2015 National Youth Tobacco Survey

Methodology Report

For questions about this report, please email Sean Hu at shu@cdc.gov

Prepared for Centers for Disease Control and Prevention

Prepared by ICF International, Rockville, Maryland

Centers for Disease Control and Prevention

Office on Smoking and Health

Atlanta, GA

March 2016

Recommended Citation: Office on Smoking and Health. 2015 National Youth Tobacco Survey: 

Methodology Report. Atlanta, GA: U.S. Department of Health and Human Services, Centers for 

Disease Control and Prevention, National Center for Chronic Disease Prevention and Health 

Promotion, Office on Smoking and Health, 2015.

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TABLE OF CONTENTS

Contents

Table of Contents........................................................................................................... i

Chapter 1—NYTS Sampling Design............................................................................. 1

1.1 Overview of the National Youth Tobacco Survey (NYTS) .................................... 1

1.2 Overview of the 2015 NYTS Methodology............................................................ 1

Chapter 2—NYTS Sampling Methods.......................................................................... 3

2.1 Overview .............................................................................................................. 3

2.1.1 Oversampling of Racial/Ethnic Minorities.....................................................3

2.1.2 Frame Construction.........................................................................................4

2.1.2 Sampling Stages and Measure of Size ............................................................5

2.2 Stratification and Linking ...................................................................................... 7

2.2.1 Primary Sampling Unit (PSU)........................................................................7

2.2.2 Schools............................................................................................................9

2.2.3 Sample Sizes.................................................................................................10

2.3 Sampling Methods.............................................................................................. 14

2.3.1 Primary Sampling Unit .................................................................................14

2.3.2 Schools..........................................................................................................15

2.3.3 Grades...........................................................................................................16

2.3.4 Classes...........................................................................................................17

Chapter 3—NYTS Data Collection.............................................................................. 18

3.1 Survey Instrument .............................................................................................. 18

3.2 Recruitment Procedures..................................................................................... 18

3.3 Survey Administration......................................................................................... 18

3.3.1 Data Collection Staffing ...............................................................................18

3.3.2 Field Procedures............................................................................................19

3.3.3 Classroom Selection......................................................................................19

3.4 Participation Rates ............................................................................................. 20

Chapter 4—Weighting of NYTS Response Data ....................................................... 21

4.1 Overview ............................................................................................................ 21

4.2 Sampling Weights............................................................................................... 21

4.2.1 Adjusted Conditional Student Weights.........................................................21

4.2.2 School Sampling Weights.............................................................................22
4.2.3 Grade Sampling Weights..............................................................................23

4.2.4 PSU Sampling Weights.................................................................................23

4.2.5 Overall Sampling Weight .............................................................................23

4.3 Nonresponse Adjustments ................................................................................. 23

4.4 Post-stratification and Trimming ......................................................................... 26

4.5 Analysis Strata and Variance Estimation............................................................ 32

Appendix A. Questionnaire ........................................................................................ 38

Appendix B. Student Weight Detail ........................................................................... 40

Appendix C. Common Core of Data Race/Ethnicity Definitions.............................. 44
List of Exhibits and Figures

Figure 2.1 Process Leading to Number of Sampled Schools....................................... 6

Exhibit 2-2: Stratum Definition and PSU Allocation to Strata ....................................... 9

Figure 2-2: Linked School Construction and Grade Sampling for High Schools .......... 9

Exhibit 2-3: Historical Summary of NYTS Participation Rates.................................... 11

Exhibit 3-1: Sample Yields for Non-Hispanic Black and Hispanic Students by School 

Level .......................................................................................................................... 20

Exhibit 3-2: Sample Yields for Students by Grade ..................................................... 20

Exhibit 4-1. Response Rates and Corresponding Non-response Adjustment by Nonresponse Adjustment Cells ...................................................................................... 263

Exhibit 4-2. Eligibility Criteria for Control Totals ....................................................... 285

Exhibit 4-3. Counts of Schools and Students by School Type and Eligibility Status .. 26

Exhibit 4-4a. Control Totals Used in Even Iterations of the Raking and Trimming Method

27

Exhibit 4-4b. Control Totals Used in Odd Iterations of the Raking and Trimming Method

28

Exhibit 4-5. Percent Change in Weighted Estimates and Standard Errors Comparing 

New and Previous Methods of Post-stratification in the NYTS................................... 29

EXHIBIT 4-6. Sampling and Analysis Stratum Coding Schemes ............................. 341

Exhibit 4-7a: Current Use Estimates for Selected Tobacco Products for High School 

Students................................................................................................................... 352

Exhibit 4-7b: Current Use Estimates for Selected Tobacco Products for Middle School 

Students................................................................................................................... 352

EXHIBIT 4-8: Example: Estimates, Current Use by School Type ............................ 363
Methodology Report: 2015 National Youth Tobacco Survey

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CHAPTER 1—NYTS SAMPLING DESIGN

1.1 Overview of the National Youth Tobacco Survey (NYTS)

In conjunction with the State Youth Tobacco Survey (YTS), the National Youth Tobacco Survey 

(NYTS) was developed to provide the data necessary to support the design, implementation, and 

evaluation of state and national tobacco prevention and control programs (TCPs).1,2In addition, 

NYTS data supplement other existing surveys, such as the Youth Risk Behavior Surveillance 

System (YRBSS), by providing more comprehensive data on tobacco-related indicators for both 

middle school (grades 6–8) and high school (grades 9–12) students. Tobacco-related indicators 

included in the NYTS are: tobacco use (e.g., bidis, cigarettes, cigars, tobacco pipes, smokeless 

tobacco, snus, dissolvable tobacco products, hookahs, and electronic cigarettes); exposure to 

secondhand smoke; smoking cessation; minors’ ability to purchase or obtain tobacco products; 

and, knowledge and attitudes about tobacco and familiarity with pro-tobacco and anti-tobacco 

media messages. NYTS data also serve as essential benchmarks against which TCPs can assess 

the extent of youth tobacco use. The NYTS provides multiple measures and data for six of the 20 

tobacco-related Healthy People 2020 objectives (USDHHS, 2010): TU-2, TU-3, TU-7, TU-11, 

TU-18 and TU-19.

First conducted during fall 1999 and again during spring 2000, 2002, 2004, 2006, 2009, 2011, 

2012, 2013, 2014 and 2015, the NYTS provides data that are representative of all middle school 

and high school students in the 50 States and the District of Columbia. Beginning in 2011, the 

Centers for Disease Control and Prevention (CDC) and the Food and Drug Administration (FDA) 

have collaborated to administer the NYTS annually.

