate weight and the base weight of the last unit was multiplied by zero; in the second

variance stratum, the base weight of a different group of two units was multiplied by 1.5, and the base

weight of the third unit was multiplied by zero. Any adjustments done to the full sample weights were

repeated for the replicate weights. For each full sample weight, there are 90 replicate weights with the
4-28

same weight prefix. For example, the replicate weights for child-level weights C1CW0 are C1CW1

through C1CW90.

4.4.2 Taylor Series Method

The Taylor Series method produces a linear approximation to the survey estimate of interest;

then the variance of the linear approximation can be estimated by standard variance formulas. The stratum

and first-stage unit identifiers needed to use the Taylor Series method were assigned taking care to ensure

that there were at least two responding units in each stratum. A stratum that did not have at least two

responding units was combined with an adjacent stratum. For the ECLS-K, the method of stratifying firststage units was the same for each type of weight in each round of data collection and in the panel, while

the combining of strata due to inadequate sample size and the sequential numbering of strata and firststage units were done separately. Consequently, there is a different set of stratum and first-stage unit

identifiers for each set of weights.

Stratum and first-stage unit identifiers are provided as part of the ECLS-K data file and can

be used with software such as SUDAAN and STATA. They are described in table 4-11.

4.4.3 Specifications for Computing Standard Errors

Specifications for computing standard errors are given in table 4-12. For each type of

analysis described in the table, users can choose between the replication method or the Taylor Series

method for computing standard errors.

For the replication method using WesVar, the case identification (ID), the full sample

weight, the replicate weights, and the method of replication are required parameters. All analysis of the

ECLS-K data should be done using the paired jackknife method (JK2). As an example, to compute child

level estimates (e.g., mean reading scores) and their standard errors, users need to specify CHILDID in

the ID box of the WesVar data file screen, C1CW0 as the full sample weight, C1CW1 to C1CW90 as the

replicate weights, and JK2 as the method of replication.
4-29

Table 4-11. ECLS-K Taylor Series stratum and first-stage unit identifiers

Variable name Description

Child level C1TCWSTR Sampling stratum – fall-kindergarten C-weights

C1TCWPSU First-stage sampling unit within stratum – fall-kindergarten Cweights

C1TPWSTR Sampling stratum – fall-kindergarten P-weights

C1TPWPSU First-stage sampling unit within stratum – fall-kindergarten Pweights

CICPTSTR Sampling stratum – fall-kindergarten CPT-weights

C1CPTPSU First-stage sampling unit within stratum – fall-kindergarten CPTweights

C2TCWSTR Sampling stratum – spring-kindergarten C-weights

C2TCWPSU First-stage sampling unit within stratum – spring-kindergarten Cweights

C2TPWSTR Sampling stratum – spring-kindergarten P-weights

C2TPWPSU First-stage sampling unit within stratum – spring-kindergarten Pweights

C2CPTSTR Sampling stratum – spring-kindergarten CPT-weights

C2CPTPSU First-stage sampling unit within stratum – spring-kindergarten

CPT-weights

BYCWSTR Sampling stratum – base year panel C-weights

BYCWPSU First-stage sampling unit within stratum – base year panel Cweights

BYPWSTR Sampling stratum – base year panel P-weights

BYPWPSU First-stage sampling unit within stratum – base year P-weights

BYCPTSTR Sampling stratum – base year panel CPT-weights

BYCPTPSU First-stage sampling unit within stratum – base year CPT-weights

BYCOMSTR Sampling stratum – base year panel COM-weights

BYCOMPSU First-stage sampling unit within stratum – base year COM-weights

Teacher level B1TTWSTR Sampling stratum – fall-kindergarten weights

B1TTWPSU First-stage sampling unit within stratum – fall-kindergarten

B2TTWSTR Sampling stratum – spring-kindergarten

B2TTWPSU First-stage sampling unit within stratum – spring-kindergarten

School level S2SAQSTR Sampling stratum

S2SAQPSU First-stage sampling unit within stratum
Table 4-12. Specifications for computing standard errors

Computing Standard Errors

Replication Method (WesVarPC) Taylor Series Method (SUDAAN 

Type of Analysis Full Sample Weight ID Replicate Weights Jacknife Method Sample Design Nesting VFall-KindergartenCross-sectional# Child-level# Teacher-level

C1CW0

C1PW0

C1CPTW0

B1TW0

CHILDID

PARENTID

CHILDID

T_ID

C1CW1 - C1CW90

C1PW1 - C1PW90

C1CPTW1 - C1CPTW90

B1TW1 - B1TW90

JK2JK2JK2JK2

WRWRWRWR

C1TCWSTR 

C1TPWSTR 

C1CPTSTR 

B1TTWSTR 

Spring-KindergartenCross-sectional# Child-level# Teacher-level# School-level

C2CW0

C2PW0

C2CPTW0

B2TW0

S2SAQW0

CHILDID

PARENTID

CHILDID

T_ID

S_ID

C2CW1 - C2CW90

C2PW1 - C2PW90

C2CPTW1 - C2CPTW90

B2TW1 - B2TW90

S2SAQW1 - S2SAQW90

JK2JK2JK2JK2JK2

WRWRWRWRWR

C2TCWSTR 

C2TPWSTR 

C2CPTSTR 

B2TTWSTR 

S2SAQSTR 

Base Year Panel# Child-level

BYCW0

BYPW0

BYCPTW0

BYCOMW0

CHILDID

PARENTID

CHILDIDCHILDID

BYCW1 - BYCW90

BYPW1 - BYPW90

BYCPTW1 - BYCPTW90

BYCOMW1 - BYCOMW90

JK2JK2JK2JK2

WRWRWRWR

BYCWSTR 

BYPWSTR 

BYCPTSTR 

BYCOMSTR 

4-30
4-31

For the Taylor Series method using either SUDAAN or STATA, the full sample weight, the

sample design, the nesting stratum and PSU variables are required. For the same example above, the full

sample weight (C1CW0), the without replacement sample design (WR), the stratum variable

(C1TCWSTR) and the PSU variable (C1TCWPSU) must be specified.

The last column in table 4-12 gives the average root design effect that can be used to

approximate the standard errors for each type of analysis. For a discussion of the use of design effects, see

section 4.5.

4.5 Design Effects

An important analytic device is to compare the statistical efficiency of survey estimates with

what would have been obtained in a hypothetical and usually impractical simple random sample (SRS) of

the same size. In a stratified clustered design like the ECLS-K, stratification generally leads to a gain in

efficiency over simple random sampling, but clustering has the opposite effect because of the positive

intracluster correlation of the units in the cluster. The basic measure of the relative efficiency of the

sample is called the design effect, defined as the ratio, for a given statistic, of the variance estimate under

the actual sample design to the variance estimate that would be obtained with an SRS of the same sample

size:

DEFF

Var

Var

DESIGN

SRS

=

The root design effect, DEFT, is defined as:

DEFT

SE

SE

DESIGN

SRS

=

where SE is the standard error of the estimate.
4-32

4.5.1 Use of Design Effects

One method of computing standard errors for the ECLS-K is the paired jackknife method, as

described in section 4.4, using programs designed specifically for analyzing complex survey data such as

WesVar. Another approach, Taylor Series linearization (and software designed for it), is also discussed in

the same section. If a statistical analysis software package such as SPSS (Statistical Program for the

Social Sciences) and SAS (Statistical Analysis System) is used, the standard errors should be corrected

using DEFT, since these programs calculate standard errors, assuming the data were collected with a

simple random sample. The standard error of an estimate under the actual sample design can be calculated

as follows:

SE DEFF Var DEFT SE DESIGN SRS SRS = ×=×

Packages such as SAS or SPSS can be used to obtain VarSRS and SESRS. Alternatively, VarSRS

and SESRS can be computed using the formulas below for means and proportions.

Means

( ) 2

2

1

1 1 n SRS

i

i w

n

i

SRS SE

w

w x x

n Var =

−

=

∑

∑

where wi are the sampling weights, n is the number of respondents in the sample, and the sample mean xw

is calculated as follows:

x

w x

w w

i i

n

i

= n

∑

∑

1

1

Proportions

( ) 1 2

srs SESRS

n

p p Var = − =
4-33

where p is the weighted estimate of proportion for the characteristic of interest and n is the number of

cases in the sample.

In both cases of means and proportions, the standard error assuming SRS should be

multiplied by DEFT to get the standard error of the estimate under the actual design.

4.5.2 Average Design Effects for the ECLS-K

In the ECLS-K, a large number of data items were collected from students, parents, teachers,

and schools. Each item has its own design effect that can be estimated from the survey data. One way to

produce design effects for analysts’ use is to calculate them for a number of variables and average them.

The averaging can be done overall and for selected subgroups. The tables that follow show estimates,

standard errors, and design effects for selected means and proportions based on the ECLS-K child, parent,

teacher, and school data. For each survey item, the tables present the number of cases, the estimate, the

standard error taking into account the actual sample design (Design SE), the standard error assuming SRS

(SRS SE), the root design effect (DEFT), and the design effect (DEFF). Standard errors (Design SE) were

produced using the paired jackknife replication method (JK2).

For each survey estimate, the variable name as it appears in the ECLS-K Base Year PublicUse Electronic Code Book (ECB) is also provided in the table. If multiple variables were combined to

arrive at the estimate, then the names of all the variables used are provided. For example, the estimate of

the proportion of fall-kindergarten children whose home language was not English was computed using

two different survey items, P1ANYLNG (parent questionnaire item PLQ020, whether another language

was used at home) and P1PRMLNG (parent questionnaire item PLQ060, what was the primary language

used at home). The first letter of the variable name indicates the source of the item: C – child assessment,

P – parent instrument, A – teacher instrument part A, B – teacher instrument part B, T – teacher

instrument part C, and S – school administrator questionnaire. The second letter of the variable name

indicates when the data were collected: 1 – round 1, fall-kindergarten; 2 – round 2, spring-kindergarten.

For more information on the variables used in this section, refer to chapter 3, which describes the

assessment and rating scale scores used in the ECLS-K, and chapter 7, which has a detailed discussion of

the other variables.
4-34

Child-Level Design Effects

Standard errors and design effects for the child-level items are presented in tables 4-13 to

4-15 for fall-kindergarten, spring-kindergarten, and for the base year. The survey items were selected so

that there was a mix of items common to both fall- and spring-kindergarten and items that were specific to

each round of data collection. For fall- and spring-kindergarten, the student-level items include the

different scores from the assessment data, the social rating scores as provided by the parents and teachers,

some characteristics of the parents, and some characteristics of the students as reported by the parents. For

a small number of estimates, the data were subset to cases where the estimate is applicable; for example,

the score for the Spanish math assessment applies only to students who were assessed in Spanish; the type

of primary child care is only for children who had regular scheduled child care; the number of hours that

the mothers work is only for women in the labor force; and the question on whether the parents have a

happy relationship is only for parents in current relationships. For the base year student panel, design

effects were calculated for the difference in scores between the two rounds of data collection, and also for

some spring-kindergarten items.

The median design effect is 4.7 for fall-kindergarten (compared with 2.2 for the NELS:88

base year student questionnaire data) and 4.1 for spring-kindergarten (compared with 3.4 for the NELS:88

first followup). The size of the ECLS-K design effects is largely a function of the number of children

sampled per school. With about 20 children sampled per school, an intraclass correlation of 0.2 might

result in a design effect of about 5. The median design effect is 3.4 for the panel of students common to

both fall- and spring-kindergarten, and the lower median design effect is due to the smaller cluster size in

the panel. The ECLS-K design effects are slightly higher than the average of 3.8 that was anticipated

during the design phase of the study, both for estimates for proportions and for score estimates.

Table 4-16 presents the median design effects for subgroups based on school type, child’s

gender and race-ethnicity, geographic region, and level of urbanicity. For fall-kindergarten, the median

design effects vary from 2.0 (Hispanic) to 5.9 (children in small towns and rural areas). For springkindergarten and the panel, the range of variability of the median design effects is similar to that for fallkindergarten; that is, 2.0 for Hispanic children to 6.7 for children in small towns and rural areas for

spring-kindergarten and 1.6 for children of other race-ethnicity to 5.3 for children in small towns and rural

areas for the panel. Once again, the variation in the design effects is largely a function of the sample size

as well as the homogeneity of the children within schools.
4-35

Table 4-13. ECLS-K, fall-kindergarten: standard errors and design effects for the full sample – child

level

Survey item Variable name

Number

of cases Estimate

Design

SE

SRS

SE DEFT DEFF

Child scores (Mean)

Reading score (English) C1RSCALE 17,625 22.02 0.156 0.063 2.480 6.148

Math score (English) C1MSCALE 17,615 19.52 0.141 0.054 2.606 6.789

Math score (Spanish) C1MSCALE 1,021 12.88 0.279 0.132 2.115 4.472

General knowledge (English) C1GSCALE 17,566 22.09 0.171 0.056 3.077 9.471

Composite motor skills C1CMOTOR 18,422 12.08 0.050 0.023 2.176 4.734

Approaches to learning-Parent P1LEARN 18,029 3.11 0.006 0.004 1.678 2.816

Self-control-Parent P1CONTRO 18,023 2.83 0.006 0.004 1.430 2.045

Social interaction-Parent P1SOCIAL 18,026 3.32 0.007 0.004 1.702 2.896

Withdrawn-Parent P1SADLON 18,010 1.54 0.006 0.003 2.131 4.542

Impulsive/overactive-Parent P1IMPULS 17,902 1.98 0.011 0.005 2.156 4.647

Approaches to learning-Teacher T1LEARN 18,839 2.96 0.010 0.005 2.110 4.452

Self-control-Teacher T1CONTRO 18,135 3.07 0.011 0.005 2.371 5.623

Interpersonal-Teacher T1INTERP 17,923 2.96 0.010 0.005 2.125 4.516

Externalizing problems-Teacher T1EXTERN 18,609 1.64 0.010 0.005 2.006 4.025

Internalizing problems-Teacher T1INTERN 18,356 1.55 0.007 0.004 1.867 3.484

Child characteristics (Percent)

Lived in single-parent family P1HFAMIL 18,097 23.57 0.728 0.315 2.309 5.331

Lived in two-parent family P1HFAMIL 18,097 74.43 0.790 0.324 2.438 5.942

Home language is non-English P1ANYLNG,

P1PRMLNG

18,059 12.09 0.724 0.243 2.983 8.896

Primary care is center-based P1PRIMNW 8,173 36.82 1.195 0.533 2.240 5.019

Primary care is home-based P1PRIMNW 8,173 63.18 1.195 0.533 2.240 5.019

Expected to graduate from college P1EXPECT 17,968 74.74 0.703 0.324 2.168 4.702

Being read to every day P1READBO 18,068 44.44 0.656 0.370 1.774 3.146

Was in excellent/very good/good health P1HSCALE 18,055 96.92 0.164 0.129 1.274 1.624

Parents had high school or less WKPARED 17,754 37.99 0.912 0.364 2.503 6.267

Mom worked 35 hours+/week P1HMEMP 12,519 64.17 0.701 0.429 1.635 2.672

Child characteristics (Mean)

Age of child in months R1_KAGE 19,073 68.50 0.077 0.032 2.378 5.656

Child’s household size P1HTOTAL 18,097 4.52 0.022 0.010 2.106 4.436

Number of children <18 in child’s HH P1LESS18 18,097 2.50 0.020 0.009 2.247 5.047

Median 2.162 4.675

Mean 2.154 4.801

Standard deviation 0.406 1.775

Coefficient of variation 0.188 0.370

Minimum 1.274 1.624

Maximum 3.077 9.471
4-36

Table 4-14. ECLS-K, spring-kindergarten: standard errors and design effects for the full sample – child

level

Survey item Variable name

Number

of cases Estimate

Design

SE

SRS

SE DEFT DEFF

Child scores (Mean)

Reading score (English) C2RSCALE 18,937 31.64 0.213 0.075 2.855 8.151

Math score (English) C2MSCALE 18,925 27.43 0.173 0.063 2.728 7.443

Math score (Spanish) C2MSCALE 724 18.76 0.454 0.242 1.874 3.513

General knowledge (English) C2GSCALE 18,903 26.81 0.158 0.056 2.801 7.844

Approaches to learning-Parent P2LEARN 18,252 3.12 0.005 0.004 1.279 1.637

Self-control-Parent P2CONTRO 18,251 2.87 0.006 0.004 1.685 2.839

Social interaction-Parent P2SOCIAL 18,270 3.42 0.006 0.004 1.579 2.494

Withdrawn-Parent P2SADLON 18,232 1.55 0.006 0.003 1.966 3.867

Impulsive/overactive-Parent P2IMPULS 18,091 1.96 0.010 0.005 1.856 3.445

Approaches to learning-Teacher T2LEARN 18,979 3.08 0.010 0.005 1.992 3.967

Self-control-Teacher T2CONTRO 18,847 3.15 0.011 0.005 2.294 5.261

Interpersonal-Teacher T2INTERP 18,767 3.09 0.009 0.005 1.999 3.997

Externalizing problems-Teacher T2EXTERN 18,907 1.69 0.010 0.005 2.173 4.723

Internalizing problems-Teacher T2INTERN 18,806 1.59 0.008 0.004 2.062 4.251

Child characteristics (Percent)

Lived in single-parent family P2HFAMIL 18,906 23.51 0.584 0.308 1.895 3.591

Lived in two-parent family P2HFAMIL 18,906 74.33 0.645 0.318 2.030 4.120

Home language is non-English P2ANYLNG,

P2PRMLNG

18,862 12.53 0.739 0.241 3.064 9.391

Child used home computer P2HOMECM 18,910 52.61 0.818 0.363 2.253 5.078

Child read outside school everyday P2CHREAD 18,877 39.82 0.649 0.356 1.823 3.322

Parents had high school or less WKPARED 17,607 37.14 0.910 0.364 2.499 6.244

Parent attended PTA P2ATTENP 18,914 33.12 0.902 0.342 2.635 6.941

Parent thinks not safe for child to play

outside

P2SAFEPL 18,898 96.17 0.220 0.140 1.574 2.479

Parents had happy relationship P2MARRIG 14,291 97.77 0.164 0.123 1.333 1.776

Parent too busy to play with child P2TOOBUS 18,600 45.55 0.591 0.365 1.618 2.617

Child characteristics (Mean)

Age of child in months R2_KAGE 19,890 74.76 0.070 0.032 2.209 4.881

Child’s household size P2HTOTAL 18,906 4.54 0.023 0.010 2.218 4.919

Number of children <18 in child’s HH P2LESS18 18,906 2.52 0.020 0.009 2.302 5.298

Median 2.030 4.120

Mean 2.096 4.596

Standard deviation 0.458 1.993

Coefficient of variation 0.218 0.434

Minimum 1.279 1.637

Maximum 3.064 9.391
4-37

Table 4-15. ECLS-K, panel: standard errors and design effects for the full sample – child level

Survey item Variable name

Number

of cases Estimate

Design

SE

SRS

SE DEFT DEFF

Child scores

(Difference between fall- and spring-kindergarten scores)

Reading score (English) C2RSCALE – C1RSCALE 16,751 9.88 0.123 0.047 2.603 6.778

Math score (English) C2MSCALE – C1MSCALE 16,748 8.18 0.084 0.040 2.126 4.520

Math score (Spanish) C2MSCALE – C1MSCALE 637 6.28 0.165 0.175 0.944 0.892

General knowledge (English) C2GSCALE – C1GSCALE 16,697 5.16 0.070 0.031 2.255 5.084

Approaches to learning-Parent P2LEARN - P1LEARN 16,326 0.01 0.005 0.003 1.467 2.151

Self-control-Parent P2CONTRO - P1CONTRO 16,326 0.05 0.005 0.003 1.461 2.135

Social interaction-Parent P2SOCIAL - P1SOCIAL 16,331 0.10 0.006 0.005 1.315 1.729

Withdrawn-Parent P2SADLON – PISADLON 16,298 0.01 0.005 0.003 1.652 2.730

Impulsive/overactive-Parent P2IMPULS - P1IMPULS 16,109 -0.01 0.009 0.005 1.670 2.790

Approaches to learning-Teacher T2LEARN - T1LEARN 17,208 0.11 0.007 0.004 1.861 3.462

Self-control-Teacher T2CONTRO - T1CONTRO 16,538 0.07 0.008 0.004 1.840 3.387

Interpersonal-Teacher T2INTERP - T1INTERP 16,296 0.12 0.008 0.004 1.919 3.681

Externalizing problems-Teacher T2EXTERN - T1EXTERN 16,944 0.06 0.005 0.003 1.481 2.193

Internalizing problems-Teacher T2INTERN - T1INTERN 16,681 0.04 0.007 0.004 1.707 2.915

Child characteristics (Percent)

Lived in single-parent family P2HFAMIL 16,870 23.11 0.603 0.325 1.857 3.447

Lived in two-parent family P2HFAMIL 16,870 74.80 0.671 0.334 2.007 4.029

Home language is non-English P2ANYLNG, P2PRMLNG 16,906 11.65 0.706 0.247 2.860 8.182

Child used home computer P2HOMECM 16,881 53.54 0.860 0.384 2.242 5.025

Child read outside school everyday P2CHREAD 16,855 39.73 0.663 0.377 1.760 3.099

Parents had high school or less WKPARED 15,733 36.19 0.939 0.383 2.451 6.006

Parent attended PTA P2ATTENP 16,880 33.24 0.940 0.362 2.594 6.727

Parent thinks not safe for child to

play outside P2SAFEPL 16,866 96.19 0.219 0.147 1.487 2.212

Parents had happy relationship P2MARRIG 12,823 97.83 0.168 0.129 1.305 1.702

Parent too busy to play with child P2TOOBUS 16,642 45.73 0.590 0.386 1.528 2.335

Child characteristics (Mean)

Age of child in months R2_KAGE 18,146 74.76 0.072 0.033 2.171 4.713

Child’s household size P2HTOTAL 16,870 4.55 0.023 0.011 2.141 4.585

Number of children <18 in child’s

HH P2LESS18 16,870 2.52 0.020 0.009 2.167 4.695

Median 1.857 3.447

Mean 1.884 3.748

Standard deviation 0.454 1.767

Coefficient of variation 0.241 0.471

Minimum 0.944 0.892

Maximum 2.860 8.182
4-38

Table 4-16. ECLS-K: median design effects for subgroups – child level

Fall-kindergartena Spring-kindergartenb Panelc

Subgroups DEFT DEFF DEFT DEFF DEFT DEFF

All students 2.162 4.675 2.030 4.120 1.857 3.447

Type of school

Public 2.064 4.258 1.932 3.734 1.781 3.171

Private 1.995 3.979 1.954 3.817 1.782 3.174

Catholic private 1.771 3.136 1.738 3.022 1.654 2.736

Other private 1.937 3.754 1.706 2.910 1.709 2.920

Gender

Male 1.771 3.135 1.735 3.011 1.533 2.349

Female 1.645 2.704 1.656 2.741 1.572 2.471

Race-ethnicity

White 1.777 3.159 1.802 3.246 1.654 2.736

Black 1.594 2.546 1.462 2.137 1.417 2.009

Hispanic 1.397 1.952 1.406 1.977 1.366 1.865

Asian/Pacific Islander 1.971 3.883 2.107 4.438 1.422 2.021

Other 1.629 2.654 1.503 2.260 1.279 1.635

Region

Northeast 1.760 3.099 1.824 3.328 1.541 2.374

Midwest 2.366 5.599 2.306 5.319 2.102 4.418

South 2.122 4.502 1.969 3.876 1.945 3.784

West 1.647 2.712 1.666 2.775 1.532 2.347

Urbanicity

Central city 2.136 4.563 1.952 3.812 1.752 3.068

Urban fringe and large town 1.814 3.291 1.775 3.151 1.586 2.516

Small town and rural area 2.421 5.861 2.594 6.727 2.306 5.319

a

Each median is based on 28 items.

b

Each median is based on 27 items.

c

Each median is based on 27 items.

Items with the highest design effects are those related to teacher data. For example, in

spring-kindergarten, the estimate of the percent of children whose teachers have a master’s degree or a

higher degree is 35.6 percent with a design effect of 16.814; the estimate of the percent of children whose

teachers spoke only English in class is 89.5 percent with a design effect of 17.871; the estimate of the

mean number of years that these children’s teachers taught in schools is nine years with a design effect of

12.157. The median design effect for these three items is about 14.8 for fall-kindergarten and 16.8 for
4-39

spring-kindergarten. The high design effects are reasonable for this type of data because children in the

same class have the same teacher, and the intraclass correlation is thus high.

Teacher-Level Design Effects

Standard errors and design effects for the teacher-level items are presented in table 4-17 for

fall-kindergarten and table 4-18 for spring-kindergarten. Survey items were selected from both teacher

instruments, part A and part B. In part A of the teacher instrument, teachers were asked to report about

their children’s and class’s characteristics, for classes that they taught, whether they were morning,

afternoon, or all day classes. In part B, they were asked about class organization, class activities,

evaluation methods and also about their views on kindergarten readiness, school environment, and overall

school climate. The topics covered in part B are not class-specific. Based on data collected in part A,

teachers were classified as full-day (if they taught all day classes) or part-day teachers (if they taught

morning, or afternoon, or both morning and afternoon classes). For both fall- and spring-kindergarten, a

small number of teachers who filled out information for morning and all day classes or afternoon and all

day classes could not be classified as part-day or full-day teachers and were excluded from the

computation of design effects. This affects items such as the language that the teachers spoke in class and

the class size since these estimates were computed over all classes taught, whether they were morning,

afternoon, or all day classes.

The median design effect is 2.5 for both fall- and spring-kindergarten. These are lower than

the child-level design effects because the number of responding teachers per school is relatively small.

The design effect for teachers is largely a result of selecting a sample using a design most effective for

child-level statistics.

Table 4-19 presents the median design effects for subgroups based on school type,

geographic region, level of urbanicity, teacher type, and percent of minority enrollment in the school. For

fall-kindergarten, the median design effect varies from 1.4 (teachers in Catholic private schools) to 3.2

(teachers in schools with 75 percent minority enrollment or more). The median design effects are

generally lower for spring-kindergarten, but the range of variability is similar to that for fall-kindergarten

(from 1.3 for teachers in Catholic private schools to 3.0 for teachers in schools with 25 to 49 percent

minority enrollment).
4-40

Table 4-17. ECLS-K, fall-kindergarten: standard errors and design effects for the full sample – teacher

level

Survey item Variable name

Number

of cases Estimate

Design

SE

SRS

SE DEFT DEFF

Teacher characteristics (Percent)

Used only English in class A1ATNOOT,

A1PTNOOT,

A1DTNOOT

2,828 84.97 1.008 0.672 1.501 2.252

Used Spanish in class A1ATSPNH,

A1PTSPNH,

A1DTSPNH

2,828 13.83 0.925 0.649 1.425 2.031

Had math area in class B1MATHAR 3,037 94.71 0.712 0.406 1.753 3.072

Had computer area in class B1COMPAR 3,031 83.31 1.095 0.677 1.617 2.615

Used 5-9 unpaid prep hours B1NOPAYP 3,032 37.14 1.022 0.877 1.165 1.357

Had preschoolers in kindergarten B1INKNDR 2,975 43.00 1.629 0.908 1.795 3.221

Teacher is Hispanic B1HISP 2,973 6.53 0.766 0.453 1.692 2.862

Had at least a bachelor’s degree B1HGHSTD 2,919 95.20 0.627 0.395 1.587 2.517

Had no teaching certification B1TYPCER 2,923 3.20 0.401 0.325 1.233 1.520

Had highest teaching certification B1TYPCER 2,923 62.05 1.409 0.897 1.570 2.464

Certified in early childhood B1ERLYCT 2,941 52.95 1.607 0.920 1.746 3.048

Taught all day class only A1ACLASS,

A1PCLASS,

A1DCLASS

2,860 62.38 2.231 0.906 2.463 6.068

Teacher characteristics (Mean)

Age of teacher B1AGE 2,923 41.29 0.249 0.191 1.301 1.693

Class size A1ATOTAG,

A1PTOTAG,

A1DTOTAG

2,398 19.70 0.305 0.121 2.511 6.303

Had control of teaching techniques and

discipline (scale 1 to 5)

B1CNTRLC 3,023 4.43 0.021 0.015 1.423 2.026

Number of years teaching kindergarten B1YRSKIN 3,024 8.11 0.173 0.135 1.281 1.642

Median 1.579 2.491

Mean 1.629 2.793

Standard deviation 0.386 1.444

Coefficient of variation 0.237 0.517

Minimum 1.165 1.357

Maximum 2.511 6.303
4-41

Table 4-18. ECLS-K, spring-kindergarten: standard errors and design effects for the full sample –

teacher level

Survey item Variable name

Number

of cases Estimate

Design

SE

SRS

SE DEFT DEFF

Teacher characteristics (Percent)

Used only English in class A2AENGLS

A2PENGLS

A2DENGLS

3,037 89.22 0.925 0.563 1.643 2.700

Used Spanish in class A2ACSPNH

A2PCSPNH

A2DCSPNH

3,037 11.68 0.935 0.583 1.604 2.573

Directed 2 hours of whole class

activities

A2WHLCLS 3,032 33.80 1.132 0.859 1.318 1.737

Had daily reading and language arts A2OFTRDL 3,063 94.82 0.720 0.400 1.798 3.234

Had math 3-4 times per week A2OFTMTH 3,051 15.30 0.926 0.652 1.421 2.018

Had physical education 1-2 times per

week

A2TXPE 3,060 54.61 2.301 0.900 2.557 6.539

Had adequate child size furniture A2FURNIT 3,061 72.30 1.137 0.809 1.406 1.977

Attended 3 or more in-service training

days

A2INSRVC 3,044 87.32 0.975 0.603 1.617 2.615

Parents see child’s work 15 times or

more

A2SHARED 3,046 22.36 1.226 0.755 1.624 2.637

Had math area in class B1MATHAR 3,225 94.67 0.749 0.395 1.894 3.588

Had computer area in class B1COMPAR 3,220 82.85 1.039 0.664 1.565 2.448

Used 5-9 unpaid prep hours B1NOPAYP 2,970 37.13 1.054 0.887 1.188 1.412

Had preschoolers in kindergarten B1INKNDR 3,170 43.42 1.582 0.880 1.797 3.230

Teacher is Hispanic B1HISP 3,167 6.43 0.754 0.436 1.729 2.991

Had at least a bachelor’s degree B1HGHSTD 3,113 94.76 0.606 0.399 1.517 2.302

Had no teaching certification B1TYPCER 3,114 3.44 0.445 0.326 1.363 1.858

Had highest teaching certification B1TYPCER 3,114 61.50 1.290 0.872 1.479 2.187

Certified in early childhood B1ERLYCT 3,137 52.50 1.565 0.892 1.755 3.079

Teacher characteristics (Mean)

Age of teacher B1AGE 3,111 41.11 0.253 0.187 1.354 1.834

Number of paid aides A2PDAIDE 3,053 0.90 0.030 0.014 2.148 4.615

Had control on teaching techniques and

discipline (scale 1 to 5)

B1CNTRLC 2,962 4.43 0.022 0.015 1.461 2.134

Number of years teaching kindergarten B1YRSKIN 3,219 8.09 0.162 0.131 1.234 1.522

Median 1.585 2.511

Mean 1.612 2.692

Standard deviation 0.311 1.139

Coefficient of variation 0.193 0.423

Minimum 1.188 1.412

Maximum 2.557 6.539
4-42

Table 4-19. ECLS-K: median design effects for subgroups – teacher level

Subgroups Fall-kindergartena Spring-kindergartenb

DEFT DEFF DEFT DEFF

All teachers 1.579 2.491 1.585 2.511

Type of school

Public 1.542 2.379 1.548 2.397

Private 1.319 1.738 1.243 1.544

Catholic private 1.162 1.360 1.147 1.315

Other private 1.226 1.503 1.212 1.467

Region

Northeast 1.410 1.987 1.415 2.005

Midwest 1.518 2.305 1.512 2.284

South 1.561 2.437 1.559 2.429

West 1.532 2.349 1.549 2.398

Urbanicity

Central city 1.682 2.830 1.547 2.393

Urban fringe and large town 1.534 2.356 1.484 2.202

Small town and rural area 1.617 2.616 1.700 2.893

Type of teacher

Full day 1.513 2.290 1.638 2.683

Part day 1.411 1.990 1.339 1.793

Minority enrollment

0 – 25% 1.368 1.871 1.367 1.869

25 – 49% 1.765 3.113 1.738 3.022

50 – 74% 1.424 2.027 1.406 1.977

75 – 100% 1.776 3.166 1.453 2.110

a Each median is based on 16 items.

b Each median is based on 22 items.

School-Level Design Effects

Standard errors and design effects for the school-level items are presented in table 4-20.

Survey items are selected from the school administrator questionnaire. For items having to do with

children with limited English proficiency (LEP), the data were subset to schools with LEP children. The

median design effect is 1.6. Table 4-21 presents the median design effects for subgroups based on school

type, geographic region, level of urbanicity, and percent of minority enrollment in the school. They vary

from 1.1 for schools in the Northeast region to 2.1 for schools in small towns and rural areas.
4-43

Table 4-20. ECLS-K, spring-kindergarten: standard errors and design effects for the full sample – school

level

Survey item Variable name

Number

of cases Estimate

Design

SE

SRS

SE DEFT DEFF

School characteristics (Percent)

Had a particular focus or emphasis S2FOCUS 859 19.94 1.736 1.363 1.274 1.622

Used standardized achievement

assessment as requirement for

admission

S2STNDTE

856

10.84 1.392 1.063 1.310 1.715

Funding levels decreased significantly S2FUNDLV 854 12.50 1.382 1.132 1.221 1.491

Received federal Title 1 funds this year S2TT1 862 51.51 1.450 1.704 0.851 0.725

Required kindergartners to wear

uniform

S2UNIFRM

858

18.92 1.936 1.337 1.448 2.096

Gave children readiness or placement

assessment

S2RDITST

860

61.74 2.391 1.657 1.443 2.082

Tested kindergartners with

standardized assessments

S2TESTK

838

33.40 2.030 1.629 1.246 1.552

Offered after- school child care S2AFTSCH 856 56.61 2.187 1.694 1.291 1.666

Offered pre-kindergarten S2PREKIN 856 45.47 2.149 1.702 1.263 1.594

Had LEP children S2LIMENG 857 39.07 1.980 1.667 1.188 1.412

Had translators for LM-LEP families S2TRANSL 414 77.69 2.928 2.046 1.431 2.047

Offered IEP to disabled children S2ONIEP 853 80.77 1.714 1.350 1.270 1.613

Principal is male S2GNDER 848 39.16 2.186 1.676 1.304 1.701

Principal is black S2RACE3 820 6.94 0.991 0.888 1.116 1.246

Principal has master’s degree S2EDLVL 806 87.49 1.665 1.166 1.428 2.040

School characteristics (Mean)

Percent LEP students S2LEPSCH 387 11.99 1.542 1.002 1.539 2.370

Total years as principal S2TOTPRI 840 10.38 0.300 0.268 1.118 1.249

Median 1.274 1.622

Mean 1.279 1.660

Standard deviation 0.162 0.395

Coefficient of variation 0.127 0.238

Minimum 0.851 0.725

Maximum 1.539 2.370
4-44

Table 4-21. ECLS-K: median design effects for subgroups – school level

Subgroups Spring-kindergartena

DEFT DEFF

All schools 1.274 1.622

Type of school

Public 1.312 1.721

Private 1.150 1.323

Catholic private 1.105 1.220

Other private 1.079 1.165

Region

Northeast 1.045 1.092

Midwest 1.202 1.445

South 1.374 1.888

West 1.414 2.000

Urbanicity

Central city 1.279 1.635

Urban fringe and large town 1.223 1.496

Small town and rural area 1.445 2.088

Minority enrollment

0 – 25% 1.213 1.471

25 – 49% 1.246 1.552

50 – 74% 1.182 1.396

75 – 100% 1.422 2.023

a Each median is based on 17 items.
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5. DATA COLLECTION METHODS AND RESPONSE RATES

5.1 Data Collection Methods

The following sections discuss the procedures used in the data collection phase of the Early

Childhood Longitudinal Study-Kindergarten Class of 1998-99 (ECLS-K) base year.

The ECLS-K base year data collection was conducted in the fall and spring of the 1998-99

school year. The timeline for base year data collection is shown in exhibit 5-1. Computer-assisted

personal interviewing (CAPI) was the mode of data collection for the child assessment, and computerassisted telephone/personal interviewing (CATI/CAPI) was the mode of data collection for the parent

interview; self-administered questionnaires were used for gathering information from teachers, school

administrators, student records, and about teacher salary and benefits. The field staff was organized into

100 work areas, each with a data collection team consisting of one field supervisor and three assessors.

The data collection teams were responsible for all data collection activities in their work areas; they

conducted the direct child assessments and the parent interviews, distributed and collected all school and

teacher questionnaires, and completed a school facilities checklist.

5.2 Study Endorsements and School Recruitment

Prior to contacting state agencies or dioceses for a commitment to participate, key

educational organizations were contacted for endorsement. The ECLS-K study received the endorsement

of many national associations and organizations representing parents, school administrators, teachers, and

private religious and nonreligious schools.

Before the data collection effort could begin, it was first necessary to secure a commitment

to participate in the study from the administrator of each sampled school. Several levels of cooperation

were sought before school administrators were approached. The process of notifying states, districts,

dioceses, and schools about the study began in January 1998. The contact procedures varied for public,

Catholic, and non-Catholic private schools. Public schools had three levels of contact—state, school

district, and school; Catholic schools had two levels—diocese and school; and non-Catholic private

schools had one—the school.
5-2

Exhibit 5-1. Timeline of base year data collection

1998 1999

School Recruitment Kindergarten

Winter - Winter

1998 1999 Fall Winter Spring

State 

Contacts

District/Diocese 

Contacts

School 

Contacts

Baseline 

assessment data

Parent 

interviews 

conducted

Teacher 

information 

collected

Child 

assessments

Parent 

interviews 

conducted

Teacher 

information 

collected

School and school 

administrator data

Student 

record data

Fall

Head Start

verification

State Contacts for Public Schools

Letters were sent to the chief state school officer of each of the 41 states that contained

ECLS-K sampled schools to explain the objectives of the study and the data collection procedures,

especially those for protecting individual and institutional confidentiality.1

 All states agreed to participate.

The letter indicated the state test director would be called to discuss the study. Information packets that

included a list of the public school districts and sampled public schools within the state were then sent to

the state test directors. The state test directors were called to identify the procedures to be followed in

contacting public school districts. Once approval was obtained at the state level, contact was made with

the district superintendents.

 

1 The ECLS-K area sample was not stratified by state and as a result not all states contain sampled schools. The smallest geographic area for

which direct representation was guaranteed is Census region. For more information concerning the sample design see chapter 4.
5-3

District and Catholic Diocese Contacts

For the public schools, a letter that included a list of the sampled schools within the district

was sent to the district superintendent. A similar package of materials was sent to the Catholic dioceses

and Archdioceses in the sample to obtain permission to contact Catholic schools about the study.

Beginning in late January, calls were placed to district superintendents and Catholic dioceses to explain

the study, answer questions, and obtain permission to contact sampled schools within the district or

diocese. Once approval was obtained at the district or diocesan level, contact was made with each school

administrator. A total of 584 public school districts contained sampled schools, and 75 percent of them

cooperated with the study. (See table 5-4 for school response rates.) A total of 55 Catholic dioceses or

Archdioceses contained sampled schools, and 93 percent of them cooperated with the study (see table 5-4

for school response rates).

School Contacts

A letter and other study materials were mailed to school administrators in all the sampled

schools. The school administrators in non-Catholic private schools were also contacted at this time.

Telephone contacts began in March 1998 and continued through the end of the school year in June 1998;

school recruitment continued in fall 1998 with the start of the new school year. To increase the number of

participating public schools, substitute schools were recruited in primary sampling units (PSUs) where the

public school response rate was less than 65 percent. Once the school administrator agreed to participate,

he or she was asked to set an appointment for two visits by the ECLS-K field staff to the school in the fall

of the 1998-99 school year. The first visit, the preassessment visit, was to select the sample of children

(see section 5.4.2 for more detail on this visit), and the second visit was to conduct the child assessments

(see section 5.4.3 for more detail on this visit). The school administrator was also asked to identify an

individual, referred to as the school coordinator, to act as the school liaison with the ECLS-K.

Increasing Response Rates for Originally Sampled Schools

Recruitment of originally sampled schools continued into winter 1999 to improve school

response rates before school substitution. Beginning in January 1999, a small group of highly experienced

field supervisors converted originally sampled schools that had initially refused to participate in the fall-
5-4

kindergarten assessments. Refusal conversion materials were developed that included endorsements from

school principals, kindergarten teachers, and parents. Also, a fact sheet presented general information

about the direct child assessment and the response to it by children, teachers, and parents who participated

in the fall ECLS-K. Field supervisors made telephone or in-person contacts to refusal schools to

encourage them to participate in the study. The refusal conversion effort continued through March 1999

and improved original school response rates by five percentage points.

5.3 Field Staff Training

In-person training sessions were conducted to prepare field staff for fall data collection tasks.

Three different training sessions for fall-kindergarten were conducted: one for staff recruiting schools into

the study, one for field supervisors, and one for assessors. Two different training sessions for springkindergarten were conducted: one for continuing staff and one for new staff.

School Recruitment Training

School recruitment staff—primarily field supervisors with experience on other education

studies such as the National Assessment of Educational Progress (NAEP) and the Third International

Mathematics and Science Study (TIMSS)—were trained for three days in March 1998 to recruit public

school districts, Catholic dioceses, and private schools into the study. The topics covered in the training

included an introduction to the study, practice exercises in recruiting schools, refusal avoidance

techniques, and exercises on scheduling schools efficiently within a work area.

Fall-Kindergarten Field Staff Training

Field supervisor and assessor training was held during two time periods between late August

and late September 1998. Field staff were selected who had experience on NAEP and TIMSS, as well as

the Panel Study of Income Dynamics—Child Supplement and the Monitoring the Future Study. The

majority of selected field staff were retired teachers, former educators, people experienced in conducting

assessments, or people experienced in working in schools or with school-age children. Prior to the inperson training session, all field staff received eight hours of home study training on the study design,
5-5

including a training video demonstrating the conduct of the direct child assessment, the field procedures,

and computer keyboard skills, and eight hours of general interviewing techniques training. Field

supervisors were trained to conduct supervisor activities in groups of 15. Field supervisors and assessors

were trained for assessment activities and parent interviewing in groups of 20 to 24. In total, 112 field

supervisors and 343 assessors completed training.

Field Supervisor Training. Field supervisor training preceded the assessor training and

lasted for two days. Field supervisor training was conducted using the automated field management

system (FMS). The FMS was used throughout the data collection period to enter information about the

sampled children, parents, teachers, and schools and to monitor production on all data collection

activities. The field supervisors entered information into the FMS during training presentations, providing

them with hands-on experience with the FMS and all field procedures prior to beginning data collection.

The topics covered in the field supervisor training session included reviewing materials for prior contacts

with schools; role plays to practice contacting school coordinators; conducting the preassessment

activities such as selecting the student samples, identifying student home language, obtaining parent

consent, and parent contacting procedures; meeting with teachers; and quality control observation

procedures.

Assessor Training. One major goal of the assessor training was to train field staff in the

proper procedures to conduct the direct child assessment. This included following standardized

procedures for administration of all assessment items as well as maintaining a neutral rapport with the

sampled children. Another major goal was to engage parents in the study to obtain consent to conduct the

direct child assessment and complete the parent interview. The topics covered in the assessor training

session included an introduction to the study, interactive lectures based on the direct child assessment and

parent interview, practicing parent interviews in pairs using role-play scripts, practicing the direct child

assessment using role-play scripts, techniques for parent refusal avoidance, and strategies for building

rapport with kindergarten children. The sessions provided trainees with hands-on experience with all the

direct child assessment materials and procedures and the CAPI programs prior to beginning data

collection. Assessor training lasted for five days; field supervisors were also trained to perform all

assessor activities. Trainees practiced entering information into the CAPI system on laptop computers

during training presentations on conducting the direct child assessment and parent interview. The majority

of training time (about 30 hours) was spent on conducting the direct child assessment, establishing

rapport with the child, and maintaining a neutral approach. The trainees practiced the direct child

assessment with kindergarten children brought onsite to the training session. These practice sessions and
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direct interactions with children provided useful experience prior to assessing sample children. The rest of

the time was spent on conducting the parent interview. Spanish bilingual interviewers underwent an

additional six hours of training during which they practiced the Spanish version of the direct child

assessment [a Spanish Oral Language Development Scale (OLDS), a Spanish warm-up booklet, and

translated versions of the mathematics and the psychomotor assessment] and the parent interview, and

practiced gaining cooperation and answering parents’ questions in Spanish (see section 5.7 on data

collection quality control.)

Spring-Kindergarten Training

Field supervisors and assessors were trained over a four-week period in March 1999. The

majority of field staff was trained primarily using a home study training program. The purpose of the

home study training program was to introduce changes to the instruments between the fall and spring data

collection and provide sufficient practice with the instruments and provide review and practice of the

procedures to conduct child assessments and parent interviews. An in-person attrition training session was

conducted for staff new to the study in the spring. This training session replicated the comprehensive fall

training program using the revised instruments for the spring data collection. In total, 101 field

supervisors and 321 assessors completed training.

Home Study Training Program. Field supervisor training was again conducted using the

FMS. The field supervisor home study program was 24 hours long. The topics covered in the field

supervisor training session included conducting the preassessment activities such as selecting the student

samples in converted schools, identifying children who moved from the originally sampled school,

identifying regular and special education teachers, linking them to children and distributing teacher

questionnaires, distributing school administrator questionnaires and completing the facility checklist, and

following quality control observation procedures. The field supervisors again entered training information

into the FMS during the home study training exercises. The topics covered in the field supervisor training

session included contacts with schools, conducting the preassessment activities, identifying children no

longer in their originally sampled school, and following quality control observation procedures. A total of

89 field supervisors completed the home study training.

Child assessment home study materials included written exercises, self-study role-plays,

scripted role-plays with partners, a training video focusing on improving assessment skills, and a final
5-7

practice direct child assessment observed by the field supervisor. Parent interview home study materials

included written exercises and trainees practicing the parent interviews in pairs on the telephone and

completing a final role-play on the parent interview with their field supervisor. Assessors again entered

training information into the CAPI system on laptop computers using role-play scripts to complete the

home study exercises. The assessor home study program was 16 hours long. Field supervisors ensured

that the assessors on their teams were proficient in conducting the direct child assessment by observing a

practice assessment with a child and the parent interview by conducting the final role play. A total of 223

assessors completed the home study training (see section 5.7 on data collection quality control) .

Attrition Training Session. The in-person attrition training for new interviewers was held

in March 1999 for nine days. The fall-kindergarten training materials were slightly modified for this

attrition training to incorporate some spring-kindergarten changes, such as the elimination of the

psychomotor assessment in the direct child assessment. The new spring-kindergarten parent and child

home study materials were also incorporated into the attrition training sessions. A total of eight field

supervisors and 98 assessors completed the attrition training.

5.4 Fall-Kindergarten Data Collection

5.4.1 Advance Mailings

Beginning in late summer 1998, letters were mailed to school administrators to confirm

scheduled visits for the schools. A packet of material was also mailed to the school coordinators asking

them to prepare for the preassessment visit to the school. Beginning in September, field supervisors called

school coordinators to confirm the dates of the preassessment and assessment visits, answer any

questions, and prepare for the preassessment visits. The school coordinators were asked to prepare a list

of kindergartners for selecting the sample and to distribute materials such as the study brochure, summary

sheets describing the role of teachers and parents in the study, and a letter to teachers to the kindergarten

teachers.
5-8

5.4.2 Preassessment Visits

Most preassessment visits were scheduled two weeks before the assessment visit to allow

time to contact parents to obtain their consent for the sampled children to participate in the study. During

the preassessment visit, the field supervisor met with the school coordinator to obtain the lists of

kindergarten children and teachers, draw the student sample (see chapter 4, section 4.2.4), meet with

kindergarten teachers to secure their cooperation, distribute teacher questionnaires, and identify home

language and accommodations information about the sampled children. In year-round schools, which

comprised four percent of cooperating schools, field supervisors selected the child sample across the

multiple tracks within the school. Field supervisors also prepared and distributed parent information

packets that included consent forms.

Distribution of Teacher Questionnaires

During the meeting with the kindergarten teachers, the self-administered teacher

questionnaires were distributed. All kindergarten teachers were asked to complete teacher questionnaire A

and teacher questionnaire B; teacher questionnaire C was child-specific and completed by the teacher

about each of the sampled children. If more than one teacher was in the kindergarten classroom,

supervisors determined the primary teacher for completing teacher questionnaire C. The primary teacher

was defined as the teacher who had primary academic responsibility for the children and could best report

on the information being sought about the sampled children. Field supervisors made arrangements with

the kindergarten teachers to collect completed questionnaires during the assessment visit.

Identifying Home Language and Accommodations for the Direct Assessment of

Sampled Children

Field supervisors obtained information about each child who was scheduled to be assessed to

determine whether the measure of oral English language (OLDS) would be administered and whether any

administrative accommodations had to be made in the direct assessment. The reading and general

knowledge assessment batteries were designed to be administered only in English. The warm-up booklet,

mathematics, and psychomotor assessments were, however, translated into Spanish for children who

could not be assessed in English but were able to take the assessment in Spanish. To determine whether
5-9

language minority children could be validly and reliably assessed in English using the core direct

assessment battery, the OLDS, a measure of basic oral English proficiency, was administered (see

chapter 2, section 2.1 for details on the OLDS).

To determine whether languages other than English were used in the home, the school

coordinator was asked to consult school records of the sampled children. If the information was not in the

school records, the field supervisor asked the teachers to report on home language during the group

teacher meeting. A series of four questions was used to ask the teacher about the children’s home

language:

 What are the names of the children in your classroom who speak a language other

than English?

 What other children in the classroom have families who speak a language other than

English in the home?

 What other children in your classroom have you observed participating in a

conversation with peers or adults where a language other than English was used?

 What language other than English does the child speak?

Children identified with a home language other than English were first assessed using the

English OLDS. Depending on the child’s English OLDS score, the laptop was programmed to administer

the appropriate version of the assessment (see section 5.4.3 for details on data collection for children with

a home language other than English).