1.2 Overview of the 2015 NYTS Methodology

The 2015 NYTS employed a stratified, three-stage cluster sample design to produce a nationally 

representative sample of middle school and high school students in the United States. NonHispanic black students were oversampled. Sampling procedures were probabilistic and conducted 

without replacement at all stages, and entailed selection of: 1) Primary Sampling Units (PSUs)

(defined as a county, or a group of small counties, or part of a very large county) within each 

created stratum; 2) Secondary Sampling Units (SSUs), (defined as schools or linked schools) 

within each selected PSU; and 3) students within each selected school. Participating students 

completed the survey via pencil and paper using a self-administered, scannable questionnaire 

booklet.

 

1 Centers for Disease Control and Prevention. (CDC) (2014). Best Practices for comprehensive tobacco control 

programs-2014. Atlanta, GA: US Department of Health and Human Services, Public Health Service, CDC.

2 MacDonald, G., Starr, G., Schooley, M., Yee, S. L., Klimowski. K., Turner, K. (2001). Introduction to program 

evaluation for comprehensive tobacco control programs. Atlanta, GA: US Department of Health and Human 

Services, CDC.
Methodology Report: 2015 National Youth Tobacco Survey

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Participation in the NYTS was voluntary at both the school and student levels. At the student level, 

participation was anonymous. Schools used either passive or active permission forms at their 

discretion to fulfill requirements of the No Child Left Behind Act, whereby parents must be 

provided with a means to opt out of their child’s participation.

The final sample consisted of 255 schools, of which 185 participated, yielding a school 

participation rate of 72.6%. A total of 17,711 student questionnaires were completed out of a 

sample of 20,259 students, yielding a student participation rate of 87.4%. The overall participation 

rate, defined as the product of the school-level and student-level participation rates, was 63.4%. 

A weighting factor was applied to each student record to adjust for nonresponse and for varying 

probabilities of selection. Weights were adjusted to ensure that the weighted proportions of 

students in each grade matched national population proportions. 

The remainder of this report provides detailed information on the methodology used in the 2015

NYTS sample selection (Chapter 2), data collection (Chapter 3), and weighting of student response 

data (Chapter 4).
Methodology Report: 2015 National Youth Tobacco Survey

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CHAPTER 2—NYTS SAMPLING METHODS

2.1 Overview

The objective of the NYTS sampling design was to support estimation of tobacco-related 

knowledge, attitudes, and behaviors in a national population of public and private school students 

enrolled in grades 6 through 12 in the United States. More specifically, the study was designed to 

produce national estimates at a 95% confidence level with a margin of error of 5% by school level 

(middle school and high school), by grade (6, 7, 8, 9, 10, 11, and 12), by sex (male and female), 

and by race/ethnicity (non-Hispanic white, non-Hispanic black, and Hispanic). Additional 

estimates were also supported for subgroups defined by grade, by sex, and by race/ethnicity, each 

within school level domains; however, precision levels will vary considerably according to 

differences in subpopulation sizes.

The universe for the study consisted of all public and private school students enrolled in regular 

middle schools and high schools in grades 6 through 12 in the 50 U.S. States and the District of 

Columbia. Alternative schools, special education schools, Department of Defense operated 

schools, vocational schools that serve only pull-out populations, and students enrolled in regular 

schools unable to complete the questionnaire without special assistance, were excluded. 

The 2015 NYTS is a continuation of the NYTS cycles that took place in 1999, 2000, 2002, 2004, 

2006, 2009, 2011, 2012, 2013 and 2014. The NYTS employs a repeat cross-sectional design to 

develop national estimates of tobacco use behaviors and exposure to pro- and anti-tobacco 

influences among students enrolled in grades 6–12. The general sampling design framework used 

for the 2014 NYTS was also employed for the 2015 NYTS. 

2.1.1 Oversampling of Racial/Ethnic Minorities

To facilitate accurate prevalence estimates among racial/ethnic minority groups, prior cycles of 

the NYTS have employed multiple strategies to increase the number of non-Hispanic black and 

Hispanic students included in the sample. These approaches have included over-sampling PSUs in 

high racial/ethnic minority strata, the use of a weighted measure of size (MOS), and double class 

selection in large schools that contain a sufficient proportion of minority students.

The design development process examines parameters such as thresholds for double class selection 

and PSU allocation to strata, to balance the dual goals of overall precision and minority group 

targets. 

The sampling design balances increasing yields for minority students with overall precision as 

oversampling leads to larger variances for overall estimates. As described below, the only 

oversampling that remains in the more efficient design for the 2015 NYTS is double class 

sampling. This method has been shown to reduce design effects for survey estimates. The design 

effect, defined as the variance of actual survey estimates divided by the variance of a simple 

random sample of the same size, is a common useful measure of the precision of survey estimates.

A weighted measure of size (MOS) was previously used to increase the probability of selection of 

high racial/ethnic minority PSUs and schools using a Probability Proportional to Size (PPS) 

sampling design. The effectiveness of a weighted MOS in achieving oversampling is dependent 
Methodology Report: 2015 National Youth Tobacco Survey

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upon the distributions of non-Hispanic black and Hispanic students in schools. The need for a 

weighted MOS is predicated on a relatively low prevalence of minority students in the population; 

however, this premise has become less tenable with the increase of nonwhite students in the 

population overall, and specifically, Hispanic students. The need for oversampling Hispanic 

students has been gradually reduced with the increasing numbers of Hispanics among the student 

population. As seen below, some degree of oversampling non-Hispanic black students remains in 

the sampling design.

In 1990, the contactor, ICF (formerly Macro International Inc.), conducted the first in a series of 

simulation studies to investigate the impact of various weighting functions on the numbers and 

percentages of racial/ethnic minority students reached in YRBS.3 Sampling strategies based on 

this work were incorporated into the NYTS, and these simulations have been updated with each 

cycle of the NYTS to ensure that the minimum amount of weighting in the MOS is being used, 

while still achieving adequate representation of non-Hispanic black and Hispanic students. When 

the possibility of using an unweighted measure of enrollment size was investigated for the 2012 

NYTS, results demonstrated that adequate representation of non-Hispanic black and Hispanic 

students would be achieved through the use of an unweighted MOS. Thus, starting with the 2013 

NYTS and continuing for 2014 and 2015 NYTS, student enrollment was used as the unweighted 

MOS, leading to improvements in the statistical efficiency of the design.

The MOS used in the 2015 NYTS sampling design no longer oversampled schools with heavier 

minority concentrations directly. In addition, the allocation to strata was proportional so the second 

oversampling approach was also no longer in effect. Nevertheless, double class selection was still 

implemented in the 2015 NYTS sampling.

In previous NYTS cycles, schools with high racial/ethnic populations were subject to double class 

selection. More specifically, two classes per grade were selected in these schools, compared to one 

class per grade in other schools, to increase the number of racial/ethnic minority students sampled. 

In the 2015 NYTS, double class selection was used only in large schools that had greater than 3% 

non-Hispanic black student enrollment. The threshold was developed, and updated, to generate the 

necessary numbers of participating non-Hispanic black students to ensure estimation precision for

this subgroup.

2.1.2 Frame Construction

The frame was constructed from separate sources obtained from the National Center for Education 

Statistics (NCES) and from a commercial vendor, Market Data Retrieval Inc. (MDR Inc.). The 

NCES files were the Common Core of Data (CCD) for public schools and Private School Survey 

(PSS) for private schools. In addition, the frame incorporated data from the MDR dataset.

The reason for moving to a frame built from multiple data sources was to increase the coverage of 

schools nationally. This dual-source frame build method was implemented for the 2014 NYTS 

survey for the first time4, and the method was replicated for the 2015 NYTS. Including schools 

 

3 Errecart, M. T. (1990, October 5). Issues in Sampling African-Americans and Hispanics in School-Based Surveys.

Atlanta, GA: Centers for Disease Control.

4 Redesigning National School Surveys: Coverage and Stratification Improvement using Multiple Datasets. William 
Methodology Report: 2015 National Youth Tobacco Survey

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sourced from the two NCES files resulted in a coverage increase among all public and non-public 

high schools of 23%. There was a 15.5% increase of coverage among public schools and a 46% 

increase in coverage among non-public high schools.

2.1.3 Sampling Stages and Measure of Size

The three-stage cluster sample was stratified by racial/ethnic composition and urban versus rural

status at the first (primary) stage. PSUs were defined as a county, a portion of a county, or a group 

of counties. PSUs were classified as “urban” if they are in one of the 54 largest metropolitan 

statistical areas (MSAs) in the U.S using 2012 American Community Survey (ACS) data from the 

US Census Bureau. Otherwise, they were classified as “nonurban.” The 16 primary strata are 

defined in Exhibit 2-2. Additionally, implicit stratification was imposed by geography by sorting 

the PSU frame by State and by 5-digit ZIP Code (within State). Within each stratum, a PSU was 

randomly sampled without replacement at the first stage.

In subsequent sampling stages, a probabilistic selection of schools and students was made from 

the sample PSUs. It may be helpful to stress that unlike the 2014 NYTS, the 2015 NYTS is 

designed to balance the yields across grades. Therefore, the PSU subsampling is simplified to vary 

across school sizes but not between school level categories.

The sampling stages may be summarized as follows, with additional details provided in Section 2.2:

 Selection of PSUs: Eighty five PSUs were selected from 16 strata, with probability 

proportional to the total number of eligible students enrolled in all eligible schools located 

within a PSU.

 Selection of Schools: At the second sampling stage, a total of 170 large schools or secondstage units (SSUs) were selected from the 85 sample PSUs. Two large schools were 

selected per sample PSU, one per level (middle or high), for a total of 170 large sample 

SSUs. An additional 20 medium SSUs and 30 small SSUs were selected from subsample 

PSUs, for a total of 220 sample SSUs (220= 170+30+20). Figure 2.1 shows the sample 

sizes for SSUs and the number of sampled schools. The PSU subsample was drawn as a 

simple random sample, and the schools were drawn with probability proportional to the 

total number of eligible students enrolled in a school.

 Selection of Students: Students were selected via whole classes, whereby all students enrolled 

in any one selected class were by default chosen for participation. Classes were selected from 

course schedules provided by each school that agreed to participate. Schedules were 

constructed such that all eligible students were represented one time only.

 

Robb, Kate Flint, Alice Roberts, Ronaldo Iachan - ICF International, FEDCASIC March 2014
Methodology Report: 2015 National Youth Tobacco Survey

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FIGURE 2.1 PROCESS LEADING TO NUMBER OF SAMPLED SCHOOLS

Schools were stratified into large, medium, and small schools based on their ability to support two, 

one, or less than one class selection per grade. In large schools, an average of 1.46 classes were 

selected per grade by selecting two classes per grade in 46% of selected large schools and one class 

per grade in the remaining schools. The double class sampling took place in schools with nonHispanic black enrollments over the established 3% threshold. Specifically, double class sampling 

took place by design in 78 of the 170 sample large schools, or 45.8% of these schools.

The sampling approach utilized PPS sampling methods. In PPS sampling, when the MOS is 

defined as the count of final-stage sampling units, and a fixed number of units are selected in the 

final stage, the result is an equal probability of selection for all members of the universe. For the 

NYTS, we approximate these conditions, and thus obtain a roughly self-weighting sample.

The MOS also was used to compute stratum sizes and PSU sizes. Assigning an aggregate measure 

of size to PSU, the sample allocates the PSU sample in proportion to the student population. 

Exhibit 2-1 presents a high-level summary of the key sampling design features that will be 

described in detail in the next sections.

EXHIBIT 2-1: KEY SAMPLING DESIGN FEATURES

Sampling Units Stratification Measure of Size Designed Sample Size Actual Sample Size

1

Counties, 

portions of a 

county, or groups 

of counties

Urban vs. Nonurban

(2 strata);

Minority concentration

(8 strata)

Aggregate school 

size in target 

grades

85 Counties, portions of a 

county, or groups of 

counties

85 Counties, portions of 

a county, or groups of 

counties

2 Schools

Small, medium and large;

High school vs. middle 

school

Eligible enrollment

220 SSU (school) 

selections: 170 large 

schools, 20 medium 

schools and 30 small 

schools

220 SSUs; 255 physical 

schools; 185 participated

3

Classes / 

students

1 or 2 classes per grade (2 

per grade in large, highminority schools)

27,789 students sampled; 

21,000 participants

20,259 students 

sampled;

17,711 students 

participating
Methodology Report: 2015 National Youth Tobacco Survey

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2.2 Stratification and Linking

This section describes the following steps that are necessary for the selection of the first- and 

second-stage samples of PSUs and schools: organizing counties into Primary Sampling Units; 

linking schools into SSUs; and implementing the stratification and allocation methods at each 

of these stages.