Field supervisors identified what accommodations, if any, needed to be made for children

with disabilities to administer the direct assessment battery appropriately. For the purpose of the ECLS-K,

children with disabilities are those who meet the federal eligibility requirements for participation in

special education programs or services. All children with disabilities are expected to have an

Individualized Education Plan (IEP), an Individualized Family Service Plan (IFSP), or a 504 Plan on file

with the school district as it is a required component of the eligibility process.

The ECLS-K permitted the following accommodations for children with disabilities:

 Setting. Any environmental modification typically used by the student to do his or her

schoolwork, such as special lighting, a quiet room, an adaptive chair or table was used

for the assessment.
5-10

 Scheduling/timing. Assessments of children with disabilities were scheduled at

particular times of the day that were best suited for the child or for longer or shorter

(split) periods.

 Health care aides (personal attendants). Individuals who were actually written into

the student’s IEP to perform a variety of services for both the student and his or her

teacher were allowed to be present during the assessment, but they could not provide

answers or hints to the student. A form entitled “Guidelines for Health Care Aides”

was reviewed with the aide prior to the assessment.

 Assistive devices. Assistive devices such as a brace, a hearing aid, a cane, or a voice

synthesizer were legitimate to use during the ECLS-K to improve access to the

assessment without providing answers.

Braille administration, enlarged print, and sign language administration were not valid

accommodations for the ECLS-K. Children who required these accommodations were excluded from the

direct assessment portion of the ECLS-K. These children, however, remain in the sample and all other

data are captured for them.

A series of questions was asked of the teacher to determine if the student would be:

 Included in the assessment without accommodations;

 Included in the assessment with accommodations; or

 Excluded from a part or all of the assessment.

The questions were:

1. What are the names of the children in your class who have an IEP, IFSP, or 504 Plan

on file?

2. Does the child’s IEP or equivalent state that the child cannot participate in

standardized assessments?

3. Does the child normally use and require any of the accommodations that are NOT

offered in the ECLS-K? (Braille, enlarged print, sign language)?

4. Does the child normally use any of the accommodations that ARE offered in the

ECLS-K (setting, scheduling/timing, health care aide, assistive device)?

5. What are the names of the children in your class who cannot perform the following

fine and gross motor tasks?

a. Hold a pencil or crayon to make marks on a piece of paper?
5-11

b. Grasp small objects and release them at will?

c. Demonstrate his or her skill at hopping, skipping, and/or walking backward due

to impaired mobility or use of assistive devices such as wheelchairs, crutches,

and braces?

6. What are the names of the children in your class who normally wear glasses or

hearing aids?

7. Are there any children with other kinds of special needs that should be noted for the

assessment (e.g., medications, allergies, shyness, etc.)?

Question 5 was asked to determine if the child could perform the fine and gross motor

activities for the psychomotor assessment. If the child had an IEP or equivalent that required the use of an

accommodation not offered by the ECLS-K, or could not perform the question 5 activities, the laptop

computer was programmed to exclude the child from those sections of the assessment battery in which the

child could not participate.

Obtaining Parent Contact Information and Consent

Obtaining parental contact information and consent was another important procedure of the

preassessment period. Parent contact information was necessary to obtain consent and to conduct the

parent interview. Two types of consent forms—implicit and explicit—were developed for the ECLS-K.

Implicit consent did not require parent/guardian signature for the child to participate. Approximately onehalf of the cooperating schools required implicit parent consent forms. If the consent form was not

returned to the school with a signature and indicating that the child was not to participate by the date

recorded, permission for the child to participate was assumed. Explicit consent required a

parent/guardian’s signature to allow his or her child to participate in the study. The consent form was

included in parent information packets, which were mailed directly to the parents of sampled children.

The study followed the consent procedure required by the school or district.

Preassessment visits to schools requiring explicit consent were scheduled as early as possible

in the field period to allow for sufficient time to obtain consent before the assessment date. If a parent

indicated that he or she did not want the child to participate in the assessment or did not return the consent

form, refusal conversion attempts were made. Generally, a team member would place up to four calls to

the home to gain the parent’s cooperation. If there was no positive response to the telephone contacts, a

visit to the home was made to encourage participation. The assessment team also attempted to contact
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parents to obtain parent consent while they were dropping off their children before school or picking them

up after school. If the parent refusal could not be converted, the child was not assessed.2

5.4.3 Conducting the Direct Child Assessment

For the fall-kindergarten wave, the direct child assessment was administered during a

14-week field period that began in September and ended in early December. In year-round schools,

assessment teams made multiple visits to the school to conduct direct child assessments. The assessment

team visited the school when each track was in session to assess the sampled children. The direct child

assessment was normally conducted in a school classroom or library. Before conducting the assessment,

field supervisors and assessors set up the room for the assessment. They followed procedures for meeting

children that were agreed upon during the preassessment visit at the school. Each child was signed out of

his or her classroom prior to the assessment and signed back into the classroom upon the conclusion of

the assessment. When scheduling schools in the fall and the spring, an attempt was made to conduct the

direct child assessments at about the same point in time from the beginning of school year and at the end

of the year to increase the chances that children’s exposure to instruction was about the same for all

children.

The direct child assessment took approximately 50 to 70 minutes per child. Children

identified as having a home language other than English were administered the OLDS prior to the

assessment. Children passing the cut score for the OLDS were administered the English direct child

assessment. Children who fell below the cut score for the OLDS and whose language was Spanish were

administered the Spanish language version of the OLDS and parts of the direct child assessment that were

translated into Spanish (the warm-up booklet, math, and psychomotor). These children also had their

height and weight measured. Children who fell below the cut score for the OLDS and whose language

was other than Spanish had only their height and weight measured. Table 5-1 presents the percentage of

children who were routed into the various assessment alternatives in fall-kindergarten. Overall 15 percent

of the sampled children were screened using the OLDS in the fall-kindergarten. Of the children whose

home language was Spanish, 42 percent were at or above the cut score, and of the children whose home

language was a language other than English or Spanish, 61 percent were at or above the cut score.

 

2 Schools requiring signed explicit parent consent forms accounted for 69 percent of the final refusals.
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Table 5-1. OLDS routing results in fall-kindergarten

Total screened

(%)

At or above cut score on OLDS

(%)

Below cut score on OLDS

(%)

Total screened 15 49 51

Spanish language 62 42 58

Other language 38 61 39

Fewer than 1 percent of participating children were excluded from the direct child

assessment or required an accommodation offered in the assessment. Table 5-2 presents the number of

children excluded from or requiring an accommodation to the direct child assessment.

Table 5-2. Number of children excluded from or accommodated in the fall-kindergarten assessment

Number of children

Excluded for disability 88

Setting 48

Scheduling/timing 93

Health care aide 37

Assistive device 4

A very small number of children who participated in the ECLS-K assessment were excluded

from different parts of the psychomotor assessment because they could not physically perform the task.

Table 5-3 presents the number of children excluded from different parts of the psychomotor assessment.

Table 5-3. Number of children excluded from parts of the psychomotor assessment

Number of children

Excluded from gross motor assessment 70

Excluded from fine motor assessment 5

Excluded from both gross and fine motor assessment 7
5-14

5.4.4 Conducting the Parent Interview

For the fall-kindergarten wave, the parent interview was administered from September 1998

through January 1999. The parent interview averaged 50 minutes. To administer the parent interview,

assessors began by locating parents using the contact information obtained from the school. The contact

information contained parents’ names, addresses, and telephone numbers. Upon completing the interview,

parents were mailed a thank-you letter and a copy of “Learning Partners – A Guide to Educational

Activities for Families,” May 1997, U.S. Department of Education, Office of Educational Research and

Improvement.

The ECLS-K parent interview was conducted primarily by telephone, by field staff using a

CATI. The parent interview was conducted in person if the respondent did not have a telephone. Three

percent of all completed parent interviews were conducted in person. The parent interview was conducted

primarily in English, but provisions were made to interview parents who spoke only Spanish, Lakota,

Hmong, or Chinese. The questionnaire was translated into Spanish, Lakota, Hmong, and Chinese and

printed on a hard copy parent interview form. Bilingual interviewers were trained to conduct the parent

interview in either English or the other language they spoke. When the person answering the telephone

was not able to speak English, and the field staff were not bilingual and were unable to identify an

English-speaking household member, the case was coded as a “language problem.” The field supervisor

reviewed the case and assigned it to a bilingual field staff person if the language was offered by the

ECLS-K. Slightly more than seven percent of the parent interviews were conducted in a language other

than English; 94 percent of the non-English parent interviews were conducted in Spanish. One percent of

parent interviews could not be conducted because of language problems.

In order to build response rates for the parent interview, a special refusal conversion effort

was implemented. A refusal conversion workshop was developed from three focus groups with field staff

to determine the types of resistance being expressed by parents. The focus group information was used to

generate a framework for the workshop materials and procedures. A written conference guide was

distributed to field staff identified as effective at converting refusals, and they completed written exercises

on refusal conversion. Conference calls were held with the identified field staff to review the refusal

conversion exercises and discuss strategies for converting refusals.

In addition to the specialized field staff training, a parent refusal conversion mailing was

prepared that included a refusal conversion letter; copies of letters from participating school principals
5-15

and kindergarten teachers endorsing the study; and the parent thank-you incentive, “Learning Partners –

A Guide to Educational Activities for Families.” These endorsement letters from principals and teachers

were addressed to the parents in the school with very positive statements about the study, encouraging the

parents to cooperate and participate in the ECLS-K. The school principals and teachers agreed to allow

the use of their letters and quotes about the study. The refusal conversion cases were assigned to a group

of specially trained field staff. All refusal cases were reviewed by the field managers, and a refusal

conversion strategy was discussed with the refusal converter. The refusing parents were all called, and a

conversion attempt was made. The refusal conversion effort was implemented for a month, from

December 20, 1998, to January 19, 1999, and response rates improved by five percentage points.

5.4.5 Teacher Data Collection

Data were collected from teachers between September and December 1998. Field

supervisors distributed the teacher questionnaires to kindergarten teachers in the sampled schools during

the preassessment visit. The field supervisors collected the teacher questionnaires while at school during

the assessment visits. Field supervisors contacted teachers by telephone or by visiting the school to collect

completed teacher questionnaires that were not available during the assessment visits. Teachers were

asked to complete individual ratings for the sampled children in their classrooms, and they were

reimbursed five dollars for each child rating (Teacher Questionnaire C) they completed.

5.5 Fall-Kindergarten Response Rates

Table 5-4 presents the number of schools cooperating during the fall-kindergarten data

collection, as well as weighted and unweighted before-substitution school response rates. Substitute

schools were recruited in PSUs where the public school response rate was low, i.e., where the unweighted

school response rate was less than 65 percent and fewer than six originally selected schools were

cooperating. Substitutes were purposively selected. For each refusing school, the frame was searched for

a suitable replacement school, if any, based on characteristics such as size, percent minority, and type of

locale. The number of cooperating schools was increased from 879 to 953 by recruiting substitutes.

Substitutes were not selected for private schools. The ECLS-K design called for 20 percent of the base

year student sample to come from private schools. Projections indicated that 20 percent would be attained
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Table 5-4. The ECLS-K, fall-kindergarten: number of cooperating schools and before-substitution

school-level response rates

Number cooperatingb Before-substitution response rates

School characteristica

Before

substitution

After

substitution

School weight ×

KG enrollment Unweighted

All schools 879 953 69.4 68.8

School type

Public 641 715 70.1 70.1

Private 238 238 65.9 65.6

Catholic 100 100 83.0 83.3

Other private 138 138 56.2 56.8

Type of locale

Large city 191 198 78.1 78.0

Mid-size city 180 197 73.6 72.9

Urban fringe of large city 255 271 67.8 66.8

Urban fringe of mid-size city 59 66 62.1 60.8

Large town 20 20 57.5 60.6

Small town 71 82 65.3 63.4

Rural 103 119 64.2 64.0

School size (Kindergartners)

1 to 12 55 55 74.5 69.6

13 to 24 96 98 63.2 64.0

25 to 36 75 79 71.5 74.3

37 or more 653 721 69.7 69.0

Percent minority enrolled

0 – 10% 225 249 66.1 64.8

11 – 49% 285 324 68.4 67.5

50 – 89% 180 188 70.9 72.0

90 – 100% 135 138 78.5 78.5

Unknown 54 54 62.9 62.8

Region

Northeast 160 181 65.1 65.8

Midwest 231 238 77.4 77.5

South 268 294 67.0 64.1

West 220 240 68.7 69.2

High grade

Trans K, K or trans 1 40 40 78.4 78.4

01-12 800 874 69.4 69.0

Ungraded 4 4 100.0 100.0

Unknown 35 35 57.5 56.5

aBased on frame data.

bOne or more children or parents participated, or school is one of two schools in the Archdiocese of New Orleans where student sampling was

performed too late in fall to field child or parent data collection.
5-17

through the private school freshening process (see section 4.2.3), without resorting to substitution, and

those projections were later borne out by experience. The response rates presented in table 5-4 reflect

levels of school cooperation prior to substitution. The presence of substitutes increases the sample size

and, if chosen skillfully, may also reduce nonresponse bias. However, the bedrock upon which the study’s

quality rests is the originally selected sample of schools.

Since the primary analytic focus of the ECLS-K is the child, weighted school response rates

are reported in terms of eligible children. For example, overall, the kindergartners enrolled in the ECLS-K

fall-kindergarten cooperating schools are weighted up to 69.4 percent of the fall 1998 kindergarten

population. The unweighted response rate is 68.8 percent. The weighted response rate for public schools

(70.1 percent) was about four points higher than for private (65.9 percent). However, within the private

school domain there was a much greater disparity between Catholic schools (83.0 percent) and other

private schools (56.2 percent). During the computation of sampling weights, nonresponse adjustment cells

were constructed with an eye toward reducing the potential biases associated with these rates.

Table 5-5 presents weighted and unweighted child-level completion rates for fallkindergarten data collection, broken out by school characteristics. A completion rate is a response rate

conditioned on the results of an earlier stage of data collection. For example, a weighted 89.9 percent of

all children sampled in cooperating schools completed the child assessment. Of course as noted earlier,

not all sampled schools cooperated. In this table and the ones that follow, data from cooperating substitute

schools have been included along with those from original schools. Since parent and teacher data appear

on the child data file (see chapter 8), rates have been presented at the child-level for these instruments as

well. If one looks at subpopulations defined by school characteristics, it is striking how characteristics

that were important during school recruitment are no longer so for schools that agreed to participate. For

example, comparing public and private schools, the completion rate for the child assessment differs only

by 0.3 percent, with private schools having the higher rate (90.2 vs. 89.9), and Catholic and other private

schools differ by 2.2 percent (91.3 vs. 89.1).

Table 5-6 continues presenting child-level fall-kindergarten weighted and unweighted

completion rates, this time broken out by child characteristics. Again, completion rates do not differ

greatly except possibly for the parent interview where Asian and Pacific Islander parents responded at a

rate of 74.1 percent, largely because of language problems, in contrast with a rate of 85.3 percent for

parents overall.
5-18

Table 5-5. The ECLS-K fall-kindergarten: number of completed child-level cases and child-level completion rates, by sc

Child assessment Parent interview

Completion rates Comple

School characteristica Completesb Weighted Unweighted Completesc Weighted 

All children 19,173 89.9 89.8 18,097 85.3 School type

Public 15,229 89.9 89.7 14,283 85.0 Private 3,944 90.2 90.1 3,814 87.5 

Catholic 2,007 91.3 91.3 1,947 88.6 Other private 1,937 89.1 88.9 1,867 86.5 

Type of locale

Large city 3,954 88.2 88.1 3,611 80.8 Mid-size city 4,135 91.1 91.1 3,977 87.8 Urban fringe of large city 5,452 89.5 89.0 5,092 84.4 Urban fringe of mid-size city 1,359 89.5 90.1 1,302 85.6 Large town 416 91.8 92.0 415 91.9 Small town 1,601 91.0 91.1 1,532 86.9 Rural 2,256 90.8 90.6 2,168 87.6 

School size (Kindergartners)

1 to 12 366 86.1 85.7 358 85.7 13 to 24 1,550 90.5 91.7 1,478 86.9 25 to 36 1,640 91.8 91.6 1,577 88.0 37 or more 15,617 89.8 89.5 14,684 84.9 

Percent minority enrolled

0 – 10% 5,134 90.0 89.9 5,027 88.2 11 – 49% 6,704 90.0 89.6 6,405 85.8 50 – 89% 3,637 90.0 89.8 3,381 84.6 90 – 100% 2,905 89.9 89.7 2,514 78.8 Unknown 793 88.9 90.3 770 87.9 

Region

Northeast 3,605 89.0 88.9 3,373 83.4 Midwest 4,769 89.8 90.1 4,575 86.1 South 6,126 91.5 91.1 5,853 87.7 West 4,673 88.4 88.3 4,296 82.3 

High grade

Trans K, K or trans 1 552 90.4 88.9 526 85.8 01-12 18,174 90.0 89.9 17,135 85.3 Ungraded 40 74.6 71.4 35 56.7 Unknown 407 90.0 89.8 401 88.7 
5-19

Table 5-5. The ECLS-K, fall-kindergarten: number of completed child-level cases and child-level completion rates, by sc

(continued)

Teacher questionnaire A Teacher questionnaire B 

Completion rates Completion rates 

School characteristica Completesd Weighted Unweighted Completesd Weighted Unweighted Completes

All children 19,444 90.8 91.0 20,647 96.6 96.7 19,492 School type

Public 15,468 90.7 91.1 16,382 96.6 96.5 15,489 Private 3,976 91.2 90.9 4,265 97.1 97.5 4,003 

Catholic 2,015 91.1 91.7 2,186 99.4 99.5 2,036 Other private 1,961 91.4 90.0 2,079 95.0 95.5 1,967 

Type of locale

Large city 4,022 88.8 89.7 4,215 93.1 94.0 3,996 Mid-size city 4,314 95.7 95.0 4,496 99.4 99.1 4,238 Urban fringe of large city 5,372 89.0 87.7 5,836 95.6 95.3 5,522 Urban fringe of mid-size city 1,452 97.0 96.3 1,497 99.4 99.3 1,388 Large town 425 96.3 94.0 452 100.0 100.0 423 Small town 1,589 91.3 90.4 1,746 99.7 99.4 1,631 Rural 2,270 85.3 91.2 2,405 95.7 96.6 2,294 

School size (Kindergartners)

1 to 12 415 97.8 97.2 420 98.4 98.4 371 13 to 24 1,561 82.3 92.4 1,604 92.0 94.9 1,564 25 to 36 1,660 92.3 92.7 1,766 98.5 98.6 1,668 37 or more 15,808 91.3 90.6 16,857 96.9 96.6 15,889 

Percent minority enrolled

0 – 10% 5,190 91.3 90.9 5,601 98.4 98.1 5,196 11 – 49% 6,808 91.8 91.0 7,254 97.1 97.0 6,804 50 – 89% 3,856 95.2 95.2 3,948 97.8 97.5 3,744 90 – 100% 2,839 87.7 87.7 2,972 91.1 91.8 2,943 Unknown 751 73.4 85.5 872 95.4 99.3 805 

Region

Northeast 3,657 90.8 90.2 3,858 95.3 95.2 3,626 Midwest 4,997 94.0 94.4 5,203 98.4 98.3 4,858 South 6,209 91.2 92.4 6,515 96.7 96.9 6,258 West 4,581 86.9 86.6 5,071 95.8 95.8 4,750 

High grade

Trans K, K or trans 1 527 88.1 84.9 615 99.6 99.0 561 01-12 18,464 90.9 91.3 19,536 96.6 96.6 18,479 Ungraded 43 70.1 76.8 43 70.1 76.8 38 Unknown 410 92.9 90.5 453 100.0 100.0 414 

aBased on frame data.bReading, math, or general knowledge assessment was scorable or child was language minority (not Spanish) and scored below cut score in OLDS or disabled (IEP).cFamily structure portion of parent interview was completed.dQuestionnaire was not blank.
5-20

Table 5-6. The ECLS-K, fall-kindergarten: number of completed child-level cases and child-level completion rates, by chi

Child assessment Parent inte

Completion rates Co

Child characteristica Completesb Weighted Unweighted Completesc Weighte

All children 19,173 89.9 89.8 18,097 85.3 Gender

Male 9,751 89.5 89.4 9,171 84.6 Female 9,367 90.4 90.2 8,884 86.3 Unknown gender 55 79.1 76.4 42 52.7 

Race-ethnicity

White (not Hispanic) 10,975 90.2 90.2 10,692 87.8 Black (not Hispanic) 3,021 90.8 90.6 2,784 84.3 Hispanic 2,631 89.6 89.4 2,428 82.7 Asian or Pacific Islander 1,652 88.6 88.5 1,392 74.1 American Indian or Alaskan Native 339 93.4 93.1 292 80.0 Other 221 83.3 85.3 206 79.5 Unknown race-ethnicity 334 79.5 78.0 303 68.9 

Year of birth

1992 5,466 91.3 91.1 5,206 87.2 1993 13,410 89.8 89.8 12,611 85.0 1994 53 83.5 86.9 49 77.6 Other/unknown 244 71.7 67.8 231 65.1 

Kindergarten type

Full-day 10,640 90.4 90.2 10,061 85.8 Part-day 8,533 89.5 89.5 8,036 84.9 Kindergarten type unknown 0 0.0 0.0 0 0.0 
5-21

Table 5-6. The ECLS-K, fall-kindergarten: number of completed child-level cases and child-level completion rates, by chi

(continued)

Teacher questionnaire A Teacher questionnaire B T

Completion rates Completion rates 

Child characteristica Completesd Weighted Unweighted Completesd Weighted Unweighted Complet

All children 19,444 90.8 91.0 20,647 96.6 96.7 19,492 Gender

Male 9,871 90.3 90.5 10,536 96.6 96.6 9,942 Female 9,524 91.6 91.7 10,062 96.9 96.9 9,504 Unknown gender 49 59.6 68.1 49 59.6 68.1 46 

Race-ethnicity

White (not Hispanic) 11,114 91.0 91.4 11,861 97.5 97.5 11,104 Black (not Hispanic) 3,089 92.8 92.7 3,224 96.9 96.7 3,103 Hispanic 2,579 88.1 87.6 2,776 94.1 94.3 2,673 Asian or Pacific Islander 1,731 92.9 92.7 1,816 97.4 97.3 1,721 American Indian or AlaskanNative 359 98.6 98.6 337 94.0 92.6 344 Other 237 89.3 91.5 251 97.1 96.9 225 Unknown race-ethnicity 335 78.8 78.3 382 87.3 89.3 322 

Year of birth

1992 5,551 91.9 92.5 5,845 97.1 97.4 5,552 1993 13,581 90.8 90.9 14,449 96.8 96.8 13,635 1994 54 90.7 88.5 57 92.9 93.4 52 Other/unknown 258 71.1 71.7 296 83.0 82.2 253 

Kindergarten type

Full-day 10,930 93.0 92.6 11,370 96.4 96.4 10,866 Part-day 8,514 88.4 89.3 9,277 97.1 97.3 8,626 Kindergarten type unknown 0 0.0 0.0 0 0.0 0.0 0 

aDemographic data were captured from school records prior to student sampling.bReading, math, or general knowledge assessment was scorable or child was language minority (not Spanish) and scored below cut score in OLDS or disabled (IEP).

cFamily structure portion of parent interview was completed.dQuestionnaire was not blank.
5-22

Table 5-7 presents the results of the fall-kindergarten teacher data collection, with teachers,

not children, considered the unit of analysis. As all kindergarten teachers within a school were sampled,

the weight used to calculate weighted completion rates is the school weight. Teachers were slightly more

willing to complete questionnaire B that asked for personal background information (94.3 percent overall)

than questionnaire A that focused on classroom characteristics (90.8 percent).

The school-level response rates presented in table 5-4 can be combined with the child-level

and teacher-level completion rates from tables 5-5 and 5-7 to produce overall response rates at the child

and teacher levels, by school characteristic. Table 5-8 displays the results at the child level. Children

enrolled in Catholic schools are noteworthy for having high response rates across all survey instruments.

Also notable are the relatively high response rates for children in schools with over 90 percent minority

enrollment, schools in the Midwest, and schools whose highest grade is kindergarten. Table 5-9 presents

overall response rates at the teacher level, by school characteristic. The same subgroups exhibit a better

than average level of response.

5.6 Spring-Kindergarten Data Collection

5.6.1 Advance Contact with Respondents

In March 1999, letters were mailed to school administrators confirming the scheduled visits

for the school that were set up in the fall. Letters were mailed to the school coordinators reminding them

of the upcoming visits to the school. Letters were also mailed to parents reminding them of the spring

data collection activities.

5.6.2 Preassessment Contact

Field supervisors conducted most preassessment activities by telephone except in the schools

that had refused to participate in the fall-kindergarten collection but had agreed to participate in the spring

collection. In these converted schools, field supervisors made personal visits to select the child sample

and complete the other preassessment activities as described for fall-kindergarten data collection.
5-23

Table 5-7. The ECLS-K, fall-kindergarten: number of completed teacher questionnaires and teacherlevel completion rates

Teacher questionnaire A Teacher questionnaire B

Completion rates Completion rates

School characteristica Completesb Weighted Unweighted Completesb Weighted Unweighted

All schools 2,931 90.8 90.4 3,047 94.3 94.0

School type

Public 2,577 90.3 90.2 2,684 94.2 93.9

Private 354 93.3 91.9 363 94.7 94.3

Catholic 146 92.8 94.2 152 97.2 98.1

Other private 208 93.5 90.4 211 93.5 91.7

Type of locale

Large city 668 90.5 87.2 686 92.4 89.6

Mid-size city 599 95.5 94.9 614 97.7 97.3

Urban fringe of large city 854 88.3 88.1 903 93.1 93.2

Urban fringe of mid-size

city 230 95.8 95.8 237 99.0 98.8

Large town 44 95.6 93.6 45 96.7 95.7

Small town 262 95.1 94.6 273 98.2 98.6

Rural 274 84.0 87.8 289 89.7 92.6

School size (Kindergartners)

1 to 12 60 94.2 93.8 61 95.1 95.3

13 to 24 139 84.7 90.3 140 87.3 90.9

25 to 36 101 92.9 93.5 105 96.6 97.2

37 or more 2,631 91.2 90.2 2,741 95.0 94.0

Percent minority enrolled

0 – 10% 650 91.5 91.9 679 96.0 96.0

11 – 49% 999 93.4 92.7 1,024 95.4 95.0

50 – 89% 662 90.9 92.2 684 92.9 95.3

90 – 100% 525 86.9 84.0 548 89.9 87.7

Unknown 95 82.5 83.3 112 97.6 98.2

Region

Northeast 437 89.6 89.2 457 92.4 93.3

Midwest 573 95.3 93.9 589 95.9 96.6

South 1,129 91.2 92.4 1,156 95.1 94.6

West 792 87.0 86.1 845 92.9 91.8

High grade

Trans K, K or trans 1 130 90.9 86.7 145 96.4 96.7

01-12 2,743 90.6 90.5 2,842 94.1 93.8

Ungraded 11 91.7 91.7 11 91.7 91.7

Unknown 47 96.0 95.9 49 100.0 100.0

aBased on frame data.

bNonblank questionnaire.
5-24

Table 5-8. The ECLS-K, fall-kindergarten: number of completed child-level cases and overall childlevel response rates, by school characteristic

Child assessment Parent interview

Overall response rates Overall response rates

School characteristica Completesb Weighted Unweighted Completesc Weighted Unweighted

All children 19,173 62.4 61.8 18,097 59.2 58.3

School type

Public 15,229 63.0 62.9 14,283 59.6 59.0

Private 3,944 59.4 59.1 3,814 57.7 57.2

Catholic 2,007 75.8 76.1 1,947 73.5 73.8

Other private 1,937 50.1 50.5 1,867 48.6 48.7

Type of locale

Large city 3,954 68.9 68.7 3,611 63.1 62.8

Mid-size city 4,135 67.0 66.4 3,977 64.6 63.9

Urban fringe of large city 5,452 60.7 59.5 5,092 57.2 55.5

Urban fringe of mid-size city 1,359 55.6 54.8 1,302 53.2 52.5

Large town 416 52.8 55.8 415 52.8 55.6

Small town 1,601 59.4 57.8 1,532 56.7 55.3

Rural 2,256 58.3 58.0 2,168 56.2 55.7

School size (Kindergartners)

1 to 12 366 64.1 59.6 358 63.8 58.3

13 to 24 1,550 57.2 58.7 1,478 54.9 56.0

25 to 36 1,640 65.6 68.1 1,577 62.9 65.5

37 or more 15,617 62.6 61.8 14,684 59.2 58.1

Percent minority enrolled

0 – 10% 5,134 59.5 58.3 5,027 58.3 57.1

11 – 49% 6,704 61.6 60.5 6,405 58.7 57.8

50 – 89% 3,637 63.8 64.7 3,381 60.0 60.1

90 – 100% 2,905 70.6 70.4 2,514 61.9 61.0

Unknown 793 55.9 56.7 770 55.3 55.1

Region

Northeast 3,605 57.9 58.5 3,373 54.3 54.7

Midwest 4,769 69.5 69.8 4,575 66.6 67.0

South 6,126 61.3 58.4 5,853 58.8 55.8

West 4,673 60.7 61.1 4,296 56.5 56.2

High grade

Trans K, K or trans 1 552 70.9 69.7 526 67.3 66.4

01-12 18,174 62.5 62.0 17,135 59.2 58.4

Ungraded 40 74.6 71.4 35 56.7 62.5

Unknown 407 51.8 50.7 401 51.0 50.0

aBased on frame data.

bReading, math, or general knowledge assessment was scorable or child was language minority (not Spanish) and scored below cut score in

OLDS or disabled (IEP).

cFamily structure portion of Parent Interview was completed.

dQuestionnaire was not blank.
5-25

Table 5-9. The ECLS-K, fall-kindergarten: number of completed child-level cases and overall child-level response rates, by school 

Teacher questionnaire A Teacher questionnaire B T

Overall response rates Overall response rates 

School characteristica Completesd Weighted Unweighted Completesd Weighted Unweighted Completesd 

All children 19,444 63.0 62.6 20,647 67.0 66.5 19,492 

School type

Public 15,468 63.6 63.9 16,382 67.7 67.6 15,489 Private 3,976 60.1 59.6 4,265 64.0 64.0 4,003 

Catholic 2,015 75.6 76.4 2,186 82.5 82.9 2,036 Other private 1,961 51.4 51.1 2,079 53.4 54.2 1,967 

Type of locale

Large city 4,022 69.4 70.0 4,215 72.7 73.3 3,996 Mid-size city 4,314 70.4 69.3 4,496 73.2 72.2 4,238 Urban fringe of large city 5,372 60.3 58.6 5,836 64.8 63.7 5,522 Urban fringe of mid-size city 1,452 60.2 58.6 1,497 61.7 60.4 1,388 Large town 425 55.4 57.0 452 57.5 60.6 423 Small town 1,589 59.6 57.3 1,746 65.1 63.0 1,631 Rural 2,270 54.8 58.4 2,405 61.4 61.8 2,294 

School size (Kindergartners)

1 to 12 415 72.9 67.7 420 73.3 68.5 371 13 to 24 1,561 52.0 59.1 1,604 58.1 60.7 1,564 25 to 36 1,660 66.0 68.9 1,766 70.4 73.3 1,668 37 or more 15,808 63.6 62.5 16,857 67.5 66.7 15,889 

Percent minority enrolled

0 – 10% 5,190 60.3 58.9 5,601 65.0 63.6 5,196 11 – 49% 6,808 62.8 61.4 7,254 66.4 65.5 6,804 50 – 89% 3,856 67.5 68.5 3,948 69.3 70.2 3,744 90 – 100% 2,839 68.8 68.8 2,972 71.5 72.1 2,943 Unknown 751 46.2 53.7 872 60.0 62.4 805 

Region

Northeast 3,657 59.1 59.4 3,858 62.0 62.6 3,626 Midwest 4,997 72.8 73.2 5,203 76.2 76.2 4,858 South 6,209 61.1 59.2 6,515 64.8 62.1 6,258 West 4,581 59.7 59.9 5,071 65.8 66.3 4,750 

High grade

Trans K, K or trans 1 527 69.1 66.6 615 78.1 77.6 561 01-12 18,464 63.1 63.0 19,536 67.0 66.7 18,479 Ungraded 43 70.1 76.8 43 70.1 76.8 38 Unknown 410 53.4 51.1 453 57.5 56.5 414 

aBased on frame data.bReading, math, or general knowledge assessment was scorable or child was language minority (not Spanish) and scored below cut score in OLDS or disabled (IEP).

cFamily structure portion of Parent Interview was completed.dQuestionnaire was not blank.
5-26

5.6.3 Conducting Direct Child Assessments

The spring direct child assessment was identical to the one used in the fall except that the

psychomotor assessment was dropped. The direct child assessments were conducted between March and

June 1999. For children with a language other than English in the home, the child’s score on the fallkindergarten OLDS determined what assessment was administered in spring. Table 5-10 summarizes the

OLDS routing patterns in spring-kindergarten.

Table 5-10. OLDS routing patterns in spring-kindergarten for fall-kindergarten respondents

Home Language

Fall-Kindergarten

OLDS Score

OLDS Required

in Spring-Kindergarten?

Spring

assessment path

English Not applicable No English

Spanish At or above cut

score

No English

Below cut score Yes

At or above cut score English

Below cut score Spanish

Other language At or above cut

score

No English

Below cut score Yes

At or above cut score English

Below cut score Height/Weight

Children who scored at or above the cut point on the OLDS in fall-kindergarten were

automatically routed by CAPI to take the assessment in English; the OLDS was not administered again.

Children who scored below the cut point in the OLDS in fall-kindergarten were administered the OLDS

again in spring-kindergarten and routed according to the new spring-kindergarten OLDS score. Children

taking the direct assessment for the first time in the spring, with a language other than English in the

home, were routed according to their home language, just as in fall-kindergarten (see section 5.4.3).

Table 5-11 presents the percentage of children who were routed into the various assessment alternatives

in spring-kindergarten. Eight percent of the sampled children were screened using the OLDS in the

spring-kindergarten. Overall almost one-half (42%) of the screened children were at or above the cut

score on the OLDS and were administered the English direct child assessment. Of the children whose

home language was Spanish, 36 percent were at or above the cut score, and of the children whose home

language was a language other than English or Spanish, 55 percent were at or above the cut score.
5-27

Table 5-11. OLDS routing results in spring-kindergarten

Total screened

(%)

At or above cut score on OLDS

(%)

Below cut score on OLDS

(%)

Total screened 8 42 58

Spanish language 69 36 64

Other language 31 55 45

As in the fall, children identified as requiring an accommodation for the direct child

assessment were provided the accommodation during the assessment. The patterns for accommodations

were the same as in the fall-kindergarten data collection. Less than one percent of children were excluded

from the spring direct child assessment due to physical or mental disability. Table 5-12 presents the

number of children excluded from or requiring an accommodation to the direct child assessment in the

spring of kindergarten.

Table 5-12. Number of children excluded from or accommodated in the spring-kindergarten assessment

Number of children

Excluded for disability 70

Setting 35

Scheduling/timing 70

Health care aide 8

Assistive device 5

5.6.4 Conducting the Parent Interview

Parent interview procedures mirrored those of the fall-kindergarten. The parent interview

was administered, primarily by telephone interview, from March through July 1999. The parent interview

averaged 65 minutes. As in the fall, the parent interview was conducted in person if the respondent did

not have a telephone. Three percent of all completed parent interviews were conducted in person. As in

the fall, seven percent of the completed parent interviews were conducted in a language other than

English with 61 percent of completed non-English interviews conducted in Spanish. The refusal

conversion workshop developed for fall-kindergarten was implemented at the end of the spring field

period to improve response rates. The special effort to build parent response rates was conducted between
5-28

June 15 and July 4, 1999, and yielded an additional two percentage points. Five percent of the cases were

not completed because of locating problems.

5.6.5 Teacher and School Data Collection

Data were collected from school administrators, regular classroom teachers, and special

education teachers between March and June 1999. Teachers were again asked to complete individual

ratings for the sampled children in their classrooms, and they were reimbursed seven dollars for each

child rating (teacher questionnaire C) they completed. In addition, school staff were asked to complete a

student record abstract after the school year closed. The school staff were reimbursed five dollars for

every student record abstract they completed. Field supervisors also completed a facilities checklist for

each sampled school.

Field supervisors distributed the school and teacher questionnaires in a variety of ways,

depending on the preference of the school staff. Questionnaires were distributed during the preassessment

visit (if one was held), by mail, and during the assessment visits. During the field period, field supervisors

followed up with school administrators and teachers by telephone and visits to the schools to ensure

completed questionnaires were mailed to Westat, the company contracted to conduct the survey. In late

June, a package was mailed to all schools with outstanding school administrator questionnaires, teacher

questionnaires, or student record abstracts with a request to complete and return questionnaires. Field staff

were prompted by telephone and in person for the return of school administrator questionnaires and

student record abstracts through October 1999.

Data about annual salary and benefits of identified teachers and principals in the sampled

schools were collected between April and July 1999 from public school district administrators,

headmasters of private schools, and Catholic dioceses or schools. The majority of teachers were identified

in early April 1999; a smaller number were identified and fielded in mid-May 1999. The package mailed

to these respondents included a list by school of the names of the principals and teachers about whom the

salary and benefit data were requested. Responses were returned by facsimile or in the included return

mailer. Respondents were prompted by telephone for the return of the salary and benefit information

through July 1999.
5-29

5.6.6 Conducting Data Collection on Children Who Withdrew from Their Original School

During the preassessment contacts, field supervisors asked school staff to identify children

who had withdrawn from the school since the fall of kindergarten. School staff were asked whether they

knew the name and address of the school the child transferred into as well as any new information about

the child’s household address. This information was entered into the FMS and processed at Westat for

refielding. A total of 1,568 children (8.2 percent) were identified as movers from their original school

between fall-kindergarten and spring-kindergarten. Different data collection strategies were followed for

children who moved, depending on how they were classified. Data collection was attempted for children

who moved as follows:

 Parent interviews were attempted regardless of children’s mover status.

 Data collected for children moving into cooperating sampled schools included the

child assessment in the school, school administrator questionnaire, regular or special

education teacher questionnaires, and student record abstract forms.

 Data collected for children moving into nonsampled schools in cooperating districts

included the child assessment in the school, school administrator questionnaire,

regular or special education teacher questionnaires, salary and benefits questionnaire,

and student record abstract forms.

 Data collected for children moving into sample schools that refused, schools in

sampled districts that refused, or ineligible sampled schools included only the direct

child assessment conducted in the home. No school or teacher data were collected.

 Data collected for children moving into schools in nonsampled districts or dioceses

depended on whether the school was within or outside of the PSU.

- For children in schools within the PSU, data collected included only the child

assessment in the home.

- For children in schools outside the PSU, no child or teacher data were

collected.

 For children who were not in school in the spring (home school or at home children),

data collected included the child assessment in the home, if the children were in the

sample PSU, and no data if they were outside the sample PSU.

Almost one-half (46.7 percent, 733 children) of the children who moved were identified as

moving into a school in the sampled ECLS-K PSUs, 18 percent moved into a school outside the PSU, 3

percent were identified as in home school, 27 percent of the movers were unlocatable because there was

not enough information from the parent locating information to search the frame, and for 5 percent the
5-30

schools could not be found on the frame. Of the 1,568 movers, data collection was attempted for 52

percent (816 children) of the located movers beginning in late April 1999. Almost 70 percent of the

movers were fielded for in-home assessments and the remaining 30 percent of movers were fielded for inschool assessment. Sixty-two percent (510 children) of the fielded movers had assessments completed, 31

percent were nonresponse, and 1 percent were classified as out-of-scope. Table 5-13 presents the

classification of the 1,568 children who moved and the number of completed assessments.

Table 5-13. Number of children who moved by category and assessment result

Child in Number of children Number of complete assessments

Cooperating sampled school 103 85

Refusal school 41 24

Nonsampled district within sample PSU 589 348

Nonsampled district outside sample PSU 302 0

Home school 45 32

Nonsampled private school 38 21

Unknown location 450 0

Total 1,568 510

5.6.7 Collection of Head Start Data

As mentioned in chapter 2, children’s Head Start participation data were collected in the fall

parent interview and in the spring-kindergarten student record abstract. The fall-kindergarten parent

interview included a directory of Head Start centers names and addresses to assist the respondent in

identifying the correct Head Start center name and address. The Head Start directory was sorted by state

and alphabetically within state. In the child care section of the respondent was asked if the child attended

Head Start in the year before kindergarten. If the answer was “Yes,” then the directory was searched for

the center name. If a match was found, the center name and address was verified with the respondent,

selected from the directory and the identification number stored as the response to the question. If the

center could not be found on the directory, then the field staff collected the name and address of the

center, the name of the director and where the Head Start center was located, for example, in its own

building, or in a school or church.
5-31

Head Start Data Collection-Spring-Kindergarten

In the student record abstract, collected in spring-kindergarten, one question asked whether

or not the sampled child had attended a Head Start center before entering kindergarten. If the answer was

“Yes,” then the school staff person was asked to record the name, address, and telephone number of the

Head Start center and the name of the Head Start director.

The process of verifying the Head Start centers and child participation began in the fall of

1999 and will continue through winter 2000.

5.7 Data Collection Quality Control

Data collection quality control efforts began with the development and testing of the

CATI/CAPI applications and FMS. As these applications were programmed, extensive testing of the

system was conducted. This testing included review by project design staff, statistical staff, and the

programmers themselves. This testing by staff members, representing different aspects of the project, was

designed to ensure that the systems were working properly from all perspectives. Quality control

processes continued with the development of field procedures that maximized cooperation and thereby

reduced the potential for nonresponse bias.

A live pilot test of the systems and procedures, including field supervisor and assessor

training, was conducted in April and May 1998 with 12 elementary schools in the Washington, DC,

metropolitan area. The purpose of the pilot test was to ensure that all the systems were working properly.

Modifications to the data collection procedures, training programs, and systems were made to improve

efficiency and reduce respondent burden. Modifications to the parent interview to address some issues

raised by pilot test respondents were also made at this time.

Quality control activities continued during training and data collection. During the assessor

training, field staff practiced conducting the parent interview in pairs and practiced the direct child

assessment with kindergarten children brought to the training site for this purpose. When the fieldwork

began, field supervisors observed each assessor conducting child assessments and made telephone calls to

parents to validate the interview. Field managers made telephone calls to the schools to collect
5-32

information on the school activities for validation purposes. A sample of the assessor-completed OLDS

score sheets were rescored in the home office for quality control purposes.

5.7.1 Child Assessment Observations

Field supervisors conducted on-site observations of the child assessments. During the fallkindergarten collection, one observation was completed for each assessor within the first two weeks of the

field period. In spring-kindergarten, two observations were completed for each assessor. The first

observation was within two weeks after the assessments began, and the second observation was

completed within three weeks of the first observation.

A standardized observation form was used to evaluate the assessor’s performance in

conducting the child assessment. The assessor was rated in three areas:

 Rapport building and working with the child – such as use of neutral praise and the

assessor’s response to various child behaviors.

 Cognitive assessment activities – such as reading questions verbatim, the use of

acceptable probes, the use of appropriate hand motions, and the absence of coaching.

 Specific assessment activities – such as correctly coding answers to open-ended

questions in the assessment, weighing and measuring the child correctly, and

following administration procedures.

The field supervisors recorded their observations on the form and then reviewed the form

with the assessor. The most frequent problems observed were not reading the items verbatim and

inappropriate gesturing. Feedback was provided to the assessors on the strengths and weaknesses of their

performance and, when necessary, remedial training was provided in areas of weakness. A training video

reinforcing appropriate gesturing and reading items verbatim was created and used in the spring training.

5.7.2 Parent Validations

Parent validation forms were generated for approximately ten percent of the completed

parent interviews. The first parent interview completed by an assessor was always validated. Over the

course of the field period, a running count of an assessor’s completed parent interviews was maintained,

and each tenth completed parent interview was selected for validation. This ensured that ten percent of
5-33

each assessor’s cases were selected for validation. The parent validation was approximately five minutes

long.

Field supervisors used a standardized parent validation script when calling the parents. The

script covered the following topics:

 Verification of the child’s name, date of birth, and gender; and

 Between eight and ten questions from the current round interview were re-asked of the

parent.

During the validation process, no evidence was found of parent interviews being falsified.

5.7.3 School Validations

To ensure that assessments proceeded smoothly, a validation call was completed with the

school principal in approximately ten percent of each supervisor’s assigned schools in both the fall- and

spring-kindergarten collections.

Field managers conducted the school validations by telephone. The first school that each

team completed was called to ascertain how well the preassessment and assessment activities went. If the

feedback from the school was positive, the fifth school that each team completed was called. If any

problems were indicated in the first validation call, immediate action was taken with the field supervisor.

The validation feedback was discussed with the supervisor and remedial action was provided, including

in-person observation of the supervisor’s next school if necessary.

Field managers used a standardized script when calling the school principals. The script

covered the following topics:

 How well the ECLS-K supervisor organized and executed the sampling tasks;

 An overall rating of how the assessments went;

 Feedback about the study from the children and kindergarten teachers;

 Suggestions for improving procedures and making it easier for a school to participate;

and

 General comments and suggestions.
5-34

5.7.4 Quality Control of the OLDS Scoring

The OLDS used to screen children for English language proficiency included the “Let’s Tell

Stories” subtest. This subtest involved reading the child a short story and having the child repeat it back to

the assessor. The child’s responses were recorded verbatim and scored by the assessor. Responses to this

subtest are unique to each child, and it was important for interviewers’ and coders’ scoring of the child’s

responses to match the preLAS®2000 standards.

ECLS-K assessors were trained to conduct the OLDS using audiotapes of the stories and

children’s responses to the stories. Assessors listened to the audiotaped stories and to the child’s

responses and recorded the child’s responses verbatim. Then the assessor scored the story using the

preLAS®2000 rules. Reasons for scoring each story a particular way were discussed in detail. Differences

between the assessor’s scores and the correct scores were discussed during training, so assessors could

understand the difference between the scores. Several stories in each scoring category were provided for

practice to fine tune the assessor’s scoring. Then the scoring ability of each assessor was tested. Only

assessors who scored a 90 percent accuracy in scoring the training stories as matched against the

preLAS®2000 samples were allowed to conduct the OLDS.

A ten percent sample of each assessor’s OLDS stories were recoded in the home office. The

coders received the same training as the assessors. Coders then scored the stories independently. If the

home office coders’ scores differed from the assessor, their scores were verified by their supervisor. All

cases were adjudicated by lead trainers for the OLDS. Approximately 66 percent of the stories had

complete score agreement between the assessor, coder, and lead trainer. The additional 33 percent of the

stories had score agreement by two of the three scorers.

5.7.5 Assessor Effects

Individual Test Administrator effects and Design Effects

A multi-level analysis3

 was carried out to estimate components of variance in fall- and

spring-kindergarten cognitive scores associated with the: (1) student level, (2) school level, (3) team

 

3

 Bryk, A. & Raudenbush, S.W. (1992). Hierarchical Linear Models: Applications and data analysis methods. New York, Sage Publications.

Snijders, T. & Bosker, R. (1999). Multilevel Analysis – An introduction to basic and advanced multilevel modeling. London, Sage Publications.
5-35

leader, and (4) individual test administrator. This secondary analysis was motivated by Westat’s earlier

finding of larger than expected design effects. In addition, the impact on the above sources of variance of

the SES indicator (parent’s education) was also estimated. It was expected that much of the clustering of

students within neighborhood schools (hence higher design effects) could be explained by SES.

In addition to the potential clustering effects related to shared parent SES within schools,

there was a concern that the individual mode of administration might inject additional and unwanted

variance to both the individual and the between school components of variance in the cognitive scores.

Since it is more difficult to standardize test administrations when tests are individually administered, this

source of variance could contribute to the high design effects if the individual assessors differed

systematically in their modes of administration.

It was found that the component of variance associated with the individual test

administration effect was negligible in all three cognitive areas and thus had little or no impact on the

design effects. Much of the design effects with respect to cognitive scores could be explained by parents’

SES.

5.8 Spring-Kindergarten Response Rates

Table 5-14 presents school-level response rates for spring-kindergarten data collection.

Response rates increased for both public and private schools from fall to spring because of a concerted

effort at refusal conversion. This was most dramatic for Catholic schools where the rate increased from

83.0 percent to 94.1 percent, but even non-Catholic private schools increased by over four percentage

points. Although not directly evident from the table, all but three schools cooperating in the fall data

collection continued to do so in the spring.