2.2.1 Primary Sampling Unit (PSU)

Defining a PSU

In general, PSUs are geographic areas defined as counties or groupings of counties. In defining a 

PSU, several issues are considered:

1. Each PSU should be large enough to contain the requisite numbers of schools and students 

by grade, yet not so large as to be selected with near certainty.

2. Each PSU should be compact geographically so that field staff can go from school to school 

easily.

3. Recent data should be available to characterize each PSU.

4. Each PSU should contain at least four middle and five high schools.

Generally, counties were equivalent to PSUs with two exceptions: 

1. Low population counties are combined to provide sufficient numbers of schools and 

students; and 

2. Counties that are very large may be split to avoid becoming certainty or near-certainty 

PSUs. 

Certainty PSUs are those whose size is large enough to ensure selection with probability one (1.0) 

with a PPS sampling design that selects larger PSUs with larger probabilities. As certainty PSUs 

lead to inefficiencies in the design, they are split so that the new smaller units are no longer selected 

with a probability of one. Near-certainty units also are split to build in a safety buffer in the PSU 

sizes. County population figures were aggregated from school enrollment data for the grades of 

interest.

The 2015 NYTS PSU definitions were based on the definitions developed in the coordinated 2013 

YRBS-NYTS cycles, and also used in the 2014 NYTS cycle. The exact PSUs defined in the 2015

NYTS sampling frame were updated to ensure that all PSUs met the criteria above. The frame had 

1,259 PSUs, 520 of which were comprised of one single county. 

Stratification of PSUs

The PSUs were organized into 16 strata, based on urban/rural location (as defined above) and 

racial/ethnic minority enrollment of non-Hispanic blacks and Hispanics. In the traditional 

stratification used by the NYTS, the classification of PSUs into the two racial/ethnic minority 

strata, non-Hispanic black and Hispanic, is based on the predominant minority in the PSU. This 

classification is coupled with the density distribution of non-Hispanic blacks and Hispanics to 
Methodology Report: 2015 National Youth Tobacco Survey

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subdivide each of the four primary strata into four substrata, indexed by 1-4 according to this 

density. The approach for computing stratum boundaries follows the cumulative square root of “f” 

method developed by Dalenius and Hodges.5 The boundaries or cutoffs change as the frequency 

distribution (“f”) for the racial groupings change from one survey cycle to the next. These rules 

are summarized below.

 If the PSU is within one of the 54 largest MSA in the U.S. it is classified as “urban,” 

otherwise it is classified as “nonurban.”

 If the percentage of Hispanic students in the PSU exceeded the percentage of non-Hispanic 

black students, then the PSU is classified as Hispanic. Otherwise it is classified as nonHispanic black.

 Hispanic urban and Hispanic nonurban PSUs were classified into four density groupings, 

depending upon the percentages of Hispanics in the PSU.

o For urban, high Hispanic PSU, the percentage cut points used to define the 

groups were 26, 42, and 58%.

o For nonurban, high Hispanic PSU, the percentage cut points used to define the 

groups were 24, 48, and 68%.

 Non-Hispanic Black urban and non-Hispanic black nonurban PSUs also were classified 

into four groupings, depending upon the percentages of non-Hispanic blacks in the PSU. 

o For urban non-Hispanic black PSUs, the percentage cut points used to define the 

groups were 26, 40, and 54%.

o For nonurban High non-Hispanic black PSUs, the percentage cut points used to 

define the groups were 20, 34, and 54%.

Allocation of the PSU Sample

We designed and selected a sample of 85 PSUs that were allocated in proportion to student 

enrollment to maximize overall precision. We made adjustments to the initial allocation to ensure 

that racial/ethnic minority targets would be met. Specifically, the adjustments rounded fractional 

allocations, ensured that each stratum would have at least two sampled PSUs, and added balance 

to the distribution across strata.

 

5 Dalenius, T., & Hodges, J. L. (1959). Minimum Variance Stratification. Journal of American Statistical 

Association, 54, 88−101.
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Exhibit 2-2 presents the allocation of the PSU sample to strata. 

EXHIBIT 2-2: STRATUM DEFINITION AND PSU ALLOCATION TO STRATA

Predominant 

Minority Urban/Rural

Density 

Group 

Number

Stratum 

Code

Student 

Population

Number 

of 

Sample 

PSUs

NonHispanic 

Black

Urban

1 BU1 2,720,181 7

2 BU2 975,490 4

3 BU3 908,299 2

4 BU4 516,712 2

Nonurban

1 BR1 3,937,157 9

2 BR2 1,503,403 5

3 BR3 1,026,612 3

4 BR4 313,063 2

Hispanic

Urban

1 HU1 3,530,556 10

2 HU2 2,429,442 8

3 HU3 1,865,988 6

4 HU4 2,106,242 5

Nonurban

1 HR1 4,427,215 14

2 HR2 1,284,402 3

3 HR3 988,655 3

4 HR4 523,491 2

2.2.2 Schools

Linking into Second-stage Sampling Unit (SSU)

Schools were classified as “whole” for high 

schools if they have all high-school grades 9 

through 12, and whole for middle schools if they 

had all grades 6–8. Otherwise, they were 

considered a “fragment” school. Fragment 

schools formed component schools that were 

linked with other schools (fragment or whole) to 

form a linked school that has all four grades. This 

process is illustrated in Figure 2-2, where 

“Component School A” is linked with “Component School B,” to form a linked school. We linked 

schools before sampling using an algorithm developed for use in the national YRBS that links 

geographically proximate schools. Linked schools were treated as second-stage sampling units

(SSUs) with selection performed at the grade level, as described below.

FIGURE 2-2: LINKED SCHOOL CONSTRUCTION AND 

GRADE SAMPLING FOR HIGH SCHOOLS

Component School B

(Whole)

Component School A

(Fragment)

Grade 9

Grade 10 Grade 10

Grade 11 Grade 11

Grade 12 Grade 12
Methodology Report: 2015 National Youth Tobacco Survey

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Stratification

SSUs were stratified by school level (middle and high) and by size. Middle schools were those 

that contained any of grades 6 through 8, and high schools were those that contained any of grades 

9 through 12. Schools that contained a mix of high and middle school grades were split into two 

sampling units, or one for each level.

SSUs also were stratified by school size into small, medium, and large strata on the basis of their 

ability to support less than one, one, or two class selections per grade. Operationally, large SSUs 

contained at least 56 students at each grade level, medium SSUs contained between 28 and 55

students per grade, and small SSUs contained less than 28 students at any grade level.

2.2.3 Sample Sizes

This section provides the derivation of the NYTS sample sizes driven by target precision 

requirements overall and in key subgroups. The required student yields, or numbers of 

participating students, are translated into the necessary numbers of sample schools, and sample

PSUs, using historical participation rates. 

The NYTS is designed to produce accurate estimation within a margin of error (MOE) of 5% at a 

95% precision level for the following key subgroup estimates:

 Middle and high school (school level): middle school students in total (grades 6–8 

combined) and high school students in total (grades 9–12 combined);

 Grade: individual grades 6, 7, 8, 9, 10, 11, and 12;

 Sex: males and females in total, by school level (male middle school students, female high

school students), and by individual grade (6-grade males, 6-grade females);

 Race/Ethnicity: in total and by school level (e.g., Hispanic middle school students).

The sample sizes are developed to support analysis by individual grade and by sex without any

special considerations in the sampling plan. Design effects were assumed by the design to be 

relatively small for subgroups that cut across schools; therefore, estimates by sex will have better 

precision than other subgroups. Thus, the designed confidence intervals were ± 3%. Because the 

design is expected to yield a greater number of completed surveys from high school students than 

from middle school students, overall estimates are anticipated to be more precise at the high school 

level than those at the middle school level. Moreover, because within grade estimates by sex have 

slightly larger standard errors than those for estimates by grade alone, estimates of sex are expected 

within ± 5%.

The 2015 NYTS sampling design aimed at balancing student yields by grade unlike the 2012 and 

2014 sample designs which aimed at balance by school level (middle and high school). Previous 

designs aimed at balance by school level had targets of 10,000 students per level. For the 2015 

NYTS, the target sample sizes correspond to approximately 3,000 participating students per grade

and so they also ensure the precision of estimates by individual grade (e.g., sex by grade subgroup 

estimates on the basis of about 1,500 students).
Methodology Report: 2015 National Youth Tobacco Survey

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Across the ten previous cycles of the NYTS, the school participation has averaged 86.5%, with a 

low of 75.4%. Student participation has averaged 90.5% with a low of 87.6%. Historical 

participation rates at both school and student levels, which guide the sampling design and sample 

sizes, are summarized in Exhibit 2.3. In calculating the sample sizes for the 2015 NYTS, we made 

our approach more robust by assuming a conservative combined rate (student x school) of 77%, 

which was slightly lower than the historical overall response rate from 1999-2013 of 78.3% (90% 

school x 85.5% student). The 2014 response rates were not yet available when the 2015 sampling 

plan was created. 

EXHIBIT 2-3: HISTORICAL SUMMARY OF NYTS PARTICIPATION RATES

YEAR School Participation Student Participation Overall

1999 90.3% 93.2% 84.2%

2000 90.0% 93.4% 84.1%

2002 83.1% 90.6% 75.3%

2004 92.7% 87.9% 81.5%

2006 91.6% 87.6% 80.2%

2009 92.3% 91.9% 84.8%

2011 83.2% 88.0% 73.2%

2012 80.3% 91.7% 73.6%

2013 75.4% 90.7% 68.4%

2014 80.2% 91.4% 73.3%

Average over all previous 

cycles 85.91% 90.64% 79.61%

Schools were classified by size based on grade-level enrollments. This ensures that a sampled 

school of a given size classification is able to support the student sample sizes summarized in 

Exhibit 2-4.

The NYTS sample size calculations are based on the following assumptions:

 The main structure of the sampling design will be consistent with the design used to draw 

the sample for prior cycles of the NYTS.

 The selection of a minimum of one SSU at the high school level and one SSU at the middle 

school level within each PSU. Some PSUs are selected to provide up to four extra schools. 

A PSU is a county, a group of contiguous counties, or a section of a county if too large. 

 SSUs with at least 56 students per grade are considered large, and those among the others 

with 28 students per grade are considered medium; otherwise, they are considered small.

 
Methodology Report: 2015 National Youth Tobacco Survey

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 On average, each selected class includes 28 students (on the basis of historical averages).

 For SSUs classified as large, we sample double the amount of students in 46% of these 

schools, by sampling eight classes instead of four. 

 A 77% overall response rate (based on historical averages) calculated as the product of the 

school and student response rate.

Based on these assumptions, 85 PSUs were selected as the basis for the sample. Within each of 85 

sample PSUs, two large schools were drawn, one at the middle school level to supply students in 

grades 6 through 8, and one at the high school level to supply students in grades 9 through 12. In 

addition, 10 and 15 PSUs were independently sub-sampled to supply medium and small SSUs at 

each level, respectively. The anticipated number of students selected from all sample schools was

27,789 students (before non-response). 

Exhibit 2-4 provides a detailed calculation of designed sample sizes across school level and school 

size categories.6 Section 3.4 compares these projections to the actual sample yields.

 

6

In this exhibit, the schools are secondary sampling units (SSUs), or “virtual schools”, created by combining actual, 

physical schools so that each virtual school unit has a complete set of grades for the level. The virtual schools are 

expanded to physical schools.
Methodology Report: 2015 National Youth Tobacco Survey

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EXHIBIT 2-4: PLANNED SAMPLE SIZES FOR THE 2015 NYTS

*Note that this was anticipated number of students in all sampled schools, and the actual number 

of sampled students is derived only from participating schools (and is thus considerably lower). 

2.2.3.1 Middle School and High School Estimates

Estimates by school level are required to support separate analysis of students across middle school 

grades (6, 7, and 8) and high school grades (9, 10, 11, and 12). However, schools tend to vary in 

their grade structures, an inconsistency that compromises the ability to easily and efficiently link 

schools for sampling purposes in a manner that also uniformly divides students by grade. For 

example, 9th grade students are served by both junior high schools with grades 7–9 and by high 

schools with grades 9–12. As a result, we have developed the school linking approach described in 

Section 2.2.2 that was applied independently for high schools and middle schools.

2.2.3.2 Grade Estimates

The designed sample sizes are approximately balanced for school-level and for grade-level 

groupings. By targeting nearly 3,000 students per grade, the sample ensures that estimates at the 

grade level achieve the required precision levels. 
Methodology Report: 2015 National Youth Tobacco Survey

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2.2.3.3 Sex Group Estimates

The large sample size permitted analysis by sex without any special considerations in the sampling 

plan. During the class selection process, frames of eligible classes from co-educational schools in 

which classrooms were segregated by sex (i.e., an all-male or all-female class) were avoided, if 

possible.