Table 5-15 presents child-level completion rates for the spring data collection, broken out by

school characteristics. Because of the refusal converted schools, the absolute number of completed cases

increased from fall- to spring-kindergarten. However, the completion rates fell by a point or two for the

child assessment and parent interviews between the two rounds. Overall the child-level rates for teacher

questionnaires A, B, and C dropped by four to six points between fall-kindergarten and spring, largely
5-36

Table 5-14. The ECLS-K, spring-kindergarten: number of cooperating schools and before-substitution

school-level response rates

Number cooperatingb Before-substitution response rates

School characteristica

Before

substitution

After

substitution

School weight ×

KG enrollment Unweighted

All schools 940 1014 74.0 73.7

School type

Public 678 752 74.2 74.2

Private 262 262 72.8 72.4

Catholic 113 113 94.1 94.2

Other private 149 149 60.6 61.6

Type of locale

Large city 195 202 79.0 79.6

Mid-size city 189 206 76.8 76.5

Urban fringe of large city 271 287 71.6 70.9

Urban fringe of mid-size

city 63 70 66.9 65.6

Large town 31 31 96.3 93.9

Small town 77 88 71.6 68.8

Rural 114 130 69.4 70.8

School size (Kindergartners)

1 to 12 57 57 79.0 73.1

13 to 24 108 110 69.7 72.0

25 to 36 82 86 78.0 81.2

37 or more 693 761 73.9 73.2

All schools 940 1014 74.0 73.7

Percent minority enrolled

0 – 10 % 249 273 73.0 71.8

11 – 49 % 302 341 71.5 71.6

50 – 89% 191 199 75.8 76.7

90 – 100% 140 143 81.3 81.4

Unknown 58 58 67.7 67.4

Region

Northeast 168 189 68.4 69.1

Midwest 246 253 82.3 82.6

South 301 327 74.3 72.2

West 225 245 69.6 70.8

High grade

Trans K, K or trans 1 43 43 83.8 84.3

01-12 855 929 73.9 73.8

Ungraded 4 4 100.0 100.0

Unknown 38 38 62.0 61.3 aBased on frame data.

bOne or more children or parents participated, or school is one of two schools in the Archdiocese of New Orleans where student sampling was

performed too late in fall-kindergarten to field child or parent data collection.
5-37

Table 5-15. The ECLS-K, spring-kindergarten: number of completed child-level cases and child-level completion rates

characteristic

Child assessment Parent interview School a

Completion rates Completion rates 

School characteristica Completesb Weighted Unweighted Completesc Weighted Unweighted Completesd 

All children 19,967 88.0 88.3 18,950 83.9 83.8 19,282 School type

Public 15,581 87.7 87.8 14,695 83.3 82.8 14,930 Private 4,386 90.1 90.0 4,255 87.9 87.3 4,352 

Catholic 2,296 91.7 91.5 2,206 88.4 87.9 2,334 Other private 2,090 88.5 88.4 2,049 87.5 86.7 2,018 

Type of locale

Large city 3,933 86.6 86.9 3,599 79.4 79.5 3,289 Mid-size city 4,219 89.0 89.7 4,050 85.5 86.1 4,284 Urban fringe of large city 5,674 87.6 87.9 5,360 83.6 83.0 5,305 Urban fringe of mid-size city 1,404 86.7 87.3 1,349 83.2 83.9 1,514 Large town 636 89.2 89.1 624 87.1 87.4 666 Small town 1,696 89.5 89.4 1,641 87.2 86.5 1,683 Rural 2,405 88.6 88.4 2,327 85.8 85.6 2,541 

School size (Kindergartners)

1 to 12 383 83.9 84.2 395 87.7 86.8 405 13 to 24 1,725 89.8 91.1 1,642 85.3 86.7 1,701 25 to 36 1,780 91.0 91.1 1,712 87.6 87.6 1,852 37 or more 16,079 87.7 87.8 15,201 83.4 83.0 15,324 

Percent minority enrolled

0 – 10% 5,538 87.9 88.4 5,427 86.4 86.6 5,853 11 – 49% 6,878 87.9 87.9 6,622 84.6 84.6 7,223 50 – 89% 3,746 88.0 87.9 3,478 82.6 81.6 3,375 90 – 100% 2,952 88.7 89.0 2,592 78.6 78.1 2,054 Unknown 853 87.7 88.9 831 85.6 86.7 777 

Region

Northeast 3,746 88.2 88.5 3,513 82.5 83.0 3,417 Midwest 4,951 87.6 88.2 4,761 84.3 84.8 5,069 South 6,685 89.4 89.6 6,358 85.4 85.2 6,476 West 4,585 85.9 86.3 4,318 82.1 81.2 4,320 

High grade

Trans K, K or trans 1 597 90.3 89.4 573 87.8 85.8 582 01-12 18,880 87.9 88.3 17,895 83.8 83.7 18,227 Ungraded 43 80.5 76.8 38 62.8 67.9 56 Unknown 447 88.0 87.5 444 87.5 86.9 417 

All children 19,637 87.0 87.0 20,523 90.5 90.9 19,382 
5-38

Table 5-15. The ECLS-K, spring-kindergarten: number of completed child-level cases and child-level completion rates

characteristic (continued)

Teacher questionnaire A Teacher questionnaire B 

Completion rates Completion rates 

School characteristica Completesd Weighted Unweighted Completesd Weighted Unweighted Compl

School type

Public 15,389 86.9 86.9 15,880 89.7 89.6 15,23Private 4,248 87.9 87.3 4,643 95.2 95.5 4,14

Catholic 2,296 91.7 91.5 2,206 88.4 87.9 2,33Other private 2,069 88.3 87.7 2,197 92.7 93.2 1,95

Type of locale

Large city 3,526 77.8 78.1 3,872 84.5 85.8 3,74Mid-size city 4,214 89.6 89.8 4,359 92.2 92.9 4,09Urban fringe of large city 5,718 89.2 88.6 5,855 90.7 90.8 5,46Urban fringe of mid-size city 1,418 87.7 88.3 1,482 91.7 92.3 1,38Large town 632 89.0 88.5 655 91.5 91.7 63Small town 1,673 88.7 88.4 1,795 95.0 94.8 1,67Rural 2,456 89.1 90.6 2,505 91.4 92.4 2,40

School size (Kindergartners)

1 to 12 394 88.4 87.0 423 93.2 93.4 3613 to 24 1,671 85.6 88.4 1,756 89.9 92.9 1,6525 to 36 1,771 90.6 90.9 1,866 94.7 95.7 1,7137 or more 15,801 86.9 86.4 16,478 90.1 90.1 15,64

Percent minority enrolled

0 – 10% 5,701 90.9 91.2 5,901 94.1 94.4 5,4811 – 49% 6,735 86.5 86.3 7,158 91.2 91.7 6,6650 – 89% 3,682 86.7 86.5 3,810 89.5 89.5 3,6490 – 100% 2,716 82.4 82.1 2,742 82.0 82.9 2,78Unknown 803 83.8 83.8 912 92.6 95.2 80

Region

Northeast 3,836 90.6 90.8 3,871 91.6 91.6 3,60Midwest 4,773 85.3 85.2 5,176 91.9 92.4 4,87South 6,699 89.2 89.9 6,781 90.1 91.0 6,54West 4,329 82.3 81.6 4,695 88.6 88.6 4,36

High grade

Trans K, K or trans 1 529 83.4 79.3 631 95.3 94.6 5701-12 18,627 87.3 87.2 19,358 90.2 90.7 18,34Ungraded 29 41.8 51.8 42 68.5 75.0 3Unknown 452 87.5 88.6 492 96.6 96.5 42

aBased on frame data.bReading, math, or general knowledge assessment was scorable or child was language minority (not Spanish) and score below cut score in OLDS or disabled (IEP).

cFamily structure portion of parent interview was completed. dQuestionnaire was not blank.
5-39

because of the data collection protocol for students who transferred to another school (for details see

section 5.6.6). For teacher A and B, this disproportionately affected children in large cities, who showed a

loss of 9 to 11 percentage points. Catholic schools too showed an 11-point drop in teacher B completion,

though the spring-kindergarten rate was still in excess of 88 percent. The weighted completion rate for the

school administrator questionnaire was 85.9 percent overall with the lowest rate observed for large cities

(71.4 percent).

Table 5-16 also presents child-level response rates for the spring data collection, this time

broken out by child characteristics. The same gains and losses of sample size and completion rates hold as

in table 5-15 and for the same reasons. However, for the teacher A and B questionnaires, the decrease in

the completion rate between fall and spring was higher for black students, i.e., in the range of 8 to 9

points. The lowest rate of completion for the school administrator questionnaire was for black and

Hispanic students (76.4 and 75.1 percent, respectively), largely because of their clustering in large cities.

Table 5-17 presents teacher-level completion rates for the spring-kindergarten data

collection broken out by school characteristics. Relative to the fall data collection experience, the overall

completion rate for the teacher A questionnaire increased by nearly three percentage points to 93.4

percent and the teacher B rate held steady (94.1 percent). There was an 11-point gain in the teacher A rate

for rural schools but a nearly four percentage point loss in the Midwest, which, however, still remained at

nearly 92 percent.

Table 5-18 displays school-level completion rates for the school administrator questionnaire

broken out by school characteristics. The overall weighted rate is 87.2 percent. The private school

completion rate is about five points higher than that for public schools (90.7 vs. 85.5 percent). Rates

tended to be lower for schools in large cities (77.6 percent complete), those having 90 percent or more

minority students enrolled (65.0 percent), and those located in the West (80.1 percent).

The spring-kindergarten school-level response rates presented in table 5-14 can be combined

with the child-level and teacher-level completion rates from tables 5-15 and 5-17 to produce overall

response rates at the child and teacher levels, by school characteristic. Table 5-19 displays the results at

the child-level. For the most part, the response patterns notable in the fall-kindergarten assessment

persisted into the spring. Response rates for children enrolled in Catholic schools were higher than

average across all survey instruments, as were those for children in schools in the Midwest, and schools
5-40

Table 5-16. The ECLS-K, spring-kindergarten: number of completed child-level cases and child-level completion rates, by 

Child assessment Parent interview Scho

Completion rates Completion rates 

Child characteristica Completesb Weighted Unweighted Completesc Weighted Unweighted Comple

All children 19,967 88.0 88.3 18,950 83.9 83.8 19,282 Gender

Male 10,191 88.1 88.3 9,679 84.0 83.8 9,864 Female 9,720 88.2 88.5 9,231 84.3 84.1 9,370 Unknown gender 56 59.1 56.6 40 35.2 40.4 48 

Race-ethnicity

White (not Hispanic) 11,602 88.2 88.5 11,370 86.4 86.7 12,011 Black (not Hispanic) 3,130 88.7 88.6 2,847 80.8 80.6 2,60Hispanic 2,668 88.4 88.5 2,494 83.0 82.7 2,223 Asian or Pacific Islander 1,696 89.6 89.9 1,455 77.0 77.1 1,614 American Indian or Alaskan

Native 327 88.1 88.4 288 77.7 77.8 332 

Other 219 83.4 85.2 201 78.0 78.2 227 Unknown race-ethnicity 325 72.8 71.4 295 64.0 64.8 274 

Year of birth

1992 5,720 88.8 89.1 5,465 85.1 85.2 5,689 1993 13,936 88.1 88.4 13,199 84.0 83.7 13,244 1994 54 85.2 85.7 52 83.9 82.5 46 Other/unknown 257 70.1 68.7 234 62.2 62.6 303 

Kindergarten type

Full day 11,036 93.0 92.9 10,246 86.4 86.3 10,149 Part day 8,543 91.1 91.1 8,016 86.0 85.5 8,017 Kindergarten type unknown 388 27.0 28.3 688 49.9 50.1 1,062 
5-41

Table 5-16. The ECLS-K, spring-kindergarten: number of completed child-level cases and child-level completion rates, by 

(continued)

Teacher questionnaire A Teacher questionnaire B 

Completion rates Completion rates 

Child characteristica Completesd Weighted Unweighted Completesd Weighted Unweighted Comple

All children 19,637 87.0 87.0 20,523 90.4 90.9 19,382 Gender

Male 10,010 87.2 86.9 10,455 90.5 90.7 9,875 Female 9,584 87.3 87.4 10,011 90.8 91.3 9,477 Unknown gender 43 46.0 43.4 57 49.5 57.6 30 

Race-ethnicity

White (not Hispanic) 11,623 89.0 88.8 12,187 92.7 93.1 11,323 Black (not Hispanic) 2,983 85.1 84.6 3,086 87.7 87.6 3,057 Hispanic 2,502 83.4 83.2 2,593 86.2 86.2 2,544 Asian or Pacific Islander 1,647 88.3 87.4 1,745 92.9 92.6 1,632 American Indian or Alaskan

Native 331 89.3 89.9 313 85.8 85.1 332 

Other 215 86.1 83.7 237 90.6 92.2 208 Unknown race-ethnicity 336 70.5 73.8 362 77.4 79.6 286 

Year of birth

1992 5,581 86.9 87.0 5,885 91.1 91.8 5,599 1993 13,750 87.6 87.4 14,303 90.6 90.9 13,498 1994 51 87.0 83.6 56 90.8 91.8 47 Other/unknown 255 65.7 68.4 279 71.9 74.8 238 

Kindergarten type

Full-day 11,106 94.4 93.5 11,437 96.2 96.3 10,800 Part-day 8,531 91.0 91.0 9,062 96.5 96.6 8,412 Kindergarten type unknown 0 0.0 0.0 24 1.9 1.8 170 

aDemographic data were captured from school records prior to student sampling.bReading, math, or general knowledge assessment was scorable or child was language minority (not Spanish) and scored below cut score in OLDS or disabled (IEP).

cFamily structure portion of parent interview was completed.dQuestionnaire was not blank.
5-42

Table 5-17. The ECLS-K, spring-kindergarten: number of completed teacher questionnaires and teacherlevel completion rates

Teacher questionnaire A Teacher questionnaire B

Completion rates Completion rates

School characteristica Completesb Weighted Unweighted Completesc Weighted Unweighted

All schools 3,215 93.4 93.0 3,243 94.1 93.8

School type

Public 2,819 93.7 93.2 2,830 93.9 93.6

Private 396 92.4 91.2 413 95.3 95.2

Catholic 168 93.5 93.9 176 98.1 98.3

Other private 228 91.8 89.4 237 93.8 92.9

Type of locale

Large city 688 88.8 88.1 703 92.4 90.0

Mid-size city 640 96.6 96.2 646 97.6 97.1

Urban fringe of large city 955 93.1 93.1 953 93.0 92.9

Urban fringe of mid-size

city 244 95.0 95.3 253 98.7 98.8

Large town 85 89.4 89.5 86 90.6 90.5

Small town 288 94.8 95.7 291 96.4 96.7

Rural 315 94.9 94.3 311 90.8 93.1

School size (Kindergartners)

1 to 12 64 94.2 92.8 67 96.7 97.1

13 to 24 157 88.4 90.2 158 87.7 90.8

25 to 36 114 96.7 95.8 116 96.9 97.5

37 or more 2,880 93.8 93.0 2,902 94.6 93.7

Percent minority enrolled

0 – 10% 736 95.5 94.8 740 95.5 95.4

11 – 49% 1,070 93.3 93.6 1,083 95.0 94.8

50 – 89% 704 92.4 92.5 726 93.7 95.4

90 – 100% 596 91.9 91.0 573 89.5 87.5

Unknown 109 92.8 88.6 121 97.1 98.4

Region

Northeast 486 93.5 92.7 490 93.1 93.5

Midwest 574 91.6 89.0 613 94.6 95.0

South 1,288 96.0 96.2 1,267 95.0 94.6

West 867 90.7 91.3 873 92.9 91.9

High grade

Trans K, K or trans 1 134 83.4 79.8 160 94.4 95.2

01-12 3,019 94.1 93.7 3,017 94.0 93.6

Ungraded 10 77.2 76.9 12 92.4 92.3

Unknown 52 94.3 94.5 54 98.5 98.2

aBased on frame data.

bRound 2 questionnaire completed by teacher employed in a base-year probability sampled school.

cFall-kindergarten or spring-kindergarten questionnaire completed by teacher employed in a base-year probability sampled school.
5-43

Table 5-18. The ECLS-K, spring-kindergarten: number of completed school administrator questionnaire

and school-level completion rates

Number Completion rates

School characteristica cooperatingb Weightedc Unweighted

All schools 866 87.2 85.4

School type

Public 631 85.5 83.9

Private 235 90.7 89.7

Catholic 105 93.9 92.9

Other private 130 88.9 87.2

Type of locale

Large city 148 77.6 73.3

Mid-size city 189 92.8 91.7

Urban fringe of large city 237 84.1 82.6

Urban fringe of mid-size city 66 95.5 94.3

Large town 29 94.7 93.5

Small town 78 88.6 88.6

Rural 119 89.5 91.5

School size (Kindergartners)

1 to 12 50 85.0 87.7

13 to 24 98 90.2 89.1

25 to 36 82 96.0 95.3

37 or more 636 84.8 83.6

Percent minority enrolled

0 – 10% 256 94.7 93.8

11 – 49% 315 92.2 92.4

50 – 89% 158 80.8 79.4

90 – 100% 89 65.0 62.2

Unknown 48 86.3 82.8

Region

Northeast 154 84.7 81.5

Midwest 228 92.1 90.1

South 286 89.6 87.5

West 198 80.1 80.8

High grade

Trans K, K or trans 1 37 87.1 86.0

01-12 793 87.2 85.4

Ungraded 4 100.0 100.0

Unknown 32 85.2 84.2

aBased on frame data.

bSchool is part of the original base-year probability sample and questionnaire was not blank.

cWeighted by school base weight.
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Table 5-19. The ECLS-K, spring-kindergarten: child-level sample sizes and overall response rates, by school characteristic

Child assessment Parent interview Scho

Overall response rates Overall response rates 

School characteristicsa Completesb Weighted Unweighted Completesc Weighted Unweighted Complet

All children 19,967 65.1 65.1 18,950 62.1 61.8 19,282 School type

Public 15,581 65.1 65.1 14,695 61.8 61.4 14,93Private 4,386 65.6 65.2 4,255 64.0 63.2 4,35

Catholic 2,296 86.3 86.2 2,206 83.2 82.8 2,33Other private 2,090 53.6 54.5 2,049 53.0 53.4 2,01

Type of locale

Large city 3,933 68.4 69.2 3,599 62.7 63.3 3,289 Mid-size city 4,219 68.4 68.6 4,050 65.7 65.9 4,284 Urban fringe of large city 5,674 62.7 62.3 5,360 59.9 58.8 5,305 Urban fringe of mid-size city 1,404 58.0 57.3 1,349 55.7 55.0 1,514 Large town 636 85.9 83.7 624 83.9 82.1 666 Small town 1,696 64.1 61.5 1,641 62.4 59.5 1,683 Rural 2,405 61.5 62.6 2,327 59.5 60.6 2,541 

School size (Kindergartners)

1 to 12 383 66.3 61.6 395 69.3 63.5 405 13 to 24 1,725 62.6 65.6 1,642 59.5 62.4 1,701 25 to 36 1,780 71.0 74.0 1,712 68.3 71.1 1,852 37 or more 16,079 64.8 64.3 15,201 61.6 60.8 15,324 

Percent nonwhite enrolled

0 – 10% 5,538 64.2 63.5 5,427 63.1 62.2 5,853 11 – 49% 6,878 62.8 62.9 6,622 60.5 60.6 7,223 50 – 89% 3,746 66.7 67.4 3,478 62.6 62.6 3,375 90 – 100% 2,952 72.1 72.4 2,592 63.9 63.6 2,054 Unknown 853 59.4 59.9 831 58.0 58.4 777 

Region

Northeast 3,746 60.3 61.2 3,513 56.4 57.4 3,417 Midwest 4,951 72.1 72.9 4,761 69.4 70.0 5,069 South 6,685 66.4 64.7 6,358 63.5 61.5 6,476 West 4,585 59.8 61.1 4,318 57.1 57.5 4,320 

High grade

Trans K, K or trans 1 597 75.7 75.4 573 73.6 72.3 582 01-12 18,880 65.0 65.2 17,895 61.9 61.8 18,227 Ungraded 43 80.5 76.8 38 62.8 67.9 56 Unknown 447 54.6 53.6 444 54.3 53.3 417 

.
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Table 5.19. The ECLS-K, spring-kindergarten: child-level sample sizes and overall response rates, by school characteristi

Teacher questionnaire A Teacher questionnaire B 

Overall response rates Overall response rates 

School characteristicsa Completesd Weighted Unweighted Completesd Weighted Unweighted Compl

All children 19,637 64.4 64.1 20,523 67.0 67.0 19,38School type

Public 15,389 64.5 64.5 15,880 66.6 66.5 15,23Private 4,248 64.0 63.2 4,643 69.3 69.1 4,14

Catholic 2,179 82.3 82.0 2,446 92.1 91.9 2,19Other private 2,069 53.5 54.0 2,197 56.2 57.4 1,95

Type of locale

Large city 3,526 61.5 62.2 3,872 66.8 68.3 3,74Mid-size city 4,214 68.8 68.7 4,359 70.8 71.1 4,09Urban fringe of large city 5,718 63.9 62.8 5,855 64.9 64.4 5,46Urban fringe of mid-size city 1,418 58.7 57.9 1,482 61.3 60.5 1,38Large town 632 85.7 83.1 655 88.1 86.1 63Small town 1,673 63.5 60.8 1,795 68.0 65.2 1,67Rural 2,456 61.8 64.1 2,505 63.4 65.4 2,40

School size (Kindergartners)

1 to 12 394 69.8 63.6 423 73.6 68.3 3613 to 24 1,671 59.7 63.6 1,756 62.7 66.9 1,6525 to 36 1,771 70.7 73.8 1,866 73.9 77.7 1,7137 or more 15,801 64.2 63.2 16,478 66.6 66.0 15,64

Percent nonwhite enrolled

0 – 10% 5,701 66.4 65.5 5,901 68.7 67.8 5,4811 – 49% 6,735 61.8 61.8 7,158 65.2 65.7 6,6650 – 89% 3,682 65.7 66.3 3,810 67.8 68.6 3,6490 – 100% 2,716 67.0 66.8 2,742 66.7 67.5 2,78Unknown 803 56.7 56.5 912 62.7 64.2 80

Region

Northeast 3,836 62.0 62.7 3,871 62.7 63.3 3,60Midwest 4,773 70.2 70.4 5,176 75.6 76.3 4,87South 6,699 66.3 64.9 6,781 66.9 65.7 6,54West 4,329 57.3 57.8 4,695 61.7 62.7 4,36

High grade

Trans K, K or trans 1 529 69.9 66.8 631 79.9 79.7 5701-12 18,627 64.5 64.4 19,358 66.7 66.9 18,34Ungraded 29 41.8 51.8 42 68.5 75.0 3Unknown 452 54.3 54.3 492 59.9 59.2 42

aBased on frame data.bReading, math, or general knowledge assessment was scorable or child was language minority (not Spanish) and scored below the cut score in the OL

cFamily structure portion of Parent Interview was completed. dQuestionnaire was not blank.
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whose highest grade is kindergarten. For schools with over 90 percent minority enrollment, response rates

were higher than average for all but the school administrator questionnaire, which was a full 13 points

below the average rate. Table 5-20 presents overall response rates at the teacher-level, by school

characteristic. The same subgroups exhibit a better than average level of response.

In addition to the child assessment, parent interview, teacher questionnaires, and school

administrator questionnaires whose completion rates have been summarized in the preceding tables,

various other types of data were collected during spring of kindergarten as well. Table 5-21 presents

counts of completes and weighted and unweighted completion rates at the overall student level for these

other data collection efforts. The facilities checklist has a 96.6 percent weighted completion rate at the

student level. (At the school level, it is 95.7 percent.) Also very high are the special education teacher

questionnaire A and the school principal’s salary and benefits information, at 94.1 and 91.0 percent

completion respectively. The salary and benefits data for special education teachers is largely unavailable

(31.4 percent completion) as is the Adaptive Behavior Scale (45.6 percent), which was collected only for

students who were not assessed in spring of kindergarten because of physical or mental disability. The

Adaptive Behavior Scale was collected for a very small number of children, less than 100, and seemed to

be burdensome for the special education teachers to complete. The majority of teachers were identified

for teacher and salary benefits data collection in early April 1999; a smaller number were identified and

fielded in mid-May 1999. It appears that the timing of this data collection effort may not have included all

of the special education teachers. In addition, these teachers often had other sources of payment about

which the school district could not report; for example, the county had a special education cooperative

that paid the teacher salary or the salary was paid by other special services contracts.

5.9 Base Year Response Rates

Thus far cooperation rates have been presented for the two rounds of data collection

separately. It is also reasonable to consider such rates from the perspective of the base year as a whole.

Table 5-22 presents base year rates for schools, children, parents, and teachers. Within this table a child,

parent, or teacher is considered a respondent if he or she cooperated in either the fall- or springkindergarten data collection. A school is counted as responding if one or more parents or children from

that school cooperated in either round. Rates are given both weighted and unweighted. Weighted rates

were calculated using the school base weight for schools and teachers and the child base weight for
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Table 5-20. The ECLS-K, spring-kindergarten: number of completed teacher questionnaires and teacher-level response 

Teacher questionnaire A Teacher questionnaire B

Response rates Response rates

School characteristica Completesb Weighted Unweighted Completesb Weighted Unweighted

All schools 3,215 69.1 68.5 3,243 69.6 69.1School type

Public 2,819 69.5 69.2 2,830 69.7 69.5Private 396 67.3 66.0 413 69.4 68.9

Catholic 168 88.0 88.5 176 92.3 92.6Other private 228 55.6 55.1 237 56.8 57.2

Type of locale

Large city 688 70.2 70.1 703 73.0 71.6Mid-size city 640 74.2 73.6 646 75.0 74.3Urban fringe of large city 955 66.7 66.0 953 66.6 65.9Urban fringe of mid-size city 244 63.6 62.5 253 66.0 64.8Large town 85 86.1 84.0 86 87.2 85.0Small town 288 67.9 65.8 291 69.0 66.5Rural 315 65.9 66.8 311 63.0 65.9

School size (Kindergartners)

1 to 12 64 74.4 67.8 67 76.4 71.013 to 24 157 61.6 64.9 158 61.1 65.425 to 36 114 75.4 77.8 116 75.6 79.237 or more 2,880 69.3 68.1 2,902 69.9 68.6

Percent minority enrolled

0 – 10% 736 69.7 68.1 740 69.7 68.5

11 – 49% 1,070 66.7 67.0 1,083 67.9 67.950 – 89% 704 70.0 70.9 726 71.0 73.290 – 100% 596 74.7 74.1 573 72.8 71.2Unknown 109 62.8 59.7 121 65.7 66.3

Region

Northeast 486 64.0 64.1 490 63.7 64.6Midwest 574 75.4 73.5 613 77.9 78.5South 1,288 71.3 69.5 1,267 70.6 68.3West 867 63.1 64.6 873 64.7 65.1

High grade

Trans K, K or trans 1 134 69.9 67.3 160 79.1 80.301-12 3,019 69.5 69.2 3,017 69.5 69.1Ungraded 10 77.2 76.9 12 92.4 92.3Unknown 52 58.5 57.9 54 61.1 60.2

aBased on frame data.bNonblank questionnaire.
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Table 5-21. The ECLS-K, spring-kindergarten: number of completed instruments and child-level

completion rates for additional data collected

Completion rates

Instrument Completes Weighted Unweighted

School Facilities Checklist 21,140 96.6 93.6

Student Record Abstract 16,902 75.9 74.8

Salary and Benefits (principal) 19,628 91.0 86.9

Salary and Benefits (regular teacher) 18,700 88.1 87.3

Salary and Benefits (special education teacher) 240 31.4 30.0

Special Education Teacher Questionnaire A 737 94.1 92.2

Special Education Teacher Questionnaire B 698 87.2 87.4

Adaptive Behavior Scale 32 45.6 45.7

Table 5-22. The ECLS-K: number sampled and number and percent cooperating during fall- or springkindergarten, with percent of fall respondents who also responded in the spring

Base year response rate

Percent of fall respondents

who also responded in spring

Sampled

Responded

fall or spring

kindergarten Weighted Unweighted Weighted Unweighted

Schools

 Before substitution 1,277 944 74.2 73.9 99.3 99.5

 After substitution 1,277 1,018 80.7 79.7 99.4 99.6

Children 22,782 20,929 92.0 91.9 95.1 95.3

Parents 22,782 20,141 88.8 88.4 93.7 93.7

Teachers 3,551 3,305 93.5 93.1 -a -

a

a

 The conditional response rate is not presented for teachers because in the spring a teacher was considered to be a respondent if the teacher

questionnaire B had been completed in either the spring or the fall. Therefore, the nominal fall to spring retention rate would be meaningless,

i.e., 100 percent.

children and parents. In a longitudinal study such as the ECLS-K, another informative measure is the rate

at which respondents in one round of data collection continue to cooperate in the next round. Typically

the retention rates from round to round are higher than the initial cooperation rate. This is definitely true

of the ECLS-K where the fall-kindergarten child assessment completion rate was 89.9 percent (see table

5-5), but 95.1 percent of fall-assessed children were also assessed in spring-kindergarten. The difference

for parents was even more dramatic: 85.3 percent cooperated in the fall, of whom 95.1 percent also

participated in the spring. Of schools that cooperated in the fall, over 99 percent continued in the study

during the spring.
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6. DATA PREPARATION

As described in chapter 5, two types of data collection instruments were used for the Early

Childhood Longitudinal Study-Kindergarten Class of 1998-99 (ECLS-K)—computer-assisted and selfadministered paper forms (hard copy). The data preparation approach differs with the type of data

collection. The direct child assessment and parent interview were conducted using computer-assisted

personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). Editing

specifications were built into the computer program used by field staff to collect the data. The teacher and

school administrator forms were self-administered. When the field supervisors returned these forms,

coders recorded the receipt of these forms into a project-specific forms tracking system. Coders reviewed

the questionnaires to ensure data readability for transfer into an electronic format. The visual review

included changing any “other” responses that actually fit within the numerical answer structure of the

question. (This is called upcoding.) There were some items where upcoding was conducted after the data

were keyed due to the large volume of other responses. Once they finished this review, the coders sent the

instruments to data entry to be manually transferred to an electronic format and reviewed for range and

logic consistency. This section presents the data preparation activities for both types of data collection in

more detail.

6.1 Coding and Editing Specifications for CATI/CAPI

The very nature of designing a computer-assisted interview forces decisions about edit

specifications to be made up front. Both acceptable ranges and logic consistency checks were preprogrammed into the electronic questionnaire. The next few sections describe the coding and editing of

the data collected using CATI/CAPI.

6.1.1 Range Specifications

Within the CATI/CAPI instruments, respondent answers were subjected to both “hard” and

“soft” range edits during the interviewing process. A “soft range” is one that represents the reasonable

expected range of values but does not include all possible values. Reponses outside the soft range were

confirmed with the respondent and entered a second time. For example, the number of hours each week a
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child attends a day care center on a regular basis had a soft range of 1 to 50. A value outside this range

could be entered and confirmed as correct by the assessor as long as it was within the hard range of values

(1 to 70).

“Hard ranges” are those that have a finite set of parameters for the values that can be entered

into the computer, for example, “1-13 pounds” for birth weight. Out-of-range values for closed-ended

questions were not accepted. If the respondent insisted that a response outside the hard range was correct,

the assessor could enter the information in a comments data file. Data preparation and project staff

reviewed these comments. Out-of-range values were accepted if the comments supported the response.

6.1.2 Consistency Checks (Logical Edits)

Consistency checks, or logical edits, examine the relationship between responses to ensure

that they do not conflict with one another or that the response to one item does not make the response to

another item unlikely. For example, in the household roster, one could not be recorded as a mother and

male. When a logical error such as this occurred during a session, the assessor saw a message requesting

verification of the last response and a resolution of the discrepancy. In some instances, if the verified

response still resulted in a logical error, the assessor recorded the problem either in a comment or on a

problem report.

6.1.3 Coding

Additional coding was required for some items of data collected in the CAPI/CATI

instruments. These items included “Other, specify” text responses, occupation, race-ethnicity, and

language. Staff were recruited and trained to code these data using coding manuals designed by Westat

and NCES to support the coding process. In this section, we describe the coding activities for the

CAPI/CATI instruments.
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Review of “Other, specify” Items

The “Other, specify” open-ended parent interview responses were reviewed to determine if

they should be coded into one of the existing code categories. During the data collection, when a

respondent selected an “other” response in the parent interview, the assessor entered the text into a

“specify” overlay that appeared on the screen. These text “specify” responses were reviewed by the data

preparation staff and, where appropriate, coded into one of the existing response categories. If a text

“specify” response for which there was no code occurred frequently enough, new codes were added.

Fall-Kindergarten Parent Interview

Review of the “specify” text responses revealed over 1,000 cases where respondents spoke

languages other than the ones specified in the questionnaire as spoken in the household. Over 50

languages beyond the options provided were recorded, and there were over 100 cases with other

languages (892 for other languages, 306 for primary language). Given the frequency with which some of

the languages appeared, groups of languages were created based on geographic boundaries. These

additions were: African language; Eastern European language; Native American language; Sign language;

Middle Eastern language; Western European language; Indian subcontinent language; Southeast Asian

language; Pacific Islander language; and other language.

Spring-Kindergarten Parent Interview

No additional codes were added from the review of the spring-kindergarten parent interview

“Other, specify” items.

Parent Occupation Coding

Occupations were coded using the “Manual for Coding Industries and Occupations,” March

1999 (National Household Education Survey, NHES: 99). This coding manual was created for NHES and

used an aggregated version of industry and occupation codes. The industry and occupation codes used by

NHES were originally developed for the National Postsecondary Student Aid Study (NPSAS, 1990) and
6-4

contained one to four digits. Analysis of the NPSAS categories revealed that some categories had very

small numbers of cases and some categories that are similar in industry or occupation had similar

participation rates, suggesting that the separate codes could be collapsed without significant loss of

information. The NHES industry and occupation code categories use a one-digit code, the highest level of

aggregation, to have sufficient numbers of cases to support analysis without collapsing categories. There

are 13 industry codes and 22 occupation codes in the NHES coding scheme. If an industry or occupation

could not be coded using this manual, the “Index of Industries and Occupations - 1980” and “Standard

Occupational Classification Manual—1980” were used. Both of these manuals use an expanded coding

system and at the same time are directly related to the much more condensed NHES coding scheme. The

1980 manuals were used for reference in cases where the NHES coding scheme did not adequately cover

a particular situation (see chapter 7, section 7.4 for an expanded description of the industry and

occupation codes).1

Occupation coding began with an autocoding procedure using a computer string match

program developed for the NHES. The program searched the responses for strings of text for each

record/case and assigned an appropriate code. About 25 percent of the cases were autocoded and were not

verified because there was an exact match between the respondent’s answer and the occupation code.

Cases that could not be coded using the autocoding system were coded manually by coders

using a customized coding utility program designed for coding occupations. The customized coding utility

program brought up each case for coders to assign the most appropriate codes. In addition to the text

strings, other information such as main duties, highest level of education, and name of the employer was

available for the coders. The coders used this information to ensure that the occupation code assigned to

each case was appropriate.

Verification of coding is an important tool to assure quality control and as an extension of

coder training. As a verification step, two coders independently assigned codes, i.e., double-blind coding, to

industry and occupation cases. A coding supervisor arbitrated disagreements between the two codes, the

initial code and the verification code. In the early stages of each coder’s work, 100 percent of each coder’s

work was reviewed. Once the coder’s error rate had dropped to one percent or less, ten percent of the

coder’s work was reviewed.

 

1 Office of Budget and Management, Executive Office of the President (1980). Standard Industrial Classification Manual. Springfield, VA, and

Office of Federal Statistical Policy and Standards, U.S. Department of Commerce (1980). Standard Occupational Classification Manual (2nd

ed.). Washington, DC: Superintendent of Documents, U.S. Government Printing Office.
6-5

Partially Complete Parent Interviews

A “completed” parent instrument was defined by whether the section on family structure

(FSQ) was completed by the interviewer. Only completed interviews were retained in the final data file. A

small number of interviews in each round, approximately 50 (<1 percent) in fall-kindergarten and 75 (<1

percent) in spring-kindergarten, terminated the parent interview after the FSQ section but before the end

of the instrument. These interviews were defined as “partially complete” cases and were included in the

data file. All instrument items after the interview termination point were set to -9 for “not ascertained.”

Household Roster in the Parent Interview

Several tests were run on the household roster to look for missing or inaccurate information.

First, the relationship of an individual to the focal child was compared to the individual’s listed age and

gender. There were 235 cases with inconsistencies (such as a male mother or a biological mother over age

80) that were examined more closely, and any problems found were corrected wherever possible; 231

cases were corrected. Second, households with more than one mother or more than one father were

scrutinized for errors. While it is possible to have more than one mother in a household—for example, a

household could contain one biological and one foster mother of the focal child—such cases warranted

closer inspection. There were 37 cases with more than one mother or father in the household. Corrections

were made wherever clear errors existed. Twenty-one cases were corrected, and the other 16 appeared to

be correct. Lastly, the relationship of an individual to both the focal child and the reference person was

examined, as there are cases in which the relationship of an individual to the focal child conflicts with his

status as the spouse/partner of the reference person. For example, in a household containing a child’s

grandparents, but not his or her parents, we may designate the grandmother as the “mother” figure, and

the grandfather thus becomes the “father” by virtue of his marriage to the grandmother. These cases were

examined but left unchanged. Both the original—and correct (grandfather)—relationship data and the

new “parent-figure” designation (father) that had been constructed were kept.

Race-Ethnicity Coding

Just under 5,000 “Other, Specify” responses were received on the race-ethnicity questions

because respondents were allowed to indicate that they belonged to more than one race. Many of these
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“others” included more than one response (e.g., African American/Asian or American Indian/white). The

open responses were coded into one or more of the following seven categories: one Hispanic category;

White, non-Hispanic; Black or African American, non-Hispanic; American Indian or Alaskan Native;

Asian; Native Hawaiian, or other Pacific Islander; and one unspecified multirace-ethnicity category.

The same coding rules were used to code all race-ethnicity variables for children, resident

parents, and nonresident parents. See chapter 7, section 7.4.1 for details on how the race variables were

coded and how the race-ethnicity composite was created.

6.2 Coding and Editing Specifications for Hard Copy Questionnaires

6.2.1 Receipt Control

In order to monitor the more than 125,000 documents that were to be received in the base

year, a project-specific receipt and document control system was established. The receipt and document

control system was initially loaded with the identifying information such as identification numbers for

schools, teachers, and children; the links between teachers and children; and the questionnaires that were

expected from each school and teacher, for each cooperating school in the sample. As data were collected

in the field, field staff completed transmittal forms for each school to indicate which questionnaires were

being mailed to the home office. Once data collection started, receipt control clerks compared the

transmittal forms to the questionnaires sent in from the field for accuracy and completeness. The

identification number on each form was matched against the identification numbers in the tracking system

to verify that the appropriate number of forms for each school was returned. The forms were then logged

into the receipt and document control system. Once forms were logged in, if they had any data (some

forms had no data due to refusal by the respondent to complete them), they were then coded; the data

were entered into electronic format, after which the data were edited. The following sections describe the

coding, data entry, and editing processes for hard copy questionnaires.

6.2.2 Coding

The hard copy questionnaires required coding of race-ethnicity for teachers, review of

“Other, specify” text responses, and a quick visual review of particular questions in each questionnaire.
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The quick visual review was to assure that the questionnaire values were accurate and complete and were

consistent across variables and that the numbers were converted to the appropriate unit of measurement

prior to converting data to an electronic format. The coding staff were trained on the coding procedures

and had coding manuals to support the coding process. This staff also did the data editing after data entry

was complete. Senior coders verified coding. The verification rate was set at 100 percent for each coder

until accuracy of less than one percent error rate was established. After that point, work was reviewed at a

rate of ten percent.

Review of “Other, specify” Items

The “Other, specify” text responses were reviewed by the data editing staff and, where

appropriate, upcoded into one of the existing response categories. Reexamination of uncoded specify text

responses in the fall-kindergarten teacher questionnaires, the spring-kindergarten teacher questionnaires,

and the school administrator questionnaire revealed that all responses could be coded into preexisting

categories. The small number of specify responses that remained after upcoding those did not fit into any

preexisting category and were of insufficient numbers to warrant an additional category. No new codes

were added.

Coding Teacher Race-Ethnicity

“Other, specify” text responses for race-ethnicity in the teacher questionnaire B were coded

using the procedures described above in section 6.1.3.

Coding Teacher Language

“Other, specify” text responses for language in the teacher questionnaire A were coded using

the procedures described above in section 6.1.3.
6-8

Coding the Psychomotor Assessment

In fall-kindergarten, the copy form items—copy a plus sign, a square, a triangle, an opensquare with a circle, and an asterisk—and the draw-a-person item from the fine motor section of the

psychomotor assessment required special coding. The coding protocol for the measure from which the

fine motor assessment was adapted was used to code all drawings. A coding sheet was developed that

contained both the scoring decisions for the copy forms and the draw-a-person. The coding sheet and

coding protocol were reviewed prior to coder training by the authors of the measure from which the fine

motor assessment was adapted.

Each copy form was scored separately based on the coding protocol. The first 1,100 forms

were sent to one of the authors of the measure from which the fine motor assessment was adapted for

additional review as a check on the quality of coding. These forms had ambiguous responses that required

more expert adjudication and establishment of additional coding rules. The author made decisions about

the drawings, and she coded the forms and established rules that were followed for the remainder of the

forms. The coding was verified by the coding supervisor at 100 percent until the coder error rate fell

below one percent. Coders were retrained as necessary to follow the coding protocol.

Coding the Facilities Checklist

On question 3a, parts a, b, c, and d, the valid options were “a little,” “some” and “a lot.”

There was no response option for the value “none.” However, in approximately 35 to 45 percent of the

cases the field staff wrote in a category of “none.” Thus a new category of “none” was included as a

response option, even though it was not directly provided in the questionnaire.

6.2.3 Data Entry

Westat data entry staff keyed the forms in each batch. The data were rekeyed by more senior

data entry operators at a rate of 100 percent to verify the data entry. The results of the two data entry

passes were compared and differences identified. The hard copy form was pulled and examined to

determine what corrections had to be made to the keyed data. These corrections were rekeyed. An
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accuracy rate exceeding 99 percent was the result of this process. The verified batches were then

transmitted electronically to Westat’s computer system for data editing.

6.2.4 Data Editing

The data editing process consisted of running range edits for soft and hard ranges, running

consistency edits, and reviewing frequencies of the results.

Range Specifications

Hard copy range specifications are the set parameters for high and low acceptable values for

a question. Where values were preprinted on the forms, these were used as the range parameters. For

open-ended questions, high and low ranges were established as acceptable values. Data frequencies were

run on the range of values to identify any errors. Values outside the range fell out as an error and were

printed on hard copy for a data editor to review. Cases identified with range errors were extracted from

the data set, and the original response was updated. Data frequencies were then rerun and reviewed. This

iterative process was repeated until no further range errors were found.

Consistency Checks (Logical Edits)

Consistency between variables not involved in a skip pattern was accomplished by

programming logical edits between variables (for example, in teacher questionnaire A, the sum of the

number of boys and girls in the class could not exceed the total number of children enrolled in the

classroom). These logical edits were run on the whole database after all data entry and range edits were

complete. The logical edits were run separately for each form. All batches of data were combined into one

large data file, and data frequencies were produced. The frequencies were reviewed to ensure the data

remained logically consistent within the form. When an inconsistency was found, the case was identified

and the inconsistency was printed on paper for an editor to review. The original value was replaced, and

the case was then rerun through the consistency edits. Once the case passed the consistency edits, it was

appended back into the main data set. The frequencies were then rerun and reviewed. This was an

iterative process; it was repeated until no further inconsistencies were found.
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Frequency and Cross-Tabulation Review

Frequencies and cross-tabulations were run to determine consistency and accuracy across the

various forms and matched against the data in the field management system. If discrepancies could not be

explained, no changes were made to the data. For example, in teacher questionnaire A, an item asking

about languages other than English spoken in the classroom includes a response option of “No language

other than English.” If a respondent circled that response but also answered that other languages besides

English were spoken in the classroom, then the response was left as recorded by the respondent.
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7. DATA FILE CONTENT AND COMPOSITE VARIABLES

This chapter describes the content of the Early Childhood Longitudinal Study-Kindergarten

Class of 1998-99 (ECLS-K) Base Year Public-Use Data Files. There are three data files: one each for

school, teacher, and child. The school file contains one record for each of the 866 schools providing a

school administrator questionnaire; the teacher file contains one record for each of the 3,305 responding

teachers; and the child file contains one record for each of the 21,260 responding students. School- and

teacher-level data, including composites, are also stored on the child catalog for the convenience of users

performing child-level analyses that require school or teacher data. Each of these data files is stored in the

root directory of the CD-ROM as an ASCII file; the file names are school.dat, teacher.dat, and child.dat.

However, it is strongly recommended that users access the data using the Electronic Code Book (ECB)

software available on the CD-ROM rather than access the ASCII files directly. Appendix B contains the

record layout for the school, teacher, and child files.

7.1 Identification Variables

Each data file contains an identification (ID) variable that uniquely identifies each record.

For the school file, the ID variable is S_ID; for the teacher file, the ID variable is T_ID; and for the child

file, the ID variable is CHILD_ID.

Each type of respondent has a unique ID number. The school ID number is the base for all

the subsequent ID numbers as children, parents, and teachers were sampled from schools. The school ID

number is a four-digit number assigned sequentially to sampled schools. The school ID has a series of

ranges: 0001-1299 for originally sampled schools; 2000 series for new schools added to the sample

during the sample freshening process; 3000 series for substitute schools that replaced nonresponding

original sample schools; and 4000 series for transfer schools, which were assigned during processing at

the home office. (See chapter 4 for a complete description of the ECLS-K sample.)

The child ID number is a concatenation of the school ID where the child was sampled, a

three-digit student number and the letter “C.” For example, 0001001C is the ID number of the first child

sampled in school 0001. The teacher ID number is a concatenation of the school ID where the teacher was

sampled, the letter “T” and a two-digit teacher number. For example, 0001T01 is the ID number for the
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first teacher sampled in school 0001. The parent ID number is linked to the child ID number and is a

concatenation of the school ID, the child ID and the letter “P.” For example, 0001001P is the ID number

of the parent of the first child sampled in school 0001. If twins are sampled in a particular household, the

ID of the first child sampled is used to generate the parent ID. For twins, there will be two child-level

records with the same parent ID. Children with the same teacher can be identified by finding all children

on the child file with the same teacher ID.

7.2 Missing Values

All variables on these files use a standard scheme for missing values. Codes are used to

indicate item nonresponse, legitimate skips, unit nonresponse, and suppressed data.

-1 Not Applicable (legitimate skip)

-2 Data Suppressed

-7 Refused

-8 Don’t Know

-9 Not Ascertained

(blank) System Missing

The “not applicable” code (-1) indicates that the respondent did not answer the question due

to skip instructions within the instrument or external reasons that lead a respondent to not participate. For

the child file where the respondents were directly interviewed, a “not applicable” is coded for items that

were not asked of the respondent because of a previous answer given. For example, an item about a

sibling’s age is not asked when the respondent has indicated that the child has no siblings. A “not

applicable” code is also used in the direct child assessment if a child did not participate in any section due

to language or a disability. For the teacher and school files where the instruments are self-administered, a

“not applicable” is coded for items that the respondent left blank because the written directions instructed

them to skip the item due to a certain response on a previous item.

Depending on the research question being addressed, cases with a value of -1 may need to be

recoded. For example, a researcher interested in the average number of hours kindergarten children spend

in relative care, would include both children who are in such care and those who do not receive relative

care, i.e. children whose overall exposure is zero hours. These cases would have a -1 for the question on
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number of hours in relative care, since they would have been skipped out of the series once the

respondent had answered that the child had no relative care. Thus, the -1s would first have to be converted

to zero before being included in the computations. If the same researcher was interested in the average

time children in relative care spend in such care, he would simply average the cases with valid data

values, i.e. those with no missing values or -1s.

The “data suppressed” code (-2) indicates that the data for that variable are suppressed in

order to protect the identity of the respondent or child. When the data for a variable are suppressed, all the

cases have a value of -2 for that variable. A comment, “This data is suppressed for respondent

confidentiality,” is displayed in the comment field in the electronic code book.

The “refused” code (-7) indicates that the respondent specifically told the interviewer that he

or she would not answer the question. This, along with the “don’t know” code and the “not ascertained”

code, indicates item nonresponse. This code rarely appears in the school and teacher files because it

indicates the respondent specifically wrote something on the questionnaire indicating an unwillingness to

answer the question.

The “don’t know” code (-8) indicates that the respondent specifically told the interviewer

that he or she does not know the answer to the question (or in rare cases on the self-administered

questionnaires, “I don’t know” was written in for the item). The “don’t know” code was also used in the

direct child assessment when children did not answer a particular question after procedures had been

followed to repeat the question and try it again. For items where “don’t know” is one of the options

explicitly provided, a “-8” will not be coded for those that choose this option; instead the “don’t know”

response will be coded as indicated in the value label information for that item.

The “not ascertained” code (-9) indicates that the respondent left the item blank that he or

she should have answered. For the school and teacher self-administered questionnaires, this is the primary

code for item nonresponse. For data outside the instruments, e.g., direct assessment scores, a –9 means

that a value was not ascertained or could not be calculated due to nonresponse.

System missing appears as a blank when viewing code book frequencies and in the ASCII

data files. System missing codes (blanks) indicate that an entire instrument or assessment is missing due

to unit nonresponse. For example, if a child’s parent did not participate in the parent interview, then all

items from the parent interview will have a system missing. These may be translated to another value
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when the data are extracted into specific processing packages. For instance, SAS will translate these

blanks into periods (“.”) for numeric variables.

Missing values for composite variables were coded in a similar fashion. If a particular

composite was inappropriate for a given household—as the variable P1MOMID was for a household with

no resident mother—that variable was given a value of -1. In instances where a variable was appropriate,

but complete information to construct the composite was not available, the composite was given a value

of -9. The “Refused” and “Don’t Know” codes were not used for the composites, except in the

calculations of the height, weight, and body mass index (BMI) composites for fall-kindergarten and

spring-kindergarten.1

The ECLS-K Base Year Public-Use Data Files are provided on a CD-ROM and are

accessible through an ECB that allows data users to view variable frequencies, tag variables for

extraction, and create the SAS, SPSS for Windows, or STATA code needed to create an extract each file

for analysis. The three data files on the ECB—school, teacher, and child—are each referred to as a

“catalog.” Instructions for using the CD-ROM and ECB are provided in chapter 8.

7.3 Variable Naming Conventions

Variables were named according to the data source, (e.g., parent interview, teacher

questionnaire) and the data collection point (e.g., fall-kindergarten, spring-kindergarten). These variable

names are used consistently throughout all three catalogs. In general, variable names start with the

following prefixes:

A1 Data collected/derived from fall-kindergarten teacher questionnaire A.

A2 Data collected/derived from spring-kindergarten teacher questionnaire A.

B1 Data collected/derived from fall-kindergarten teacher questionnaire B.

 

1

 Children’s height and weight measurements were each taken twice to prevent error and provide an accurate reading. Children’s BMI was

calculated based on height and weight. The rules for using “don’t know” and “not ascertained” codes for these values was as follows. If both the

first and second measurement of height in the child assessment were coded as -8 (don’t know), then the height composite was coded as -8

(don’t know). If both the first and second measurements of weight were coded as -8 (don’t know), the weight composite was coded as -8 (don’t

know). If either the height or weight composites were coded as not ascertained (-9), the BMI composite was coded as not ascertained (-9). If

neither the height nor weight composites were coded as not ascertained, and either the height or weight composite was coded as -8 (don’t

know), then the BMI composite was coded as -8 (don’t know).
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B2 Data collected/derived from spring-kindergarten teacher questionnaire B.

BY Base year panel weight variables.

C1 Data/scores collected/derived from fall-kindergarten direct child assessment

and fall-kindergarten weight variables.

C2 Data/scores collected/derived from spring-kindergarten direct child assessment

and spring-kindergarten weight variables.

F1 Data from fall-kindergarten field management system (FMS).

F2 Data from spring-kindergarten FMS.

FK Data from base year (cross round) FMS.

IF Imputation flags.

K2 Data collected/derived from spring-kindergarten school facility checklist.

P1 Data/scores collected/derived from fall-kindergarten parent interview.

P2 Data/scores collected/derived from spring-kindergarten parent interview.

R1 Derived child demographic or child status variables for fall-kindergarten.

R2 Derived child demographic or child status variables for spring-kindergarten.

S2 Data collected/derived from spring-kindergarten school administrator questionnaire.

T1 Data/scores collected/derived from fall-kindergarten teacher questionnaire C.

T2 Data/scores collected/derived from spring-kindergarten teacher questionnaire C.

WK Base year (cross round) parent composite variables.

A few exceptions that do not follow the above-mentioned prefix convention are:

The identifiers CHILDID, PARENTID, T1_ID, T2_ID, S1_ID, and S2_ID.

School demographic variables from the sample frame that are named CREGION (Census

region), and CS_TYPE2 (type of school), KURBAN (location type, e.g., large city,

small town).

In general, all composites derived from a given source maintain the same prefix as the

source. Some composite variables, however, combined information across data

collection points and/or several sources and are consequently not associated with any
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prefixes. Derived child demographic variables, gender, race-ethnicity, and date of

birth, were created from the best source of data and are named GENDER, RACE,

DOBMM, DOBDD, and DOBYY. Other such derived variables include CREGION,

FKCHGSCH, FKCHGTCH, KURBUN, R1_KAGE and R2_KAGE. Sources and

other details for these and all other composite variables can be found in table 7-6 .

7.4 Composite Variables

To facilitate analysis of the survey data, a group of composite variables were created and

added to the child, teacher and school data files. Variables based on the child assessment include height,

weight, and BMI. Variables based on the teacher data include class type (e.g., AM, PM, or all-day

kindergarten class), student-teacher instructional ratio, and teacher’s age. Variables constructed from the

school data include the percentage of minority students, school type, and school instructional level.

Variables constructed from the parent interview data include parent identifiers, parent demographics,

household composition, household income and poverty, childcare, and child demographics.

Table 7-6 lists all the composite variables. All basic child demographic items (gender, age,

race-ethnicity, and date of birth) are listed first. Child care variables follow the demographics, and then

household composition and imputed variables are listed. Demographics for parents are next; resident

father and mother characteristics are first, followed by characteristics of nonresident biological parents

and nonresident adoptive parents. Teacher, classroom, and school variables are listed last. Once the user

identifies the composites of interest, he or she can refer to table 8-8 for instructions on accessing the

variables from the ECB.

7.4.1 Child Variables

Child Height Composite (C1HEIGHT and C2HEIGHT)

Children’s height was measured twice. For the height composite (C1HEIGHT and

C2HEIGHT), if the two height values from the instrument were less than two inches apart, the average of

the two height values was computed and used as the composite value. Otherwise, the value that was

closest to 43 inches (the average height for a five year old child) was used as the composite.
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Child Weight Composite (C1WEIGHT and C2WEIGHT)

Children’s weight was measured twice. For the weight composite (C1WEIGHT and

C2WEIGHT), if the two weight values from the instrument were less than five pounds apart, the average

of the two values was computed and used as the composite value. Otherwise, the value that was closest to

40 pounds (the average weight for a five year old child) was used as the composite value.

Child BMI Composite (C1BMI and C2BMI)

Composite BMI (C1BMI and C2BMI) was calculated by multiplying the composite weight

in pounds by 703.0696261393 and dividing by the square of the child’s composite height in inches.

Child Date of Birth Composite (DOBYY, DOBMM, and DOBDD)

The child date of birth composite was created using parent interview data and, in cases in

which the parent interview data did not exist or were outside of the criteria for inclusion, using the FMS

data. If the date of birth given was before June 1, 1990, or after March 31, 1995, the data were excluded.

Child Age at Assessment Composite (R1_KAGE, R2_KAGE)

The child’s age was calculated by determining the number of days between the child

assessment date and the child’s date of birth. The value was then divided by 30 to calculate the age in

months.

Gender Composite (GENDER)

The gender composite was derived using the gender indicated in the parent interview and, if

it was missing, the FMS. Also, if the gender was different in the fall-kindergarten parent interview and

spring-kindergarten parent interview, the FMS gender was used.
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Race-Ethnicity Composites

The data on race-ethnicity is presented in the ECLS-K files in two ways. Since a respondent

was allowed to indicate that they belonged to more than one of the five race categories (White, Black or

African American, American Indian or Alaskan Native, Asian, Native Hawaiian or other Pacific

Islander), a series of five dichotomous race variables were created that indicated separately whether the

respondent belonged to any of the five specified race groups. In addition one more dichotomous variable

was created for those who had simply indicated that they were multiracial without specifying the race

(e.g., biracial).

Data was collected on ethnicity as well. Respondents were asked if they were Hispanic or

not. Using the six race dichotomous variables and the Hispanic ethnicity variable, a race-ethnicity

composite variable was created. The categories were: White, non Hispanic; black or African American,

non-Hispanic; Hispanic, race specified; Hispanic, no race specified; Asian; Native Hawaiian or other

Pacific Islander; American Indian or Alaskan Native, and more than one race specified, non-Hispanic.

The retention of the dichotomous on the file allows users to create different composites as

needed.

7.4.2 Family and Household Variables

The list of all composite variables, including variables used to derive the composites, is

given in table 7-6 at the end of this chapter. Several household and family composite variables were

created. The creation of one of these, socioeconomic status (SES), is described below.

The socioeconomic scale (SES) variable was computed at the household level for the set of

parents who completed the parent interview in fall-kindergarten or spring-kindergarten. The SES variable

reflects the socioeconomic status of the household at the time of data collection for spring-kindergarten

(Spring 1999). The components used for the creation the SES were:

Father/male guardian’s education;

Mother/female guardian’s education;
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Father/male guardian’s occupation;

Mother/female guardian’s occupation; and

Household income.

The parent’s occupation was recoded to reflect the average of the 1989 General Social

Survey (GSS) prestige score2

 of the occupation. It was computed by averaging the corresponding prestige

score of the 1980 Census occupational category codes covered by the ECLS-K occupation. Table 7-6

provides details on the prestige score values.

The variables were collected as follows:

1. Income. The information about income was collected in spring-kindergarten. As a

result, income is missing for all households with parents who did not participate in the

survey in spring-kindergarten.

2. Parent’s education. The information about parent’s education was collected in

round 1. For households not interviewed in fall-kindergarten (e.g., parents of children

in refusal-converted schools), this information was collected in spring-kindergarten.

3. Parent’s occupation. The information about parent’s education was collected in fallkindergarten only.

Because not all the parents responded to all the questions or were respondents in both

rounds, there were missing values for some of the components of the SES indicator. The amounts of

missing data for these variables were small percentages, with income having the largest percentage

missing (see table 7-1 below).

Table 7-1. Missing data for SES variables

Variable Number Missing Percent

Mother’s Education 414 2.1%

Father’s Education 756 3.8%

Mother’s Occupation 2256 11.3%

Father’s Occupation 2252 11.3%

Household Income 5630 28.2%

 

2

 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: NORC.
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A hot deck imputation methodology was used to impute for missing values of all

components of the SES. In hot deck imputation, the value reported by a respondent for a particular item is

given or “donated” to a “similar” person who failed to respond to that question. Groups or cells use

auxiliary information known for both donors and nonrespondents. Ideally, donors and nonrespondents

have similar characteristics in the cell. The value to impute is from the randomly selected donor among

the respondents within the cell.

The SES component variables were highly correlated so a multivariate analysis was more

appropriate for examining the relationship of the characteristics of donors and nonrespondents. A

categorical search algorithm called CHAID (Chi-squared Automatic Interaction Detector) was used to

divide the data into cells based on the distribution of the variable to be imputed. The analysis used the

records with no missing values for the variable being imputed. CHAID not only analyzed and determined

the best predictors but also created the cells that were used for hot deck imputation.

The variables were imputed in a sequential order and separately by type of household

(female single parent, male single parent, and both parents present). For households with both parents

present, the mother’s and father’s variables were imputed separately. The new imputed values were used

in the creation of the imputation cells if these values had been already imputed. If this was not the case, an

“unknown” or missing category was created as an additional level for the CHAID analysis. As a rule, no

imputed value was used as a donor. In addition, the same donor was not used more than two times. The

order of the imputation for all the variables was from the lowest percent missing to the highest.

Occupation imputation involved two steps. First, the labor force status of the parent was imputed, i.e.,

whether the parent was employed or not. Then the parent’s occupation was imputed only for those parents

whose status was identified as employed either through the parent interview or the first imputation step.

The variable for income was imputed last using a three-stage procedure, where if a respondent provided

some partial information about income, that was used in the imputation process as well. For example,

some respondents did respond in the parent interview to the item on whether they earned more or less

than $25,000 but did not respond to the items asking for further details.

For example, for households where both parents are present, variables were imputed in the

following order:

Mother’s education;

Father’s education;
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Mother’s labor force status;

Mother’s occupation if the mother was employed or imputed as employed;

Father’s labor force status;

Father’s occupation if the father was employed or imputed as employed;

Income figure for households where the detailed income ranges are known;

Income figure for households where the broad income ranges are known; and

Income figure for households with no information about income.

Once the components of the SES variable were imputed, their corresponding z-score or

normalized value was computed. The expression of z-score zhi for the h-th component in the i-th

household is

( ) w

hi w

hi

se x

x x

z − =

where hi x is the value of the h-th SES component for the i-th household;

wi is the base weight for the i-th record;

xw is the weighted mean of hi x ; and

( ) w se x is the standard error of xw .

That is, each component converted to a z-score with mean of 0 and a standard deviation of

one. For income, the component i x is defined as the logarithm of the income for i-th household. The

logarithm of income was used as its distribution is less skewed than the direct income values. The SES

value for the i-th household was then computed as

i

m

h

hi

i

m

z

SES

i

∑= = 1

where mi is number of non-missing SES components for the i-th household. WKSESL is a continuous

variable that ranges from –4.75 to 2.75. As described, the SES composite is the average of up to five

measures, each of which was standardized to have a mean of 0 and a standard deviation of 1, hence the
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negative valves. For analyses that require a continuous SES measure, such as multivariate regressions,

WKSESL is the variable to use. For categorical analyses, use WKSESQ5.

Note that for households with only one parent present, not all the components were defined.

In these cases, the SES was computed averaging the available components.

A categorical SES variable was created to indicate the quintile for the value of the SES. The

quintiles were computed at the child level using the spring-kindergarten parent weights.

The imputed income variable was also used to create a household-level poverty variable.

Income was compared to preliminary Census poverty thresholds for 1998, which vary by household size.

Households whose income fell below the appropriate threshold were classified as poor (see table 7-2). For

example, if a household contained two members, and the household income was lower than $10,973, then

the household was considered to be in poverty.

Table 7-2. Preliminary Census poverty thresholds for 19981

Household Size Weighted Average Thresholds

2 $10,973

3 $13,001

4 $16,655

5 $19,682

6 $22,227

7 $25,188

8 $28,023

9+ $33,073

1

 U.S. Census Bureau, Current Population Survey. http://www.census.gov/hhes/poverty/threshld/thresh98.html.

7.4.3 Teacher Variables

The list of all composite variables, including the variables used to derive the composites, is

given in table 7-6 at the end of this chapter. Details about how one of the teacher composites, class type

was created are provided below.
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Base Year Class Type Composite (KGCLASS)

This composite indicates the type(s) of kindergarten class(es) teachers taught, (i.e., AM only,

PM only, AM and PM, all day). If the fall-kindergarten class type and spring-kindergarten class type were

the same, then that class type was used as KGCLASS. If either data collection point was missing, the

remaining class type was used as KGCLASS. If one of the data collection points showed an answer that

indicated overlapping times of day for instruction (the teacher indicated that he or she taught AM and all

day classes, PM and all day classes, or AM, PM, and all day classes) and the other data collection point

had a more typical answer (AM only, PM only, AM and PM, or all day), then the typical answer was used

as KGCLASS. If the two data collection points both had consistent answers but they were different from

each other, then the FMS data were used if it was in agreement with one of the data collection points. If

there were two different overlapping answers in the two data collection points or the FMS was not in

agreement with either of the two data collection points, then the fall-kindergarten answer was used.

Overlapping answers, e.g., AM and all day, were used only when they were provided in both fall- and

spring-kindergarten. If both data collection points had missing data, then the FMS data were used. Since

KGCLASS is a teacher level variable it is only present in the teacher level dataset.

There were 1,063 respondents who chose the “other specify” category for question 3e in the

teacher questionnaire C, student information section. A new variable, TZTT1OTG, was created that

classified the “other specifies” from question 3e into the following five categories: ESL/Bilingual,

Handicapped/Special Education, School-wide Title I, Computer, and other. It is important to remember

that these responses were not offered to all respondents, and thus, should not be used to make estimates

such as percentage of children participating in Title I computer programs. The goal of this variable was to

provide some information on the large number of “other specifies” in this question.

7.4.4 School and Class Characteristics

The list of all composite variables, including the variables used to derive the composites, is

given in table 7-6 at the end of this chapter. Variables on school and class characteristics were constructed

from the teacher and school data. Details on how some of the variables were created follow.
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Percent Minority Students (S2KMINOR)

The percent of minority students was derived from answers to the spring-kindergarten school

administrator questionnaire by determining the percentage of children who were either of Hispanic

origins (question 14) or who were American Indian or Alaskan Native, Asian, Black or AfricanAmerican, or Native Hawaiian or Other Pacific Islander (question 15) to create the percent minority

composite. In the questionnaire design, it was assumed that the school administrator would allow for

overlap between the ethnicity and race. For example, 20 percent of the children could be listed as

Hispanic, and these same children’s races could be indicated in the next question, such that the

percentages for different races in the school would add to 100 percent. However, this is not how all the

questionnaires were answered; therefore, rules were established for the following five types of schools to

accommodate different answer patterns. It is important to note that the term “race variables” refers to the

variables obtained from question 15 that pertain solely to race, whereas the terms “ethnicity” or

“Hispanic” refer to the ethnicity variable obtained from question 14. The term “race-ethnicity” variables

refer to the sum of the variables from questions 14 and 15, i.e., both the race and ethnicity variables.

 Type 1: Schools with missing race or Hispanic data. For the schools that had all

race variables missing, the Hispanic variables missing, or both race and the Hispanic

variables missing, data from the school sample frame was used to create the percent

minority composite. If there was no school sample frame data for certain records, then

the composite was not created for those records.

 Type 2: Schools with race and Hispanic variables totaling less than 97 percent.

For the schools that had race and Hispanic variables with percentages totaling less

than 97 percent, school sample frame data were used to create composite.

 Type 3: Schools with race-ethnicity variables (questions 14 and 15) greater than

103 percent but race variables sum to less than 97 percent. For the schools that

had a total of race and Hispanic variable percentages greater than 103 percent but with

the sum of the race variables less than 97 percent, school sample frame data were used

to create the composite.

 Type 4: Schools with total race-ethnicity percentages (sum of questions 14 and

15) between 97 percent and 103 percent. Several schools had the total percentages

of Hispanic and race variables (question 14 and question 15) total to between 97
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percent and 103 percent. Overall, these cases included two types of cases: a) where

items in question 14 (Hispanicity) and question 15 (race) cumulatively added up to

100 percent, it was assumed that this indicated that the school administrator did not

allow an overlap between ethnicity and race and therefore none of the percentages for

whites were meant to indicate Hispanic whites; and b) cases where the percentages

given for Hispanic was so small that it was less relevant how the principal interpreted

these items and the total of question 14 and question 15 was not equal to 100 percent,

but lay between 97 and 103 percent. Of these cases, some add up to exactly 100

percent, and so no adjustment was needed prior to calculating percentage minority.

For the latter cases, the percent white was adjusted so that the total equaled 100. In

order to do so, the percent white was divided by reported total (sum of question 14

and question 15) then multiplied by 100 to get adjustment. Then, 100 minus adjusted

percent white was used for the percent minority.

 Type 5: Schools with race-ethnicity percentages greater than 103 percent and

race variables sum to between 97 percent and 103 percent. Many schools that had

Hispanic and race variables that added to more than 103 percent and totals for race

variables that were between 97 percent and 103 percent. This indicated that the school

administrator interpreted these items to allow for overlap (e.g., there could be

Hispanic whites, Hispanic African-Americans, etc.)—some of the whites may be

Hispanic whites. It could not be known with certainty how many of the whites were

Hispanic and how many were non-Hispanic. For these schools, the first step was to

determine the range of possible values for percent minority (100 minus non-Hispanic

whites) given the data. The following example illustrates this process:

Example: Hispanic = 60 percent; and Asian = 1 percent, Native Indian = 1 percent,

African-American = 12 percent, Pacific Islander = 0 percent, white = 88 percent, and

Total Races = 100 percent. Therefore, total of percent given for all races and Hispanic

= 160 percent.

The next step was to determine the highest possible percent minority. This value was

calculated assuming that all Hispanics are white-Hispanics. The Hispanic percent was subtracted from the

percent white. (If percent Hispanic is greater than percent white, then percent white becomes zero.) Then

remaining white percentage was subtracted from 100 to equal highest possible percent minority.
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Example: Non-Hispanic whites = 88 percent (whites) – 60 percent (Hispanic) = 28 percent.

Therefore, highest percent minority = 100 percent - 28 percent (non-Hispanic whites) = 72

percent.

The next step was to determine the lowest possible percent minority. This value was

calculated assuming that all of the African-Americans were African-American-Hispanics and that any

remaining Hispanics were white-Hispanics. Total African-Americans were subtracted from Hispanics,

then only those remaining Hispanics were subtracted from whites to estimate the lowest possible

percentage of non-Hispanic whites. Next, non-Hispanic whites were subtracted from 100 to get the lowest

possible percent minority.

Example: Non-African-American Hispanics = 60 percent (Hispanics) - 12 percent (AfricanAmericans) = 48 percent; Non-Hispanic whites = 88 percent (whites) - 48 percent (nonAfrican-American Hispanics) = 40. Therefore, lowest percent minority = 100 percent - 40

percent (non-Hispanic whites) = 60 percent.

The last step was to compare this range of possible percent minority values to the school

sample frame value. If the school sample frame value was within range of the possible values, then the

school sample frame value was used as the percent minority composite. If the school sample frame value

was outside of the range of possible values, then school sample frame values were not used because the

range of values in the school administrator questionnaire offered at least some improvement for an

estimate over the school sample frame alone. If the school sample frame value was lower than the range

of values, the lowest possible percent minority was used as the composite estimate. If the school sample

frame value was higher than the range of values, the highest possible percent minority was used as the

composite estimate. For example, if the range of possible percent minority was 60 to 72 and the school

sample frame value was 73, then the percent minority was estimated as 72.

School Type Composite (S2KSCTYP)

Questions 5 (whether school is public) and 7 (type of private school) from the school

administrator questionnaire, along with school sample frame data, were used to create this variable. If the

response to question 5 (Is this a public school?) was “Yes,” then S2KSCTYP was coded “public.” If the

response to question 5 was “No,” and the response to question 7.a. (Is your school a Catholic school) was
7-17

“Yes,” then the school was coded as “Catholic.” If the responses to both questions 5 and 7.a. were No,”

and the response to question 7.b. (Is your school private with another religious affiliation?) was “Yes,”

then S2KSCTYP was coded as “private, other religious.” Otherwise, a school administrator questionnaire

with valid answers to questions 5 and 7, then S2KSCTYP was coded as “private, non-religious.” If there

was no school administrator questionnaire, then school sample frame data were used to create the

composite.

School Instructional Level Composite (S2KSCLVL)

The purpose of this composite is to classify schools based on the highest grade taught in the

school. Question 4 (grade levels included in the school) of the school administrator questionnaire was

used to create this composite. The highest grade level circled on the form was determined, and the grade

level was classified accordingly. If question 4 was left blank, question 10 from the school administrator

questionnaire (grade levels that participate in special programs, if applicable) was used as a proxy for

question 4. If the respondent did not answer questions 4 and 10, or there was no school administrator

questionnaire, then school sample frame data were used to determine the value for the composite.

7.4.5 Parent Identifiers and Household Composition

The construction of parent identifiers and the household composition variables from the

parent interview data was a two-step process. First, individuals identifying themselves as the child’s

mother/father were located within the household roster, and their type (biological, adoptive, foster, step,

partner, or unknown) was established. For households containing more than one father or mother, a

hierarchy was used to designate the “current,” or residential, parent of each sex. The biological parent, if

present, was always the current mother or father. In the absence of a biological parent, the current

mother/father designation was given to the adoptive, step, foster/guardian, partner, or “unknown-type”

parent. This information, along with household size and presence or absence of grandparents, siblings,

and other relatives was used to construct the household composition variables P1HPARNT, P2HPARNT,

P1HDAD, P2HDAD, P1HMOM, P2HMOM, P1HFAMIL, and P2HFAMIL and parent type variables

P1MOMTYP, P1DADTYP, P2MOMTYP, and P2DADTYP.
7-18

After the actual residential parents were identified and the composite variables were

constructed, in any household without a parent the household respondent (and his or her spouse, if

applicable) was assigned as a “parent figure.” Parent demographic variables (including age, raceethnicity, and education) were then constructed for all parents/parent “figures.” It should be noted,

however, that these parent “figures” were not incorporated into the construction of the household

composition variables described above.

It should be noted that because the composite construction identifies only one resident

mother and/or one resident father, same sex parents are not readily identified in the composites

themselves. Two approaches can be used to identify these couples. First, the user should search the

relationship variables (P1REL_1, P2REL_1, etc.) to identify households in which more than one person

identifies himself or herself as a father/mother to the focal child. Secondly, since not all same-sex partners

identify themselves as “mother” or “father” to the focal child, the user should also search for households

in which the respondent (identified by P1PER_1, P2PER_1, etc.) is the child’s parent and the

respondent’s spouse (identified from P1SPOUSE, P2SPOUSE) is the same sex as the respondent.

There are four sections in the parent interview that asked about parent-figure-specific

questions:

ALQ Years child lived with person

PLQ Primary language

PEQ Parent education

EMQ Employment

Each of these sections may be completed during the parent interview about a different

household member but about no more than four household members. Rather than reserve space (mostly

unused) for these sections with each of the 17 household members (the FSQ section), these sections are

repeated four times, with “pointer” or “foreign key” variables that hold the original number of the

household member. For instance, if household member #3 is the first person to receive the ALQ section,

then the pointer variable P1ALQHH1 will hold the value “3,” and the actual ALQ variable P1YRS_1 will

hold the number of years the child spent with the household member specified by the value of

P1ALQHH1. Table 7-3 identifies the pointer variables.
7-19

Table 7-3. Pointers to parent figure questions

Person Pointer Interview Item

P1ALQHH1 P1 ALQ010 HH PERSON P1YRS_1 P1 ALQ010 YEARS CHD LIVED WITH PERSON 1

POINTER 1 P1MON_1 P1 ALQ010 MONTHS CHD LIVED WITH PERSON 1

P1ALQHH2 P1 ALQ010 HH PERSON P1YRS_2 P1 ALQ010 YEARS CHD LIVED WITH PERSON 2

POINTER 2 P1MON_2 P1 ALQ010 MONTHS CHD LIVED WITH PERSON 2

P1ALQHH3 P1 ALQ010 HH PERSON P1YRS_3 P1 ALQ010 YEARS CHD LIVED WITH PERSON 3

POINTER 3 P1MON_3 P1 ALQ010 MONTHS CHD LIVED WITH PERSON 3

P1ALQHH4 P1 ALQ010 HH PERSON P1YRS_4 P1 ALQ010 YEARS CHD LIVED WITH PERSON 4

POINTER 4 P1MON_4 P1 ALQ010 MONTHS CHD LIVED WITH PERSON 4

P1PLQHH1 P1 PLQ083-090 HH P1RES_1 P1 PLQ083 PERSON 1 LANGUAGE TO CHILD

PERSON POINTER 1 P1CHL_1 P1 PLQ090 CHILD’S LANGUAGE TO PERSON 1

P1PLQHH2 P1 PLQ083-090 HH P1RES_2 P1 PLQ083 PERSON 2 LANGUAGE TO CHILD

PERSON POINTER 2 P1CHL_2 P1 PLQ090 CHILD’S LANGUAGE TO PERSON 2

P1PLQHH3 P1 PLQ083-090 HH P1RES_3 P1 PLQ083 PERSON 3 LANGUAGE TO CHILD

PERSON POINTER 3 P1CHL_3 P1 PLQ090 CHILD’S LANGUAGE TO PERSON 3

P1PLQHH4 P1 PLQ083-090 HH P1RES_4 P1 PLQ083 PERSON 4 LANGUAGE TO CHILD

PERSON POINTER 4 P1CHL_4 P1 PLQ090 CHILD’S LANGUAGE TO PERSON 4

P1HIG_1 P1 PEQ020 PERS 1 HIGHEST EDUCATION LEVEL

P1HIS_1 P1 PEQ030 IF PERS 1 HIGH SCHOOL DIPLOMA

P1ENR_1 P1 PEQ050 IF PERS 1 ENROLLED IN COURSES

P1FPT_1 P1 PEQ060 PERS 1 COURSE FULL/PART TIME

P1TRN_1 P1 PEQ070 IF PERSON 1 GETS JOB TRAINING

P1PEQHH1 P1 PEQ020-080 HH

PERSON POINTER 1

P1WKL_1 P1 PEQ080 PERS 1 WEEK HOURS IN TRAINING

P1HIG_2 P1 PEQ020 PERS 2 HIGHEST EDUCATION LEVEL

P1HIS_2 P1 PEQ030 IF PERS 2 HIGH SCHOOL DIPLOMA

P1ENR_2 P1 PEQ050 IF PERS 2 ENROLLED IN COURSES

P1FPT_2 P1 PEQ060 PERS 2 COURSE FULL/PART TIME

P1TRN_2 P1 PEQ070 IF PERSON 2 GETS JOB TRAINING

P1PEQHH2 P1 PEQ020-080 HH

PERSON POINTER 2

P1WKL_2 P1 PEQ080 PERS 2 WEEK HOURS IN TRAINING

P1HIG_3 P1 PEQ020 PERS 3 HIGHEST EDUCATION LEVEL

P1HIS_3 P1 PEQ030 IF PERS 3 HIGH SCHOOL DIPLOMA

P1ENR_3 P1 PEQ050 IF PERS 3 ENROLLED IN COURSES

P1FPT_3 P1 PEQ060 PERS 3 COURSE FULL/PART TIME

P1TRN_3 P1 PEQ070 IF PERSON 3 GETS JOB TRAINING

P1PEQHH3 P1 PEQ020-080 HH

PERSON POINTER 3

P1WKL_3 P1 PEQ080 PERS 3 WEEK HOURS IN TRAINING
7-20

Table 7-3. Pointers to parent figure questions (continued)

Person Pointer Interview Item

P1HIG_4 P1 PEQ020 PERS 4 HIGHEST EDUCATION LEVEL

P1HIS_4 P1 PEQ030 IF PERS 4 HIGH SCHOOL DIPLOMA

P1ENR_4 P1 PEQ050 IF PERS 4 ENROLLED IN COURSES

P1FPT_4 P1 PEQ060 PERS 4 COURSE FULL/PART TIME

P1TRN_4 P1 PEQ070 IF PERSON 4 GETS JOB TRAINING

P1PEQHH4 P1 PEQ020-080 HH

PERSON POINTER 4

P1WKL_4 P1 PEQ080 PERS 4 WEEK HOURS IN TRAINING

P1PAY_1 P1 EMQ020 PERS 1 HAD PAID JOB LAST WEEK

P1VAC_1 P1 EMQ030 IF PERS 1 ON LEAVE PAST WEEK

P1JOB_1 P1 EMQ040 PERSON 1 NUMBER OF ALL JOBS

P1HRS_1 P1 EMQ050 PERSON 1 HOURS/WK AT ALL JOBS

P1LOK_1 P1 EMQ060 PERS 1 SOUGHT JOB LAST 4 WEEKS

P1DO1_1 P1 EMQ070 PERS 1 CHKD W/PUB EMPL AGNCY

P1DO2_1 P1 EMQ070 PERS 1 CHKD W/PRIV EMP AGNCY

P1DO3_1 P1 EMQ070 PERS 1 CHKD W/EMPLOYR DIRECTLY

P1DO4_1 P1 EMQ070 PERS 1 CHKD W/FRIENDS & REL

P1DO5_1 P1 EMQ070 PERS 1 PLACED OR ANSWERED ADS

P1DO6_1 P1 EMQ070 PERS 1 READ WANT ADS

P1DO7_1 P1 EMQ070 PERS 1 DID SOMETHING ELSE

P1DOW_1 P1 EMQ080 WHAT PERSON 1 DOING LAST WEEK

P1TAK_1 P1 EMQ100 PERS 1 JOB AVAILABLE LAST WEEK

P1EMQHH1 P1 EMQ020-150 HH

PERSON POINTER 1

P1OCC_1 P1 EMQ130-50 1ST PERSON OCCUPATION CODE

P1PAY_2 P1 EMQ020 PERS 2 HAD PAID JOB LAST WEEK

P1VAC_2 P1 EMQ030 IF PERS 2 ON LEAVE PAST WEEK

P1JOB_2 P1 EMQ040 PERSON 2 NUMBER OF ALL JOBS

P1HRS_2 P1 EMQ050 PERSON 2 HOURS/WK AT ALL JOBS

P1LOK_2 P1 EMQ060 PERS 2 SOUGHT JOB LAST 4 WEEKS

P1DO1_2 P1 EMQ070 PERS 2 CHKD W/PUB EMPL AGNCY

P1DO2_2 P1 EMQ070 PERS 2 CHKD W/PRIV EMP AGNCY

P1DO3_2 P1 EMQ070 PERS 2 CHKD W/EMPLOYR DIRECTLY

P1DO4_2 P1 EMQ070 PERS 2 CHKD W/FRIENDS & REL

P1DO5_2 P1 EMQ070 PERS 2 PLACED OR ANSWERED ADS

P1DO6_2 P1 EMQ070 PERS 2 READ WANT ADS

P1DO7_2 P1 EMQ070 PERS 2 DID SOMETHING ELSE

P1DOW_2 P1 EMQ080 WHAT PERSON 2 DOING LAST WEEK

P1TAK_2 P1 EMQ100 PERS 2 JOB AVAILABLE LAST WEEK

P1EMQHH2 P1 EMQ020-150 HH

PERSON POINTER 2

P1OCC_2 P1 EMQ130-50 2ND PERSON OCCUPATION CODE
7-21

7.4.6 Changes in Household Composition between Fall-Kindergarten and SpringKindergarten

Changes in household composition between fall-kindergarten and spring-kindergarten may

complicate analyses. A household may have two different mothers in the base year file, one for each data

collection point. For most composite variables, a change in parent across rounds will result in missing

values in spring-kindergarten, since much of the information collected in fall-kindergarten is either not

collected in spring-kindergarten or is only asked of new households in spring-kindergarten. Consequently,

the household with a change in mother between rounds will have a nonmissing value for mother’s

employment in fall-kindergarten, but a missing value for mother’s employment in spring-kindergarten.

For several composites—education, mother’s marital status, and mother’s work history—a

cross-round, “base year” composite was constructed to compensate for missing values resulting from

household changes. These composites have a prefix of “w.” In general, if a nonmissing value existed for

spring-kindergarten, the composite was assigned that value; otherwise, if a nonmissing value existed for

fall-kindergarten, that value was used; otherwise the variable was set to missing. The result of this scheme

maximizes available information across rounds, on the theory that “replacement” parents in springkindergarten do not differ much demographically from their fall-kindergarten counterparts.

7.4.7 The Supplemental Section (SPQ) in the Spring-Kindergarten Parent Interview

The SPQ section was administered in spring-kindergarten to households that were

nonrespondents in fall-kindergarten. The SPQ was constructed to allow respondents participating for the

first time in spring-kindergarten to provide data for some of the more vital items from fall-kindergarten,

rather than respond to the entire fall-kindergarten instrument. For example, an entire section of fallkindergarten is devoted to education of parents and other adults in the household, while the springkindergarten SPQ contains only four questions on the education of the respondent and respondent’s

spouse (if applicable).

The topics covered in SPQ section include child care, Head Start participation, child health

and health services received, marital status of the biological parents at the time of the child’s birth, the

primary language spoken in the home, respondent (and spouse) education, and receipt of WIC benefits.
7-22

Relevant items from the SPQ section were used to construct composites for families new to

the survey in spring-kindergarten. Composites were calculated for child care (WKCAREPK), parental

education (WKMOMED, WKDADED, WKPARED), primary language (WKLANGST), mother’s work

status (WKHEARLY), and parent’s marital status at the time of the focal child’s birth (WKHMOMAR,

WKBMOMAR). These “cross-round” composites facilitate analyses across fall-kindergarten and springkindergarten.

When using SPQ data for cross-round analyses, users should combine the data items from

SPQ with the appropriate items from fall-kindergarten. Table 7-4 presents instrument item matches across

the two data collection points.

Care should be exercised when doing cross-round analysis of parental education, since the

spring-kindergarten SPQ education items are asked only of the respondent and the respondent’s spouse,

and it is conceivable that the respondent is not the parent. The cross-round parental education composites

(WKPARED, WKMOMED, and WKDADED) are more appropriate for such analyses.

The only section that is asked in its entirety to fall nonrespondents is the FSQ section, where

information on the household roster is collected for the first time.

7.4.8 Industry and Occupation Codes Used in ECLS-K

This section describes the aggregated categories that were used for coding occupation in the

ECLS-K.3

 Executive, Administrative, and Managerial Occupations

This category includes top and middle management occupations and occupations that directly support

management. Top level managers are persons concerned with policy making, planning, staffing, directing, and/or

controlling activities. Middle managers include persons who plan, or organize, direct and/or control activities at the

operational level. Legislators are also included in this category. Workers in this category are not directly concerned

with the fabrication of products or with the provision of services. Other officials and administrators include consultants,

library directors, customer-house brokers, and location managers.

 

3

 Office of Budget and Management, Executive Office of the President (1980). Standard Industrial Classification Manual. Springfield, VA, and

Office of Federal Statistical Policy and Standards, U.S. Department of Commerce (1980). Standard Occupational Classification Manual (2nd

ed.). Washington, DC: Superintendent of Documents, U.S. Government Printing Office.
7-23

Table 7-4. SPQ item matches to fall-kindergarten items

Fallkindergarten

Variable

Fallkindergarten

Item

Springkindergarten

Variable

Springkindergarten

Item

P1SAMETW CCQ003 P2SAMETW SPQ005

P1RPREK CCQ025 P2RPREK SPQ010

P1NPREK CCQ130 P2NPREK SPQ020

P1HSPREK CCQ215 P2HSPREK SPQ030

P1CPREK CCQ280 P2CPREK SPQ040

P1WEIGHP CHQ005 P2WEIGHP SPQ060

P1WEIGHO CHQ005 P2WEIGHO SPQ065

P1WEIGH5 CHQ010 P2WEIGH5 SPQ070

P1WEIGH6 CHQ015 P2WEIGH6 SPQ080

P1PREMAT CHQ025 P2PREMAT SPQ090

P1EARLY CHQ030 P2EARLY SPQ100

P1EARDAY CHQ030 P2EARDAY SPQ105

P1SIGHT CHQ285 P2SIGHT SPQ107

P1CORREC CHQ315 P2CORREC SPQ108

P1DIFFHR CHQ230 P2DIFFHR SPQ109

P1THERAP CHQ340 P2THERAP SPQ110

P1THERA2 CHQ345A P2THERA2 SPQ120A

P1THERA3 CHQ345B P2THERA3 SPQ120B

P1THERA5 CHQ345D P2THERA5 SPQ120D

P1THERA6 CHQ345E P2THERA6 SPQ120E

P1THERA7 CHQ345F P2THERA7 SPQ120F

P1THERA8 CHQ345G P2THERA8 SPQ120G

P1THERA9 CHQ345H P2THERA9 SPQ120H

P1THER10 CHQ345I P2THER10 SPQ120I

P1THER11 CHQ345J P2THER11 SPQ120J

P1THER12 CHQ345K P2THER12 SPQ120K

P1THER13 CHQ345L P2THER13 SPQ120L

P1THER14 CHQ375 P2THER14 SPQ130A

P1THER15 CHQ375U P2THER15 SPQ130B

P1BIOLOG MHQ060 P2BIOLOG SPQ150

P1ANYLNG PLQ020 P2ANYLNG SPQ155

P1PRMLNG PLQ060 P2PRMLNG SPQ157

P1MOMWRK EMQ170 P2MOMWRK SPQ200

P1WICMOM WPQ030 P2WICMOM SPQ210

P1WICCHD WPQ040 P2WICCHD SPQ220

 Natural Scientists and Mathematicians

This category includes those engaged primarily in the application of scientific principles to research and

development. Natural scientists are those in the physical sciences (e.g., chemistry, physics) and the life sciences (e.g.,

biology, agriculture, medicine). In addition, this category includes those in computer science, mathematics (including

statistics), and operations research.

 Social Scientists, Social Workers, Religious Workers, and Lawyers

This division includes occupations concerned with the social needs of people and in basic and applied research in

the social sciences.

 Teachers: College, University, and other Postsecondary Institution; Counselors, Librarians,

Archivists

This NHES category includes those who teach at higher education institutions and at other postsecondary (after

high school) institutions, such as vocational institutes. In addition, vocational and educational counselors, librarians,

and archivists are included here.
7-24

 Teachers, except Postsecondary Institution

This category includes prekindergarten and kindergarten teachers, elementary and secondary teachers, special

education teachers, instructional coordinators, and adult education teachers (outside postsecondary).

 Health Diagnosing and Treating Practitioners

This category includes health care professionals who diagnose and treat patients. In addition to physicians, dentists,

and veterinarians, this category includes optometrists, podiatrists, and other diagnosing and treating professionals, such

as optometrists, podiatrists, chiropractors, hypnotherapists, and acupuncturists.

 Registered Nurses, Pharmacists, Dieticians, Therapists, and Physician’s Assistants

This category includes occupations concerned with the maintenance of health, the prevention of illness, and the

care of the ill through the provision and supervision of nursing care; compounding drugs, planning food service or

nutritional programs; providing assistance to physicians; and the provision of therapy and treatment as directed by

physicians.

 Writers, Artists, Entertainers, and Athletes

This occupational category includes occupations concerned with creating and executing artistic works in a

personally interpreted manner, by painting, sculpturing, drawing, engraving, etching, and other methods; creating

designs for products and interior decorations; designing and illustrating books, magazines, and other publications;

writing; still, motion picture and television photography/filming; producing, directing, staging, acting, dancing, singing

in entertainment; and participating in sports and athletics as competitor or player and administering and directing

athletic programs.

 Health Technologists and Technicians

This category includes occupations concerned with providing technical assistance in the provision of health care.

For example, clinical laboratory technologists and technicians, dental hygienists, radiologic technicians, licensed

practical nurses (LPN’s), and other health technologists are included here.

 Technologists and Technicians, except Health

This group includes those providing technical assistance in engineering and scientific research, development,

testing, and related activities, as well as operating and programming technical equipment and systems.

 Marketing and Sales Occupations

This category includes occupations involving selling goods or services, purchasing commodities and property for

resale, and conducting wholesale or retail business.

 Administrative Support Occupations, including Clerical

Occupations involving preparing, transcribing, transferring, systematizing, and preserving written communications

and records; collecting accounts; gathering and distributing information; operating office machines and data processing

equipment; operating switchboards; distributing mail and messages; and other support and clerical duties such as bank

teller, data entry keyer, etc.

 Service Occupations

The category includes occupations providing personal and protective services to individuals, and current

maintenance and cleaning for building and residences. Some examples include food service, health service (e.g., aides

or assistants), cleaning services other than household, and personal services.

 Agricultural, Forestry, and Fishing Occupations

This category is concerned with the production, propagation (breeding/growing), gathering, and catching of

animals, animal products, and plant products (timber, crop, and ornamental); the provision of services associated with
7-25

agricultural production; and game farms, fisheries, and wildlife conservation. “Other agricultural and related

occupations” include occupations concerned with the production and propagation of animals, animals products, plants

and products (crops and ornamental).

 Mechanics and Repairers

Mechanics and repairers are persons who do adjustment, maintenance, part replacement, and repair of tools,

equipment, and machines. Installation may be included if installation is usually done in conjunction with other duties of

the repairers.

 Construction and Extractive Occupations

This category includes occupations that normally are performed at a specific site, which will change over time, in

contrast to production workers, where the work is usually at a fixed location. Construction workers include those in

overall construction, brickmasons, stonemasons, carpenters, electricians, drywall installers, paperhangers and painters,

etc. Extractive occupations include oil well drillers, mining machine operators, and so on.

 Precision Production Occupations

Precision production includes occupations concerned with performing production tasks that require a high degree

of precision or attainment of rigid specification and operating plants or large systems. Examples are tool and die

makers, pattern and model makers, machinists, jewelers, engravers, and so on. Also included are some food-related

occupations including butchers and bakers. Plant and system operators include water and sewage, gas, power,

chemical, petroleum, and other plant or system operators.

 Production Working Occupations

This category includes occupations concerned with setting up, operating, and tending of machines and hand

production work usually in a factory or other fixed place of business.

 Transportation and Material Moving Occupations

This category includes occupations concerned with operating and controlling equipment used to facilitate the

movement of people or materials and the supervising of those workers.

 Handlers, Equipment Cleaners, Helpers, and Laborers

This category includes occupations that involve helping other workers and performing routine nonmachine tasks.

A wide variety of helpers, handlers, etc., are included in this category. Examples include construction laborers, freight,

stock, and material movers, garage and service station related occupations, parking lot attendants, and vehicles washers

and equipment cleaners.

7.5 Using Teacher Data Collected Either in Fall-kindergarten or Spring-kindergarten

Teachers were asked about classroom characteristics in teacher questionnaire part B (TQB).

If the data were collected in fall-kindergarten, they were not collected again in spring-kindergarten. If

they were not collected in fall-kindergarten, i.e., a teacher was added to the sample in spring-kindergarten,

then they were collected in spring-kindergarten. There are flags on the file to indicate in which round the

data were collected. The TQB flags and data are set on the teacher and child catalogs as follows:
7-26

 Teacher catalog: There are two flags: B1TQUEX and B2TQUEX. For each, a “1”

means that the TQB for that teacher was collected at that data collection point. All

teachers will have one but not both of these variables set. Each teacher record has only

one set of TQB data, regardless of data collection point.

 Child catalog: There are two flags and corresponding sets of data. The first flag,

B1TQUEX, and variables (approximately 80 variables all starting with the B1 prefix)

are set if the teacher with whom the child was linked in fall-kindergarten has a

completed TQB. This is regardless of which round the TQB on the teacher record was

collected. The second flag, B2TQUEX, and variables (also approx. 80 variables, all

starting with B2) are set if the teacher with whom the child was linked in springkindergarten has a TQB. That is, the B1TQUEX and B2TQUEX on the child catalog

refer to the data collection point in which the child was linked to the teacher, not the

data collection point in which that teacher’s TQB data was collected.

7.6 The Relationship Among Composite Variables on the Child, Teacher, and School

Catalogs

The school catalog contains ONLY school composites.

The teacher catalog contains all teacher composites and five of the school composites

(S2KSCTYP, S2KENRLS, S2KMINOR, S2KSCLVL, and S2KPUPR).