2.2.3.4 Race/Ethnicity Group Estimates

In order to support separate analysis of the data for non-Hispanic white, non-Hispanic black and 

Hispanic students, in total and by school level, adequate sample sizes were required by the design 

for subgroups defined by: 1) school level by racial grouping; or 2) by sex grouping. Sample sizes 

were not designed, however, to support detailed analyses by sex and school level within 

racial/ethnic subgroups (e.g., middle school Hispanic males).

2.3 Sampling Methods

This section describes the methods used in the selection of PSUs, schools, grades, and classes of 

students. In this process, we define the probabilities of selection associated with the various 

sampling stages as follows:

 Probability of selecting PSUs;

 Probability of selecting schools;

 Probability of selection of grades;

 Probability of selecting classes and students.

These probabilities provide the basis for the sampling weights discussed in Chapter 4.

The overall probability of selection for a student is the product of the probability of selection of 

the PSU, which contains a group of schools, multiplied by the conditional probability of selecting 

the student's school and the conditional probability of selecting the student's class. These steps are 

detailed in the selection below.

2.3.1 Primary Sampling Unit

Selection

Within each first-stage stratum, the PSUs were sorted by five-digit ZIP Code to attain a form of 

implicit geographic stratification. Implicit stratification, coupled with the PPS sampling method 

described below, ensures geographic sample representation. With PPS sampling, the selection 

probability for each PSU is proportional to the PSU’s measure of size.

The following systematic sampling procedures were applied to the stratified frame to select a PPS 

sample of PSUs. 

 Select 85 PSUs with a systematic random sampling method within each stratum. The 

method applies within each stratum a sampling interval computed as the sum of the 
Methodology Report: 2015 National Youth Tobacco Survey

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measures of size for the PSUs in the stratum divided by the number of PSUs to be selected 

in the stratum.

 Subsample at random 10 of the sample PSUs for the medium school sample for each 

school level. 

 Subsample at random 15 of the sample PSUs for the small school sample for each school 

level. 

Although the revised sampling design is focused on balancing student yields by grade, rather than 

by school level, sampling is still done separately for middle schools and high schools.

Probability

If MOSklm is the measure of size for school k in PSU l in stratum m and if Km is the number of 

PSUs to be selected in stratum m, then Pp

lm is the probability of selection of PSU l in stratum m:

















MOS

MOS

P = K

.m

lm

m

P

lm

2.3.2 Schools

Selection

For large schools, one high school and one middle school were selected with PPS systematic 

sampling within a PSU. The schools were selected into the sample with probability proportional 

to the measure of size. (MOS). 

Small and medium schools were sampled independently from large schools; they were set in two 

separate strata sampled at lower rates. This approach was implemented by drawing subsample

PSUs for small and medium school sampling as described earlier. One small school or medium 

school was then selected in each subsampled PSU with probability proportional to the MOS.

Replacement of Schools/School Systems

We did not replace refusing school districts, schools, classes, or students. We allowed for school 

and student nonresponse by inflating the sample sizes to account for nonresponse. With this 

approach, all schools can be contacted in a coordinated recruitment effort, which is not possible 

for methods that allow for replacing schools.
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Probability

The probability of selecting large school k in PSU l and stratum m, PLS

klm, at each level was 

computed as follows:

















MOS

MOS

P =

.lm

LS klm

klm

For medium schools, one school was drawn from each of 10 subsampled PSU at each level, so the 

probability of selection of a medium school then becomes,

 

















MOS

MOS

P =

.lm

MS klm

klm 10 / 85

For small schools, one school was drawn from each of 15 subsampled PSU at each level, so the 

probability of selection of a small school then becomes (the same for each level)

 

















MOS

MOS

P =

.lm

SS klm

klm 15/85

2.3.3 Grades

Selection

Except for linked schools, all eligible grades were included in the class selection for each school.

In linked schools, grades were selected independently. One component school was selected to 

provide classes at each grade level, and grades within component schools were drawn with 

probability proportional to grade enrollment.

Probability

Most SSUs in the sample contained one component school. In these cases, all eligible grades were 

selected so that the probability of selecting a grade was 1.0.

In SSUs that were made up of component schools, the selection of each component school at each 

grade is made with PPS sampling. The school selections from each component school at each 

grade level were made independently.

We denote this PG

jklm the probability of selecting grade j in SSU k, in PSU l, stratum m. For the j

th

grade within SSU k, this probability is equal to the ratio of the number of students at grade j in the 

component school to the total enrollment in grade j across all component schools within the SSU.
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2.3.4 Classes

Selection

In large schools, an average of 1.46 classes per grade were selected by selecting 2 classes per grade 

in 46% of the selected large schools and one class per grade in the remaining large schools. The 

double class sampling took place in schools with greater than 3% non-Hispanic black enrollment 

and one class per grade in the remaining schools.

One class per grade was selected in medium schools. In small schools, that is, those that could not 

support a full class selection at each grade, all students in all eligible grades were taken into the 

sample.

All students in a selected class who could complete the survey without special assistance were 

considered eligible and offered the opportunity to participate in the survey. Refusing students were 

not replaced. Nonresponse at the student level was accounted for in the sample size using an 

average per class yield that assumed student response rates derived from historical experience with 

the NYTS.

A set of classes was identified for each school at each grade level such that every student in a given 

grade level was enrolled in exactly one of the classes in the set. For example, a required English 

course might be used. If the school’s estimated non-Hispanic black enrollment exceeded 3%, two 

classes were randomly selected, without replacement, from the list. Otherwise, one class was 

randomly selected. Selections were made at all eligible grade levels in the school.

Probability

The probability of selection of a class when there are Cjklm classes at grade j in school k, PSUi, 

stratum m is just 1/Cjklm or 2/Cjklm depending on whether one or two classes are taken in the school. 

All students in a selected class were chosen, so the probability of selection of a student is the same 

as the class (i.e., 1/Cjklm or 2/Cjklm). 

Note that the probability of student selection within a class does not vary by race, ethnicity, or sex. 