The child catalog contains all child, teacher (except KGCLASS which is in the teacher

catalog only), and school composites, but it should be noted that some of the teacher variables on the

child catalog are slightly altered when they are brought to the child level. While some teacher variables

are class specific, i.e., there will be three versions of each of these class-specific variables (AM, PM, and

all day) on the teacher file, the child file will only contain data from only one of each of the three

variables—depending on whether the child attends an AM, PM, or all day class. For example, there are

three variables (one for AM, one for PM, and one for all day classes) on the teacher catalog that represent

percent of African-Americans in the teacher’s class. Since the child can only be in one class, the child-file

version of percent African-American in class is only one variable. The same is true for percent Hispanic

and percent minority variables. The opposite is true for the teacher age variable—since a child could have

two teachers, one in each data collection point, there are two teacher ages (B1AGE, B2AGE) on the child

file, but only one teacher age (B1AGE) on the teacher file. Table 7-5 presents the relationship of the

composite variables on the three catalogs.
7-27

Table 7-5. Relationship among composite variables on the three ECB catalogs

School

Catalog

Teacher

Catalog

Child

Catalog Variable Name Variable Label

X DOBMM CHILD COMPOSITE DOB MONTH

X DOBDD CHILD COMPOSITE DOB DAY

X DOBYY CHILD COMPOSITE DOB YEAR

X GENDER CHILD COMPOSITE GENDER

X RACE CHILD COMPOSITE RACE

X R1_KAGE R1 COMPOSITE CHILD ASSESSMENT AGE(MNTHS)

X R2_KAGE R2 COMPOSITE CHILD ASSESSMENT AGE(MNTHS)

X R2ELIG R2 CHILD ELIGIBILITY IN ROUND 2

X FKCHGTCH FK CHD CHANGED TCHRS BETWEEN ROUND 1 & 2

X FKCHGSCH FK CHD CHANGED SCHLS BETWEEN ROUND 1 & 2

X C1HEIGHT C1 ROUND 1 CHILD COMPOSITE HGT (INCHES)

X C1WEIGHT C1 ROUND 1 CHILD COMPOSITE WGT (POUNDS)

X C1BMI C1 ROUND 1 CHILD COMPOSITE BMI

X C2HEIGHT C2 ROUND 2 CHILD COMPOSITE HGT (INCHES)

X C2WEIGHT C2 ROUND 2 CHILD COMPOSITE WGT (POUNDS)

X C2BMI C2 ROUND 2 CHILD COMPOSITE BMI

X P1HMEMP P1 CURRENT MOTHER EMPLOYMENT STATUS

X P1HDEMP P1 CURRENT FATHER EMPLOYMENT STATUS

X P1HMAGE P1 AGE - CURRENT MOTHER (YRS)

X P1HDAGE P1 AGE - CURRENT FATHER (YRS)

X P1DADID P1 HOUSEHOLD ROSTER NUMBER OF FATHER

X P1MOMID P1 HOUSEHOLD ROSTER NUMBER OF MOTHER

X P1HMRACE P1 RACE OF CURRENT MOTHER

X P1HMHISP P1 MOTHER HISPANIC

X P1HMIND P1 MOTHER AMERICAN INDIAN

X P1HMASN P1 MOTHER ASIAN

X P1HMBLCK P1 MOTHER BLACK

X P1HMPACI P1 MOTHER PACIFIC ISLANDER

X P1HMWHT P1 MOTHER WHITE

X P1HMMT1R P1 MOTHER MORE THAN ONE RACE

X P1HDRACE P1 RACE OF CURRENT FATHER

X P1HDHISP P1 FATHER HISPANIC

X P1HDIND P1 FATHER AMERICAN INDIAN

X P1HDASN P1 FATHER ASIAN

X P1HDBLCK P1 FATHER BLACK

X P1HDPACI P1 FATHER PACIFIC ISLANDER

X P1HDWHT P1 FATHER WHITE

X P1HDMT1R P1 FATHER MORE THAN ONE RACE

X P1HMAFB P1 AGE AT 1ST BIRTH - CURRENT MOM (YRS)

X P1BMAGE P1 AGE OF NONRES BIO MOTHER (YRS)

X P1BDAGE P1 AGE OF NONRES BIO FATHER (YRS)

X P1BMRACE P1 RACE OF NONRES BIOLOGICAL MOTHER

X P1BMHISP P1 NONRES BIOLOGICAL MOTHER HISPANIC
7-28

Table 7-5. Relationship among composite variables on the three ECB catalogs (continued)

School

Catalog

Teacher

Catalog

Child

Catalog Variable Name Variable Label

X P1BMIND P1 NONRES BIO MOTHER AMERICAN INDIAN

X P1BMASN P1 NONRES BIOLOGICAL MOTHER ASIAN

X P1BMBLCK P1 NONRES BIOLOGICAL MOTHER BLACK

X P1BMPACI P1 NONRES BIO MOTHER PACIFIC ISLANDER

X P1BMWHT P1 NONRES BIOLOGICAL MOTHER WHITE

X P1BDMT1R P1 NONRES BIO FATHER MORE THAN 1 RACE

X P1BDRACE P1 RACE OF NONRES BIOLOGICAL FATHER

X P1BDHISP P1 NONRES BIOLOGICAL FATHER HISPANIC

X P1BDIND P1 NONRES BIO FATHER AMERICAN INDIAN

X P1BDASN P1 NONRES BIOLOGICAL FATHER ASIAN

X P1BDBLCK P1 NONRES BIOLOGICAL FATHER BLACK

X P1BDPACI P1 NONRES BIO FATHER PACIFIC ISLANDER

X P1BDWHT P1 NONRES BIOLOGICAL FATHER WHITE

X P1BMMT1R P1 NONRES BIO MOTHER MORE THAN 1 RACE

X P1BMAFB P1 AGE AT 1ST BIRTH NONRES BIO MOM (YRS)

X P1AMOMED P1 EDUCATION LVL NONRES ADOPTIVE MOTHER

X P1ADADED P1 EDUCATION LVL NONRES ADOPTIVE FATHER

X P1BMOMED P1 EDUCATION LVL NONRES BIOLOGICAL MOM

X P1BDADED P1 EDUCATION LVL NONRES BIOLOGICAL DAD

X P1MOMOCC P1 RESIDENT MOTHER’S OCCUPATION

X P1DADOCC P1 RESIDENT FATHER’S OCCUPATION

X P1AGEENT P1 AGE (MONTHS) AT KINDERGARTEN ENTRY

X P1AGEFRS P1 AGE (MNTHS) AT FIRST NONPARENTAL CARE

X P1CARNOW P1 CURRENT NONPARENTAL CARE ARRANGEMENTS

X P1NUMNOW P1 # NONPARENTAL CARE ARRANGEMENTS NOW

X P1HRSNOW P1 # HOURS SPENT IN NONPARENTAL CARE NOW

X P1PRIMNW P1 PRIMARY TYPE OF NONPARENTAL CARE

X P1CENTER P1 CHILD EVER IN CENTER-BASED CARE

X P1DISABL P1 CHILD W/ DISABILITY

X P1HMLANG P1 MOTHER’S LANGUAGE TO CHILD

X P1HMLTOM P1 CHILD’S LANGUAGE TO MOTHER

X P1HDLANG P1 FATHER’S LANGUAGE TO CHILD

X P1HDLTOD P1 CHILD’S LANGUAGE TO FATHER

X P1LANGUG P1 BOTH PARENT LANGUAGE TO CHILD

X P1HMOM P1 RESIDENT MOTHER TYPE

X P1HDAD P1 RESIDENT FATHER TYPE

X P1NUMSIB P1 NUMBER OF SIBLINGS IN HOUSEHOLD

X P1LESS18 P1 NUMBER IN HOUSEHOLD AGED <18

X P1OVER18 P1 NUMBER IN HOUSEHOLD AGED 18+

X P1HTOTAL P1 TOTAL NUMBER IN HOUSEHOLD

X P1HFAMIL P1 FAMILY TYPE

X P1HPARNT P1 TYPES OF PARENTS IN HOUSEHOLD

X P1MOMTYP P1 TYPE OF RESIDENT MOTHER

X P1BMVITL P1 BIOLOGICAL MOTHER IS LIVING/DEAD

X P1ABSMOM P1 TYPE OF NONRESIDENT MOTHER(S)
7-29

Table 7-5. Relationship among composite variables on the three ECB catalogs (continued)

School

Catalog

Teacher

Catalog

Child

Catalog Variable Name Variable Label

X P1DADTYP P1 TYPE OF RESIDENT FATHER

X P1BDVITL P1 BIOLOGICAL FATHER IS LIVING/DEAD

X P1ABSDAD P1 TYPE OF NONRESIDENT FATHER(S)

X P1TWINST P1 CHILD BIRTH STATUS

X P1FIRKDG P1 FIRST-TIME KINDERGARTNER

X P2HMOM P2 RESIDENT MOTHER TYPE

X P2HDAD P2 RESIDENT FATHER TYPE

X P2NUMSIB P2 NUMBER OF SIBLINGS IN HOUSEHOLD

X P2LESS18 P2 NUMBER IN HOUSEHOLD AGED <18

X P2OVER18 P2 NUMBER IN HOUSEHOLD AGED 18+

X P2HTOTAL P2 TOTAL NUMBER IN HOUSEHOLD

X P2HFAMIL P2 FAMILY TYPE

X P2HPARNT P2 TYPES OF PARENTS IN HOUSEHOLD

X P2MARSTA P2 MARITAL STATUS

X P2MOMID P2 HOUSEHOLD ROSTER NUMBER OF MOTHER

X P2DADID P2 HOUSEHOLD ROSTER NUMBER OF FATHER

X P2HMAGE P2 AGE - CURRENT MOTHER (YRS)

X P2HDAGE P2 AGE - CURRENT FATHER (YRS)

X P2HMRACE P2 RACE OF CURRENT MOTHER

X P2HMHISP P2 MOTHER HISPANIC

X P2HMIND P2 MOTHER AMERICAN INDIAN

X P2HMASN P2 MOTHER ASIAN

X P2HMBLCK P2 MOTHER BLACK

X P2HMPACI P2 MOTHER PACIFIC ISLANDER

X P2HMWHT P2 MOTHER WHITE

X P2HMMT1R P2 MOTHER MORE THAN ONE RACE

X P2HDRACE P2 RACE OF CURRENT FATHER

X P2HDHISP P2 FATHER HISPANIC

X P2HDIND P2 FATHER AMERICAN INDIAN

X P2HDASN P2 FATHER ASIAN

X P2HDBLCK P2 FATHER BLACK

X P2HDPACI P2 FATHER PACIFIC ISLANDER

X P2HDWHT P2 FATHER WHITE

X P2HDMT1R P2 FATHER MORE THAN ONE RACE

X P2DADCHK P2 FATHER IN HOUSEHOLD

X P2DADTYP P2 TYPE OF RESIDENT FATHER

X P2MOMCHK P2 MOTHER IN HOUSEHOLD

X P2MOMTYP P2 TYPE OF RESIDENT MOTHER

X WKMOMED WK MOTHER’S EDUCATION LEVEL

X IF_M_ED IF MOTHER’S EDUCATION IMPUTED

X WKDADED WK FATHER’S EDUCATION LEVEL

X IF_D_ED IF FATHER’S EDUCATION IMPUTED

X WKHEARLY WK MOM WORKED BTWN CHILD’S BIRTH & KINDG

X WKPARED WK PARENT HIGHEST EDUCATION LEVEL

X WKHMOMAR WK MOTHER MARRIED AT TIME OF BIRTH

X WKBMOMAR WK NONRES MOM MARRIED AT TIME OF BIRTH

X WKRACETH WK CHILD RACE- COMPOSITE
7-30

Table 7-5. Relationship among composite variables on the three ECB catalogs (continued)

School

Catalog

Teacher

Catalog

Child

Catalog Variable Name Variable Label

X WKHISP WK CHILD HISPANIC

X WKAMERIN WK CHILD AMERICAN INDIAN

X WKASIAN WK CHILD ASIAN

X WKBLACK WK CHILD BLACK

X WKPACISL WK CHILD PACIFIC ISLANDER

X WKWHITE WK CHILD WHITE

X WKMT1RAC WK CHILD MORE THAN ONE RACE

X WKCAREPK WK NONPARENTAL CARE ARRANGEMENTS PRE-K

X WKNUMPRK WK # NONPARENTAL CARE ARRANGEMENTS PRE-K

X P1HRSPRK P1 # HRS SPENT IN NONPARENTAL CARE PRE-K

X P1PRIMPK P1 PRIMARY TYPE NONPARENTAL CARE PRE-K

X P1COSTPK P1 COST NONPARENTAL CHILD CARE PRE-K ($)

X WKLANGST WK HOME LANGUAGE OF CHILD

X WKMOMSCR WK AVG PRESTIGE SCORE FOR MOM OCCUPATION

X IF_M_OCS IF MOM OCCUPATION PRESTIGE SCORE IMPUTED

X WKDADSCR WK AVG PRESTIGE SCORE FOR DAD OCCUPATION

X IF_D_OCS IF DAD OCCUPATION PRESTIGE SCORE IMPUTED

X WKSESL WK CONTINUOUS SES MEASURE

X WKSESQ5 WK CATEGORICAL SES MEASURE

X WKPOVRTY WK POVERTY LEVEL

X WKINCOME WK INCOME (IMPUTED)

X IF_INC IF INCOME VALUE IMPUTED

X A1PBLK A1 PERCENT OF BLACKS IN CLASS

X A1APBLK A1 PERCENT OF BLACKS IN CLASS-AM

X A1PPBLK A1 PERCENT OF BLACKS IN CLASS-PM

X A1DPBLK A1 PERCENT OF BLACKS IN CLASS-AD

X A1PHIS A1 PERCENT OF HISPANICS IN CLASS

X A1APHIS A1 PERCENT OF HISPANICS IN CLASS-AM

X A1PPHIS A1 PERCENT OF HISPANICS IN CLASS-PM

X A1DPHIS A1 PERCENT OF HISPANICS IN CLASS-AD

X A1PMIN A1 PERCENT OF MINORITIES IN CLASS

X A1APMIN A1 PERCENT OF MINORITIES IN CLASS-AM

X A1PPMIN A1 PERCENT OF MINORITIES IN CLASS-PM

X A1DPMIN A1 PERCENT OF MINORITIES IN CLASS-AD

X X B1AGE B1 TEACHER’S AGE

X B2AGE B2 TEACHER’S AGE

X KGCLASS KINDERGARTEN TEACHER CLASS COMPOSITE

X X X S2KSCTYP S2 SCHOOL TYPE FROM THE SCH ADMIN QUEST

X X S2KENRLK S2 TOTAL SCHOOL K ENROLLMENT

X X X S2KENRLS S2 TOTAL SCHOOL ENROLLMENT

X X X S2KMINOR S2 PERCENT MINORITY STUDENTS

X X S2KFLNCH S2 PCT FREE LUNCH ELIGIBLE STUDENTS

X X S2KRLNCH S2 PCT REDUCED LUNCH ELIGIBLE STUDENTS

X X X S2KSCLVL S2 SCH INSTRUCTNL LEVEL FROM ADMIN QUEST

X X S2KLNGTH S2 SCHOOL YEAR LENGTH

X X S2KGFTED S2 PERCENT GIFTED/TALENTED STUDENTS

X X X S2KPUPRI S2 PUBLIC OR PRIVATE SCHOOL
7-31

7.7 Creating a Child File for Classroom Level Analysis

Using T1_ID or T2_ID in the child catalog a user can identify all students belonging to a

given teacher. And given a T1_ID and A1CLASS or T2_ID and A2CLASS combination, a classroom

level file can also be created in the child catalog. For example, in the child catalog, records with T1_ID =

0001T01 and A1CLASS = AM are all children from teacher 0001T01’s morning class.

7.8 Creating a Classroom Level File Using Teacher Data

For researchers interested in doing classroom level analysis on the characteristics of

kindergarten classrooms, a classroom level file can be constructed. Each record in the teacher file

represents one teacher regardless of whether that teacher teaches one or two classes. (Approximately 16

percent of the kindergarten teachers in the ECLS-K sample teach both a morning and an afternoon class.)

For situations when the class rather than the teacher is the unit of analysis of interest to the researcher, a

new class-level file may be constructed so that each record represents a kindergarten class. Teachers

provided separate information about each of their kindergarten classes – morning, afternoon, or all-day –

for a set of items on the fall part A and spring part A teacher questionnaires. These class-specific

variables contain information about class characteristics such as class size and race-ethnicity composition.

In order to create a classroom level file a user must perform the following steps:

1. Identify the variables that are to be included in the classroom level file including the AM,

PM, and all day versions of identical variables. These are the variables that will comprise the classroom

file, so non-class specific data, e.g., teacher’s educational background, should be included as well.

A1AASIAN, A1PASIAN, and A1DASIAN are examples of class-specific variables.

2. Create three files, an AM, PM, and all day file, each with the appropriate class specific

variables (e.g., keep only A1AASIAN for the AM file, drop A1PASIAN and A1DASIAN) and the

remaining non-class specific variables. The aim of this exercise is to isolate into three groups those

records with data belonging to each of the three groups. Those teachers with more than one class type will

appear in more than one file. Thus on the AM file, there will be data for all the non-class specific

variables, and the AM variables. The PM and all day variables will be dropped.
7-32

3. Rename the class-specific variables in each of the three files to be the same across each of

the files. For example, on the AM file, one might rename the variable A1AASIAN (the number of Asian

children in the AM class) to A1ASIAN. Similarly, A1PASIAN (the number of Asian children in the PM

class) gets renamed to A1ASIAN, and A1DASIAN gets renamed to A1ASIAN in the all day file.

4. On each of these three files, create a class type variable with the same name across the

three files, that indicates the class type. The class type variable can use values of 1=AM, 2=PM, and 3=all

day. In each of the files the class type variable value will be the same for all cases (e.g., all records in the

AM file will have a value of ‘AM’ for class type), but when the records from the three files (AM, PM,

and all day) are merged, the class type variable will be used to identify whether the classroom is an AM,

PM, or all day class.

5. Merge the three files. Now each case represents one classroom. This file will have no

classroom ID. An ID can be created if needed but is not necessary for analysis.

With this file users can do analysis such as examining characteristics of classrooms with

high percent minority, etc.

KGCLASS, F1CLASS, F2CLASS, A1CLASS, and A2CLASS are all class type variables—

AM/PM/All-day. However, they carry different information and serve different purposes. KGCLASS was

created to reflect the teacher’s base year class type and is not related to the child data. (See section 7.4 for

details.) F1CLASS and F2CLASS are the child’s fall- and spring-kindergarten class types respectively in

the FMS. A1CLASS and A2CLASS were created for linking teacher and child data in fall- and springkindergarten, respectively.

The teacher report of class type in the teacher questionnaire A defines the teacher’s reported

class type. This report does not always match the child’s class type. A1CLASS and A2CLASS were

created to report a child class type. In fall-kindergarten, if the teacher’s reported class type in teacher

questionnaire A matches F1CLASS, then A1CLASS is defined as the matching class type. If the teacher’s

reported class type does not match F1CLASS and the teacher reported only one class in teacher

questionnaire A, then A1CLASS is defined as the teacher’s reported class type and the child is linked to

the only class that the teacher reported. If the teacher’s reported class type does not match F1CLASS and

the teacher reported more than one class in questionnaire A, then A1CLASS is missing and the teacher
7-33

and child are not linked. The same rules were followed with F2CLASS and A2CLASS with springkindergarten data to link the teacher and child data.

Table 7-6 lays out the composite and derived variables that are on the ECLS-K child,

teacher, and school datasets. Please note that some of the variables specified in the ‘derived from’ column

are intermediary variables that were not included in the final datasets. This column also contains the item

numbers from the questionnaire, which help in identifying the items that were used in the creation of

these composites and derived variables.
7-34

Table 7-6. Composite variablesVariable Name Category Description Derived from VCHILDID Child Child Identification Number OCHILDIDR1_KAGER2_KAGE

Child Child’s age at the time the direct child assessment

occurred. This age will be in month and years, nodays.

DOBMO, DOBDA, DOBYR,C1ASMTMM, C1ASMTDD,C1ASMTYY, C2ASMTMM,C2ASMTDD, C2ASMTYY

C

GENDER Child Child’s gender FMS and parent interview

fall (INQ050)spring (INQ 160)

1 2 

DOBMMDOBDDDOBYY

ChildChildChild

Child’s date of birth monthChild’s date of birth dayChild’s date of birth year

FMS, parent interviewfall (INQ060)spring (INQ 170)

11

WKRACETH Child Race and ethnicity of the child P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 3 4 5 6 7 8 
7-35

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKAMERIN Child Child is American Indian or Native Alaskan P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

WKASIAN Child Child is Asian P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-36

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKPACISL Child Child is Native Hawaiian or other Pacific Islander P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

WKBLACK Child Child is African American P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-37

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKWHITE Child Child is white P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

WKHISP Child Child is Hispanic P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-38

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKMT1RAC Child Child is more than one race P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

C1BMIC2BMI

ChildChild

Round 1 body mass index (BMI)Round 2 body mass index (BMI)

C1HEIGHT, C1WEIGHTC2HEIGHT, C2WEIGHT

C

C1HEIGHT Child Child’s round 1 composite height HEIGHTIN, HEIGHTI2 CC2HEIGHT Child’s round 2 composite height HEIGHTI3, HEIGHTI4 CC1WEIGHT Child Child’s round 1 composite weight WEIGHTIN, WEIGHTI2 CC2WEIGHT Child’s round 2 composite weight WEIGHTI3, WEIGHTI4 CP1DISABL Child Child currently has a disability P1DIAGNO (CHQ120),

P1PROFFD (CHQ155),P1CLIMB (CHQ185),P1COMMU2 (CHQ215),P1DIFFH3 (CHQ245),P1VISIO2 (CHQ300),P1THERAP (CHQ340)

1 2 

P1TWINST Child Multiple birth status P1MULTIP (CHQ035),

P1IDENTI (CHQ070)

1 2 3 4 9 
7-39

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1AGEFRS Child The age in months when the focal child first began

any type of nonparental care on a regular basis

P1AGEMRL (CCQ020), P1YRSFIR(CCQ125), P1AGEMHS (CCQ259),P1CAGEMO (CCQ275)

C

P1CARNOW Child Focal child is currently receiving any nonparental

care

P1RELAT (CCQ010),P1NONREL (CCQ115),P1CTRNOW (CCQ260)

1 2 

WKCAREPK Child Focal child received any nonparental care the year

before entering kindergarten

P1RLCAR2 (CCQ070),P1NONRL2 (CCQ.120),P1HEADST (CCQ215),P1CPREK (CCQ.280),P2RPREK (SPQ010),P2NPREK (SPQ020),P2HSPREK (SPQ030),P2CPREK (SP040)

1 2 

P1CENTER Child Child has ever been in center-based care P1EVERHD (CCQ210),

P1HEADST (CCQ215),P1CTRNOW (CCQ260),P1ATTEND (CCQ265)

1 2 

P1COSTPK Child The total amount paid for all primary care

arrangements for the child the year beforekindergarten

P1AMTREL (CCQ053),P1PAYNON (CCQ158),P1AMTHS (CCQ256),P1AMNTF4 (CCQ318)

C

P1HRSNOW Child Total number of hours per week the focal child

currently spends in all primary, nonparental/nonkindergarten care

P1NUMHR2 (CCQ090),P1HRSNR (CCQ190), P1NUMHRS(CCQ335)

C

P1HRSPRK Child Total number of hours per week the focal child

spent in all types of primary, nonparental care theyear before kindergarten

P1NUMHRS (CCQ335),P1HRSNON (CCQ150),P1HSHRS (CCQ251),P1NUMHR4 (CCQ310)

C

P1NUMNOW Child Total number of all types of care arrangements the

focal child currently has on a regular basis

P1NUMREG (CCQ060),P1NUMCAR (CCQ165),P1NUMNO2 (CCQ325)

C
7-40

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKNUMPRK Child Care Total number of all types of care arrangements the

focal child had on a regular basis the year beforestarting kindergarten(Note: Information for this variable was onlycollected in round 1. Data for round 2 participantsare coded as “not ascertained” or -9.)

P1NUMARR (CCQ.030),P1NUMNON (CCQ.135),P1HEADST (CCQ.215),P1NUMCNT (CCQ.285),

C

P1PRIMPK Child Primary, nonparental arrangement in which the

child spent the most hours per week during the yearbefore kindergarten

P1NHRSPK (CCQ150),P1NUMHR4 (CCQ310),P1HSHRS (CCQ251),P1HOMREL (CCQ035)

0 1 2 3 4 5 6 7 8 

P1PRIMNW Child Primary, nonparental/non-kindergarten

arrangement in which the child currently spends themost hours per week

P1NUMHR2 (CCQ090), P1RLCAR2(CCQ070), P1HOMNON (CCQ140)

0 1 2 3 4 5 6 7 

P1AGEENT Child The age in months at which the focal child first

entered kindergarten. For children in their first yearof kindergarten, this will be their age as ofSeptember 1, 1998; for those in their second year ofkindergarten, it will be their age as of September 1,1997.

CHILDDOB (INQ.060), P1WHICHY(PIQ080)

C

P1FIRKDG Child First time kindergartner P1WHICHY (PIQ080) 1 

2 
7-41

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VA1CLASS Child Student attend a morning, afternoon or full-day

kindergarten class in the fall

FMS data, A1ACLASS, A1DCLASS,A1PCLASS

1 2 3 

A2CLASS Child Student attends a morning, afternoon, or full-day

kindergarten class in the spring

FMS data, A2ACLASS, A2DCLASS,A2PCLASS

1 2 3 

FKCHGTCH Child Child changed teachers between round 1 and round

2

R1T_ID, R2T_ID 0 

1 

FKCHGSCH Child Child changed schools between round 1 and round

2

R1S_ID, R2S_ID 0 

1 

P1MOMIDP2MOMID

Family/HH Household roster number of resident mother P1REL (FSQ130), P1UNR (FSQ180),

P2REL (FSQ130), P2UNR (FSQ180)

1

P1DADIDP2DADID

Family/HH Household roster number of resident father P1REL (FSQ130), P1UNR (FSQ180),

P2REL (FSQ130), P2UNR (FSQ180)

1

P1HPARNTP2HPARNT

Family/HH Classification of the focal child’s parents who

reside in the household

P1REL (FSQ130), P1UNR (FSQ180)P1HMOM, P1HDAD (composites),P2REL (FSQ130), P2UNR (FSQ180),P2HMOM, P2HDAD (composites)

1 2 3 4 5 6 7 8 9 

P1HFAMILP2HFAMIL

Family/HH Family type categories using both parent and

sibling information

P1REL (FSQ130), P1UNR (FSQ180)P1HMOM, P1HDAD, P1NUMSIB(composites)P2REL (FSQ130), P2UNR (FSQ180),P2HMOM, P2HDAD, P2NUMSIB(composites)

1 2 3 4 5 
7-42

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1NUMSIBP2NUMSIB

Family/HH Total number of siblings with whom the focal child

lives, including anyone reporting him/herself as thechild of the focal child’s foster parent/guardian

P1REL (FSQ130), P1UNR (FSQ180),P1AGE, P2REL (FSQ130),P2UNR (FSQ180), P2AGE

C

P1LESS18P2LESS18

Family/HH Total number of household members younger than

18 years old

P1REL (FSQ130), P1UNR (FSQ180),P1AGE, P2REL (FSQ130),P2UNR (FSQ180), P2AGE

C

P1OVER18P2OVER18

Family/HH Total number of household members older than 18

years old

P1REL (FSQ130),P1UNR (FSQ180), P1AGE,P2REL (FSQ130),P2UNR (FSQ180),P2AGE

C

P1HTOTALP2HTOTAL

Family/HH Total number of household members P1REL (FSQ130), P1UNR (FSQ180),

P2REL (FSQ130), P2UNR (FSQ180)

C

P1TWINP2TWIN

Family/HH Household has sampled twins P1PER, P2PER 0 

1 

WKPOVRTY Family/HH Poverty indicator WKINCOME, P2HTOTAL

(composites), and Census-definedthresholds

1 2 

WKINCOME Family/HH Household income INCOME (PAQ100).

Imputed using hotdeck imputation ifmissing. Imputation used partialincome information collected in theparent interview if available.

C
7-43

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKSESL Family/HH Socioeconomic status (SES) scale Derived from the logarithm of

WKINCOME, WKMOMED,WKDADED, WKMOMSCR,(mother’s occupation GSS prestigescore), WKDADSCR (father’soccupation GSS prestige score)(composites)

C

WKSESQ5 Family/HH Quintile indicator for WKSESL (SES scale) Derived from WKSESL at the child

level using round 2 P2 weights

1 2 3 4 5 

WKPARED Family/HH Highest level of education for the child’s parents or

nonparent guardians who reside in the household.If only one parent or guardian resides in thehousehold, WKPARED reflects that parent’seducation level.

WKMOMED, WKDADED(composites)

1 2 3 4 5 6 7 8 9 
7-44

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKMOMSCR Family/HH Mother’s occupation GSS prestige score Average of the 1998 GSS prestige

scores of the 1998 censusoccupational category codespreviously matched to the ECLS-Koccupation codes. The ECLS-Koccupation codes were collected inEMQ120, EMQ130, and EMQ140

23333333334555566667

7
7-45

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKDADSCR Family/HH Father’s occupation GSS prestige score Average of the 1998 GSS prestige

scores of the 1998 censusoccupational category codespreviously matched to the ECLS-Koccupation codes. The ECLS-Koccupation codes were collected inEMQ120, EMQ130, and EMQ140

233333333345555666677
7-46

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VWKLANGST Family/HH The primary language other than English spoken in

the child’s home

P1ANYLNG (PLQ.020),P1PRMLNG (PLQ060),PRIMARYLA (PLQ060),OTHERLAN (PLQ040),P2ANYLNG (SPQ155)P2PRMLNG (SPQ157)

1 2 

P1LANGUG Family/HH Language(s) spoken most often at home by the

parent(s)/guardian(s) in the household

P1ANYLNG (PLQ020),OTHERLAN (PLQ040)

1 2 3 

P2MARSTA Family/HH Marital status of parent(s) in household P2CURMAR (FSQ200) 1 

2 3 4 5 7 

P2ERRFLG Family/HH A dichotomous variable indicating clear errors in

the household roster

P1REL (FSQ130),P1UNR (FSQ180), P2JOI, P2RDP,P2REAS1

0 1 

P2KSHCHG Family/HH Household had roster change. A dichotomous

variable set to 1 if a household experienced a truechange between rounds. In this context, true changemeans that at least one individual entered or left thehousehold between rounds, and this change did notinvolve interviewer corrections for round 1 rostererrors.

P1REL (FSQ130), P1UNR (FSQ180),P2JOI, P2REAS1

0 1 

P2SPQDAT Family/HH Record in section SPQ (fall nonrespondent) PARENTID 0 

1 

P1DADTYPP2DADTYP

Family/HH Type of resident father P1REL (FSQ130),

P1UNR (FSQ180),P2REL (PSQ130)P2UNR (FSQ180)

1 2 3 
7-47

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HDADP2HDAD

Family/HH Indicates whether the birth, adoptive, step, or foster

father of the focal child resides in the householdwith the focal child

P1REL (FSQ130),P1UNR (FSQ180),P2REL (FSQ130),P2UNR (FSQ180)

1 2 3 4 5 6 7 

P1HDAGEP2HDAGE

Family/HH Age of resident father P1AGE

P2AGE

C

P1HDRACEP2HDRACE

Family/HH Race and ethnicity of the father or male guardian in

the household

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 3 4 5 6 7 8 
7-48

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HDASNP2HDASN

Family/HH Father or male guardian in the household is Asian P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

P1HDBLCKP2HDBLCK

Family/HH Father or male guardian in the household is African

American

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-49

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HDINDP2HDIND

Family/HH Father or male guardian in the household is

American Indian or Native Alaskan

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

P1HDPACIP2HDPACI

Family/HH Father or male guardian in the household is Native

Hawaiian or other Pacific Islander

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-50

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HDWHTP2HDWHT

Family/HH Father or male guardian in the household is white P1RC_1 (FSQ190),

P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

P1HDHISPP2HDHISP

Family/HH Father or male guardian in the household is

Hispanic

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-51

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HDMT1RP2HDMT1R

Family/HH Father or male guardian in the household is more

than one race

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

WKDADED Family/HH Father’s highest level of education P1HIG (PEQ020),

P1HIS (PEQ030)P2HIGHGR (SPQ160),P2HSDIPL (SPQ170),P2HIGHSP (SPQ180),P2PRTDIP (SPQ190),Imputed using hotdeck imputation ifmissing.

1 2 3 4 5 6 7 8 9 
7-52

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HDEMP Family/HH The work status of the father/male guardian in the

household.

P1HRS (EMQ050),P1PAY_1 (EMQ020),P1VAC (EMQ030),P1LOK (EMQ060),P1DO1 (EMQ070),P1DO2 (EMQ070),P1DO3 (EMQ070),P1DO4 (EMQ070),P1DO5 (EMQ070),P1DO6 (EMQ070),P1DO7 (EMQ070)

1 2 3 4 

P1DADOCC Family/HH Father’s occupation FORWHOM (EMQ120), KINDOFIN

(EMQ130), and KINDOFWO(EMP140)

1 2 3 4 5 6 7 8 9 1111
7-53

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1DADOCC(continued)

Family/HH Father’s occupation FORWHOM (EMQ120), KINDOFIN

(EMQ130), and KINDOFWO(EMP140)

111111222

P1HDLANG Family/HH Language used by the father when speaking to the

child

P1ANYLNG (PLQ020), RESLA(PLQ.080)

1 2 3 4 
7-54

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HDLTOD Family/HH Language used by the child when speaking to the

father

P1ANYLNG (PLQ020), RESLA(PLQ.080)

1 2 3 4 

P1MOMTYPP2MOMTYP

Family/HH Type of resident mother P1REL (FSQ130),

P1UNR (FSQ180), P2REL (FSQ130),P2UNR (FSQ180)

1 2 3 

P1HMOMP2HMOM

Family/HH Indicates whether the birth, adoptive, step, or foster

mother of the focal child resides in the householdwith the focal child

P1REL (FSQ130), P1UNR (FSQ180),P2REL (FSQ130), P2UNR (FSQ180)

1 2 3 4 5 6 7 

P1HMAGEP2HMAGE

Family/HH Age of resident mother P1AGE

P2AGE

C

P1HMRACEP2HMRACE

Family/HH Race and ethnicity of the mother or female

guardian in the household

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 3 4 5 6 7 8 9 
7-55

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HMASNP2HMASN

Family/HH Mother or female guardian in the household is

Asian

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

P1HMBLCKP2HMBLCK

Family/HH Mother or female guardian in the household is

African American

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-56

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HMINDP2HMIND

Family/HH Mother or female guardian in the household is

American Indian or Native Alaskan

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

P1HMPACIP2HMPACI

Family/HH Mother or female guardian in the household is

Native Hawaiian or other Pacific Islander

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-57

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HMWHTP2HMWHT

Family/HH Mother or female guardian in the household is

white

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

P1HMHISPP2HMHISP

Family/HH Mother or female guardian in the household is

Hispanic

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 
7-58

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HMMT1RP2HMMT1R

Family/HH Mother or female guardian in the household is

more than one race

P1RC_1 (FSQ190),P1RC_2 (FSQ190),P1RC_3 (FSQ190),P1RC_4 (FSQ190),P1RC_5 (FSQ190),P1RC_6 (FSQ190),RACEOS (FSQ190),P1HSP (FSQ190),P2RC_1 (FSQ195),P2RC_2 (FSQ195),P2RC_3 (FSQ195),P2RC_4 (FSQ195),P2RC_5 (FSQ195),RACEOS (FSQ198),P2HSP (FSQ190)

1 2 

WKMOMED Family/HH Mother’s highest level of education P1HIG (PEQ020),

P1HIS (PEQ030),P2HIGHGR (SPQ160),P2HSDIPL (SPQ170),P2HIGHSP (SPQ180),P2PRTDIP (SPQ190),Imputed using hotdeck imputation ifmissing.

1 2 3 4 5 6 7 8 9 
7-59

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HMEMP Family/HH The work status of the mother or female guardian

in the household

P1PAY_1 (EMQ020),P1HRS (EMQ050),P1VAC (EMQ030),P1LOK (EMQ060),P1DO1 (EMQ070),P1DO2 (EMQ070),P1DO3 (EMQ070),P1DO4 (EMQ070),P1DO5 (EMQ070),P1DO6 (EMQ070),P1DO7 (EMQ070)

1 2 3 4 

P1MOMOCC Family/HH Mother’s occupation Combination of FORWHOM

(EMQ120), KINDOFIN (EMQ130),and KINDOFWO (EMP140)

1 2 3 4 5 6 7 8 9 1111
7-60

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1MOMOCC(continued)

Family/HH Mother’s occupation Combination of FORWHOM

(EMQ120), KINDOFIN (EMQ130),and KINDOFWO (EMP140)

111111222

P1HMLANG Family/HH Language used by the mother when speaking to the

child

P1ANYLNG (PLQ020),RESLA (PLQ.080)

1 2 3 4 
7-61

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1HMLTOM Family/HH Language used by the child when speaking to the

mother

P1ANYLNG (PLQ020), RESLA(PLQ.080)

1 2 3 4 

P1HMAFB Family/HH Resident biological mother’s age when she first

gave birth

P1AGE, P1OLDMOM (MHQ165) C

WKHEARLY Family/HH Mother worked between the child’s birth and the

start of kindergarten

P1MOMWRK (EMQ170),P2MOMWRK (SPQ200)

1 2 

WKHMOMAR Family/HH Resident biological mother was married to the

biological father at the time of the child’s birth

P1LEGMAR (MHQ020),P1BIOLOG (MHQ.060),P1BIOPAR (MHQ125),P1WHNMAR (MHQ025),P1BIOLO2 (MHQ065),P1BIOLO3 (MHQ065),P1MDWHN (MHQ130),P1MDWH2 (MHQ130),P2BIOLOG (SPQ150)

1 2 

P1ABSDAD Family/HH Type of nonresident father P1REL (FSQ130), P1BIO (HRQ030) 1 

2 

P1BDVITL Family/HH The vital status of the nonresident biological father P1BIO (HRQ030) 1 

2 

P1BDAGE Family/HH Age of biological nonresident father P1DB1 (HRQ040), P1BIOLOG

(HRQ030)

C
7-62

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1BDRACE Family/HH Race and ethnicity of nonresident biological father P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 3 4 5 6 7 8 9 

P1BDASN Family/HH Nonresident biological father is Asian P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BDBLCK Family/HH Nonresident biological father is African American P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BDIND Family/HH Nonresident biological father is American Indian or

Alaskan Native

P1BM1 (HRQ100),P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 
7-63

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1BDPACI Family/HH Nonresident biological father is Native Hawaiian or

other Pacific Islander

P1BM1 (HRQ100),P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BDWHT Family/HH Nonresident biological father is white P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BDHISP Family/HH Nonresident biological father is Hispanic P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BDMT1R Family/HH Nonresident biological father is more than one race P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 
7-64

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1BDADED Family/HH Education of nonresident biological father P1HIG (PEQ020),

P1HIS (PEQ030)

1 2 3 4 5 6 7 8 9 

P1ABSMOM Family/HH Type of nonresident mother P1REL (FSQ130), P1BIO (HRQ030) 1 

2 

P1BMVITL Family/HH The vital status of the nonresident biological

mother

P1BIO (HRQ030) 1 

2 

P1BMAGE Family/HH Age of biological nonresident mother P1DB1 (HRQ040), P1BIO

(HRQ030), MONTHINT

C

P1BMRACE Family/HH Race and ethnicity of nonresident biological mother P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 3 4 5 6 7 8 9 

P1BMASN Family/HH Nonresident biological mother is Asian P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 
7-65

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1BMBLCK Family/HH Nonresident biological mother is African American P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BMIND Family/HH Nonresident biological mother is American Indian

or Alaskan Native

P1BM1 (HRQ100),P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BMPACI Family/HH Nonresident biological mother is Native Hawaiian

or other Pacific Islander

P1BM1 (HRQ100),P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BMWHT Family/HH Nonresident biological mother is white P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BMHISP Family/HH Nonresident biological mother is Hispanic P1BM1 (HRQ100),

P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 
7-66

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1BMMT1R Family/HH Nonresident biological mother is more than one

race

P1BM1 (HRQ100),P1BM2 (HRQ100),P1BM3 (HRQ100),P1BM4 (HRQ100),P1BM5 (HRQ100),P1BM6 (HRQ100),P1BMH (HRQ090)

1 2 

P1BMOMED Family/HH Education of nonresident biological mother P1HIG (PEQ020),

P1HIS (PEQ030)

1 2 3 4 5 6 7 8 9 

P1BMAFB Family/HH Nonresident biological mother’s age when she first

gave birth

P1OLDMOM (MHQ165) C

WKBMOMAR Family/HH Indicates whether the nonresident biological

mother was married to the biological father at thetime of the child’s birth

P1LEGMAR (MHQ020),P1BIOLOG (MHQ060),P1BIOPAR (MHQ125),P1WHNMAR (MHQ025),P1BIOLO2 (MHQ065),P1BIOLO3 (MHQ065),P1MDWHN (MHQ130),P1MDWH2 (MHQ130),P2BIOLOG (SPQ150)

1 2 
7-67

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VP1ADADED Family/HH Education of adoptive nonresident father P1HIG (PEQ020),

P1HIS (PEQ030)

1 2 3 4 5 6 7 8 9 

P1AMOMED Family/HH Education of adoptive nonresident mother P1HIG (PEQ020),

P1HIS (PEQ030)

1 2 3 4 5 6 7 8 9 

B1AGE Teacher Teacher’s age B1YRBORN 2A1TQUEX Teacher Presence of round 1 teacher questionnaire A AM

data

0 1 

A2ACLASS Teacher Presence of round 2 teacher questionnaire A AM

data

A2ANEW 0 

1 
7-68

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VA2DCLASS Teacher Presence of round 2 teacher questionnaire A all day

data

A2DNEW 0 

1 

A2PCLASS Teacher Presence of round 2 teacher questionnaire A PM

data

A2PNEW 0 

1 

A2TQUEX Teacher Presence or absence of round 2 teacher

questionnaire A data

0 1 

B1TQUEX Teacher Presence of round 1 teacher questionnaire B data 0 

1 

B2TQUEX Teacher Presence of round 2 teacher questionnaire B data 0 

1 

A1ACLASS Classroom Presence of round 1 teacher questionnaire A AM

data

A1ABLACK, A1ATOTRA 0 

1 

A1APBLK Classroom Percentage of blacks in class-AM A1ABLACK, A1ATOTRA CA1APHIS Classroom Percentage of Hispanics in class-AM A1AHISP, A1ATOTRA CA1APMIN Classroom Percentage of minorities in class-AM A1AASIAN, A1AHISP,

A1ABLACK, A1AAMRIN,A1ARACEO, A1ATOTRA

C

A1DCLASS Classroom Presence of round 1 teacher questionnaire A all day

(AD) data

0 1 

A1DPBLK Classroom Percentage of blacks in class-AD A1DBLACK, A1DTOTRA CA1DPHIS Classroom Percentage of Hispanics in class-AD A1DHISP, A1DTOTRA CA1DPMIN Classroom Percentage of minorities in class-AD A1DASIAN, A1DHISP,

A1DBLACK, A1DAMRIN,A1DRACEO, A1DTOTRA

C

A1PCLASS Classroom Presence of round 1 teacher questionnaire A PM

data

0 1 

A1PPBLK Classroom Percentage of blacks in Class-PM A1PBLACK, A1PTOTRA CA1PPHIS Classroom Percentage of Hispanics in Class-PM A1PHISP, A1PTOTRA CA1PPMIN Classroom Percentage of minorities in Class-PM A1PASIAN, A1PHISP, A1PBLACK,

A1PAMRIN, A1PRACEO,A1PTOTRA

C
7-69

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VCREGION School Indicates the geographic region in which the child

lives

Sampling Frame 1 

2 3 4 

KURBAN School Denoted urbanicity, from the sampling frame.

Urbanicity is assigned by the Census Bureau’sTIGER geographic information system.

Sampling Frame 1 

2 3 
7-70

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VKURBAN School Denoted urbanicity, from the sampling frame.

Urbanicity is assigned by the Census Bureau’sTIGER geographic information system.

Frame 5 

6 7 

S2KSCTYP School School type from the school administrator

questionnaire

S2PUBLIC, S2CATHOL,S2OTHREL, S2OTHPRI,CS_TYPE2

1 2 3 4 

S2KPUPRI School Public or private school S2KSCTYP 1 

2 

S2KENRLK School Total school kindergarten enrollment S2HDCHDN, S2FDCHDN,

S2KCCCHDN, S2TKCHDN,S2T1CHDN

C

S2KENRLS School Total school enrollment S2ANUMCH1, TOT_STD (from

frame)

1 2 3 4 5 

S2KLNGTH School School year length S2NUMDAY CS2KMINOR School Percentage of minority students in school F2STUMIN (from frame),

S2PCTHIS, S2INDPCT, S2ASNPCT,S2BLKPCT, S2PCFPCT,S2WHTPCT

C

S2KGFTED School Percentage of gifted/talented students in school S2GFTNBR, S2ANUMCH CS2KFLNCH School Percentage of students eligible for free lunch in

school

S2ELILNC, S2ANUMCH C
7-71

Table 7-6. Composite variables (continued)Variable Name Category Description Derived from VS2KRLNCH School Percent of students eligible for reduced price lunch

in school

S2ELIRED, S2ANUMCH C

S2KSCLVL School School instructional level from school

administrator questionnaire

S212TH, S211TH, S2TENTH,S2NINTH, S28TH, S27TH,S2SIXTH, S2FIFTH, S2FOURTH,S2THIRD, S2SECOND,S2GRADE1, S2PRE1, S2KINDER,S2TRANS, S2PRKNDR,S2TWEL12, S2ELEV11, S2TEN10,S2NIN9TH, S2ATE8TH,S2SEV7TH, S2SIX6TH, S2FIF5TH,S2FOR4TH, S2THI3RD,S2SEC2ND, S2FIR1ST, S2PRE1ST,S2KINGAR, S2REDDYN, S2PREK,HIGRADE (from frame)

1 

2 

3 

4 

K2INFAC School Presence or absence of facilities checklist data 0 

1 

S2INSAQ School Presence or absence of school administrator

questionnaire (SAQ) data

0 1 
7-72

7.9 Masked Variables

All the variables from the ECLS-K restricted-use file are included in the same order on the

ECLS-K public-use file. New variables created during the masking process are added to the end of the

files. For some of the variables, certain categories were modified. The variable labels for those masked

variables were updated from the restricted-use variables to reflect the new categories that were created

during the masking process.

Variables on the restricted-use files were modified in different ways based on the disclosure

analysis NCES conducted in order to protect the identity of the respondents and children. There are

several types of modifications on the public-use files.

1. Outliers are top- or bottom- coded to prevent identification of unique schools, teachers,

parents and children without affecting overall data quality.

2. Certain schools identified as at risk for disclosure have a 5 to 10 percent noise introduced

in those variables that pose a risk for disclosure.

3. Variables with too few cases and a sparse distribution are suppressed in the public-use

files.

4. Certain continuous variables are modified into categorical variables, and certain

categorical variables have their categories collapsed in the public-use file. While this

protects the cases from a disclosure risk, these variables can still be used in all different

kinds of analysis such as regression analysis.

There is a comment field in the variable frequency distribution view screen of the electronic

code book that displays a comment for each masked variable indicating whether the variable from the

restricted-use file has been recoded or suppressed in the public-use file. Variables that were recoded in

any way during the data masking process display the comment, “This data recoded for respondent

confidentiality.” Variables that were suppressed on the public-use file for protection of the respondent or

child from identification display the comment, “This data suppressed for respondent confidentiality” and

all values for the variable are set to equal –2 for that variable. New variables created during the masking

process also display the comment, “This data recoded for respondent confidentiality.”

The following tables present the list of the masked variables for each catalog. The tables

display the variable name, variable label and the comment displayed in the electronic code book
7-73

indicating if the variable was recoded or suppressed. A crosswalk of new variables created during

masking with the restricted-use variables is presented to help the user identify the recoded variables. For

example, A1KGTYP was created during masking and holds the values 1=regular kindergarten type and

2=other kindergarten type. Table 7-7 presents the masked variables for the child catalog. The table is

sorted sequentially by the variable Field ID (see section 8.6.1 for how to use the variable Field ID for the

child catalog.) Table 7-8 presents the masked variables for the teacher catalog. The table is sorted

sequentially by the variable Field ID (see section 8.7.1 for how to use the variable Field ID for the teacher

catalog.) Table 7-9 presents the masked variables for the school catalog. The table is sorted sequentially

by the variable Field ID (see section 8.8.1 for how to use the variable Field ID for the school catalog.)
7-74

Table 7-7. Masked variables in the child catalog

FieldID FieldName FieldLabel Comment

10 CS_TYPE2 TYPE OF SCHOOL IN SAMPLE FRAME This data recoded for respondent confidentiality.11 KURBAN LOCATION TYPE IN SAMPLE FRAME This data recoded for respondent confidentiality.14 DOBYY CHILD COMPOSITE DOB YEAR This data recoded for respondent confidentiality.17 R1_KAGE R1 COMPOSITE CHILD ASSESSMENT AGE(MNTHS) This data recoded for respondent confidentiality.

227 P1AGEENT P1 AGE (MONTHS) AT KINDERGARTEN ENTRY This data recoded for respondent confidentiality.320 A1PBLK A1 PERCENT OF BLACKS IN CLASS This data recoded for respondent confidentiality.323 B1AGE B1 TEACHER’S AGE This data recoded for respondent confidentiality.324 B2AGE B2 TEACHER’S AGE This data recoded for respondent confidentiality.326 S2KENRLK S2 TOTAL SCHOOL K ENROLLMENT This data recoded for respondent confidentiality.327 S2KENRLS S2 TOTAL SCHOOL ENROLLMENT This data recoded for respondent confidentiality.328 S2KMINOR S2 PERCENT MINORITY STUDENTS This data recoded for respondent confidentiality.329 S2KFLNCH S2 PCT FREE LUNCH ELIGIBLE STUDENTS This data recoded for respondent confidentiality.330 S2KRLNCH S2 PCT REDUCED LUNCH ELIGIBLE STUDENTS This data recoded for respondent confidentiality.332 S2KLNGTH S2 SCHOOL YEAR LENGTH This data recoded for respondent confidentiality.333 S2KGFTED S2 PERCENT GIFTED/TALENTED STUDENTS This data recoded for respondent confidentiality.816 P1WHATDI P1 CHQ125 1ST DIAGNOSIS-LEARNING ABILITY This data suppressed for respondent confidentiality.817 P1HOWOLD P1 CHQ130 AGE AT 1ST DIAGNS-LRN ABLTY This data suppressed for respondent confidentiality.818 P1OLDUNT P1 CHQ130 AGE 1ST DIAGNS-LRN ABLTY This data suppressed for respondent confidentiality.819 P1MMDIAG P1 CHQ135 MNTH AT 1ST DIAGNS-LRN ABLTY This data suppressed for respondent confidentiality.820 P1YYDIAG P1 CHQ135 YR AT 1ST DIAGNOSIS-LRN ABLTY This data suppressed for respondent confidentiality.824 P1PROFFD P1 CHQ155 IF ACTIVITY PROBLEM DIAGNOSED This data suppressed for respondent confidentiality.825 P1WHATD3 P1 CHQ160 WHAT 1ST DIAGNOSIS - ACTIVITY This data suppressed for respondent confidentiality.826 P1HOWOL2 P1 CHQ165 AGE AT 1ST DIAGNOSIS-ACTIVITY This data suppressed for respondent confidentiality.827 P1OLDUN2 P1 CHQ165 AGE UNIT AT 1ST DIAGNS-ACTVITY This data suppressed for respondent confidentiality.828 P1MMDIA2 P1 CHQ170 MNTH AT 1ST DIAGNOSIS-ACTIVITY This data suppressed for respondent confidentiality.829 P1YYDIA2 P1 CHQ170 YR AT 1ST DIAGNOSIS-ACTIVITY This data suppressed for respondent confidentiality.833 P1HOWOL3 P1 CHQ190 AGE AT 1ST DIAGNOSIS-MOBILITY This data suppressed for respondent confidentiality.834 P1OLDUN3 P1 CHQ190 AGE UNIT AT 1ST DIAGNS-MOBLITY This data suppressed for respondent confidentiality.835 P1MMDIA3 P1 CHQ190 MONTH AT 1ST DIAGNS-MOBLITY This data suppressed for respondent confidentiality.836 P1YYDIA3 P1 CHQ195 YR AT 1ST DIAGNOSIS-MOBILITY This data suppressed for respondent confidentiality.841 P1HOWOL4 P1 CHQ220 AGE AT 1ST DIAGNOSIS-SPEECH This data suppressed for respondent confidentiality.842 P1OLDUN4 P1 CHQ220 AGE UNIT AT 1ST DIAGNS-SPEECH This data suppressed for respondent confidentiality.
Table 7-7. Masked variables in the child catalog (continued)

FieldID FieldName FieldLabel Comment

843 P1MMDIA4 P1 CHQ225 MONTH AT 1ST DIAGNOSIS-SPEECH This data suppressed for respondent confidentiality.844 P1YYDIA4 P1 CHQ225 YEAR AT 1ST DIAGNOSIS-SPEECH This data suppressed for respondent confidentiality.848 P1HOWOL5 P1 CHQ250 AGE AT 1ST DIAGNOSIS-HEARING This data suppressed for respondent confidentiality.849 P1OLDUN5 P1 CHQ250 AGE UNIT AT 1ST DIAGNS-HEARING This data suppressed for respondent confidentiality.850 P1MMDIA5 P1 CHQ255 MONTH AT 1ST DIAGNOSIS-HEARING This data suppressed for respondent confidentiality.851 P1YYDIA5 P1 CHQ255 YR AT 1ST DIAGNOSIS-HEARING This data suppressed for respondent confidentiality.852 P1HEARS P1 CHQ260 DEGREE OF CHILD’S DEAFNESS This data suppressed for respondent confidentiality.853 P1HEARAI P1 CHQ265 IF CHILD WEARS HEARING AID This data suppressed for respondent confidentiality.854 P1COCHLE P1 CHQ270 IF CHILD HAS COCHLEAR IMPLANTS This data suppressed for respondent confidentiality.855 P1HEARS2 P1 CHQ280 DEVICE EFFECT ON CHD’S HEARING This data suppressed for respondent confidentiality.859 P1HOWOL6 P1 CHQ305 AGE AT 1ST DIAGNOSIS - VISION This data suppressed for respondent confidentiality.860 P1OLDUN6 P1 CHQ305 AGE UNIT AT 1ST DIAGNOS-VISION This data suppressed for respondent confidentiality.861 P1DIA6MM P1 CHQ310 MONTH AT 1ST DIAGNOSIS-VISION This data suppressed for respondent confidentiality.862 P1DIA6YY P1 CHQ310 YR AT 1ST DIAGNOSIS-VISION This data suppressed for respondent confidentiality.863 P1CORREC P1 CHQ315 IF CHD’S VISION IS CORRECTABLE This data suppressed for respondent confidentiality.864 P1BESTEY P1 CHQ320 WHAT CAN CHILD BEST SEE This data suppressed for respondent confidentiality.882 P1THER14 P1 CHQ375 AGE AT THERAPY BEGINNING This data suppressed for respondent confidentiality.883 P1THER15 P1 CHQ375 AGE UNIT AT THERAPY BEGINNING This data suppressed for respondent confidentiality.884 P1THER16 P1 CHQ380 MONTH AT THERAPY BEGINNING This data suppressed for respondent confidentiality.885 P1THER17 P1 CHQ380 YEAR AT THERAPY BEGINNING This data suppressed for respondent confidentiality.887 P1LASTMM P1 CHQ390 LAST THERAPY MONTH This data suppressed for respondent confidentiality.888 P1LASTYY P1 CHQ390 LAST THERAPY YEAR This data suppressed for respondent confidentiality.890 P1SPECIL P1 CHQ410 IF CHD USES SPECIAL EQUIPMENT This data suppressed for respondent confidentiality.

1025 P1PRMLNG P1 PLQ060 WHAT PRIMARY LANGUAGE AT HOME This data recoded for respondent confidentiality.1482 P2CNTRYB P2 INQ310 COUNTRY OF BIRTH This data suppressed for respondent confidentiality.1517 P2PRMLNG P2 SPQ157 WHAT PRIMARY LANGUAGE AT HOME This data recoded for respondent confidentiality.1831 P2INCOME P2 PAQ100 TOTAL HOUSEHOLD INCOME ($) This data recoded for respondent confidentiality.1836 P2HOWPAY P2 PAQ137 HOW MUCH PAID IN TUITION ($) This data recoded for respondent confidentiality.1848 A1TPREK A1 Q4A TCH PREKINDERGARTEN LEVELS This data suppressed for respondent confidentiality.1849 A1TTRNK A1 Q4B TCH TRANSITIONAL KINDERGARTEN This data suppressed for respondent confidentiality.1850 A1TREGK A1 Q4C TEACHES REGULAR KINDERGARTEN This data suppressed for respondent confidentiality.1851 A1TPRE1 A1 Q4D TCH PRE-1ST GRADE LEVEL This data suppressed for respondent confidentiality.1852 A1T1ST A1 Q4E TCH 1ST GRADE LEVEL This data suppressed for respondent confidentiality.1853 A1T2ND A1 Q4F TCH 2ND GRADE LEVEL This data suppressed for respondent confidentiality.

7-75
Table 7-7. Masked variables in the child catalog (continued)

FieldID FieldName FieldLabel Comment

1854 A1T3RD A1 Q4G TCH 3RD GRADE OR HIGHER LEVEL This data suppressed for respondent confidentiality.1855 A13YROL A1 Q5A HOW MANY 3-YEAR-OLDS IN CLASS This data recoded for respondent confidentiality.1856 A14YROL A1 Q5B HOW MANY 4-YEAR-OLDS IN CLASS This data recoded for respondent confidentiality.1857 A15YROL A1 Q5C HOW MANY 5-YEAR-OLDS IN CLASS This data recoded for respondent confidentiality.1858 A16YROL A1 Q5D HOW MANY 6-YEAR-OLDS IN CLASS This data recoded for respondent confidentiality.1859 A17YROL A1 Q5E HOW MANY 7-YEAR-OLDS IN CLASS This data recoded for respondent confidentiality.1860 A18YROL A1 Q5F HOW MANY 8-YEAR-OLDS IN CLASS This data recoded for respondent confidentiality.1861 A19YROL A1 Q5G HOW MANY 9-YEAR-OLDS IN CLASS This data recoded for respondent confidentiality.1862 A1TOTAG A1 Q5H TOTAL CLASS ENROLLMENT (AGE) This data recoded for respondent confidentiality.1863 A1ASIAN A1 Q6A # OF ASIAN/PACIFIC ISLANDERS This data recoded for respondent confidentiality.1864 A1HISP A1 Q6B # OF HISPANICS (ALL RACES) This data recoded for respondent confidentiality.1865 A1BLACK A1 Q6C # OF NON-HISPANIC BLACKS This data recoded for respondent confidentiality.1866 A1WHITE A1 Q6D # OF NON-HISPANIC WHITES This data recoded for respondent confidentiality.1867 A1AMRIN A1 Q6E # OF AMERICAN INDIANS This data recoded for respondent confidentiality.1868 A1RACEO A1 Q6F # OF STUDENTS OF OTHER RACES This data recoded for respondent confidentiality.1869 A1TOTRA A1 Q6 TOTAL CLASS ENROLLMENT (RACES) This data recoded for respondent confidentiality.1896 A1TVTNM A1 Q18C TCHRS SPEAK VIETNAMESE This data suppressed for respondent confidentiality.1897 A1TCHNS A1 Q18D TCHRS SPEAK CHINESE This data suppressed for respondent confidentiality.1898 A1TJPNS A1 Q18E TCHRS SPEAK JAPANESE This data suppressed for respondent confidentiality.1899 A1TKRN A1 Q18F TCHRS SPEAK KOREAN This data suppressed for respondent confidentiality.1900 A1TFLPN A1 Q18G TCHRS SPEAK A FILIPINO LNG This data suppressed for respondent confidentiality.1901 A1TOTAS A1 Q18H TCHRS SPEAK OTHER ASIAN LNG This data suppressed for respondent confidentiality.1902 A1TOTLG A1 Q18I TCHRS SPEAK ANOTHER LANGUAGE This data suppressed for respondent confidentiality.1910 A2GIFT A2 Q2A # CLASSIFIED AS GFTED/TALENTED This data recoded for respondent confidentiality.1911 A2PRTGF A2 Q2B # TAKE PART IN GIFTED/TALENTED This data recoded for respondent confidentiality.1916 A2DISAB A2 Q3A NUMBER WITH DISABILITIES This data recoded for respondent confidentiality.1920 A2RETAR A2 Q4D MENTAL RETARDATION This data recoded for respondent confidentiality.1926 A2MULTI A2 Q4J MULTIPLE DISABILITIES This data suppressed for respondent confidentiality.1927 A2AUTSM A2 Q4K AUTISM This data suppressed for respondent confidentiality.1929 A2DEAF A2 Q4M DEAFNESS AND BLINDNESS This data suppressed for respondent confidentiality.1938 A2CVTNM A2 Q7C STUDENTS SPEAK VIETNAMESE This data suppressed for respondent confidentiality.1939 A2CCHNS A2 Q7D STUDENTS SPEAK CHINESE This data suppressed for respondent confidentiality.1940 A2CJPNS A2 Q7E STUDENTS SPEAK JAPANESE This data suppressed for respondent confidentiality.

7-76
Table 7-7. Masked variables in the child catalog (continued)

FieldID FieldName FieldLabel Comment

1941 A2CKRN A2 Q7F STUDENTS SPEAK KOREAN This data suppressed for respondent confidentiality.1942 A2CFLPN A2 Q7G STUDENTS SPEAK FILIPINO LNG This data suppressed for respondent confidentiality.1943 A2OTASN A2 Q7H STUDENTS SPEAK OTHER ASIAN LNG This data suppressed for respondent confidentiality.1944 A2OTLNG A2 Q7I STUDENTS SPEAK ANOTHER LNG This data suppressed for respondent confidentiality.1945 A2LNGOS A2 Q7I LANGUAGE OF INSTRUCTION - OTHER This data suppressed for respondent confidentiality.2255 B1YRBORN B1 Q16 TEACHER’S YEAR OF BIRTH This data recoded for respondent confidentiality.2257 B1RACE1 B1 Q18 NATIVE AMERICAN OR PACIF ISLANDER This data recoded for respondent confidentiality.2260 B1RACE4 B1 Q18 NATIVE HAWAIIAN OR OTHER PAC IS This data suppressed for respondent confidentiality.2262 B1YRSPRE B1 Q19A YRS TEACHER TAUGHT PRESCHOOL This data recoded for respondent confidentiality.2263 B1YRSKIN B1 Q19B YRS TEACHER TAUGHT KINDERGARTEN This data recoded for respondent confidentiality.2264 B1YRSFST B1 Q19C YRS TEACHER TAUGHT FIRST GRADE This data recoded for respondent confidentiality.2265 B1YRS2T5 B1 Q19D YRS TEACHER TAUGHT 2 TO 5 GRADE This data recoded for respondent confidentiality.2266 B1YRS6PL B1 Q19E YRS TEACHER TAUGHT 6 GRADE OR UP This data recoded for respondent confidentiality.2267 B1YRSESL B1 Q19F YRS TEACHER TAUGHT ESL This data recoded for respondent confidentiality.2268 B1YRSBIL B1 Q19G YRS TEACHER TAUGHT BILINGUAL ED This data recoded for respondent confidentiality.2269 B1YRSSPE B1 Q19H YRS TEACHER TAUGHT SPECIAL ED This data recoded for respondent confidentiality.2270 B1YRSPE B1 Q19I YRS TEACHER TAUGHT PHYSICAL ED This data recoded for respondent confidentiality.2271 B1YRSART B1 Q19J YRS TEACHER TAUGHT ART OR MUSIC This data recoded for respondent confidentiality.2272 B1YRSCH B1 Q20 YRS TEACHER TAUGHT AT THIS SCHOOL This data recoded for respondent confidentiality.2273 B1HGHSTD B1 Q21 HIGHEST ED LEVEL TEACHER ACHIEVED This data recoded for respondent confidentiality.2364 B2YRBORN B2 Q16 TEACHER’S YEAR OF BIRTH This data recoded for respondent confidentiality.2366 B2RACE1 B1 Q18 NATIVE AMERICAN OR PACIF ISLANDER This data recoded for respondent confidentiality.2369 B2RACE4 B2 Q18 NATIVE HAWAIIAN OR OTHER PAC IS This data suppressed for respondent confidentiality.2371 B2YRSPRE B2 Q19A YRS TEACHER TAUGHT PRESCHOOL This data recoded for respondent confidentiality.2372 B2YRSKIN B2 Q19B YRS TEACHER TAUGHT KINDERGARTEN This data recoded for respondent confidentiality.2373 B2YRSFST B2 Q19C YRS TEACHER TAUGHT FIRST GRADE This data recoded for respondent confidentiality.2374 B2YRS2T5 B2 Q19D YRS TEACHER TAUGHT 2 TO 5 GRADE This data recoded for respondent confidentiality.2375 B2YRS6PL B2 Q19E YRS TEACHER TAUGHT 6 GRADE OR UP This data recoded for respondent confidentiality.2376 B2YRSESL B2 Q19F YRS TEACHER TAUGHT ESL This data recoded for respondent confidentiality.2377 B2YRSBIL B2 Q19G YRS TEACHER TAUGHT BILINGUAL ED This data recoded for respondent confidentiality.2378 B2YRSSPE B2 Q19H YRS TEACHER TAUGHT SPECIAL ED This data recoded for respondent confidentiality.2379 B2YRSPE B2 Q19I YRS TEACHER TAUGHT PHYSICAL ED This data recoded for respondent confidentiality.2380 B2YRSART B2 Q19J YRS TEACHER TAUGHT ART OR MUSIC This data recoded for respondent confidentiality.2381 B2YRSCH B2 Q20 YRS TEACHER TAUGHT AT THIS SCHOOL This data recoded for respondent confidentiality.

7-77
Table 7-7. Masked variables in the child catalog (continued)

FieldID FieldName FieldLabel Comment

2382 B2HGHSTD B2 Q21 HIGHEST ED LEVEL TEACHER ACHIEVED This data recoded for respondent confidentiality.2476 S2NUMDAY S2 Q1 NUMBER OF DAYS MUST ATTEND This data recoded for respondent confidentiality.2477 S2ADA S2 Q2 % AVERAGE DAILY ATTENDANCE FOR YR. This data recoded for respondent confidentiality.2478 S2ANUMCH S2 Q3A # ENROLLED AROUND 10/1/1998 This data recoded for respondent confidentiality.2479 S2BNUMCH S2 Q3B # ENROLLED SINCE 10/1/1998 This data recoded for respondent confidentiality.2480 S2CNUMCH S2 Q3C # WHO LEFT - DIDN’T RETURN This data recoded for respondent confidentiality.2503 S2BIASKL S2 Q6D IS IT A TRIBAL SCHOOL This data suppressed for respondent confidentiality.2504 S2SPEDSK S2 Q6E IS IT A SPECIAL ED SCHOOL This data suppressed for respondent confidentiality.2509 S2PRIVRD S2 Q7A4 IS IT A PRIVATE ORDER This data suppressed for respondent confidentiality.2511 S2NAISKL S2 Q7C PRIVATE-ACCREDITED BY NAIS This data suppressed for respondent confidentiality.2513 S2PVTSPD S2 Q7E IS IT SPECIAL EDUCATION This data suppressed for respondent confidentiality.2516 S2EMPHAS S2 Q9 WHAT’S THE SCHOOL EMPHASIS This data suppressed for respondent confidentiality.2517 S2PREK S2 Q10 PRE-K PARTICIPATES This data suppressed for respondent confidentiality.2518 S2REDDYN S2 Q10 TRANSITIONAL PARTICIPATES This data suppressed for respondent confidentiality.2519 S2KINGAR S2 Q10 KINDERGARTEN PARTICIPATES This data suppressed for respondent confidentiality.2520 S2PRE1ST S2 Q10 PREFIRST PARTICIPATES This data suppressed for respondent confidentiality.2521 S2FIR1ST S2 Q10 1ST GRADE PARTICIPATES This data suppressed for respondent confidentiality.2522 S2SEC2ND S2 Q10 2ND GRADE PARTICIPATES This data suppressed for respondent confidentiality.2523 S2THI3RD S2 Q10 3RD GRADE PARTICIPATES This data suppressed for respondent confidentiality.2524 S2FOR4TH S2 Q10 4TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2525 S2FIF5TH S2 Q10 5TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2526 S2SIX6TH S2 Q10 6TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2527 S2SEV7TH S2 Q10 7TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2528 S2ATE8TH S2 Q10 8TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2529 S2NIN9TH S2 Q10 9TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2530 S2TEN10 S2 Q10 10TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2531 S2ELEV11 S2 Q10 11TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2532 S2TWEL12 S2 Q10 12TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.2533 S2SPENR S2 Q11 NUMBER IN SPECIAL PROGRAM This data suppressed for respondent confidentiality.2550 S2PCTHSP S2 Q14 PERCENT OF HISPANIC STUDENTS This data recoded for respondent confidentiality.2551 S2INDPCT S2 Q15A PERCENT OF NATIVE AMERICANS This data suppressed for respondent confidentiality.2552 S2ASNPCT S2 Q15B PERCENT OF ASIAN STUDENTS This data suppressed for respondent confidentiality.2553 S2BLKPCT S2 Q15C PERCENT OF BLACK STUDENTS This data recoded for respondent confidentiality.2554 S2PCFPCT S2 Q15D PERCENT OF HAWAIIAN STUDENT This data suppressed for respondent confidentiality.

7-78
Table 7-7. Masked variables in the child catalog (continued)

FieldID FieldName FieldLabel Comment

2555 S2WHTPCT S2 Q15E PERCENT OF WHITE STUDENTS This data suppressed for respondent confidentiality.2556 S2ELIBRK S2 Q16A1 ELIGIBLE FOR FREE BREAKFAST This data suppressed for respondent confidentiality.2557 S2PARBRK S2 Q16A2 PARTICIPATES IN BREAKFAST This data suppressed for respondent confidentiality.2558 S2ELILNC S2 Q16B1 ELIGIBLE FOR FREE LUNCH This data suppressed for respondent confidentiality.2559 S2PARLNC S2 Q16B2 PARTICIPATES IN FREE LUNCH This data suppressed for respondent confidentiality.2560 S2ELIRED S2 Q16C1 ELIGIBLE IN REDUCED-PRICE LUNCH This data suppressed for respondent confidentiality.2561 S2PARRED S2 Q16C2 PARTICIPATES IN RED-PRICE LUNCH This data suppressed for respondent confidentiality.2571 S2CHSRVD S2 Q20 NUMBER SERVED BY TITLE 1 This data recoded for respondent confidentiality.2572 S2HDCHDN S2 Q21A1 HALFDAY - NUMBER OF CHILDREN This data recoded for respondent confidentiality.2573 S2HDCLS S2 Q21B1 HALFDAY - NUMBER OF CLASSES This data recoded for respondent confidentiality.2574 S2HDDAYS S2 Q21C1 HALFDAY - NUMBER OF DAYS PER WK This data suppressed for respondent confidentiality.2576 S2FDCHDN S2 Q21A2 FULLDAY - NUMBER OF CHILDREN This data recoded for respondent confidentiality.2577 S2FDCLS S2 Q21B2 FULLDAY - NUMBER OF CLASSES This data recoded for respondent confidentiality.2578 S2FDDAYS S2 Q21C2 FULLDAY - NUMBER OF DAYS PER WK This data suppressed for respondent confidentiality.2580 S2KCCHDN S2 Q21A3 K COMBINATION - # OF CHILDREN This data recoded for respondent confidentiality.2581 S2KCCLS S2 Q21B3 K COMBINATION - # OF CLASSES This data suppressed for respondent confidentiality.2582 S2KCDAYS S2 Q21C3 K COMBINATION - # DAYS PER WK This data suppressed for respondent confidentiality.2584 S2TKCHDN S2 Q21A4 TRANSITIONAL K - # OF CHILDREN This data recoded for respondent confidentiality.2585 S2TKCLS S2 Q21B4 TRANSITIONAL K - # OF CLASSES This data suppressed for respondent confidentiality.2586 S2TKDAYS S2 Q21C4 TRANSITIONAL K - # DAYS PER WK This data suppressed for respondent confidentiality.2588 S2T1CHDN S2 Q21A5 TRANSITIONAL 1ST- # OF CHILDREN This data recoded for respondent confidentiality.2589 S2T1CLS S2 Q21B5 TRANSITIONAL 1ST- # OF CLASSES This data suppressed for respondent confidentiality.2590 S2T1DAYS S2 Q21C5 TRANSITIONAL 1ST - DAYS PER WK This data suppressed for respondent confidentiality.2595 S2NOCUTO S2 Q22A NO CUTOFF DATE TO TURN FIVE This data suppressed for respondent confidentiality.2607 S2CHLDNM S2 Q24 # OF CHILDREN SITE ACCOMMODATES This data recoded for respondent confidentiality.2608 S2RMNUM S2 Q25 NUMBER OF INSTRUCTIONAL ROOMS This data recoded for respondent confidentiality.2619 S2INSTCM S2 Q27A # INSTRUCTIONAL COMPUTERS This data recoded for respondent confidentiality.2620 S2ADMNCM S2 Q27B # INSTRUCT/ADMIN COMPUTERS This data recoded for respondent confidentiality.2621 S2TOTCM S2 Q27C TOTAL NUMBER OF COMPUTERS This data recoded for respondent confidentiality.2624 S2LANRMS S2 Q28A3 TOTAL ROOMS W/LAN ACCESS This data recoded for respondent confidentiality.2627 S2CDRRMS S2 Q28B3 TOTAL ROOMS W/CD-ROM ACCESS This data recoded for respondent confidentiality.2630 S2NETRMS S2 Q28C3 TOTAL RMS W/INTERNET ACCESS This data recoded for respondent confidentiality.2634 S2CMNITY S2 Q30 COMMUNITY SCHOOL IS LOCATED IN This data recoded for respondent confidentiality.2690 S2NMBKPT S2 Q40 NUMBER RETAINED GRADE-K This data recoded for respondent confidentiality.

7-79
Table 7-7. Masked variables in the child catalog (continued)

FieldID FieldName FieldLabel Comment

2732 S2NMADMN S2 Q47A2 NUMBER OF ADMINISTRATORS This data recoded for respondent confidentiality.2734 S2NMTECH S2 Q47B2 NUMBER OF TEACHERS This data recoded for respondent confidentiality.2736 S2NMDSTR S2 Q47C2 NUMBER FROM DISTRICT OFFICE This data recoded for respondent confidentiality.2738 S2NUMBRD S2 Q47D2 NUMBER OF BOARD MEMBERS This data recoded for respondent confidentiality.2740 S2NMOMDD S2 Q47E2 NUMBER OF PARENTS This data recoded for respondent confidentiality.2742 S2NMREPS S2 Q47F2 NUMBER OF REPRESENTATIVES This data recoded for respondent confidentiality.2744 S2NUMOTH S2 Q47G2 NUMBER OF OTHER GROUPS This data recoded for respondent confidentiality.2748 S2BILING S2 Q50 BILINGUAL SERVICES PERCENT This data suppressed for respondent confidentiality.2749 S2ESLONL S2 Q50 ESL SERVICES PERCENT This data suppressed for respondent confidentiality.2750 S2ESLBIL S2 Q50 ESL AND BILINGUAL PERCENT This data suppressed for respondent confidentiality.2751 S2YRSESL S2 Q51A YEARS FOR ESL SERVICES This data recoded for respondent confidentiality.2752 S2YRSBIL S2 Q51B YEARS FOR BILINGUAL SERVICES This data recoded for respondent confidentiality.2753 S2SRVBTH S2 Q51C YEARS FOR ESL & BILINGUAL SER This data recoded for respondent confidentiality.2777 S2GFTNBR S2 Q57 NUMBER GIFTED/TALENTED This data suppressed for respondent confidentiality.2778 S2ADMFTE S2 Q58A FTE ADMINISTRATIVE STAFF This data recoded for respondent confidentiality.2779 S2TCHFTE S2 Q58B FTE CLASSROOM TEACHERS This data recoded for respondent confidentiality.2780 S2GYMFTE S2 Q58C FTE GYM/DRAMA/ART TEACHERS This data recoded for respondent confidentiality.2781 S2CMPFTE S2 Q58D FTE COMPUTER SPECIALISTS This data recoded for respondent confidentiality.2782 S2HDSFTE S2 Q58E FTE HEAD START/PRE-K TEACHERS This data recoded for respondent confidentiality.2783 S2CRDFTE S2 Q58F FTE COORDINATOR/SPECIALIST This data recoded for respondent confidentiality.2784 S2TT1FTE S2 Q58G FTE TITLE 1 TEACHERS This data recoded for respondent confidentiality.2785 S2AIDFTE S2 Q58H FTE TEACHER AIDES This data recoded for respondent confidentiality.2786 S2SPEFTE S2 Q58I FTE SPECIAL ED TEACHERS This data recoded for respondent confidentiality.2787 S2SPAFTE S2 Q58J FTE SPECIAL ED AIDES This data recoded for respondent confidentiality.2788 S2ESLFTE S2 Q58K FTE ESL-BILINGUAL TEACHERS This data recoded for respondent confidentiality.2789 S2ESAFTE S2 Q58L FTE ESL-BILINGUAL AIDES This data recoded for respondent confidentiality.2790 S2LIAFTE S2 Q58M FTE PARENT LIAISONS This data recoded for respondent confidentiality.2791 S2COUFTE S2 Q58N FTE SOCIAL WORKERS This data recoded for respondent confidentiality.2792 S2LIBFTE S2 Q58O LIBRARY/MEDIA SPECIALISTS This data recoded for respondent confidentiality.2793 S2THRFTE S2 Q58P SPEECH/PHYSICAL THERAPISTS This data recoded for respondent confidentiality.2794 S2NRSFTE S2 Q58Q FTE SCHOOL NURSES This data recoded for respondent confidentiality.2795 S2NONFTE S2 Q58R FTE NON-INSTRUCTIONAL STAFF This data recoded for respondent confidentiality.2796 S2REAFTE S2 Q58S FTE READING RECOVERY TCHRS This data suppressed for respondent confidentiality.2797 S2MSCFTE S2 Q58T FTE MATH/SCIENCE SPECIALISTS This data recoded for respondent confidentiality.

7-80
Table 7-7. Masked variables in the child catalog (continued)

FieldID FieldName FieldLabel Comment

2798 S2FRLFTE S2 Q58U FTE FOREIGN LNG SPECIALISTS This data recoded for respondent confidentiality.2799 S2OTHFTE S2 Q58V OTHER FTE This data suppressed for respondent confidentiality.2800 S2FTETOT S2 Q58W TOTAL FTE SCHOOL STAFF This data recoded for respondent confidentiality.2803 S2ETHNIC S2 Q61 % HISPANIC/LATINO TEACHERS This data recoded for respondent confidentiality.2804 S2Q62IND S2 Q62 % AMERICAN NATIVE TEACHERS This data recoded for respondent confidentiality.2805 S2Q62ASN S2 Q62 % ASIAN TEACHERS This data recoded for respondent confidentiality.2806 S2Q62BLK S2 Q62 % BLACK TEACHERS This data recoded for respondent confidentiality.2807 S2Q62HAW S2 Q62 % HAWAIIAN TEACHERS This data suppressed for respondent confidentiality.2904 S2YSTCH S2 Q74A NUMBER OF YRS TEACHING This data recoded for respondent confidentiality.2905 S2TOTPRI S2 Q74B NUMBER OF YRS AS PRINCIPAL This data recoded for respondent confidentiality.2906 S2PRINHR S2 Q74C NUMBER YRS A PRINCIPAL HERE This data recoded for respondent confidentiality.2926 S2EDLVL S2 Q77 HIGHEST LEVEL OF EDUCATION This data recoded for respondent confidentiality.

New Variables Replacing Suppressed Variables Suppressed Va

3989 P1LANGS1 P1 PLQ040 OTHER LANGUAGE - SPANISH This data recoded for respondent confidentiality. P1LANG12 P1 

1=Yes2=No-1=Not applicable-7=Refused-8=Don’t know-9=Not ascertained

3990 P1LANGS2 P1 PLQ040 OTHER-OTHER EUROPEAN LANGS This data recoded for respondent confidentiality. P1LANG4 P1 

1=Yes P1LANG5 P1 2=No P1LANG6 P1 -1=Not applicable P1LANG7 P1 -7=Refused P1LANG10 P1 -8=Don’t know-9=Not ascertained

P1LANG11 P1 

PO

P1LANG16 P1 

LA

P1LANG20 P1 

LA

7-81
Table 7-7. Masked variables in the child catalog (continued)

New Variables Replacing Suppressed Variables Suppressed Va

3991 P1LANGS3 P1 PLQ040 OTHER-ASIAN LANGUAGES This data recoded for respondent confidentiality. P1LANG2 P1 

1=Yes P1LANG3 P1 2=No-1=Not applicable

P1LANG8 P1 

JA

-7=Refused P1LANG9 P1 -8=Don’t know-9=Not ascertained

P1LANG13 P1 

VI

P1LANG17 P1 

LA

P1LANG21 P1 

LA

P1LANG22 P1 

LA

P1LANG23 P1 

LA

3992 P1LANGS4 P1 PLQ040 OTHER LANGUAGE - OTHER LANGS This data recoded for respondent confidentiality. P1LANG1 P1 

1=Yes P1LANG14 P1 2=No P1LANG15 P1 -1=Not applicable P1LANG18 P1 -7=Refused-8=Don’t know-9=Not ascertained

P1LANG19 P1 

LA

3993 A1KGTYP A1 Q3 TYPE OF KINDERGARTEN PROG TCH This data recoded for respondent confidentiality. A1REGK A1 

KI

1=Regular kindergarten only A12YRK1 A1 2=Other kindergarten type A12YRK2 A1 

A1TRNK A1 

KI

A1PR1ST A1 

K

A1UNGR A1 A1MULGR A1 

3994 S2PRACE S2 Q73 PRINCIPAL’S RACE This data recoded for respondent confidentiality. S2RACE1 S2 

1=White, non-Hispanic S2RACE2 S2 2=Black, or African-American, non-Hispanic S2RACE3 S2 3=Other race-9=Not Ascertained

S2RACE4 S2 

IS

S2RACE5 S2 

7-82
Table 7-8. Masked variables in the teacher catalog

FieldID FieldName FieldLabel Comment

3 KURBAN LOCATION TYPE IN SAMPLE FRAME This data recoded for respondent confidentiality.5 CS_TYPE2 TYPE OF SCHOOL IN SAMPLE FRAME This data recoded for respondent confidentiality.

20 S2KENRLS S2 TOTAL SCHOOL ENROLLMENT This data recoded for respondent confidentiality.22 S2KMINOR S2 PERCENT MINORITY STUDENTS This data recoded for respondent confidentiality.24 A1APBLK A1 PERCENT OF BLACKS IN CLASS-AM This data recoded for respondent confidentiality.27 A1PPBLK A1 PERCENT OF BLACKS IN CLASS-PM This data recoded for respondent confidentiality.30 A1DPBLK A1 PERCENT OF BLACKS IN CLASS-AD This data recoded for respondent confidentiality.33 B1AGE B1 TEACHER’S AGE This data recoded for respondent confidentiality.43 A1ATPREK A1 Q4A TCH PREKINDERGARTEN LEVELS-AM This data suppressed for respondent confidentiality.44 A1ATTRNK A1 Q4B TCH TRANSITIONAL KINDERGARTEN-AM This data suppressed for respondent confidentiality.45 A1ATREGK A1 Q4C TEACHES REGULAR KINDERGARTEN-AM This data suppressed for respondent confidentiality.46 A1ATPRE1 A1 Q4D TCH PRE-1ST GRADE LEVEL-AM This data suppressed for respondent confidentiality.47 A1AT1ST A1 Q4E TCH 1ST GRADE LEVEL-AM This data suppressed for respondent confidentiality.48 A1AT2ND A1 Q4F TCH 2ND GRADE LEVEL-AM This data suppressed for respondent confidentiality.49 A1AT3RD A1 Q4G TCH 3RD GRADE OR HIGHER LEVEL-AM This data suppressed for respondent confidentiality.50 A1A3YROL A1 Q5A HOW MANY 3-YEAR-OLDS IN CLASS-AM This data recoded for respondent confidentiality.51 A1A4YROL A1 Q5B HOW MANY 4-YEAR-OLDS IN CLASS-AM This data recoded for respondent confidentiality.52 A1A5YROL A1 Q5C HOW MANY 5-YEAR-OLDS IN CLASS-AM This data recoded for respondent confidentiality.53 A1A6YROL A1 Q5D HOW MANY 6-YEAR-OLDS IN CLASS-AM This data recoded for respondent confidentiality.54 A1A7YROL A1 Q5E HOW MANY 7-YEAR-OLDS IN CLASS-AM This data recoded for respondent confidentiality.55 A1A8YROL A1 Q5F HOW MANY 8-YEAR-OLDS IN CLASS-AM This data recoded for respondent confidentiality.56 A1A9YROL A1 Q5G HOW MANY 9-YEAR-OLDS IN CLASS-AM This data recoded for respondent confidentiality.57 A1ATOTAG A1 Q5H TOTAL CLASS ENROLLMENT (AGE)-AM This data recoded for respondent confidentiality.58 A1AASIAN A1 Q6A # OF ASIAN/PACIFIC ISLANDERS-AM This data recoded for respondent confidentiality.59 A1AHISP A1 Q6B # OF HISPANICS (ALL RACES)-AM This data recoded for respondent confidentiality.60 A1ABLACK A1 Q6C # OF NON-HISPANIC BLACKS-AM This data recoded for respondent confidentiality.61 A1AWHITE A1 Q6D # OF NON-HISPANIC WHITES-AM This data recoded for respondent confidentiality.62 A1AAMRIN A1 Q6E # OF AMERICAN INDIANS-AM This data recoded for respondent confidentiality.63 A1ARACEO A1 Q6F # OF STUDENTS OF OTHER RACES-AM This data recoded for respondent confidentiality.64 A1ATOTRA A1 Q6 TOTAL CLASS ENROLLMENT (RACES)-AM This data recoded for respondent confidentiality.91 A1ATVTNM A1 Q18C TCHRS SPEAK VIETNAMESE-AM This data suppressed for respondent confidentiality.92 A1ATCHNS A1 Q18D TCHRS SPEAK CHINESE-AM This data suppressed for respondent confidentiality.93 A1ATJPNS A1 Q18E TCHRS SPEAK JAPANESE-AM This data suppressed for respondent confidentiality.

7-83
Table 7-8. Masked variables in the teacher catalog (continued)

FieldID FieldName FieldLabel Comment

94 A1ATKRN A1 Q18F TCHRS SPEAK KOREAN-AM This data suppressed for respondent confidentiality.95 A1ATFLPN A1 Q18G TCHRS SPEAK A FILIPINO LNG-AM This data suppressed for respondent confidentiality.96 A1ATOTAS A1 Q18H TCHRS SPEAK OTHER ASIAN LNG-AM This data suppressed for respondent confidentiality.97 A1ATOTLG A1 Q18I TCHRS SPEAK ANOTHER LANGUAGE-AM This data suppressed for respondent confidentiality.

109 A1PTPREK A1 Q4A TCH PREKINDERGARTEN LEVELS-PM This data suppressed for respondent confidentiality.110 A1PTTRNK A1 Q4B TCH TRANSITIONAL KINDERGARTEN-PM This data suppressed for respondent confidentiality.111 A1PTREGK A1 Q4C TEACHES REGULAR KINDERGARTEN-PM This data suppressed for respondent confidentiality.112 A1PTPRE1 A1 Q4D TCH PRE-1ST GRADE LEVEL-PM This data suppressed for respondent confidentiality.113 A1PT1ST A1 Q4E TCH 1ST GRADE LEVEL-PM This data suppressed for respondent confidentiality.114 A1PT2ND A1 Q4F TCH 2ND GRADE LEVEL-PM This data suppressed for respondent confidentiality.115 A1PT3RD A1 Q4G TCH 3RD GRADE OR HIGHER LEVEL-PM This data suppressed for respondent confidentiality.116 A1P3YROL A1 Q5A HOW MANY 3-YEAR-OLDS IN CLASS-PM This data recoded for respondent confidentiality.117 A1P4YROL A1 Q5B HOW MANY 4-YEAR-OLDS IN CLASS-PM This data recoded for respondent confidentiality.118 A1P5YROL A1 Q5C HOW MANY 5-YEAR-OLDS IN CLASS-PM This data recoded for respondent confidentiality.119 A1P6YROL A1 Q5D HOW MANY 6-YEAR-OLDS IN CLASS-PM This data recoded for respondent confidentiality.120 A1P7YROL A1 Q5E HOW MANY 7-YEAR-OLDS IN CLASS-PM This data recoded for respondent confidentiality.121 A1P8YROL A1 Q5F HOW MANY 8-YEAR-OLDS IN CLASS-PM This data recoded for respondent confidentiality.122 A1P9YROL A1 Q5G HOW MANY 9-YEAR-OLDS IN CLASS-PM This data recoded for respondent confidentiality.123 A1PTOTAG A1 Q5H TOTAL CLASS ENROLLMENT (AGE)-PM This data recoded for respondent confidentiality.124 A1PASIAN A1 Q6A # OF ASIAN/PACIFIC ISLANDERS-PM This data recoded for respondent confidentiality.125 A1PHISP A1 Q6B # OF HISPANICS (ALL RACES)-PM This data recoded for respondent confidentiality.126 A1PBLACK A1 Q6C # OF NON-HISPANIC BLACKS-PM This data recoded for respondent confidentiality.127 A1PWHITE A1 Q6D # OF NON-HISPANIC WHITES-PM This data recoded for respondent confidentiality.128 A1PAMRIN A1 Q6E # OF AMERICAN INDIANS-PM This data recoded for respondent confidentiality.129 A1PRACEO A1 Q6F # OF STUDENTS OF OTHER RACES-PM This data recoded for respondent confidentiality.130 A1PTOTRA A1 Q6 TOTAL CLASS ENROLLMENT (RACES)-PM This data recoded for respondent confidentiality.157 A1PTVTNM A1 Q18C TCHRS SPEAK VIETNAMESE-PM This data suppressed for respondent confidentiality.158 A1PTCHNS A1 Q18D TCHRS SPEAK CHINESE-PM This data suppressed for respondent confidentiality.159 A1PTJPNS A1 Q18E TCHRS SPEAK JAPANESE-PM This data suppressed for respondent confidentiality.160 A1PTKRN A1 Q18F TCHRS SPEAK KOREAN-PM This data suppressed for respondent confidentiality.161 A1PTFLPN A1 Q18G TCHRS SPEAK A FILIPINO LNG-PM This data suppressed for respondent confidentiality.162 A1PTOTAS A1 Q18H TCHRS SPEAK OTHER ASIAN LNG-PM This data suppressed for respondent confidentiality.163 A1PTOTLG A1 Q18I TCHRS SPEAK ANOTHER LANGUAGE-PM This data suppressed for respondent confidentiality.

7-84
Table 7-8. Masked variables in the teacher catalog (continued)

FieldID FieldName FieldLabel Comment

175 A1DTPREK A1 Q4A TCH PREKINDERGARTEN LEVELS-AD This data suppressed for respondent confidentiality.176 A1DTTRNK A1 Q4B TCH TRANSITIONAL KINDERGARTEN-AD This data suppressed for respondent confidentiality.177 A1DTREGK A1 Q4C TEACHES REGULAR KINDERGARTEN-AD This data suppressed for respondent confidentiality.178 A1DTPRE1 A1 Q4D TCH PRE-1ST GRADE LEVEL-AD This data suppressed for respondent confidentiality.179 A1DT1ST A1 Q4E TCH 1ST GRADE LEVEL-AD This data suppressed for respondent confidentiality.180 A1DT2ND A1 Q4F TCH 2ND GRADE LEVEL-AD This data suppressed for respondent confidentiality.181 A1DT3RD A1 Q4G TCH 3RD GRADE OR HIGHER LEVEL-AD This data suppressed for respondent confidentiality.182 A1D3YROL A1 Q5A HOW MANY 3-YEAR-OLDS IN CLASS-AD This data recoded for respondent confidentiality.183 A1D4YROL A1 Q5B HOW MANY 4-YEAR-OLDS IN CLASS-AD This data recoded for respondent confidentiality.184 A1D5YROL A1 Q5C HOW MANY 5-YEAR-OLDS IN CLASS-AD This data recoded for respondent confidentiality.185 A1D6YROL A1 Q5D HOW MANY 6-YEAR-OLDS IN CLASS-AD This data recoded for respondent confidentiality.186 A1D7YROL A1 Q5E HOW MANY 7-YEAR-OLDS IN CLASS-AD This data recoded for respondent confidentiality.187 A1D8YROL A1 Q5F HOW MANY 8-YEAR-OLDS IN CLASS-AD This data recoded for respondent confidentiality.188 A1D9YROL A1 Q5G HOW MANY 9-YEAR-OLDS IN CLASS-AD This data recoded for respondent confidentiality.189 A1DTOTAG A1 Q5H TOTAL CLASS ENROLLMENT (AGE)-AD This data recoded for respondent confidentiality.190 A1DASIAN A1 Q6A # OF ASIAN/PACIFIC ISLANDERS-AD This data recoded for respondent confidentiality.191 A1DHISP A1 Q6B # OF HISPANICS (ALL RACES)-AD This data recoded for respondent confidentiality.192 A1DBLACK A1 Q6C # OF NON-HISPANIC BLACKS-AD This data recoded for respondent confidentiality.193 A1DWHITE A1 Q6D # OF NON-HISPANIC WHITES-AD This data recoded for respondent confidentiality.194 A1DAMRIN A1 Q6E # OF AMERICAN INDIANS-AD This data recoded for respondent confidentiality.195 A1DRACEO A1 Q6F # OF STUDENTS OF OTHER RACES-AD This data recoded for respondent confidentiality.196 A1DTOTRA A1 Q6 TOTAL CLASS ENROLLMENT (RACES)-AD This data recoded for respondent confidentiality.223 A1DTVTNM A1 Q18C TCHRS SPEAK VIETNAMESE-AD This data suppressed for respondent confidentiality.224 A1DTCHNS A1 Q18D TCHRS SPEAK CHINESE-AD This data suppressed for respondent confidentiality.225 A1DTJPNS A1 Q18E TCHRS SPEAK JAPANESE-AD This data suppressed for respondent confidentiality.226 A1DTKRN A1 Q18F TCHRS SPEAK KOREAN-AD This data suppressed for respondent confidentiality.227 A1DTFLPN A1 Q18G TCHRS SPEAK A FILIPINO LNG-AD This data suppressed for respondent confidentiality.228 A1DTOTAS A1 Q18H TCHRS SPEAK OTHER ASIAN LNG-AD This data suppressed for respondent confidentiality.229 A1DTOTLG A1 Q18I TCHRS SPEAK ANOTHER LANGUAGE-AD This data suppressed for respondent confidentiality.310 B1YRBORN B1 Q16 TEACHER’S YEAR OF BIRTH This data recoded for respondent confidentiality.312 B1RACE1 B1 Q18 NATIVE AMERICAN OR PACIF ISLANDER This data recoded for respondent confidentiality.315 B1RACE4 B1 Q18 NATIVE HAWAIIAN OR OTHER PAC IS This data suppressed for respondent confidentiality.317 B1YRSPRE B1 Q19A YRS TEACHER TAUGHT PRESCHOOL This data recoded for respondent confidentiality.

7-85
Table 7-8. Masked variables in the teacher catalog (continued)

FieldID FieldName FieldLabel Comment

318 B1YRSKIN B1 Q19B YRS TEACHER TAUGHT KINDERGARTEN This data recoded for respondent confidentiality.319 B1YRSFST B1 Q19C YRS TEACHER TAUGHT FIRST GRADE This data recoded for respondent confidentiality.320 B1YRS2T5 B1 Q19D YRS TEACHER TAUGHT 2 TO 5 GRADE This data recoded for respondent confidentiality.321 B1YRS6PL B1 Q19E YRS TEACHER TAUGHT 6 GRADE OR UP This data recoded for respondent confidentiality.322 B1YRSESL B1 Q19F YRS TEACHER TAUGHT ESL This data recoded for respondent confidentiality.323 B1YRSBIL B1 Q19G YRS TEACHER TAUGHT BILINGUAL ED This data recoded for respondent confidentiality.324 B1YRSSPE B1 Q19H YRS TEACHER TAUGHT SPECIAL ED This data recoded for respondent confidentiality.325 B1YRSPE B1 Q19I YRS TEACHER TAUGHT PHYSICAL ED This data recoded for respondent confidentiality.326 B1YRSART B1 Q19J YRS TEACHER TAUGHT ART OR MUSIC This data recoded for respondent confidentiality.327 B1YRSCH B1 Q20 YRS TEACHER TAUGHT AT THIS SCHOOL This data recoded for respondent confidentiality.328 B1HGHSTD B1 Q21 HIGHEST ED LEVEL TEACHER ACHIEVED This data recoded for respondent confidentiality.346 A2AGIFT A2 Q2A # CLASSIFIED AS GFTED/TALENTED-AM This data recoded for respondent confidentiality.347 A2APRTGF A2 Q2B # TAKE PART IN GIFTED/TALENTED-AM This data recoded for respondent confidentiality.352 A2ADISAB A2 Q3A NUMBER WITH DISABILITIES-AM This data recoded for respondent confidentiality.356 A2ARETAR A2 Q4D MENTAL RETARDATION-AM This data recoded for respondent confidentiality.362 A2AMULTI A2 Q4J MULTIPLE DISABILITIES-AM This data suppressed for respondent confidentiality.363 A2AAUTSM A2 Q4K AUTISM-AM This data suppressed for respondent confidentiality.365 A2ADEAF A2 Q4M DEAFNESS AND BLINDNESS-AM This data suppressed for respondent confidentiality.374 A2ACVTNM A2 Q7C STUDENTS SPEAK VIETNAMESE-AM This data suppressed for respondent confidentiality.375 A2ACCHNS A2 Q7D STUDENTS SPEAK CHINESE-AM This data suppressed for respondent confidentiality.376 A2ACJPNS A2 Q7E STUDENTS SPEAK JAPANESE-AM This data suppressed for respondent confidentiality.377 A2ACKRN A2 Q7F STUDENTS SPEAK KOREAN-AM This data suppressed for respondent confidentiality.378 A2ACFLPN A2 Q7G STUDENTS SPEAK FILIPINO LNG-AM This data suppressed for respondent confidentiality.379 A2AOTASN A2 Q7H STUDENTS SPEAK OTHER ASIAN LNG-AM This data suppressed for respondent confidentiality.380 A2AOTLNG A2 Q7I STUDENTS SPEAK ANOTHER LNG-AM This data suppressed for respondent confidentiality.381 A2ALNGOS A2 Q7I LANGUAGE OF INSTRUCTION - OTHER-AM This data suppressed for respondent confidentiality.384 A2PGIFT A2 Q2A # CLASSIFIED AS GFTED/TALENTED-PM This data recoded for respondent confidentiality.385 A2PPRTGF A2 Q2B # TAKE PART IN GIFTED/TALENTED-PM This data recoded for respondent confidentiality.390 A2PDISAB A2 Q3A NUMBER WITH DISABILITIES-PM This data recoded for respondent confidentiality.394 A2PRETAR A2 Q4D MENTAL RETARDATION-PM This data recoded for respondent confidentiality.400 A2PMULTI A2 Q4J MULTIPLE DISABILITIES-PM This data suppressed for respondent confidentiality.401 A2PAUTSM A2 Q4K AUTISM-PM This data suppressed for respondent confidentiality.403 A2PDEAF A2 Q4M DEAFNESS AND BLINDNESS-PM This data suppressed for respondent confidentiality.

7-86
Table 7-8. Masked variables in the teacher catalog (continued)

FieldID FieldName FieldLabel Comment

412 A2PCVTNM A2 Q7C STUDENTS SPEAK VIETNAMESE-PM This data suppressed for respondent confidentiality.413 A2PCCHNS A2 Q7D STUDENTS SPEAK CHINESE-PM This data suppressed for respondent confidentiality.414 A2PCJPNS A2 Q7E STUDENTS SPEAK JAPANESE-PM This data suppressed for respondent confidentiality.415 A2PCKRN A2 Q7F STUDENTS SPEAK KOREAN-PM This data suppressed for respondent confidentiality.416 A2PCFLPN A2 Q7G STUDENTS SPEAK FILIPINO LNG-PM This data suppressed for respondent confidentiality.417 A2POTASN A2 Q7H STUDENTS SPEAK OTHER ASIAN LNG-PM This data suppressed for respondent confidentiality.418 A2POTLNG A2 Q7I STUDENTS SPEAK ANOTHER LNG-PM This data suppressed for respondent confidentiality.419 A2PLNGOS A2 Q7I LANGUAGE OF INSTRUCTION - OTHER-PM This data suppressed for respondent confidentiality.422 A2DGIFT A2 Q2A # CLASSIFIED AS GFTED/TALENTED-AD This data recoded for respondent confidentiality.423 A2DPRTGF A2 Q2B # TAKE PART IN GIFTED/TALENTED-AD This data recoded for respondent confidentiality.428 A2DDISAB A2 Q3A NUMBER WITH DISABILITIES-AD This data recoded for respondent confidentiality.432 A2DRETAR A2 Q4D MENTAL RETARDATION-AD This data recoded for respondent confidentiality.438 A2DMULTI A2 Q4J MULTIPLE DISABILITIES-AD This data suppressed for respondent confidentiality.439 A2DAUTSM A2 Q4K AUTISM-AD This data suppressed for respondent confidentiality.441 A2DDEAF A2 Q4M DEAFNESS AND BLINDNESS-AD This data suppressed for respondent confidentiality.450 A2DCVTNM A2 Q7C STUDENTS SPEAK VIETNAMESE-AD This data suppressed for respondent confidentiality.451 A2DCCHNS A2 Q7D STUDENTS SPEAK CHINESE-AD This data suppressed for respondent confidentiality.452 A2DCJPNS A2 Q7E STUDENTS SPEAK JAPANESE-AD This data suppressed for respondent confidentiality.453 A2DCKRN A2 Q7F STUDENTS SPEAK KOREAN-AD This data suppressed for respondent confidentiality.454 A2DCFLPN A2 Q7G STUDENTS SPEAK FILIPINO LNG-AD This data suppressed for respondent confidentiality.455 A2DOTASN A2 Q7H STUDENTS SPEAK OTHER ASIAN LNG-AD This data suppressed for respondent confidentiality.456 A2DOTLNG A2 Q7I STUDENTS SPEAK ANOTHER LNG-AD This data suppressed for respondent confidentiality.457 A2DLNGOS A2 Q7I LANGUAGE OF INSTRUCTION - OTHER-AD This data suppressed for respondent confidentiality.

New Variables Replacing Suppressed Variables Suppressed Va

886 A1AKGTYP A1 Q3 TYPE OF KINDERGARTEN PROG TCH-AM This data recoded for respondent confidentiality. A1AREGK A1 

KI

1=Regular kindergarten only A1A2YRK1 A1 2=Other kindergarten type A1A2YRK2 A1 

A1ATRNK A1 

KI

A1APR1ST A1 

K-

A1AUNGR A1 A1AMULGR A1 

7-87
Table 7-8. Masked variables in the teacher catalog (continued)

New Variables Replacing Suppressed Variables Suppressed Va

887 A1PKGTYP A1 Q3 TYPE OF KINDERGARTEN PROG TCH-PM This data recoded for respondent confidentiality. A1PREGK A1 

KI

1=Regular kindergarten only A1P2YRK1 A1 2=Other kindergarten type A1P2YRK2 A1 

A1PTRNK A1 

KI

A1PPR1ST A1 

K-

A1PUNGR A1 A1PMULGR A1 

888 A1DKGTYP A1 Q3 TYPE OF KINDERGARTEN PROG TCH-AD This data recoded for respondent confidentiality. A1DREGK A1 

KI

1=Regular kindergarten only A1D2YRK1 A1 2=Other kindergarten type A1D2YRK2 A1 

A1DTRNK A1 

KI

A1DPR1ST A1 

K-

A1DUNGR A1 A1DMULGR A1 

7-88
Table 7-9. Masked variables in the school catalog

FieldID FieldName FieldLabel Comment

3 CS_TYPE2 TYPE OF SCHOOL IN SAMPLE FRAME This data recoded for respondent confidentiality.4 KURBAN LOCATION TYPE IN SAMPLE FRAME This data recoded for respondent confidentiality.9 S2KENRLK S2 TOTAL SCHOOL K ENROLLMENT This data recoded for respondent confidentiality.

10 S2KENRLS S2 TOTAL SCHOOL ENROLLMENT This data recoded for respondent confidentiality.11 S2KMINOR S2 PERCENT MINORITY STUDENTS This data recoded for respondent confidentiality.12 S2KFLNCH S2 PCT FREE LUNCH ELIGIBLE STUDENTS This data recoded for respondent confidentiality.13 S2KRLNCH S2 PCT REDUCED LUNCH ELIGIBLE STUDENTS This data recoded for respondent confidentiality.15 S2KLNGTH S2 SCHOOL YEAR LENGTH This data recoded for respondent confidentiality.16 S2KGFTED S2 PERCENT GIFTED/TALENTED STUDENTS This data recoded for respondent confidentiality.18 S2NUMDAY S2 Q1 NUMBER OF DAYS MUST ATTEND This data recoded for respondent confidentiality.19 S2ADA S2 Q2 % AVERAGE DAILY ATTENDANCE FOR YR. This data recoded for respondent confidentiality.20 S2ANUMCH S2 Q3A # ENROLLED AROUND 10/1/1998 This data recoded for respondent confidentiality.21 S2BNUMCH S2 Q3B # ENROLLED SINCE 10/1/1998 This data recoded for respondent confidentiality.22 S2CNUMCH S2 Q3C # WHO LEFT - DIDN’T RETURN This data recoded for respondent confidentiality.45 S2BIASKL S2 Q6D IS IT A TRIBAL SCHOOL This data suppressed for respondent confidentiality.46 S2SPEDSK S2 Q6E IS IT A SPECIAL ED SCHOOL This data suppressed for respondent confidentiality.51 S2PRIVRD S2 Q7A4 IS IT A PRIVATE ORDER This data suppressed for respondent confidentiality.53 S2NAISKL S2 Q7C PRIVATE-ACCREDITED BY NAIS This data suppressed for respondent confidentiality.55 S2PVTSPD S2 Q7E IS IT SPECIAL EDUCATION This data suppressed for respondent confidentiality.58 S2EMPHAS S2 Q9 WHAT’S THE SCHOOL EMPHASIS This data suppressed for respondent confidentiality.59 S2PREK S2 Q10 PRE-K PARTICIPATES This data suppressed for respondent confidentiality.60 S2REDDYN S2 Q10 TRANSITIONAL PARTICIPATES This data suppressed for respondent confidentiality.61 S2KINGAR S2 Q10 KINDERGARTEN PARTICIPATES This data suppressed for respondent confidentiality.62 S2PRE1ST S2 Q10 PREFIRST PARTICIPATES This data suppressed for respondent confidentiality.