We denote this probability as PC

ijklm as the probability of selecting class i in grade j, school k, PSU 

l, stratum m. Since every student in a selected class is also selected, the probability of selecting 

any student in class i, grade j, school k, PSU l, stratum k, is also equal to PC

ijklm.
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CHAPTER 3—NYTS DATA COLLECTION

3.1 Survey Instrument

The NYTS collects data on key short-term, intermediate, and long-term tobacco prevention and 

control outcome indicators. The 2015 survey instrument included a total of 81 questions, with the 

first 5 collecting student demographic information and the remaining measuring a comprehensive 

set of tobacco-related topics (Appendix A). Specific areas covered by the survey included: 

prevalence of tobacco product use; knowledge of and attitudes toward tobacco use; pro- and antitobacco media and advertising; minors’ access to tobacco products; nicotine dependence; cessation 

attempts; exposure to second-hand smoke; harm perceptions; exposure to tobacco product 

warnings; and tobacco use prevention school curricula.

3.2 Recruitment Procedures

The schools selected to participate in the 2015 NYTS were located in 36 different states. 

Recruitment began in May 2014 with calls to State Departments of Education and Health. Letters 

of support were obtained from various state agencies and used in mailings to districts and schools. 

A date for survey implementation was selected to optimize the efficiency of data collection while 

accommodating school schedules. In selecting a date, convenience to the school and its calendar 

were considered. Additionally, an effort was made to schedule groups of schools from the same 

school district or PSU around the same time to facilitate efficient travel to and survey 

implementation within selected schools. Recruiters used an electronic calendar on a secure shared 

drive to facilitate communication and to avoid scheduling two schools for the same data collector 

on the same day.

3.3 Survey Administration

Survey administration in the schools began on February 9, 2015, immediately after data collector 

training, and continued until June 19, 2015. Each data collector visited an average of three schools 

per week. While the details of each data collection varied, there were six core steps followed for 

every school: 1) pre-contact call with the principal or lead contact prior to arrival at the school; 2) 

entry meeting with the principal or lead contact; 3) entry meeting with teacher or group of teachers 

prior to survey administration; 4) survey administration; 5) post-survey meeting with the teacher 

or teachers; and 6) post-survey meeting with the principal or lead contact prior to leaving the 

school. Most survey administrations could be completed in 1 day, while at other times, due to the 

number of classes selected or alternating block schedules, the data collector needed to return for a 

second day. Procedures were designed to protect students’ privacy by assuring that student 

participation was anonymous and voluntary. Students completed a self-administered scannable 

questionnaire booklet via pencil and paper.

3.3.1 Data Collection Staffing

Data collectors were recruited from a pool of previously trained data collectors, as well as retired 

teachers associations, school health networks, and a variety of health education organizations. Data 

collector training was conducted on February 4–6, 2015.
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Key components of the training included the following:

 Pre-contact activities with the schools;

 Entry and exit meetings with school officials;

 Data collection protocols;

 Follow-up activities;

 Communication with headquarters staff.

3.3.2 Field Procedures

After schools had been recruited, classes selected, and a date scheduled, each school received a 

packet of pre-survey materials. These materials included all the information necessary to prepare 

the school for data collection. Teacher packets contained the parental permission forms that had to 

be given out to all students in the selected classes prior to data collection. The timing of these presurvey packet mailings was determined in part by the type of permission form being used by the 

school. Passive parental permission forms, or forms returned only if the parents do not want their 

child to participate, were sent approximately 1 week prior to the scheduled date of data collection 

in the majority of schools. Active parental permission forms, forms that must be returned with the 

parent’s signature in order for the child to participate, were sent out at least 2 weeks prior to the 

scheduled date of data collection for schools that require active consent. Follow-up calls were 

made to the selected schools to answer any questions and to make sure materials were received 

and distributed to selected classes and students.

On a weekly basis, data collectors received mailings containing their assignments for the coming 

week, travel and logistics information, and their must-read weekly bulletin. Weekly bulletins 

underlined key performance issues, corrected misconceptions, provided consistent direction on 

any procedural changes, and kept everyone abreast of the latest must-have information. In addition 

to these mailings, boxes of survey supplies were sent to data collectors, either to the data collector’s 

home or hotel. These boxes contained all supplies necessary for completing the data collection, 

including questionnaires, data envelopes, field forms, and pencils. Data collectors were supplied 

with extra materials for emergency packs as well, which they carried with them at all times.

3.3.3 Classroom Selection

Students were selected for participation by default via the selection of whole classes (i.e., all 

students enrolled in a selected class were eligible to take the survey). The frames from which 

classes were chosen were constructed such that eligible students had one and only one chance of 

being selected. However, at times the specific method of selecting classes varied from school to 

school, according to how a school’s class schedule was structured. Typically, classes were selected 

from a list of required core courses such as English, social studies, math, or science. Among middle 

school students, and among high school students in a few States, physical education and/or health 

also were considered core courses. However, in a small number of schools, it was difficult to 

develop an appropriate frame using this approach. Therefore, in these schools, classes were

selected by using a time of day (e.g., second period) when all eligible students were scheduled to 

be attending a class of one kind or another as the frame, and randomly selecting from all classes 
Methodology Report: 2015 National Youth Tobacco Survey

Page 20

held at this time. Lastly, in some schools, school homerooms were used as the frame for class 

selection. 

3.4 Participation Rates

Across the ten previous cycles of the NYTS, the school participation has averaged 86%, with a 

low of 75%. Student participation has averaged 91% with a low of 88%, and the overall response 

rate has averaged 80%. To be conservative, we assumed slightly lower values in developing the 

sample design for the 2015 NYTS: an assumed overall participation rate of 77%.

The actual response rates in 2015 differed from our projections. The 2015 NYTS survey attained

an actual school participation rate of 72.6 % and a student participation rate of 87.4%. The overall 

participation rate, the product of the school-level and student-level participation rates was 63.4%.

While the participation rate is lower than the levels assumed in the projections, the shortfall has 

no meaningful impact on the estimation precision. As seen below, however, some design 

modifications may be recommended to increase the yields achieved for black students.

The 2015 NYTS data file contains responses from 17,711 students compared to the 21,397

responding students anticipated by the design. Exhibit 3-1 shows that student yields were lower 

than targeted for non-Hispanic Blacks. Among Hispanics, yields far exceeded the targets at both 

levels. Exhibit 3-2 shows number of respondents by grade. 