7-89
Table 7-9. Masked variables in the school catalog

FieldID FieldName FieldLabel Comment

63 S2FIR1ST S2 Q10 1ST GRADE PARTICIPATES This data suppressed for respondent confidentiality.64 S2SEC2ND S2 Q10 2ND GRADE PARTICIPATES This data suppressed for respondent confidentiality.65 S2THI3RD S2 Q10 3RD GRADE PARTICIPATES This data suppressed for respondent confidentiality.66 S2FOR4TH S2 Q10 4TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.67 S2FIF5TH S2 Q10 5TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.68 S2SIX6TH S2 Q10 6TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.69 S2SEV7TH S2 Q10 7TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.70 S2ATE8TH S2 Q10 8TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.71 S2NIN9TH S2 Q10 9TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.72 S2TEN10 S2 Q10 10TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.73 S2ELEV11 S2 Q10 11TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.74 S2TWEL12 S2 Q10 12TH GRADE PARTICIPATES This data suppressed for respondent confidentiality.75 S2SPENR S2 Q11 NUMBER IN SPECIAL PROGRAM This data suppressed for respondent confidentiality.92 S2PCTHSP S2 Q14 PERCENT OF HISPANIC STUDENTS This data recoded for respondent confidentiality.93 S2INDPCT S2 Q15A PERCENT OF NATIVE AMERICANS This data suppressed for respondent confidentiality.94 S2ASNPCT S2 Q15B PERCENT OF ASIAN STUDENTS This data suppressed for respondent confidentiality.95 S2BLKPCT S2 Q15C PERCENT OF BLACK STUDENTS This data recoded for respondent confidentiality.96 S2PCFPCT S2 Q15D PERCENT OF HAWAIIAN STUDENT This data suppressed for respondent confidentiality.97 S2WHTPCT S2 Q15E PERCENT OF WHITE STUDENTS This data suppressed for respondent confidentiality.98 S2ELIBRK S2 Q16A1 ELIGIBLE FOR FREE BREAKFAST This data suppressed for respondent confidentiality.99 S2PARBRK S2 Q16A2 PARTICIPATES IN BREAKFAST This data suppressed for respondent confidentiality.

100 S2ELILNC S2 Q16B1 ELIGIBLE FOR FREE LUNCH This data suppressed for respondent confidentiality.101 S2PARLNC S2 Q16B2 PARTICIPATES IN FREE LUNCH This data suppressed for respondent confidentiality.102 S2ELIRED S2 Q16C1 ELIGIBLE IN REDUCED-PRICE LUNCH This data suppressed for respondent confidentiality.103 S2PARRED S2 Q16C2 PARTICIPATES IN RED-PRICE LUNCH This data suppressed for respondent confidentiality.113 S2CHSRVD S2 Q20 NUMBER SERVED BY TITLE 1 This data recoded for respondent confidentiality.114 S2HDCHDN S2 Q21A1 HALFDAY - NUMBER OF CHILDREN This data recoded for respondent confidentiality.115 S2HDCLS S2 Q21B1 HALFDAY - NUMBER OF CLASSES This data recoded for respondent confidentiality.116 S2HDDAYS S2 Q21C1 HALFDAY - NUMBER OF DAYS PER WK This data suppressed for respondent confidentiality.118 S2FDCHDN S2 Q21A2 FULLDAY - NUMBER OF CHILDREN This data recoded for respondent confidentiality.119 S2FDCLS S2 Q21B2 FULLDAY - NUMBER OF CLASSES This data recoded for respondent confidentiality.120 S2FDDAYS S2 Q21C2 FULLDAY - NUMBER OF DAYS PER WK This data suppressed for respondent confidentiality.

7-90
Table 7-9. Masked variables in the school catalog (continued)

FieldID FieldName FieldLabel Comment

122 S2KCCHDN S2 Q21A3 K COMBINATION - # OF CHILDREN This data recoded for respondent confidentiality.123 S2KCCLS S2 Q21B3 K COMBINATION - # OF CLASSES This data suppressed for respondent confidentiality.124 S2KCDAYS S2 Q21C3 K COMBINATION - # DAYS PER WK This data suppressed for respondent confidentiality.126 S2TKCHDN S2 Q21A4 TRANSITIONAL K - # OF CHILDREN This data recoded for respondent confidentiality.127 S2TKCLS S2 Q21B4 TRANSITIONAL K - # OF CLASSES This data suppressed for respondent confidentiality.128 S2TKDAYS S2 Q21C4 TRANSITIONAL K - # DAYS PER WK This data suppressed for respondent confidentiality.130 S2T1CHDN S2 Q21A5 TRANSITIONAL 1ST- # OF CHILDREN This data recoded for respondent confidentiality.131 S2T1CLS S2 Q21B5 TRANSITIONAL 1ST- # OF CLASSES This data suppressed for respondent confidentiality.132 S2T1DAYS S2 Q21C5 TRANSITIONAL 1ST - DAYS PER WK This data suppressed for respondent confidentiality.137 S2NOCUTO S2 Q22A NO CUTOFF DATE TO TURN FIVE This data suppressed for respondent confidentiality.149 S2CHLDNM S2 Q24 # OF CHILDREN SITE ACCOMMODATES This data recoded for respondent confidentiality.150 S2RMNUM S2 Q25 NUMBER OF INSTRUCTIONAL ROOMS This data recoded for respondent confidentiality.161 S2INSTCM S2 Q27A # INSTRUCTIONAL COMPUTERS This data recoded for respondent confidentiality.162 S2ADMNCM S2 Q27B # INSTRUCT/ADMIN COMPUTERS This data recoded for respondent confidentiality.163 S2TOTCM S2 Q27C TOTAL NUMBER OF COMPUTERS This data recoded for respondent confidentiality.166 S2LANRMS S2 Q28A3 TOTAL ROOMS W/LAN ACCESS This data recoded for respondent confidentiality.169 S2CDRRMS S2 Q28B3 TOTAL ROOMS W/CD-ROM ACCESS This data recoded for respondent confidentiality.172 S2NETRMS S2 Q28C3 TOTAL RMS W/INTERNET ACCESS This data recoded for respondent confidentiality.176 S2CMNITY S2 Q30 COMMUNITY SCHOOL IS LOCATED IN This data recoded for respondent confidentiality.232 S2NMBKPT S2 Q40 NUMBER RETAINED GRADE-K This data recoded for respondent confidentiality.274 S2NMADMN S2 Q47A2 NUMBER OF ADMINISTRATORS This data recoded for respondent confidentiality.276 S2NMTECH S2 Q47B2 NUMBER OF TEACHERS This data recoded for respondent confidentiality.278 S2NMDSTR S2 Q47C2 NUMBER FROM DISTRICT OFFICE This data recoded for respondent confidentiality.280 S2NUMBRD S2 Q47D2 NUMBER OF BOARD MEMBERS This data recoded for respondent confidentiality.282 S2NMOMDD S2 Q47E2 NUMBER OF PARENTS This data recoded for respondent confidentiality.284 S2NMREPS S2 Q47F2 NUMBER OF REPRESENTATIVES This data recoded for respondent confidentiality.286 S2NUMOTH S2 Q47G2 NUMBER OF OTHER GROUPS This data recoded for respondent confidentiality.290 S2BILING S2 Q50 BILINGUAL SERVICES PERCENT This data suppressed for respondent confidentiality.291 S2ESLONL S2 Q50 ESL SERVICES PERCENT This data suppressed for respondent confidentiality.292 S2ESLBIL S2 Q50 ESL AND BILINGUAL PERCENT This data suppressed for respondent confidentiality.293 S2YRSESL S2 Q51A YEARS FOR ESL SERVICES This data recoded for respondent confidentiality.294 S2YRSBIL S2 Q51B YEARS FOR BILINGUAL SERVICES This data recoded for respondent confidentiality.

7-91
Table 7-9. Masked variables in the school catalog (continued)

FieldID FieldName FieldLabel Comment

295 S2SRVBTH S2 Q51C YEARS FOR ESL & BILINGUAL SER This data recoded for respondent confidentiality.319 S2GFTNBR S2 Q57 NUMBER GIFTED/TALENTED This data suppressed for respondent confidentiality.320 S2ADMFTE S2 Q58A FTE ADMINISTRATIVE STAFF This data recoded for respondent confidentiality.321 S2TCHFTE S2 Q58B FTE CLASSROOM TEACHERS This data recoded for respondent confidentiality.322 S2GYMFTE S2 Q58C FTE GYM/DRAMA/ART TEACHERS This data recoded for respondent confidentiality.323 S2CMPFTE S2 Q58D FTE COMPUTER SPECIALISTS This data recoded for respondent confidentiality.324 S2HDSFTE S2 Q58E FTE HEAD START/PRE-K TEACHERS This data recoded for respondent confidentiality.325 S2CRDFTE S2 Q58F FTE COORDINATOR/SPECIALIST This data recoded for respondent confidentiality.326 S2TT1FTE S2 Q58G FTE TITLE 1 TEACHERS This data recoded for respondent confidentiality.327 S2AIDFTE S2 Q58H FTE TEACHER AIDES This data recoded for respondent confidentiality.328 S2SPEFTE S2 Q58I FTE SPECIAL ED TEACHERS This data recoded for respondent confidentiality.329 S2SPAFTE S2 Q58J FTE SPECIAL ED AIDES This data recoded for respondent confidentiality.330 S2ESLFTE S2 Q58K FTE ESL-BILINGUAL TEACHERS This data recoded for respondent confidentiality.331 S2ESAFTE S2 Q58L FTE ESL-BILINGUAL AIDES This data recoded for respondent confidentiality.332 S2LIAFTE S2 Q58M FTE PARENT LIAISONS This data recoded for respondent confidentiality.333 S2COUFTE S2 Q58N FTE SOCIAL WORKERS This data recoded for respondent confidentiality.334 S2LIBFTE S2 Q58O LIBRARY/MEDIA SPECIALISTS This data recoded for respondent confidentiality.335 S2THRFTE S2 Q58P SPEECH/PHYSICAL THERAPISTS This data recoded for respondent confidentiality.336 S2NRSFTE S2 Q58Q FTE SCHOOL NURSES This data recoded for respondent confidentiality.337 S2NONFTE S2 Q58R FTE NON-INSTRUCTIONAL STAFF This data recoded for respondent confidentiality.338 S2REAFTE S2 Q58S FTE READING RECOVERY TCHRS This data suppressed for respondent confidentiality.339 S2MSCFTE S2 Q58T FTE MATH/SCIENCE SPECIALISTS This data recoded for respondent confidentiality.340 S2FRLFTE S2 Q58U FTE FOREIGN LNG SPECIALISTS This data recoded for respondent confidentiality.341 S2OTHFTE S2 Q58V OTHER FTE This data suppressed for respondent confidentiality.342 S2FTETOT S2 Q58W TOTAL FTE SCHOOL STAFF This data recoded for respondent confidentiality.345 S2ETHNIC S2 Q61 % HISPANIC/LATINO TEACHERS This data recoded for respondent confidentiality.346 S2Q62IND S2 Q62 % AMERICAN NATIVE TEACHERS This data recoded for respondent confidentiality.347 S2Q62ASN S2 Q62 % ASIAN TEACHERS This data recoded for respondent confidentiality.348 S2Q62BLK S2 Q62 % BLACK TEACHERS This data recoded for respondent confidentiality.349 S2Q62HAW S2 Q62 % HAWAIIAN TEACHERS This data suppressed for respondent confidentiality.446 S2YSTCH S2 Q74A NUMBER OF YRS TEACHING This data recoded for respondent confidentiality.447 S2TOTPRI S2 Q74B NUMBER OF YRS AS PRINCIPAL This data recoded for respondent confidentiality.448 S2PRINHR S2 Q74C NUMBER YRS A PRINCIPAL HERE This data recoded for respondent confidentiality.468 S2EDLVL S2 Q77 HIGHEST LEVEL OF EDUCATION This data recoded for respondent confidentiality.

7-92
Table 7-9. Masked variables in the school catalog (continued)

New Variables Replacing Suppressed Variables Suppressed Variab

New Variables Replacing Suppressed Variables Suppressed Variab

703 S2PRACE S2 Q73 PRINCIPAL’S RACE This data recoded for respondent confidentiality. S2RACE1 S2 Q73

1=White, non-Hispanic S2RACE2 S2 Q732=Black or African-American, non-Hispanic S2RACE3 S2 Q733=Other race S2RACE4 S2 Q73-9=Not ascertained S2RACE5 S2 Q73

7-93
8-1

8. GUIDE TO THE ECLS-K BASE YEAR PUBLIC-USE ELECTRONIC CODE BOOK

8.1 Introduction

The purpose of this guide is to provide data users of the Early Childhood Longitudinal

Study-Kindergarten Class of 1998-99 (ECLS-K) specific directions for using the ECLS-K Base Year

Public-Use ECB CD-ROM and Electronic Code Book (ECB). The information in this guide provides a

comprehensive tour through the ECLS-K Base Year Public-Use ECB that addresses all of the functions

and capabilities of the program. These functions allow users to interact with accompanying catalogs and

“view” the data in various ways by performing customized searches, queries, and extractions. The

organization of this document provides a “start to finish” approach through the system beginning with the

installation of the ECB, utilizing the ECB’s functions, navigating through the catalogs, and performing

user-specified data extractions.

The ECLS-K Base Year Public-Use ECB CD-ROM contains an ECB that allows users to

easily examine the variables in each of the ECLS-K Base Year Public-Use ECB data sets. The data user

can create SAS, SPSS for Windows, and STATA programs that will generate an extract data file from any

of the data files on the CD-ROM. The following flat (ASCII) data files are contained on the CD-ROM:

 Base Year Child Data (child.dat)

- Fall 1998

- Spring 1999

 Base Year Teacher Data (teacher.dat)

- Fall 1998

- Spring 1999

 Base Year School Data (school.dat)

- Spring 1999

Additionally, the CD-ROM contains Windows 95 and Portable Document Format (PDF)

files of the Base Year questionnaires in Appendix A and the record layout for each data file in Appendix

B. The menu bars are described in section 8.10.
8-2

8.1.1 Hardware/Software Requirements

The ECB program is designed to run under Windows 95® or Windows 98® on a Pentiumclass or higher PC. The PC should also have a minimum of 200 Megabytes (MB) of available disk space.

The program will visually fit best on screens set to a desktop area of 800 x 600 pixels. It will still work on

other screen settings, but it may not make the best use of the available screen space. You can check and/or

set your desktop area as follows:

1. Click on the Windows Start button.

2. Select the Settings menu and then the Control Panel folder icon.

3. In the Control Panel window, click on the Display icon.

4. Select the Settings tab.

5. Set the Desktop Area to 800 x 600 pixels with the Desktop Area slidebar.

The ECB requires approximately 20 MB of available disk space on your hard drive. If 20

MB of space is not available, you may wish to delete unnecessary files from the drive to make space for

the ECB.

8.1.2 ECLS-K Base Year Public-Use ECB Features

 Search the names and labels of variables in the databases (called catalogs) to select

variables for analysis (see section 8.3, The Variable List).

 Examine the question wording, response categories, and response frequencies for

variables the user selects (see section 8.4.7, Viewing Code Book and Variable

Information).

 Create a list of variables to be extracted from the catalog, save the list for later use,

print the list as a code book, or use a predefined list on the ECB (see section 8.4, The

Working Taglist).

 Automatically generate SAS, SPSS for Windows, or STATA programs to extract

selected variables from the whole data set or for a subset of the cases that are defined

by the user (see section 8.5, Extracting Data from the ECB).
8-3

The ECLS-K Base Year Public-Use ECB does not create a SAS, SPSS for Windows, or

STATA data file. It will prepare the statements that you can use with your own SAS or SPSS for

Windows software to create your file. As noted above, the CD-ROM contains all three ASCII data sets

that the ECB uses to extract specific sub-data files.

8.2 Installing, Starting, and Exiting the ECLS-K Base Year Public-Use ECB

The ECLS-K Base Year Public-Use ECB is provided on a CD-ROM and is intended to be

installed and run from within the Windows 95 environment. The sections in this chapter provide you with

step-by-step instructions for installing the program on your PC, starting the program, and exiting the

program once you have completed your tasks.

If you installed the ECLS-K Base Year Public-Use ECB on your PC before, you have to

uninstall the old program before installing this version. For details of how to remove the ECB program

from your PC, please see section 8.2.4.

8.2.1 Installing the ECLS-K Base Year Public-Use ECB Program on Your PC

Program installation is initiated by running the Setup.exe file found within the CD-ROM’s

root directory.

How to Install the Program:

1. Close all applications on your computer.

2. Insert the installation CD-ROM into your PC’s CD-ROM drive.

3. From the Windows 95 Start menu, select Run.

4. Type “D:\Setup.exe” into the “Open” field of the Run screen, shown in figure 8-1. If

your CD-ROM drive is assigned a different drive letter, substitute it for the “D.”
8-4

Figure 8-1. Windows Run screen

5. Click on the OK button to start the installation. You will now see several installation

screens, some of which will prompt you for a response.

Depending on your PC’s configuration, you may encounter warning messages during

installation. To respond, always keep the newer version of a file being copied and

ignore any access violation occurred during file copying.

If you are installing multiple ECB (not different versions of the same ECB) on your

PC, you may receive a message warning that setup is about to replace pre-existing

files. To respond, always insists to continue the installation although the default is to

cancel the setup. When you get a follow-up message to confirm whether the

installation should be continued, press “Yes” to continue although the default is “No.”

6. The screen shown in figure 8-2 indicates that the setup is being prepared.

Figure 8-2. InstallShield Wizard

7. You will be prompted to continue with the installation in the Welcome window shown in

figure 8-3. Click on the Next button to continue.
8-5

Figure 8-3. Welcome Window

8. When you continue, you will be prompted to choose a destination location for the installation

in the window shown in figure 8-4. If you wish to change the destination location, click on

the Browse button to change the directory. Click on the Next button when the desirable

destination folder is shown.
8-6

Figure 8-4. Choose Destination Location

9. Setup will then start installing files. Figure 8-5 shows the setup status.

Figure 8-5. Setup Status
8-7

10. Once the installation is completed, the InstallShield Wizard Complete window shown in

figure 8-6 will appear. Click on the Finish button to finish the process and return to your

PC’s desktop.

Figure 8-6. InstallShield Wizard Complete

11. The installation process should take about a minute, depending on the speed of the computer

on which the ECB is being installed.

The other option would be to go to the Start menu and go to Settings. Select Control Panel

and select Add/Remove Programs from the options. Click on the Install button and follow directions.

Make sure the ECLS-K Base Year Public-Use ECB CD-ROM is in the CD-ROM drive before starting.

The program will automatically find the file Setup.exe in the ECB and begin installation. The process will

begin at point 5 in the section above.
8-8

8.2.2 Starting the ECLS-K Base Year Public-Use ECB

Now that you have installed the ECLS-K Base Year Public-Use ECB on your PC, you can

start the program by simply selecting it from the Windows Start, Programs Menu, ECLS-K Base Year

Public-Use.

How to Start the ECB:

1. On the desktop screen (figure 8-7b), click on the Start button and then point to Programs.

Click on the ECLS-K Base Year Public-Use title to invoke the program. Alternatively,

click on the ECLS-K Base Year Public-Use desktop icon (figure 8-7a) shown below to

invoke the program.

Figure 8-7a. Desktop icon

Figure 8-7b. Desktop screen - click start
8-9

2. If you are a first time user of the ECLS-K Base Year Public-Use ECB, figure 8-8 will

display and ask if you are a new ECB user.

Figure 8-8. First time user dialog box

3. Click Yes if you are a first time user. The ECLS-K Base Year Public-Use ECB

splash-screen shown in figure 8-9 will display.

Figure 8-9. The ECLS-K Base Year ECB splash screen

4. On the Select Catalog screen (figure 8-10), highlight the catalog you want to work

with.
8-10

Figure 8-10. Select Catalog screen

5. Click OK to open the Main ECB screen, shown in figure 8-11.

Figure 8-11. Main ECB screen

6. You are now ready to use the functions of the ECLS-K Base Year Public-Use ECB as

described in the following sections.
8-11

8.2.3 Exiting the ECLS-K Base Year Public-Use ECB

The ECLS-K Base Year Public-Use ECB can be shut down at any time; however, you will

be prompted to save any unsaved information.

How to Shut Down the ECB:

1. From the File menu, click on the Exit option as shown in figure 8-12.

Figure 8-12. Exit screen

2. If you have not saved your Working Taglist, you will be prompted with the dialog box

shown in figure 8-13.

Figure 8-13. Save Working Taglist dialog box

3. If you DO NOT wish to save your Working Taglist, click on the No button. If you DO

wish to save your Working Taglist, click on the Yes button and refer to section 8.4.4,

Saving Taglists.

8.2.4 Removing the ECB Program From Your PC

How to Uninstall the ECB:

1. Click on the Windows Start button.

2. Select the Settings menu.
8-12

3. In the Control Panel window, click on the Add/Remove Programs.

4. Select “ECLS-K Base Year Public-Use” and click on the “Add/Remove button.”

5. Follow any prompts. You will be prompted by the InstallShield Wizard to confirm

the uninstallation and finish the process.

6. It is designed that the uninstallation will keep the taglists when the ECB program is

uninstalled in order that all the saved taglists will be retained when the ECB is

reinstalled. As a result, the uninstallation will not remove the directory where the

ECB was.

8.2.5 The Title Bar

The Title Bar, shown below in figure 8-14, is the horizontal bar located at the top of the

main screen. It will list the name of the program and the catalog that you have opened, and it will indicate

that you are in the “Create Taglist” mode.

Figure 8-14. Title Bar

8.2.6 The Menu Bar

Selecting items from the pull-down menus listed on the Menu Bar (figure 8-15) provides

access to the available action commands. Section 8.10 shows the choices and functions available within

each menu.
8-13

Figure 8-15. Menu Bar

How to Access the Menu Bar Items:

1. Point to an item on the Menu Bar and click.

2. Click on a command from the drop-down list.

The Menu Bar may also be activated, and its options selected using the shortcut keys

described in section 8.2.7.

8.2.7 Using Shortcut Keys to Navigate

The shortcut keys provide a means for selecting menu options and screen buttons without the

use of a mouse. These shortcut keys are identified by an underscore under the shortcut letter within the

option or button label.

The menus that appear on the windows are activated by simultaneously selecting the <ALT>

key and the underscored letter. An example of this is the activation of the Taglist Menu by selecting the

key combination of <ALT>-<T>. Once the menu is activated and all options are displayed, the options

can be selected by then pressing the underscored letter for the desired option or by pressing the arrow

keys to move between the options.
8-14

Not all screens have shortcut keys. They may, however, be used without mouse capability by

pressing the <TAB> key. The <TAB> key moves the cursor or highlight through the options and buttons

within the windows. When the desired option or button is highlighted, it can be selected by pressing the

<ENTER> key.

8.3 The Variable List

The ECLS-K Base Year Public-Use ECB main screen, shown in figure 8-16, comprises two

primary lists that each provide functions for reviewing, grouping, and extracting variable data from the

opened catalog. These lists include the Variable List and the Working Taglist.

Figure 8-16. The ECLS-K Base Year ECB main screen
8-15

The Variable List, shown in figure 8-17, is a list of all variables associated with the current

catalog. For the ECLS-K Base Year Public-Use ECB, you can open the school, teacher, or child catalogs.

When you first open a catalog, all variables contained in the catalog are displayed in the Variable List.

Once the catalog is open and the Variable List is displayed, you can scroll through the list using the

scrollbar controls at the right side of the Variable List screen. Additionally, you can press <PgUp> and

<PgDn> to scroll the list one screen at a time. <Ctrl><Home> and <Ctrl><End> will move to the first and

last variable in the list, respectively. Also, the arrow keys can be used to move through the list of variable

names.

Figure 8-17. The Variable List

The Variable List provides you with a comprehensive means of reviewing and identifying

the variables that you want to use. To help you select the desired variables, the ECB provides you with

the following capabilities:

 Perform searches of variable names and descriptions (see section 8.3.1);

 View code book information for each variable (see section 8.4.7); and

 Move selected variables to a Working Taglist (see section 8.4).
8-16

8.3.1 Searching the Code Book for Variables

The ECB allows you to search a catalog’s Variable List for variables meeting criteria you

specify. The Narrow Search and Expand Search functions are used to develop and refine the variables

listed in your Variable List before adding them to your Working Taglist. Help screens were designed for

each catalog to expedite searching. The help screen for the child catalog is provided in table 8-7, for the

teacher catalog in table 8-13, and for the school catalog in table 8-16.

8.3.1.1 Using the “Go” Button

Using the “Go” button, located at the top of the Variable List column, allows you to quickly

move to a particular variable in the Variable List. You use the field ID presented in the help screens

described above.

How to Use the GO Button:

1. Type the field ID in the input box on the left of the “Go” button.

2. Click on the “Go” button.

3. The Variable List will then scroll down automatically to show the selected variable.

4. The selected variable is highlighted.

5. The field ID of the current variable selected is showed on the left of the “Go” button

(figure 8-18).

6. Click the Reset to return to the original Variable List or enter another field ID to scroll

to another variable.
8-17

Figure 8-18. The “Go” button

For field IDs that identify different groups of variables, please refer to table 8-7 for child

catalog topical variable groupings, table 8-13 for teacher catalog topical variable groupings, and table 8-

16 for school catalog topical variable groupings.

The “Go” button will not be available in narrowed or expanded list. After a Narrow Search

or an Expand Search, you have to reset the Variable List (see section 8.3.1.4) before you can use the “Go”

button.
8-18

8.3.1.2 Narrowing Your Variable Search

The Narrow Search function can be used to narrow the list of variables displayed in the

Variable List. Since some catalogs have several thousand variables, this feature helps eliminate the

variables that do not apply to your analysis. In performing the Narrow Search, you can enter key

characters, words, or phrases as your criteria for searching the variable names; variable descriptions; or

both. Use the keywords in the “Variable Identifier” column in tables 8-7, 8-13, or 8-16 to search the child,

teacher, or school catalog variables, respectively, by variable description. If “(Variable Name)” appears

under the variable in the “Variable Identifier” column, you must search by variable name. Also, the

Narrow Search can be performed multiple times allowing you to repeatedly refine the list of variables

displayed in the Variable List column.

Performing the Narrow Search function will only search the variables listed in the Variable

List window and will not affect those in the Working Taglist window.

How to Conduct a Narrow Search:

1. Click on the Narrow button located above the Variable List window.

2. The Narrow Search dialog box appears as shown in figure 8-19.

Figure 8-19. Narrow Text Search dialog box

3. Enter a key character string, word, or phrase in the Enter Narrow Text: field.

Character strings can include a single alphanumeric character or a sequence of several

characters. The search is not case sensitive. The results returned will be all entries that

contain that exact sequence of letters, numbers, spaces, and words.
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4. Click in the Variable Name, Variable Description, or Both Variable Name and

Description radio button to specify where to search.

5. Click on the Search button to initiate the search.

6. The variables meeting the specified criteria will be displayed in the Variables List

column.

If no variable names or descriptions in the catalog contain the specified search text, then the

message shown in figure 8-20 will appear.

Figure 8-20. No matches found message

7. Repeat the Narrow Search procedure if necessary.

Example of Narrowing a Search

The following example shows you how to narrow the Variable List. In this example, you

want to include the childcare variables in the Fall-Kindergarten Parent Interview. This is what you need to

do:

1. Go to table 8-7, Child Catalog Topical Variable Groupings.

2. Look for “Fall K PI: Child Care” in the “Topic” column.

3. We will find the matching keywords in the “Variable Identifier” column to be “P1

CCQ.”

4. In the Variable List, click in the “Narrow” button.

5. In the Search Text Box (shown in figure 8-21), type in “P1 CCQ” and then click on

the “Search” button.
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Figure 8-21. Example of narrowing a search

6. The new Variable List will include only the childcare variables in the FallKindergarten Parent Interview.

8.3.1.3 Expanding Your Variable Search

The Expand Search function can be used to expand a previously narrowed list of variables

displayed in the Variable List. After performing a Narrow Search operation, you can add variables to your

current Variable List that meet your specified criteria. In performing the Expand Search, you can enter

key characters, words, or phrases as your criteria for searching the variable names, variable descriptions,

or both. Also, the Expand Search can be performed multiple times, allowing you to repeatedly refine the

list of variables displayed in the Variable List column.

Performing the Expand Search function will only search the variables listed in the Variable

List window and will not affect those in the Working Taglist window.

How to Conduct an Expand Search:

1. Click on the Expand button located above the Variable List window.

2. The Expand Search dialog box will appear as shown in figure 8-22.
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Figure 8-22. Expand Text Search dialog box

3. Enter a key character string, word, or phrase in the Enter Expand Text: field.

Character strings can include a single alphanumeric character or a sequence of several

characters. The search is not case sensitive. The results returned will be all entries that

contain that exact sequence of letters, numbers, spaces, and words.

4. Click in the Variable Name, Variable Description, or Both Variable Name and

Description radio button to specify where to search.

5. Click on the Search button to initiate the search.

6. The variables meeting the specified criteria will be added to the variables already

displaying in the Variables List column.

If no variable names or descriptions in the catalog contain the specified search text, then the

message shown in figure 8-23 will appear.

Figure 8-23. No matches found message

7. Repeat the Expand Search procedure if necessary.
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8.3.1.4 Resetting Your Variable List

Following a narrowing or expanding of the Variable List as described above, it is possible to

reset the list to display ALL of the variables available in the catalog. The Variable List is reset by clicking

on the Reset button located at the top of the Variable List column. Resetting the Variable List does not

affect the variables listed in the Working Taglist.

8.4 The Working Taglist

The Working Taglist, shown in figure 8-24, displays a list of variables that are currently

selected or tagged for extraction. All Working Taglists contain a set of variables, called required

variables, that will be automatically included in all data files that the user creates. For each catalog in the

ECLS-K Base Year Public-Use ECB, the required variables provide a foundational data set upon which

other variables rely. These required variables cannot be untagged or deleted from the Working Taglist by

the user. When a catalog is first opened, the default Working Taglist consists of only the required

variables for that catalog. (See table 8-8 for the child catalog, table 8-11 for the teacher catalog, and table

8-14 for the school catalog.) To create a taglist, add the variables you have selected to the required

variables.

Figure 8-24. The ECB Working Taglist
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8.4.1 Opening A Taglist

The ECLS-K Base Year Public-Use ECB allows you to open a predefined or previously

saved taglist and display it in the Working Taglist column. Taglists, however, are saved as part of a

particular catalog and can only be opened as part of the associated catalog.

How to Open a Taglist:

1. Open a catalog.

2. Select Open from the Taglist pulldown menu.

3. The Open Taglist dialog box, shown in figure 8-25, appears.

Figure 8-25. Open Taglist dialog box

4. Highlight the taglist that you wish to open.

5. Click on the OK button.

If you have made modifications to the taglist currently open in the Working Taglist column,

you will be prompted to save your changes.

8.4.2 Adding Variables to the Working Taglist

Variables can be added to your Working Taglist after you have identified the variables in the

ECB’s catalog that you want to extract. The user-selected variables can be added to the Working Taglist
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by selecting one of the two command buttons described in table 8-1 below. The Working Taglist may also

have variables added to it from a previously saved taglist. When moving or adding variables to the

Working Taglist, the ECB will not permit variables to be listed multiple times. This is an automatic

feature of the ECLS-K Base Year Public-Use ECB.

Table 8-1. Add variables buttons

Command Button Description

The Tag button moves variables that are

selected in the Variable List to the Working

Taglist for extraction.

The Tag All button moves all variables in the

Variable List to the Working Taglist for

extraction.

Multiple variables can be selected by using the Microsoft Windows© techniques of:

 Simultaneously pressing the <SHIFT> + Up/Down arrow keys; or

 Pressing <CTRL> + left-mouse clicking on the items to be selected (or deselected).

Also, <SHIFT> + left-mouse clicking extends the selection to include all list items

between the current selection and the location of the click.

How to Add Variables to a Working Taglist:

1. Highlight the variable(s) in the Variables List that you wish to add. (See Microsoft

Windows© techniques discussed above.)

2. Click on the Tag button, and the selected variables are added to your Working Taglist.

To add all variables from the catalog displayed in the Variable List window to your

Working Taglist, click on the Tag All button.

How to Add Variables from Another Taglist:

1. Click on the Taglist pulldown menu to display the menu options.

2. Select the Add option to display a list of previously saved taglists, shown in

figure 8-26.
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Figure 8-26. Add Taglist dialog box

3. Highlight the saved taglist whose variables you wish to add to your Working Taglist.

4. Click on the OK button.

5. The new variables are added to your Working Taglist.

8.4.3 Removing Variables from the Working Taglist

Variables are removed from your Working Taglist by selecting one or more of the nonrequired variables and clicking one of the two command buttons described in table 8-2. All variables can

be removed by clicking on the Untag All button. All but the required variables will be deleted from your

Working Taglist. Required variables are variables that are automatically extracted for all user-created files

and cannot be removed from the taglist by the user.

Table 8-2. Remove variables buttons

Command Button Description

The Untag button removes variables that are

selected from the Working Taglist.

The Untag All button removes all non-required

variables from the Working Taglist.

Attempting to remove or untag required variables from the Working Taglist is not permitted

by the ECLS-K Base Year Public-Use ECB. A message will display indicating that the required variable

cannot be untagged.
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How to Untag Variables from the Working Taglist:

1. Highlight the variable(s) in the Working Taglist that you wish to remove. (See

Microsoft Windows© techniques discussed in previous page.)

2. Click on the Untag button, and the selected variables are removed from your Working

Taglist. To remove all non-required variables from the Working Taglist, click on the

Untag All button.

8.4.4 Saving Taglists

The ECLS-K Base Year Public-Use ECB has the ability to save the newly created or

modified taglist displayed in the Working Taglist column. Taglists can be saved either under the name

already assigned or under a new name. If you have opened a new taglist and have not yet assigned it a

name, you will be presented with the Save As dialog box. If you have opened a predefined taglist and

have made modifications to it, you must save the modified taglist to a new name. You will also be

prompted to save your Working Taglist changes if you attempt to close the catalog or if you open another

taglist.

How to Save a New Taglist:

1. Complete any changes you wish to make to the new taglist.

2. Click on the Save button above the Working Taglist column. You can also select the

Save or Save As options from the Taglist pulldown menu.

3. The Save Taglist As dialog box appears as shown in figure 8-27.
8-27

Figure 8-27. Save Taglist as dialog box

4. Enter the new name for the taglist in the Taglist Name: field.

5. Click on the Save button.

6. The newly assigned taglist name now appears in the Working Taglist header bar.

If a name that already exists is entered, you will be prompted to replace the old taglist with

the new taglist. Click “YES” only if you wish to replace the old taglist with the new taglist.

How to Save an Existing Taglist Under a New Name:

1. Complete any changes you wish to make to the existing taglist.

2. Click on the Taglist pulldown menu and select the Save As option.

3. The Save Taglist As dialog box appears, shown in figure 8-28, with the current taglist

name in the Taglist Name: field.
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Figure 8-28. Save Taglist as dialog box (#2)

4. Enter the new name of the taglist in the Taglist Name: field.

5. Click on the Save button.

6. The newly assigned taglist name now appears in the Working Taglist header bar.

If a name that already exists is entered, you will be prompted to replace the old taglist with

the new taglist. Click “YES” only if you wish to replace the old taglist with the new taglist or enter a

unique name.

8.4.5 Using Predefined Taglists

The ECLS-K Base Year Public-Use ECB provides predefined taglists within each catalog,

which address specific topics. These predefined taglists can be added to your Working Taglist or can be

opened as a new Working Taglist. Opening these predefined taglists is performed using the same steps as

opening a user-saved taglist presented in section 8.4.1. Users can add as many of the predefined taglists as

desired to the open Working Taglist. See sections 8.6.2, 8.7.2, and 8.8.2 for listings and descriptions of

the predefined taglists found in the child, teacher, and school catalogs, respectively.
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8.4.6 Deleting Taglists

The ECLS-K Base Year Public-Use ECB provides the capability of permanently deleting

previously saved taglists. Predefined taglists provided with the ECB, however, can not be deleted through

this function.

How to Delete a Taglist:

1. Close the taglist currently displayed in the Working Taglist column by selecting the

Close option from the Taglist pulldown menu.

2. Neither the Working Taglist or Variable List columns are displayed.

3. Click on the Taglist pulldown menu and select the Delete option.

4. The Delete Taglist Selection screen, shown in figure 8-29, appears with the taglists

listed that may be deleted.

Figure 8-29. Delete Taglist Selection screen

5. Highlight the taglist that is to be deleted and click on the OK button.

6. A confirmation screen, shown in figure 8-30, verifies your intention to delete the

taglist.

Figure 8-30. Delete Taglist confirmation window
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7. Click on the Yes button to permanently delete the saved taglist.

8.4.7 Viewing Code Book and Variable Information

The code book for a taglist displayed in the Working Taglist column can be created, viewed,

and printed from the ECLS-K Base Year Public-Use ECB main screen. The code book displays several

pieces of information about each variable that are described in the table 8-3.

Table 8-3. Code book information

Field Description

Question Text The question that was asked the respondent by the interviewer or that

was on the self-administered instruments.

Variable Name/

Description

The name of the variable as it appears in the catalog and a brief

description of its content.

Record Number The row number of the variable within the catalog data file.

Format The format of the variable. The first character is either “A” or “N” for

alphabetical or numeric. All variables are numeric except the

identifiers—which begin with an “A.” The number following the “A” or

“N” is the length of the variable. For numeric variables, the number after

the decimal point is the number of decimal places.

Comment Information to clarify specific information about a variable.

Position The column number (position) of the variable within the catalog data file

Response A brief statement of each response code’s meaning.

Codes The numeric codes specifying each response.

Frequency The numeric count of respondents providing the corresponding response

code. The frequency counts are unweighted.

Percent The percentage of respondents providing the corresponding response

code. The percents are unweighted.
8-31

How to View the Code Book for Tagged Variables:

1. Complete any changes you wish to make to the displayed taglist.

2. Click on the Code Book pulldown menu and select the View option.

3. The ECB for the current taglist opens in a new window as shown in figure 8-31.

Figure 8-31. ECB view

4. Use the buttons described in table 8-4 to navigate through the displayed code book.

Note that the code book program will try to select a screen width percentage based on

the screen settings on your workstation. This may accidentally truncate some of the

labels shown in figure 8-31 (such as “Variable Name” or “Record Number”). If this

occurs, just select or enter “100%” in the magnification box on the toolbar. You will

always see the code book entries correctly when the magnification is set at 100%.
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Table 8-4. Navigation buttons

Command Button Description

Click this button to change the displayed page to the first

page.

Click this button to change to the previous page.

Click this button to advance to the next page.

Click this button to change the displayed page to the last

page.

Click this button to discontinue a page change. This button

is often unavailable in the ECB since page changes occur

quickly.

Click this button to print the ECB. Refer to the procedure

below for steps on printing the code book.

Click this button to send the ECB to a different destination

and save it as a different file format. Refer to the procedure

below for steps on exporting the code book.

Click the dropdown arrow to select a display magnification

of the code book.

5. Once you have finished viewing the code book, close the screen by clicking on the

Windows “X” control located in the top right corner of the window. You may also

close the window using the other standard Windows defaults: by clicking on the

windows icon in the upper left corner and selecting Close, or by pressing Alt-F4.

How to Print the ECB:

1. Complete any changes you wish to make to the displayed taglist.

2. Click on the Code Book pulldown menu and select the Print option.

3. The Printing Status screen, shown in figure 8-32, appears, and the code book prints on

your PC’s default printer.

Figure 8-32. Printing Status screen
8-33

How to Export the Code Book:

1. Complete any changes you wish to make to the displayed taglist.

2. Click on the Code Book pulldown menu and select the View option.

3. The ECB for the current taglist opens in a new window, similar to the one shown in

figure 8-31.

4. Click on the Export Code Book button: 

5. The Export Taglist selection screen, shown in figure 8-33, appears.

Figure 8-33. Export Taglist selection screen

6. Select the desired options from the Format: pulldown menu and the Destination:

pulldown menu.

7. Click on the OK button and complete any subsequent screens required for exporting

the file.

The child catalog is too large to export in its entirety.

The code book and its variables can be selected to display their information from either the

Variable List or the Working Taglist. The information that can be displayed for a variable includes the

variable name and label, the question wording associated with the variable, the position and format of the

variable on the data file, each value and its label, unweighted frequencies, and the unweighted percentage

distributions as listed on table 8-3. The entire code book can also be viewed after moving all of the

catalog’s variables to the Working Taglist. The following procedures describe how to view some or all

code book variables.
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How to Display Information for a Single Code Book Variable:

1. Locate the desired variable from either the Variable List or the Working Taglist.

2. Click on the variable name to highlight it and press <ENTER> -or- double-click on

the variable name to view the variable information. Figure 8-34 shows the Variable

Quick View that displays data for a specific variable from the Base Year School

Catalog.

The Variable Name is the only field that can be highlighted for displaying the variable’s

code book information. Clicking on the variable description field will not activate the Variable Quick

View.

Figure 8-34. Variable Quick View

3. When you are done reviewing the variable information, close the window by clicking

on the Windows control “X” in the upper right corner of the screen. You’ll return to

the main screen.
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How to Print Information for a Single Code Book Variable:

The ECB currently does not support printing the information for a single variable directly to

the printer. If you must print the information for a single variable, please follow the these steps:

1. Double-click on the variable to activate the Variable Quick View (see the previous

“How To” section for details).

2. With the Variable Quick View being the active window on top, press <Alt> + <Print

Screen> to save the image of the Variable Quick View window.

3. In any application that supports bitmap images, e.g., Microsoft Paint, Microsoft Word,

etc., paste the saved image.

4. Print the image to the printer using the print function of the application that you are

using.

How to Display and Print the Entire Code Book or Selected Pages:

1. Move all of the catalog’s variables displayed in the Variable List to the Working

Taglist by clicking on the Tag All button.

2. Click on the OK button of the Add All Variables Confirmation dialog box, shown in

figure 8-35.

Figure 8-35. Add All Variables dialog box

3. All of the variables listed in the Variable List are now displayed in the Working

Taglist.

4. Select View from the Code Book pulldown menu.

5. The entire code book displays as shown in figure 8-36. Note that this view includes

ALL variables in the catalog and can span several hundred pages. The page number is

in the upper left corner of the window.
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Figure 8-36. View of the entire code book

6. To print the entire code book, click on the printer icon displayed at the top of the code

book screen. Select “ALL” from the Printer Dialog box (figure 8-37). Enter the

number of copies you want and click on the OK button.

Figure 8-37. Printer Dialog box

7. To print selected pages of the code book, select “Pages” from the Printer Dialog box.

Enter the pages you want to print and the number of copies you want. Click on the OK

button.

8. When you are done viewing the entire code book, close the window by clicking on the

Windows control “X” in the upper right corner of the screen. You’ll return to the main

screen.
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8.5 Extracting Data from the ECB

Once the variables have been selected (tagged) for extraction and reside in the Working

Taglist, the next step is to generate the code through which the statistical analysis software can retrieve

and display the results. The ECLS-K Base Year Public-Use ECB provides options for generating the code

for analyzing data with the SAS, SPSS for Windows, or STATA statistical analysis programs.

To run these programs, you will need the appropriate statistical software and the ECLS-K

Base Year Public-Use ECB CD-ROM from which the program can extract data.

SPSS users should note that certain catalogs will produce a Frequencies command statement

with more than 500 variables. This may produce a warning of “too many variables,” and the Frequencies

command will not execute. Users may work around this limitation by dividing the Variable List into two

or more Frequencies commands.

When extracting data to be used with either the SAS, SPSS for Windows, or STATA

programs, a dialog box will be presented that allows the user to define the extract population through the

Limiting Fields. See figure 8-38. The Limiting Fields include various subgroups of respondents that are

typically of interest to analysts. These subgroups can be selected or deselected to narrow the data field

that is extracted.

Also, please note that the ECB extract function allows the user to specify the drive letter of

the CD-ROM drive. If you attempt to run the resulting SAS, SPSS, and STATA programs on a

workstation with a different CD-ROM drive letter, you must alter the program code accordingly or

regenerate the program code using the ECB.

The SAS, SPSS, or STATA source code generated by the ECB to read in the ECLS-K data

may contain code statements that are “commented” out (e.g., with * in SAS). These code statements

either run descriptive statistics, e.g., frequencies, means, etc., or associate formats with variables. They

are commented out because not all analysts will want them included in the source code.

SAS users (prior to SAS, Version 8) should note that, although the ECB will allow dataset

names larger than eight characters, the SAS system will reject these names at run-time.
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Figure 8-38. Teacher catalog limiting fields dialog box

Refer to sections 8.6.3, 8.7.3, and 8.8.3 for instructions on using and modifying the limiting

variables for the child, teacher, and school catalogs, respectively.

How to Extract a File to SAS Format:

1. Complete any changes you wish to make to the displayed taglist.

2. Click on the Extract pulldown menu and select the SAS option.

3. The Limiting Fields screen for the open catalog appears. Make your selections for

each limiting variable indicator.

4. Verify that the ECLS-K Base Year Public-Use ECB CD-ROM is mounted in your

PC’s default CD-ROM drive and then click on the OK button.

5. Type the desired name of the extract program file in the file name field of the screen

shown in figure 8-39.
8-39

Figure 8-39. Save SAS program file dialog box

6. To save the file to another directory, click on the Save in: dropdown menu button to

browse to the new location, as shown in figure 8-40.

Figure 8-40. Save SAS program file location browse screen

7. Click on the Save button to store the file.

8. In the Save Data File As window (figure 8-41) type in the file name you want the data

file to save to and then click on Save.
8-40

Figure 8-41. Save SAS data file dialog box

9. Run the saved extract program in SAS to extract the data.

How to Extract a File to SPSS Format:

1. Complete any changes you wish to make to the displayed taglist.

2. Click on the Extract pulldown menu and select the SPSS option.

3. The Limiting Fields screen for the open catalog appears. Make your selections for

each limiting variable indicator.

4. Verify that the ECLS-K Base Year Public-Use ECB CD-ROM is mounted in your

PC’s default CD-ROM drive and then click on the OK button.

5. Type the desired name of the extract program file in the file name field of the screen

shown in figure 8-42.
8-41

Figure 8-42. Save SPSS program file dialog box

6. To save the file to another directory, click on the Save in: dropdown menu button to

browse to the new location, as shown in figure 8-43.

Figure 8-43. Save SPSS program file location browse screen

7. Click on the Save button to store the file.

8. In the Save Data File As window (figure 8-44), type in the file name you want the

data file to save to and then click on Save.
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Figure 8-44. Save SPSS data file dialog box

9. Run the saved extract program in SPSS to extract the data.

How to Extract a File to STATA Format:

1. Complete any changes you wish to make to the displayed taglist.

2. Click on the Extract pulldown menu and select the STATA option.

3. The Limiting Fields screen for the open catalog appears. Make your selections for

each limiting variable indicator.

4. Verify that the ECLS-K Base Year Public-Use ECB CD-ROM is mounted in your

PC’s default CD-ROM drive and then click on the OK button.

5. Type the desired name of the extract program file in the file name field of the screen

shown in figure 8-45.
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Figure 8-45. Save STATA program file dialog box

6. To save the file to another directory, click on the Save in: dropdown menu button to

browse to the new location, as shown in figure 8-46.

Figure 8-46. Save STATA program file location browse screen

7. Click on the Save button to store the file.

8. In the Save Data File As window (figure 8-47), type in the file name you want the

data file to save to and then click on Save.
8-44

Figure 8-47. Save STATA data file dialog box

9. Run the saved extract program in STATA to extract the data.

8.5.1 Reviewing the Extract Specifications

It is recommended that users review the SAS, SPSS, or STATA program code that is

generated before running it, to check that any statements subsetting the data are correct. Note that the

ECB sometimes outputs superfluous code for selecting cases; this code is consistent with extract

specifications, but users may wish to delete it.

If a mistake in defining the criteria is made, and it is not discovered until after writing out or

running the extract program, it is very easy to correct if the taglist was saved before exiting the ECB

program. Simply restart the ECB and select the appropriate catalog, open the taglist that you saved, define

the extract criteria correctly by modifying the saved taglist as desired and saving it, and write out the

extract program again. Reviewing the program before running it is recommended because it may need to

be customized.
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8.5.2 Repairing and Compacting the Database

Periodically users may wish to repair and compact the database that contains the data of the

ECB program. If many taglists are created and deleted on a regular basis, the database will contain

lingering references to old taglists that are no longer needed. When the database is repaired and

compacted, the ECB program “cleans house” and makes the database more efficient. It also decreases the

size of the database, so space is conserved.

How to Repair and Compact the ECLS-K Database:

1. Select the Tools pulldown menu and select the Repair and Compact Database option.

2. After a few seconds, the screen shown in figure 8-48 appears indicating that the repair

and compact of the database was successfully completed.

Figure 8-48. Repair database completed screen

3. Click on the OK button.

8.6 The Child Catalog

The child catalog contains information for children selected to participate in the ECLS-K

survey and includes data collected at the child level from the child and from the child’s parents, teachers,

and schools. Table 8-5 presents the pretagged required variables that will be automatically extracted for

all user-created data files. The user cannot remove the required variables from the taglist. The last two

variables on the list, S2 Public or Private School and P1 First-Time Kindergartner, are also limiting fields,

meaning that they may be used to subset the data being extracted. See section 8.6.3 for an explanation and

description of limiting fields.
8-46

Table 8-5. Child catalog required variables

Field Name Field Label

CHILDID Child Identification Number

F1CLASS F1 Child Program Type from FMS (AM/PM/AD)

F2CLASS F2 Child Program Type from FMS (AM/PM/AD)

GENDER Child Composite Gender

RACE Child Composite Race

R1_KAGE R1 Composite Child Assessment Age (months)

P1FIRKDG P1 First-Time Kindergartner

WKSESQ5 WK Categorical SES Measure

S2KPUPRI S2 Public or Private School

The child catalog is organized into blocks of data corresponding to specific content areas.

They are:

 Identifiers are the school, child, parent, and teacher identification number and the

class type the child attended, i.e., morning, afternoon, or all-day class.

 School demographic data are from the sampling frame, e.g., Census region.

 Child demographic composite variables include age, gender, race-ethnicity, and date

of birth.

 Child status indicates if a child changed schools or teachers within the base year and

whether the child was eligible in spring-kindergarten.

 Weights are described in detail in chapter 4. Round 1 weights are for fallkindergarten; round 2 weights are for spring-kindergarten; and panel weights are for

both rounds combined.

 Flags indicate unit response for the data source, e.g., direct child assessment, parent

interview, or that the parent interview data were edited.

 Scores are for the direct or indirect child assessment, which are computed for round 1

and round 2 separately.

 Composite variables were created to facilitate data analysis. See chapter 7, section

7.4 for a detailed description of the composite variables and table 7-6 for a list of all

composite variables.

 Direct assessment data are the child’s home language, date of assessment, and

exclusions from the direct assessment. Direct assessment items are not released.

 Parent interview data are the data items from the parent interview.
8-47

 Teacher questionnaire data are the data items from the teacher questionnaires.

 School data are the data items from the school administrator questionnaire and the

facility checklist.

 Taylor Series Sampling Strata and PSUs are the stratum and first-stage unit

identifiers that are required for estimating standard errors with the Taylor Series

linearization method using software such as SUDAAN or STATA.

 Weight replicates are used to estimate standard errors using the paired jackknife

replication method (JK2) with WesVar. Each replicate is a subsample of the full

sample. The variability of the replicate estimates about the full sample estimate is

used to estimate the variance of the full sample estimate.

 New variables for public-use file are variables created for respondent confidentiality.

Table 8-6 presents the order of the data blocks on the child data file.

8.6.1 Child Catalog Topical Variable Groupings

The variables within the child catalog are organized into topical categories to help locate and

identify desired variables. These categories are identified in table 8-7. The first column of the table

describes the topic, the second column lists the variable identifiers, which generally indicate the source of

the data, e.g., the parent interview, the teacher questionnaires, as well as when the data was collected, i.e.,

fall- or spring-kindergarten. The third column is a description of the topic. The last column is the “Field

ID,” which is how you search the data file for the topics. Use the keywords in the “Variable Identifier”

column to search for variables while using the ECB Narrow or Expand function (see sections 8.3.1.2 and

8.3.1.3). If “(Variable Name)” appears under the variable in the “Variable Identifier” column, you must

search using the variable name. The field ID can be used by the “Go” function in the Variable List.

Table 8-7 is only for your convenience in searching for variables and includes only variables

that belong to certain topics. It does not reflect the contents of the entire child catalog.
8-48

Table 8-6. Child catalog blocks

Order Block

1 Identifiers

2 School demographic data

3 Child demographic composites

4 Child status flags

Weights

5 Round 1 child weight full sample

6 Round 1 parent weight full sample

7 Round 1 CPT weight full sample

8 Round 2 child weight full sample

9 Round 2 parent weight full sample

10 Round 2 CPT weight full sample

11 Panel child weight full sample

12 Panel parent weight full sample

13 Panel CPT weight full sample

14 Panel combined weight full sample

Flags

15 Round 1 child assessment data status flags

16 Round 2 child assessment data status flags

17 Round 1 parent interview data status flags

18 Round 2 parent interview data status flags

19 Round 1 teacher questionnaire A data status flags

20 Round 2 teacher questionnaire A data status flags

21 Round 1 teacher questionnaire B data status flags*

22 Round 2 teacher questionnaire B data status flags

23 Round 1 teacher questionnaire C data status flags

24 Round 2 teacher questionnaire C data status flags

25 School administrator questionnaire data status flags

26 Facility checklist data status flags

27 Parent data edit flags and error flags

Scores

28 Round 1 child assessment scores

29 Round 2 child assessment scores

30 Round 1 parent SRS scores

31 Round 2 parent SRS scores

32 Round 1 teacher questionnaire C scores

33 Round 2 teacher questionnaire C scores

Composite Variables

34 Round 1 child composites

35 Round 2 child composites

36 Round 1 parent composites

37 Round 2 parent composites

38 Cross-round Parent composites

39 Round 1 teacher questionnaire A composites

40 Cross-round teacher questionnaire A composites
8-49

Table 8-6. Child catalog blocks (continued)

Order Block

41 Round 1 teacher questionnaire B composites

42 Round 2 teacher questionnaire B composites

43 School administrator questionnaire composites

Direct Assessment Data

44 Round 1 child assessment data (language & date)

45 Round 2 child assessment data (language & date)

Parent Interview Data

46 Round 1 parent interview data

47 Round 2 parent interview data

Teacher Questionnaire Data

48 Round 1 teacher questionnaire A data

49 Round 2 teacher questionnaire A data

50 Round 1 teacher questionnaire B data

51 Round 2 teacher questionnaire B data

52 Round 1 teacher questionnaire C data

53 Round 2 teacher questionnaire C data

School Data

54 School administrator data

55 Facility checklist data

Taylor Series Sampling Strata And Primary Sampling Units (PSU)

56 Round 1 child Taylor series sampling strata and PSU

57 Round 2 child Taylor series sampling strata and PSU

58 Round 1 parent Taylor series sampling strata and PSU

59 Round 2 parent Taylor series sampling strata and PSU

60 Round 1 CPT Taylor series sampling strata and PSU

61 Round 2 CPT Taylor series sampling strata and PSU

62 Panel child Taylor series sampling strata and PSU

63 Panel parent Taylor series sampling strata and PSU

64 Panel CPT Taylor series sampling strata and PSU

65 Panel combined Taylor series sampling strata and PSU

Replicate Weights

66 Round 1 child replicate weights

67 Round 2 child replicate weights

68 Round 1 parent replicate weights

69 Round 2 parent replicate weights

70 Round 1 CPT replicate weights

71 Round 2 CPT replicate weights

72 Panel child replicate weights

73 Panel parent replicate weights

74 Panel CPT replicate weights

75 Panel combined replicate weights

76 New variables for public-use file

* Background information from teachers was collected in teacher questionnaire B for all teachers. New kindergarten teachers in springkindergarten completed this questionnaire. The child’s teacher may have changed between fall and spring. Block 50 contains the teacher

questionnaire B data for fall-kindergarten; block 51 contains the teacher questionnaire B data for spring-kindergarten.
8-50

Table 8-7. Child catalog topical variable groupings

Topic Variable Identifier Description FieldID

Identifiers CHILDID – S2_ID

(Variable Name)

School identification number

Teacher identification number

Parent/Child identification numbers

1-8

Variables from

the sampling

Variable from Sample Frame Census region, type of school, locale

of school

9-11

Status flags C1COGDAT - K2INFAC

(Variable Name)

Child assessment status indicators

(e.g., cognitive data available),

parent data available, teacher data

available, school data available

32-54

C1 Reading

C1 Math

C1 Gen

Reading, Mathematics, and General

Knowledge T-scores and IRT scale

scores

76-78

90-92

103-105

Cognitive Skills

and Knowledge

Fall-Kindergarten

Cognitive Skills

and Knowledge

SpringKindergarten

C2 Reading

C2 Math

C2 Gen. Know

Reading, Mathematics, and General

Knowledge T-scores and IRT scale

scores

117-119

131-133

144-146

Cognitive Skills

and Knowledge

Fall-Kindergarten

C1 Prof Reading

C1 Prof Math

Reading and Mathematics

proficiency scores

79-83

93-97

Cognitive Skills

and Knowledge

SpringKindergarten

C2 Prof Reading

C2 Prof Math

Reading and Mathematics

proficiency scores

120-124

134-138

Cognitive Skills

and Knowledge

Fall-Kindergarten

C1 Prob Reading

C1 Prob Math

Reading and Mathematics

probability scores

84-88

98-102

Cognitive Skills

and Knowledge

SpringKindergarten

C2 Prob Reading

C2 Prob Math

Reading and Mathematics

probability scores

125-129

139-143

Social Skills and

Knowledge

Parent Rating

Fall-Kindergarten

P1 Approaches

P1 Self

P1 Social

P1 Sad

P1 Impulsive

Five factor scores from the social

rating scale in the parent interview:

approaches to learning, self-control,

social interaction, sad/lonely,

impulsive/overactive

147

148

149

150

151
8-51

Table 8-7. Child catalog topical variable groupings (continued)

Topic Variable Identifier Description FieldID

Social Skills and

Knowledge

Parent Rating

SpringKindergarten

P2 Approaches

P2 Self

P2 Social

P2 Sad

P2 Impulsive

Five factor scores from the social

rating scale in the parent interview:

approaches to learning, self-control,

social interaction, sad/lonely,

impulsive/overactive

152

153

154

155

156

Social Skills and

Knowledge

Teacher Rating

Fall-Kindergarten

T1 Approaches

T1 Self

T1 Interpersonal

T1 Externalizing

T1 Internalizing

Five factor scores from the social

rating scale in teacher form C:

approaches to learning, self control,

interpersonal, externalizing problem

behaviors, internalizing problem

behaviors

160

161

162

163

164

Social Skills and

Knowledge

Teacher Rating

SpringKindergarten

T2 Approaches

T2 Self

T2 Interpersonal

T2 Externalizing

T2 Internalizing

Five factor scores from the social

rating scale in teacher form C:

approaches to learning, self control,

interpersonal, externalizing problem

behaviors, internalizing problem

behaviors

168

169

170

171

172

DOBMM - R2_KAGE

C1HEIGHT – C2BMI

(Variable Name)

Related to child (e.g., child DOB,

child race, child gender, etc.)

12-18

173-178

P1HMEMMP – IF_INC

(Variable Name)

Related to parent interview (e.g.,

parent occupation, parent highest

education, etc.)

179-319

A1PBLK – B2AGE

(Variable Name)

Related to teacher information (e.g.,

kindergarten teacher class type,

percentage minority in class, etc.)

320-324

Composite/

Derived

Variables

S2KSCTYP – S2KPUPRI

(Variable Name)

Related to school information (e.g.,

school type, school enrollment,

percent minority, etc.)

325-334

Fall-K PI:

Parent’s

Involvement

with Child’s

School

P1 PIQ Includes: school providing parents

with information about kindergarten,

choice of school, whether child started

kindergarten on time and whether he

or she was retained, child’s adjustment

to school, parents’ kindergarten

readiness beliefs

353-374

Fall-K PI:

Family Structure

P1 FSQ Includes: number of people in

household, relationship of people to

child, age of people, gender of people,

and race/ethnicity of people

375-628

Fall-K PI:

Home

Environment,

Activities, and

Cognitive

Stimulation

P1 HEQ Includes: how frequently family

members do such things as reading,

singing and telling stories to child,

how often child reads to self, whether

child watched Sesame Street

629-642
8-52

Table 8-7. Child catalog topical variable groupings (continued)

Topic Variable Identifier Description FieldID

Fall-K PI:

Critical Family

Processes

P1 CFQ Includes: presence of social support

to family

643-674

Fall-K PI:

Child Care

P1 CCQ Includes: child care history for child

prior to kindergarten and the present

(i.e., relative care, non-relative care,

center care, Head Start)

675-785

Fall-K PI:

Child’s Health

and Well-Being

P1 CHQ Includes: birth weight, prematurity,

twin status, early indicators for

developmental difficulties (e.g.,

attention, coordination, hearing,

vision), services for disabilities

786-890

Fall-K PI:

Parent Marital

History

P1 MHQ Includes: marital history of parents 891-933

Fall-K PI:

Child’s Living

Arrangement

History and

Historical Roster

P1 ALQ

P1 HRQ

Includes: where the child has lived

and with whom if situation has

changed within the past year

934-945

946-987

Fall-K PI:

Non-resident

Parent Questions

P1 NRQ Includes: status of biological parents

(if not in household) and contact

with non-resident parents

988-999

Fall-K PI:

Primary

Language

P1 PLQ Includes: information on languages

spoken in the home

1000-1041

3989-3992

Fall-K PI:

Parent Education

and Human

Capital

P1 PEQ Includes: educational attainment of

parents

1042-1088

Fall-K PI:

Parent

Employment

P1 EMQ Includes: employment history and

occupation of parents, history of

financial hardship

1089-1124

Fall-K PI:

Welfare and

Other Public

Transfers

P1 WPQ Includes: family’s utilization of

services such as WIC, AFDC, food

stamps

1125-1132

Fall-K PI:

Mobility

P1 CMQ Includes: how many different places

child has lived for four months or

more since birth

1133-1135

Spring-K PI:

Introduction

P2 INQ Includes: child’s place of birth 1480-1484

Spring-K PI:

Parent

Involvement

with Child’s

School

P2 PIQ Includes: parents’ contact with

school, parent attendance in school

related activities, barriers to

attendance to school related

activities

1525-1583
8-53

Table 8-7. Child catalog topical variable groupings (continued)

Topic Variable Identifier Description FieldID

Spring-K PI:

Family Structure

P2SPRHHM – P2RC6_17 Includes: number of people in

household, relationship of people to

child, age of people, gender of

people, and race/ethnicity of people

1136-1479

Spring-K PI:

Home

Environment,

Activities and

Cognitive

Stimulation

P2 HEQ Includes: attending activities in the

community (e.g., museums, zoos,

athletic events), home computer

access and use, participation in

extra-curricular activities (e.g.,

music lessons, craft classes),

information on community crime

and safety, mealtimes and bedtimes

1584-1622

Spring-K PI:

Critical Family

Processes

P2 CFQ Includes: information on parents’

relationship to one another

1623-1644

Spring-K PI:

Non-resident

Parent Question

P2 NRQ Includes: information on nonresident parent and custody

arrangements

1645-1710

Spring-K PI:

Discipline,

Warmth and

Emotional

Supportiveness

P2 DWQ Includes: parents’ interaction with

child and discipline strategies (e.g.,

spanking, time-out)

1711-1741

Spring-K PI:

Child’s Health

and Well-Being

P2 CHQ Includes: information on dentist and

doctor, updates fall information

screener on indicators for

developmental difficulties

1742-1766

Spring-K PI:

Parent’s

Psychological

Well-Being and

Health

P2 PPQ Includes: parent depression, parent

health problems

1767-1787

Spring-K PI:

Food Security

P2 FDQ Includes: food sufficiency in the

household

1788-1816

Spring-K PI:

Welfare and

Other Public

Transfers

P2 WPQ Includes: family’s utilization of

AFDC or food stamps, free/reduced

breakfast or lunch

1817-1830

Spring-K PI:

Parents Income

and Assets

P2 PAQ Includes: household income 1831-1836
8-54

Table 8-7. Child catalog topical variable groupings (continued)

Topic Variable Identifier Description FieldID

A1 Q1 – A1 Q19 Fall: type of kindergarten program,

class age and race-ethnic

composition, gender composition,

percent LEP, languages in

classroom

1839-1904

3993

Teacher

Questionnaire:

Classroom

Characteristics

A2 Q1 – A2 Q7 Spring: G&T, attendance, special

needs children

1908-1945

B1 Q1 – B1 Q6 Fall: instructional approach,

centers/learning areas

Teacher 2180-2207

Questionnaire:

Class

Organization and

Resources

A2 Q8 – A2 Q27 Spring: instructional approach,

curriculum content, teacher’s aide,

physical resources (e.g., texts,

equipment, materials for art and

music)

1946-2032

Teacher

Questionnaire:

Instructional

Activities and

Curricular Focus

A2 Q28 – A2 Q34 Spring: instructional time spent on

specific aspects of reading and

language arts, math, computers,

science and social studies

2033-2152

Teacher

Questionnaire:

Parent

Involvement

A2 Q35 – A2 Q 38 Spring: conferences, teachers

sending home information

2153-2160

Teacher

Questionnaire:

School and Staff

Activities

A2 Q39 – A2 Q41 Spring: in-service sessions, training 2161-2176

Teacher

Questionnaire:

Teacher Views

B1 Q7 – B1 Q14 Fall: views on readiness, school

climate, environment

2208-2253

Teacher

Questionnaire:

Teacher

Demographics

B1 Q15 – B1 Q24 Fall: gender, birth year, raceethnicity, experience, education2254-2285

School

Questionnaire:

School

Characteristics

S2 Q1 – S2 Q22 Includes: attendance, enrollment,

admission requirements, federal

programs

2476-2595

School

Questionnaire:

School Facilities

and Resources

S2 Q23 – S2 Q28 Includes: funding sources, facilities

(e.g., cafeteria, music room,

auditorium)

2596-2630
8-55

Table 8-7. Child catalog topical variable groupings (continued)

Topic Variable Identifier Description FieldID

School

Questionnaire:

Community

Characteristics

and School Safety

S2 Q29 – S2 Q33 Includes: community type,

community safety, school safety

2631-2652

School

Questionnaire:

School Policies

and Practices

S2 Q34 – S2 Q40 Includes: uniform code, testing and

assessments

2653-2690

School

Questionnaire:

School-FamilyCommunity

Connections

S2 Q41 – S2 Q47 Includes: programs and services for

families, parent involvement in

school

2691-2744

School

Questionnaire:

School Programs

for Special

Populations

S2 Q48 – S2 Q57 Includes: limited English proficient,

special education, gifted and

talented

2745-2777

School

Questionnaire:

Staffing and

Teacher

Characteristics

S2 Q58 – S2 Q63 Includes: school staff list, salary

information, staff’s race-ethnic

distribution

2778-2809

School

Questionnaire:

School

Governance and

Climate

S2 Q64 – S2 Q69 Includes: professional development,

process for decision making,

performance evaluation process

2810-2895

School

Questionnaire:

Principal

Characteristics

S2 Q70 – S2 Q79 Includes: administrator’s gender,

race-ethnicity, birth year, experience

and education

2896-2935

3994

Facility

Checklist

K2 Q1 – K2 Q6 Includes: information on school

facilities (e.g., presence and

condition of rooms such as libraries,

computer labs, bathrooms,

cafeteria), handicap access to

facilities, school safety

2939-3068
8-56

8.6.2 Child Catalog Predefined Taglists

There are three predefined taglists provided with the child catalog that can be used for

extracting data. These three taglists, fall-kindergarten, spring-kindergarten, and kindergarten base year,

are defined in tables 8-8, 8-9, and 8-10, respectively. The required variables, identified with “YES”

appearing in the Required Variable column, are the same as those listed in table 8-5. The limiting

variables, identified as “YES” in the Limiting Variable column, are the same as those described in

Section 8.6.3.

Table 8-8. Fall-kindergarten predefined taglist - child catalog

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

Basic Demographic Variables

Child ID CHILDID CHILD IDENTIFICATION NUMBER YES NO

Child sex GENDER COMPOSITE CHILD GENDER YES NO

Child age in the

fall of 1998

R1_KAGE R1 COMPOSITE CHILD ASSESSMENT

AGE(MONTHS)

YES NO

Child ethnicity RACE COMPOSITE CHILD RACE YES NO

Household SES WKSESQ5 WK CATEGORICAL SES MEASURE YES NO

Fall kindergarten

type

F1CLASS F1 CHILD PROGRAM TYPE FROM

FMS(AM/PM/AD)

YES NO

Springkindergarten

type

F2CLASS F2 CHILD PROGRAM TYPE FROM

FMS(AM/PM/AD)

YES NO

School type S2KPUPRI S2 PUBLIC OR PRIVATE SCHOOL YES YES

First-time

kindergartner

P1FIRKDG P1 FIRST-TIME KINDERGARTNER YES YES
8-57

Table 8-8. Fall-kindergarten predefined taglist - child catalog (continued)

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

Round Specific Assessment Scores

Reading T-scores C1RTSCOR C1 READING T-SCORE NO NO

Reading scale

scores

C1RSCALE C1 READING IRT SCALE SCORE NO NO

C1RPROB1 C1 PROB1 - LETTER RECOGNITION NO NO

C1RPROB2 C1 PROB2 - BEGINNING SOUNDS NO NO

C1RPROB3 C1 PROB3 - ENDING SOUNDS NO NO

C1RPROB4 C1 PROB4 - SIGHT WORDS NO NO

Reading

proficiency

probability scores

(5)

C1RPROB5 C1 PROB5 - WORD IN CONTEXT NO NO

C1RPROF1 C1 PROF1 - LETTER RECOGNITION NO NO

C1RPROF2 C1 PROF2 - BEGINNING SOUNDS NO NO

C1RPROF3 C1 PROF3 - ENDING SOUNDS NO NO

C1RPROF4 C1 PROF4 - SIGHT WORDS NO NO

Reading

proficiency

scores (5)

C1RPROF5 C1 PROF5 - WORD IN CONTEXT NO NO

Print familiarity

score

C1RPRINT C1 PRINT FAMILIARITY NO NO

Math T-scores C1MTSCOR C1 MATH T-SCORE NO NO

Math scale scores C1MSCALE C1 MATH IRT SCALE SCORE NO NO

C1MPROB1 C1 PROB1 - COUNT, NUMBER, SHAPE NO NO

C1MPROB2 C1 PROB2 – RELATIVE SIZE NO NO

C1MPROB3 C1 PROB3 – ORDINALITY, SEQUENCE NO NO

C1MPROB4 C1 PROB4 - ADD/SUBTRACT NO NO

Math proficiency

probability scores

(5)

C1MPROB5 C1 PROB5 – MULTIPLY/DIVIDE NO NO

C1MPROF1 C1 PROF1 - COUNT, NUMBER, SHAPE NO NO

C1MPROF2 C1 PROF2 – RELATIVE SIZE NO NO

C1MPROF3 C1 PROF3 – ORDINALITY, SEQUENCE NO NO

C1MPROF4 C1 PROF4 - ADD/SUBTRACT NO NO

Math proficiency

scores (5)

C1MPROF5 C1 PROF5 – MULTIPLY/DIVIDE NO NO

Math assessment

in Spanish

C1SPASMT C1 CHILD ASSESSMENT IN SPANISH NO NO

GK T-scores C1GTSCOR C1 GENERAL KNOWLEDGE T-SCORE NO NO

GK scale scores C1GSCALE C1 GENERAL KNOWLEDGE IRT SCALE

SCORE

NO NO

Composite motor

skill score

C1CMOTOR C1 COMPOSITE MOTOR SKILLS NO NO
8-58

Table 8-8. Fall-kindergarten predefined taglist - child catalog (continued)

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

Weights

Fall child weight C1CW0 C1 CHILD WEIGHT FULL SAMPLE NO NO

Fall parent

weight

C1PW0 C1 PARENT WEIGHT FULL SAMPLE NO NO

Fall CPT weight C1CPTW0 C1 CHILD-PARENT-TEACHER WEIGHT FULL

SAMPLE

NO NO

Table 8-9. Spring-kindergarten predefined taglist – child catalog

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

Basic Demographic Variables

Child ID CHILDID CHILD IDENTIFICATION NUMBER YES NO

Child Sex GENDER COMPOSITE CHILD GENDER YES NO

Child Age in the

fall of 1998

R1_KAGE R1 COMPOSITE CHILD ASSESSMENT

AGE(MONTHS)

YES NO

Child RaceEthnicityRACE COMPOSITE CHILD RACE YES NO

Household SES WKSESQ5 WK CATEGORICAL SES MEASURE YES NO

Fall-Kindergarten

Type

F1CLASS F1 CHILD PROGRAM TYPE FROM

FMS(AM/PM/AD)

YES NO

SpringKindergarten

Type

F2CLASS F2 CHILD PROGRAM TYPE FROM

FMS(AM/PM/AD)

YES NO

School Type S2KPUPRI S2 PUBLIC OR PRIVATE SCHOOL YES YES

First-time

Kindergartner

P1FIRKDG P1 FIRST-TIME KINDERGARTNER YES YES
8-59

Table 8-9. Spring-kindergarten predefined taglist – child catalog (continued)

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

Round Specific Assessment Scores

Reading T-scores C2RTSCOR C2 READING T-SCORE NO NO

Reading scale

scores

C2RSCALE C2 READING IRT SCALE SCORE NO NO

C2RPROB1 C2 PROB1 - LETTER RECOGNITION NO NO

C2RPROB2 C2 PROB2 – BEGINNING SOUNDS NO NO

C2RPROB3 C2 PROB3 – ENDING SOUNDS NO NO

C2RPROB4 C2 PROB4 - SIGHT WORDS NO NO

Reading

proficiency probability scores (5)

C2RPROB5 C2 PROB5 - WORD IN CONTEXT NO NO

C2RPROF1 C2 PROF1 - LETTER RECOGNITION NO NO

C2RPROF2 C2 PROF2 – BEGINNING SOUNDS NO NO

C2RPROF3 C2 PROF3 - ENDING SOUNDS NO NO

C2RPROF4 C2 PROF4 - SIGHT WORDS NO NO

Reading

proficiency

scores (5)

C2RPROF5 C2 PROF5 - WORD IN CONTEXT NO NO

Print familiarity

score

C2RPRINT C2 PRINT FAMILIARITY NO NO

Math T-scores C2MTSCOR C2 MATH T-SCORE NO NO

Math scale scores C2MSCALE C2 MATH IRT SCALE SCORE NO NO

C2MPROB1 C2 PROB1 - COUNT, NUMBER, SHAPE NO NO

C2MPROB2 C2 PROB2 – RELATIVE SIZE NO NO

C2MPROB3 C2 PROB3 – ORDINALITY, SEQUENCE NO NO

C2MPROB4 C2 PROB4 - ADD/SUBTRACT NO NO

Math proficiency

probability scores

(5)

C2MPROB5 C2 PROB5 – MULTIPLY/DIVIDE NO NO

C2MPROF1 C2 PROF1 - COUNT, NUMBER, SHAPE NO NO

C2MPROF2 C2 PROF2 – RELATIVE SIZE NO NO

C2MPROF3 C2 PROF3 – ORDINALITY, SEQUENCE NO NO

C2MPROF4 C2 PROF4 - ADD/SUBTRACT NO NO

Math proficiency

scores (5)

C2MPROF5 C2 PROF5 - MULTIPLY/DIVIDE NO NO

Math assessment in SpanishC2SPASMT C2 CHILD ASSESSMENT IN SPANISH NO NO

GK T-scores C2GTSCOR C2 GENERAL KNOWLEDGE T-SCORE NO NO

GK scale scores C2GSCALE C2 GENERAL KNOWLEDGE IRT SCALE

SCORE

NO NO
8-60

Table 8-9. Spring-kindergarten predefined taglist – child catalog (continued)

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

Weights

Spring child

weight

C2CW0 C2 CHILD WEIGHT FULL SAMPLE NO NO

Spring parent

weight

C2PW0 C2 PARENT WEIGHT FULL SAMPLE NO NO

Spring CPT

weight

C2CPTW0 C2 CHILD-PARENT-TEACHER WEIGHT FULL

SAMPLE

NO NO

Table 8-10. Kindergarten base year predefined taglist – child catalog

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

Basic Demographic Variables

Child ID CHILDID CHILD IDENTIFICATION NUMBER YES NO

Child Sex GENDER COMPOSITE CHILD GENDER YES NO

Child Age in the

fall of 1998

R1_KAGE R1 COMPOSITE CHILD ASSESSMENT

AGE(MONTHS)

YES NO

Child RaceEthnicityRACE COMPOSITE CHILD RACE YES NO

Household SES WKSESQ5 WK CATEGORICAL SES MEASURE YES NO

Fall Kindergarten

Type

F1CLASS F1 CHILD PROGRAM TYPE FROM

FMS(AM/PM/AD)

YES NO

Spring

Kindergarten

Type

F2CLASS F2 CHILD PROGRAM TYPE FROM

FMS(AM/PM/AD)

YES NO

School Type S2KPUPRI S2 PUBLIC OR PRIVATE SCHOOL YES YES

First-time

Kindergartner

P1FIRKDG P1 FIRST-TIME KINDERGARTNER YES YES
8-61

Table 8-10. Kindergarten base year predefined taglist – child catalog (continued)

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

Round Specific Assessment Scores

Fall & spring C1RSCALE C1 READING IRT SCALE SCORE NO NO

scale reading

scores

C2RSCALE C2 READING IRT SCALE SCORE NO NO

C1RPROB1 C1 PROB1 - LETTER RECOGNITION NO NO

C1RPROB2 C1 PROB2 – BEGINNING SOUNDS NO NO

C1RPROB3 C1 PROB3 - ENDING SOUNDS NO NO

C1RPROB4 C1 PROB4 - SIGHT WORDS NO NO

C1RPROB5 C1 PROB5 - WORD IN CONTEXT NO NO

C2RPROB1 C2 PROB1 - LETTER RECOGNITION NO NO

C2RPROB2 C2 PROB2 - BEGINNING SOUNDS NO NO

C2RPROB3 C2 PROB3 - ENDING SOUNDS NO NO

C2RPROB4 C2 PROB4 - SIGHT WORDS NO NO

Fall & spring

reading

proficiency probability scores

(10)

C2RPROB5 C2 PROB5 - WORD IN CONTEXT NO NO

Fall & spring C1RPRINT C1 PRINT FAMILIARITY NO NO

print familiarity

scores

C2RPRINT C2 PRINT FAMILIARITY NO NO

C1RPROF1 C1 PROF1 - LETTER RECOGNITION NO NO

C1RPROF2 C1 PROF2 - BEGINNING SOUNDS NO NO

C1RPROF3 C1 PROF3 - ENDING SOUNDS NO NO

C1RPROF4 C1 PROF4 - SIGHT WORDS NO NO

C1RPROF5 C1 PROF5 - WORD IN CONTEXT NO NO

C2RPROF1 C2 PROF1 - LETTER RECOGNITION NO NO

C2RPROF2 C2 PROF2 - BEGINNING SOUNDS NO NO

C2RPROF3 C2 PROF3 - ENDING SOUNDS NO NO

C2RPROF4 C2 PROF4 - SIGHT WORDS NO NO

Fall & spring

reading

proficiency

scores (10)

C2RPROF5 C2 PROF5 - WORD IN CONTEXT NO NO

Fall & spring C1MSCALE C1 MATH IRT SCALE SCORE NO NO

math scale scores C2MSCALE C2 MATH IRT SCALE SCORE NO NO

C1MPROB1 C1 PROB1 - COUNT, NUMBER, SHAPE NO NO

C1MPROB2 C1 PROB2 - RELATIVE SIZE NO NO

C1MPROB3 C1 PROB3 - ORDINALITY, SEQUENCE NO NO

C1MPROB4 C1 PROB4 - ADD/SUBTRACT NO NO

C1MPROB5 C1 PROB5 - MULTIPLY/DIVIDE NO NO

C2MPROB1 C2 PROB1 - COUNT, NUMBER, SHAPE NO NO

C2MPROB2 C2 PROB2 - RELATIVE SIZE NO NO

C2MPROB3 C2 PROB3 - ORDINALITY, SEQUENCE NO NO

C2MPROB4 C2 PROB4 - ADD/SUBTRACT NO NO

Fall & spring

math proficiency

probability scores

(10)

C2MPROB5 C2 PROB5 - MULTIPLY/DIVIDE NO NO
8-62

Table 8-10. Kindergarten base year predefined taglist – child catalog (continued)

Category Variable

Name Variable Label RequiredVariable

Limiting

Variable

C1MPROF1 C1 PROF1 - COUNT, NUMBER, SHAPE NO NO

C1MPROF2 C1 PROF2 - RELATIVE SIZE NO NO

C1MPROF3 C1 PROF3 - ORDINALITY, SEQUENCE NO NO

C1MPROF4 C1 PROF4 - ADD/SUBTRACT NO NO

C1MPROF5 C1 PROF5 - MULTIPLY/DIVIDE NO NO

C2MPROF1 C2 PROF1 - COUNT, NUMBER, SHAPE NO NO

C2MPROF2 C2 PROF2 - RELATIVE SIZE NO NO

C2MPROF3 C2 PROF3 - ORDINALITY, SEQUENCE NO NO

C2MPROF4 C2 PROF4 - ADD/SUBTRACT NO NO

Fall & spring

math proficiency

scores (10)

C2MPROF5 C2 PROF5 – MULTIPLY/DIVIDE NO NO

Fall & spring C1SPASMT C1 CHILD ASSESSMENT IN SPANISH NO NO

math assessment

in Spanish

C2SPASMT C2 CHILD ASSESSMENT IN SPANISH NO NO

C1GSCALE C1 GENERAL KNOWLEDGE IRT SCALE

SCORE

Fall & spring GK NO NO

scale scores

C2GSCALE C2 GENERAL KNOWLEDGE IRT SCALE

SCORE

NO NO

Weights

Base year panel

weights

BYCW0 C1C2 CHILD PANEL WEIGHT FULL SAMPLE NO NO

Base year panel

weights

BYPW0 C1C2 PARENT PANEL WEIGHT FULL

SAMPLE

NO NO

Base year panel

weights

BYCPTW0 C1C2 CHLD-PAR-TCHER PANEL WT FULL

SAMPLE

NO NO

Base year panel

weights

BYCOMW0 C1C2+P1/P2+T1/T2 PANEL WT FULL SAMPLE NO NO
8-63

8.6.3 Child Catalog Limiting Fields

The limiting fields for the child catalog include (a) first-time kindergartner or not and (b)

school type (public vs. private) and are shown in figure 8-49. These limiting fields are specific to the child

catalog and allow codes within each variable to be included or excluded from the extraction depending on

the selection indicator. For example, the user can select “No” for private schools if he or she would like to

create a data file that only includes public school kindergartners. The selection indicator will be either a

“Yes” or “No” to specify whether the variable code should be included or excluded, respectively.

Figure 8-49. Child catalog limiting fields

The limiting fields feature for each catalog allows the user to create a subset of cases based

on either “discrete” or “continuous” variables. The discrete variables have nonnumeric codes that have a

default setting of “Yes” in the select column meaning that all records will be present in the extract file. To

exclude all records with a particular variable, change the “Yes” to “No” by double-clicking on it. At least

one of the codes for each limiting variable must be selected as “Yes” or no records will be extracted for

analysis. For example, an extract using the specifications above will include kindergarten children in all

schools (public, private, and not ascertained). To restrict the extract to records for kindergarten children in

private schools, double-click the “Yes” next to “Public” to change it to “No,” and double-click the “Yes”

next to “Not Ascertained” to change it to “No.”
8-64

The continuous variables are those that contain selectable numeric ranges for upper and

lower limits (high and low columns). The upper and lower limits can be modified using the field located

in the upper right corner of the screen. The child catalog does not contain any continuous limiting fields;

therefore, the box in the upper right-hand corner of the screen is blank.

8.7 The Teacher Catalog

The teacher catalog contains information from teachers selected to participate in the ECLSK survey and includes data collected at the teacher level from the teachers. Table 8-11 presents the

pretagged required variables that will be automatically extracted for all user-created files. The user cannot

remove the required variables from the taglist. All user-defined taglists will include the required variables.

The last two variables on the list, S2 Public or Private School and Kindergarten Teacher Class Composite,

are limiting fields. For more information on limiting fields see section 8.7.3.

Table 8-11. Teacher catalog required variables

Field Name Field Label

T_ID Teacher Identification Number

B1TW0 B1 Teacher Weight Full Sample

B2TW0 B2 Teacher Weight Full Sample

S2KSCTYP S2 School Type from the SCH ADMIN QUEST

S2KPUPRI S2 Public or Private School

S2KENRLS S2 Total School Enrollment

KGCLASS Kindergarten Teacher Class Composite

The teacher catalog is organized into blocks of data corresponding to specific content areas.

The content areas are:

 Identifiers are the school and teacher identification numbers.

 School demographic data are from the sampling frame, e.g., Census region.

 Weights are described in detail in chapter 4. Round 1 weights are for fallkindergarten, and round 2 weights are for spring-kindergarten.

 Flags indicate unit response for the data source, e.g., teacher questionnaire A or

teacher questionnaire B.
8-65

 Composite variables were created to facilitate data analysis. See chapter 7, section

7.4 for a detailed description of the composite variables.

 Teacher questionnaire data are the data items from the teacher questionnaires.

 Taylor Series Sampling Strata and PSUs are the stratum and first-stage unit

identifiers that are required for estimating standard errors with the Taylor Series

linearization method using software such as SUDAAN or STATA.

 Weight replicates are used to estimate standard errors using the paired jackknife

replication method (JK2) with WesVar. Each replicate is a subsample of the full

sample. The variability of the replicate estimates about the full sample estimate is

used to estimate the variance of the full sample estimate.

 New variables for public-use file are variables created for respondent confidentiality.

Table 8-12 presents the order of the data blocks on the teacher data file.

8.7.1 Teacher Catalog Topical Variable Groupings

The variables within the teacher catalog are organized into topical categories to help locate

and identify desired variables. These categories are identified in table 8-13. The first column of the table

describes the topic, the second column lists the variable identifiers that indicate the source of the data,

e.g., teacher questionnaire A or B, as well as when the data was collected, i.e., fall- or springkindergarten. The third column is a description of the topic. The last column is the “Field ID,” which is

how you search the data for the topics. The field ID can be used by the “Go” function in the Variable List.

However, table 8-13 is only for the convenience of searching for variables and includes only

variables that belong to certain topics. It does not reflect the contents of the entire teacher catalog.
8-66

Table 8-12. Teacher catalog blocks

Order Block

1 Identifiers

2 School demographic data

Weights

3 Round 1 teacher weight full sample

4 Round 2 teacher weight full sample

Flags

5 Round 1 teacher questionnaire A data status flags

6 Round 1 teacher questionnaire B data status flags

7 Round 2 teacher questionnaire A data status flags

8 Round 2 teacher questionnaire B data status flags

Composite Variables

9 School administrator questionnaire composites

10 Cross-round teacher questionnaire A composites

11 Round 1 teacher questionnaire A composites

12 Round 1 teacher questionnaire B composites

Teacher Questionnaire Data

13 Round 1 teacher questionnaire A data

14 Round 1 teacher questionnaire B data*

15 Round 2 teacher questionnaire A data

Taylor Series Sampling Strata, PSU, and Replicate Weights

16 Round 1 teacher Taylor series sampling strata and PSU

17 Round 1 teacher replicate weights

18 Round 2 teacher Taylor series, sampling strata and PSU

19 Round 2 Teacher replicate weights

20 New variables for public-use file

* Background information from teachers was collected in teacher questionnaire B for all teachers. New kindergarten teachers in springkindergarten completed this questionnaire. Block 14 contains all the teacher questionnaire B data for both rounds of data collection. The springkindergarten teacher questionnaire B variables were merged into the fall-kindergarten teacher questionnaire B variables for the teacher catalog.

Flags are set to indicate whether teacher questionnaire B was completed in fall-kindergarten or spring-kindergarten. See chapter 7, table 7.4 for

description of flags.
8-67

Table 8-13. Teacher catalog topical variable groupings

Topic Variable

Identifier Description Field ID

School Variables S2KSCTYP -

S2KMINOR

(Variable Name)

School type, enrollment, instructional level,

percent minority in school

18-22

Teacher Composites KGCLASS –

B1AGE

(Variable Name)

Class composite, percent of minority,

black, and Hispanic students in class,

teacher’s age

23-33

A1 Q1 – A1 Q19 Fall: Type of kindergarten program, age

and race-ethnic composition, gender

composition, number of students repeating

kindergarten, exhibiting initial reading

skills, attending preschool, classroom

behavior, percent LEP, languages in

classroom,

34-231

886-888

Classroom

Characteristics

A2 Q1 – A2 Q7 Spring: G&T, attendance, special needs

children, classroom behavior, languages

used in instruction

344-457

B1 Q1 – B1 Q6 Fall: Instructional and evaluation

approaches, centers/learning areas,

preparation time

Class Organization 235-262

and Resources

A2 Q8 – A2 Q27 Spring: Instructional approach, time spent

in curriculum instruction, lunch, recess,

achievement grouping, remedial assistance,

teacher’s aides, physical resources (e.g.,

texts, equipment, materials for art and

music)

458-552

Instructional

Activities and

Curricular Focus

A2 Q28 – A2 Q34 Spring: Frequency of time spent on specific

aspects of reading and language arts, math,

computers, science and social studies

553-672

Parent Involvement A2 Q35 – A2 Q38 Spring: Conferences, volunteering,

attendance at school events, teachers

sending home information

673-682

School and Staff

Activities

A2 Q39 – A2 Q41 Spring: Meetings with staff, in-service

sessions, training, working with disabled

students

683-698

Teacher Views B1 Q7 – B1 Q14 Fall: Views on readiness, school climate

and environment, transitional activities, job

satisfaction,

263-308

Teacher

Demographics

B1 Q15 – B1 Q24 Fall: Gender, birth year, race-ethnicity,

experience, education, certification

309-340
8-68

8.7.2 Teacher Catalog Predefined Taglists

There is one predefined taglist for the teacher catalog that can be used for extracting data.

This predefined taglist is named Key Variables and contains only the required variables listed in

table 8-11.

8.7.3 Teacher Catalog Limiting Fields

The limiting fields for the teacher catalog include (a) school type (public vs. private) and (b)

a kindergarten teacher class composite (e.g., AM only, AM and PM) and are shown in figure 8-50. These

limiting fields are specific to the teacher catalog and allow codes within each variable to be included or

excluded from the extraction depending on the selection indicator. For example, the user can select “No”

for private schools if he or she would like to create a data file that only includes public school

kindergarten teachers. The selection indicator will be either a “Yes” or “No” to specify whether the

variable code should be included or excluded, respectively.

Figure 8-50. Teacher catalog limiting fields
8-69

The Limiting Fields feature for each catalog allows the user to create a subset of cases based

on either “discrete” or “continuous” variables. The discrete variables have nonnumeric codes that have a

default setting of “Yes” in the select column meaning that all records will be present in the extract file. At

least one of the codes for each limiting variable must be selected as “Yes” or no records will be extracted

for analysis. For example, an extract using the specifications above will include kindergarten teachers in

all schools (public, private, and not ascertained). To restrict the extract to records for kindergarten

teachers in private schools, double-click the “Yes” next to “Public” to change it to “No,” and double-click

the “Yes” next to “Not Ascertained” to change it to “No.”

The continuous variables are those that contain selectable numeric ranges for upper and

lower limits (high and low columns). The upper and lower limits can be modified using the field located

in the upper right corner of the screen. The teacher catalog does not contain any continuous limiting

fields; therefore the box in the upper right-hand corner of the screen is blank.

8.8 The School Catalog

The school catalog contains information from schools selected to participate in the ECLS-K

survey and includes data collected at the school level from school administrators and ECLS-K field staff.

Table 8-14 presents the pretagged required variables that will be automatically extracted for all usercreated data files. The user cannot remove the required variables from the taglist. All user-defined taglists

will include the required variables. The last variable, S2 Public or Private School, is the limiting field.

The school catalog is organized into blocks of data corresponding to specific content areas. See section

8.8.3 for more information on limiting fields.

Table 8-14. School catalog required variables

Field Name Field Label

S_ID School Identification Number

CREGION Census Region in Sample Frame

KURBAN Location Type in Sample Frame

S2SAQW0 S2 School Weight Full Sample

S2KSCTYP S2 School Type from the Sch Admin Quest

S2KENRLK S2 Total School K Enrollment

S2KENRLS S2 Total School Enrollment

S2KPUPRI S2 Public or Private School
8-70

The school catalog is organized into blocks of data corresponding to specific content areas.

The content areas are:

 Identifier is the school identification number.

 School demographic data are from the sampling frame, e.g., Census region.

 School weight full sample is described in detail in chapter 4.

 School administrator data status flag indicates unit response for school

administrator questionnaire.

 Facility checklist data status flag indicates unit response facility checklist.

 School administrator composites were created to facilitate data analysis. See

chapter 7, section 7.4 for a detailed description of the composite variables.

 School administrator data are the data items from the school administrator

questionnaire.

 Facility checklist data are the data items from the facility checklist.

 School Taylor series sampling strata and PSUs are the stratum and first-stage unit

identifiers that are required for estimating standard errors with the Taylor Series

linearization method using software such as SUDAAN or STATA.

 School weight replicates are used to estimate standard errors using the paired

jackknife replication method (JK2) with WesVar. Each replicate is a subsample of the

full sample. The variability of the replicate estimates about the full sample estimate is

used to estimate the variance of the full sample estimate. Table 8-15 presents the order

of the data blocks on the school data file.

 New variables for public-use file are variables created for respondent confidentiality.
8-71

Table 8-15. School catalog blocks

Order Block

1 Identifier

2 School demographic data

3 School weight full sample

4 School administrator questionnaire data status flag

5 Facility checklist data status flag

6 School administrator questionnaire composites

7 School administrator data

8 Facility checklist data

9 School Taylor series sampling strata and PSU

10 School replicate weights

11 New variables for public-use file

8.8.1 School Catalog Topical Variable Groupings

The variables within the school catalog are organized into topical categories to help locate

and identify desired variables. These categories are identified in table 8-16. The first column of the table

describes the topic, the second column lists the variable identifiers that indicate the source of the data,

e.g., the school administrator questionnaire or the facility checklist, as well as when the data was

collected, i.e., spring-kindergarten. The third column is a description of the topic. The last column is the

“Field ID,” which is how you search the data for the topics. Field ID can be used by the “Go” function in

the Variable List.

However, table 8-16 is only for the convenience of searching for variables and includes only

variables that belong to certain topics. It does not reflect the contents of the entire school catalog.
8-72

Table 8-16. School catalog topical variable groupings

Topic Variable

Identifier Description Field ID

School Composites S2KSCTYP to

S2KPUPRI

School type, enrollment, percent

minority, free/reduced lunch eligible,

school instructional level, school year

length, percent gifted and talented

8-17

School Characteristics S2 Q1-S2 Q22 Attendance, enrollment, grade levels,

school type, special programs, student

composition, admission requirements,

federal programs, kindergarten

programs, cutoff dates for kindergarten

entry

18-137

S2 Q23 – S2 Q28 Funding sources, facilities (e.g.,

cafeteria, music room, auditorium),

computers

School Facilities and 137-172

Resources

K2 Q1 Availability and condition of various

facilities (e.g., lighting, ventilation,

noise, handicap accessible)

481-581

S2 Q29 – S2 Q33 Busing, students attending

within/outside neighborhood,

community type, community safety,

school safety

Community 173-194

Characteristics and

School Safety

K2 Q2 – K2 Q4 Physical security in school building,

neighborhood conditions, adequacy of

safety measures

582-599

School Policies and

Practices

S2 Q34 – S2Q40 Uniform code, placement testing and

assessments, retention practices

195–232

School-FamilyCommunity ConnectionsS2 Q41 – S2Q47 Programs and services for families,

school’s communication with parents,

parent involvement in schools, schoolbased management

233–286

School Programs for

Special Populations

S2Q48 – S2Q57 Limited English Proficient, bilingual

programs, special education, gifted

and talented

287–319

Staffing and Teacher

Characteristics

S2Q58 – S2Q63 School staff list, salary information,

staff’s racial-ethnic distribution

320–351

School Governance and

Climate

S2Q64 – S2Q69 Teacher observations, professional

development, goals for kindergarten

teachers, process for decisionmaking,

school climate, principal evaluation

factors

352–437

Principal Characteristics S2 Q70 – S2 Q79 Administrator’s gender, race-ethnicity,

birth year, experience, education, time

spent in activities, familiarity with

students

438–477

703

School Atmosphere K2 Q5 – K2 Q6 Observations of children’s

expressions, learning environment

600-610
8-73

8.8.2 School Catalog Predefined Taglists

There is one predefined taglist for the school catalog that can be used for extracting data.

This predefined taglist is named Key Variables and contains only the required variables listed in

table 8-14.

8.8.3 School Catalog Limiting Fields

The Limiting Field for the School catalog is the variable “school type” (public vs. private),

which is shown in figure 8-51. This limiting field is specific to the school catalog and allows codes within

it to be included or excluded from the extraction depending on the selection indicator. For example, the

user can select “No” for private schools if he or she would like to create a data file that only includes

public schools. The selection indicator will be either a “Yes” or “No” to specify whether the cases that

match the variable code should be included or excluded, respectively.

Figure 8-51. School catalog limiting fields
8-74

The Limiting Fields feature for each catalog allows the user to create a subset of cases based

on either “discrete” or “continuous” variables. The discrete variables have nonnumeric codes that have a

default setting of “Yes” in the select column meaning that all records will be present in the extract file. At

least one of the codes for each limiting variable must be selected as “Yes” or no records will be extracted

for analysis. For example, an extract using the specifications above will include all schools (public,

private, and not ascertained). To restrict the extract to private school records, double-click the “Yes” next

to “Public” to change it to “No,” and double-click the “Yes” next to “Not Ascertained” to change it to

“No.”

The continuous variables are those that contain selectable numeric ranges for upper and

lower limits (high and low columns). The upper and lower limits can be modified using the field located

in the upper right-hand corner of the screen. The school catalog does not contain any continuous limiting

fields; therefore, the box in the upper right-hand corner of the screen is blank.

8.9 Switching Catalogs

The ECLS-K Base Year Public-Use ECB provides the capability of closing one catalog and

opening another catalog without exiting the ECB program.

How to Change Displayed Catalogs in the ECB:

1. In the main ECB screen, click on the File pulldown menu and select the Open Catalog

option.

2. If you have unsaved changes made to the Working Taglist, you will be prompted to

save the changes (figure 8-52).

Figure 8-52. Save changes
8-75

3. You can then either choose “Yes” to proceed to save the taglist or “No” to abandon

the changes.

4. The Select Catalog screen, shown in figure 8-53, appears.

Figure 8-53. Select Catalog screen

5. Highlight the catalog that you wish to open and click on the OK button. Note that the

existing catalog will be closed automatically when you open another catalog.
8-76

8.10 Menu Bar Descriptions

The File menu contains the commands needed to:

 Select and open a catalog;

 Set up your data for printing; and

 Exit the ECB.

The Taglist menu contains the commands required to manipulate

the variable lists once a catalog has been selected:

 Create a new taglist;

 Open a previously saved or predefined taglist;

 Delete a previously saved taglist;

 Add a previously saved or predefined taglist to the working

taglist;

 Save the working taglist;

 Save a taglist with another name;

The Extract menu contains options to create a syntax file for:

 SAS;

 SPSS; or

 STATA.

The Tools menu contains:

 The command for repairing and compacting the ECLS-K

database.

The Code Book menu contains the command for:

 Viewing the entire selected section of the code book; and

 Printing the entire code book or user-selections of the code

book.

The Help provides access to the detailed online help system.
8-77

In addition to the menu items found on the menu bar, the ECB screen contains various

command buttons and labels. The command buttons, when pressed, cause an event to occur, such as

moving a variable from the Variable List to the Working Taglist. They are explained in details in

section 8.3, The Variable List, and section 8.4, Working Taglist.