EXHIBIT 3-1: SAMPLE YIELDS FOR NON-HISPANIC BLACK AND HISPANIC STUDENTS BY SCHOOL LEVEL

Subgroup Projected Participants Actual Participants

Middle School non-Hispanic Blacks 1,775 1,310

Middle School Hispanics 1,775 2,294

High School non-Hispanic Blacks 1,975 1,462

High School Hispanics 1,975 2,763

EXHIBIT 3-2: SAMPLE YIELDS FOR STUDENTS BY GRADE

Subgroup Projected Participants Actual Participants

6

th Grade 3,000 2,552

7

th Grade 3,000 2,845

8

th Grade 3,000 2,773

9

th Grade 3,000 2,512

10th Grade 3,000 2,509

11th Grade 3,000 2,282

12th Grade 3,000 2,130

Unknown N/A 18
Methodology Report: 2015 National Youth Tobacco Survey

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CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA

4.1 Overview

This section describes the procedures used to weight the data collected in the NYTS 2015. The 

process involved the steps outlined below:

 Sampling weights;

 Nonresponse adjustments;

 Weight trimming;

 Poststratification to national estimates of racial totals by grade, sex and school type.

This section focuses on the development of the weights for the student response data. The final 

student level response data were weighted to reflect the initial probabilities of selection and 

nonresponse patterns, to mitigate large variations in sampling weights, and to poststratify the data 

to known sampling frame characteristics.

4.2 Sampling Weights

The base weight is the inverse of the probability of selection for each responding student. The base

weight is adjusted to compensate for nonresponse, to alleviate excess weight variation, and to 

match the weighted data to known control totals. The base weight is computed by inverting the 

probabilities of selection at each stage to derive a stage weight. For each respondent, the stage 

weights are multiplied to form the overall sampling weight assigned to each student.

The NYTS computation of sampling weights begins at the student sampling stage, and then moves 

to the school and PSU sampling stages. This sequence allows the student sampling weights to 

incorporate adjustments for student nonresponse. These adjustments, described next, use 

enrollment data by sex and by grade collected for each participating school. Because the process 

begins with the student weights within a given grade, school and PSU, we refer to these weights 

as conditional weights. 

4.2.1 Adjusted Conditional Student Weights

The adjusted conditional student weight is the student weight given the selection of the PSU, 

school, and grade. This weight is the product of the inverse of the probability of selection and a 

nonresponse adjustment within weighting classes based on grade and sex. Note that this step also 

includes an approach designed to limit the nonresponse adjustment factor, an early step to avoid 

extreme weights and hence to control the variability in the weights.

This three-step process is simplified algebraically (see Appendix B) and computed directly as the 

ratio of the number of enrolled students to the number of responding students in a given weighting

class within a school. The weighting class definition is set dynamically so as to avoid extreme 

weights, as described next.

We denote the student selection weight WR

cklm, where the subscripts k, l, and m refer to the school, 

PSU and stratum as before. The subscript c refers to the weighting class, described below. This 
Methodology Report: 2015 National Youth Tobacco Survey

Page 22

weight is computed as below, where N is the number of enrolled students for each school (the 

counts are provided by the school during data collection by grade and sex) and R is the number of 

responding students in weighting class c within a given school:

R

N

W =

cklm

R cklm

cklm

The weighting class c is defined by a sequence of rules that depends on the number of responding 

students. This is done to avoid large weights for classes with low numbers of respondents. This 

process operates entirely within schools.

Initially, the weighting class is defined by grade and sex within each school. We then combine 

weighting classes if the weight for the class exceeds a maximum value, C. This cap C is computed 

using the following equation:

min( 10, )

2

N

N

C =

cklm

cklm

cklm

The combination sequence first groups males and females within grade. Both the cap and the 

weight are then recomputed. If the weight still exceeds the cap, grades are combined. The process 

is repeated, and if the student weight still exceeds the cap, the school is taken as the weighting

class.

This has the effect, within school, of setting an upper limit on the weight of 2 in weighting classes 

with an enrollment of less than 10, and 20% of the enrollment in weighting classes with an 

enrollment of more than 10. Note that the cap could be exceeded, however, in the rare cases where 

the weighting class is collapsed to the school level.

4.2.2 School Sampling Weights

For large schools, the partial school weight is the inverse of the probability of selection of the 

school given that the PSU was selected:

P

1

=

MOS

MOS

W =

LS

klm klm

LS .lm

klm 















For small schools, the partial school weight is:

P

1

=

MOS

MOS

W =

SS

klm klm

SS .lm

klm 















(85 /15)
Methodology Report: 2015 National Youth Tobacco Survey

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For medium schools, the partial school weight for both high schools and middle schools is:

P

1

=

MOS

MOS

W =

MS

klm klm

MS .lm

klm 















(85/10)

4.2.3 Grade Sampling Weights

Grade selection occurs within linked schools where the grade is available in each of the linked 

schools, or school “components” that constitute the SSU. The partial weight for a grade, given the 

selection of the linked school containing it, is simply the inverse of the probability of selection 

described in Section 2.4. In a non-linked school, the weight is 1.0. We denote the grade weight as 

WG

jklm. 

4.2.4 PSU Sampling Weights

The weight of the PSU is the inverse of the probability of selection of that PSU:

P

1

=

MOS

MOS

K

1

W =

P

lm lm

.m

m

P

lm 











For small and medium school selections, the supporting sample PSUs were drawn as a subsample.

(Subsampling of PSUs also was conducted for the large high schools.) This PSU subsampling 

component of the PSU weight is accounted for in the school selection probability and 

corresponding weight.

4.2.5 Overall Sampling Weight

The overall sampling weight is formed as the product of the stage selection weights. This weight, 

WT1, is then adjusted for nonresponse, trimmed, and poststratified to control totals, as described in 

the following sections. This weight is computed as:











W =W W W W

W =W W W W

W =W W W W

R

hijklm

G

jklm

SS

klm

P

lm

T1

hijklm

R

hijklm

G

jklm

MS

klm

P

lm

T1

hijklm

R

hijklm

G

jklm

LS

klm

P

lm

T1

hijklm

for large, medium and small schools, respectively, where the weights in the latter portions of the 

equations are defined in the preceding sections.

4.3 Nonresponse Adjustments

Nonresponse adjustment of weights is important to reduce potential bias incorporated into surveys 

from differences between responding and nonresponding schools included in the sample. In the 

2015 NYTS cycle, the nonresponse adjustment methods were refined to further minimize 

nonresponse bias potential. As opposed to the previous method, which created adjustment cells 
Methodology Report: 2015 National Youth Tobacco Survey

Page 24

based on sampling strata, the new method defines nonresponse adjustment cells in a more tailored 

and systematic approach stemming the from non-response analysis. Specifically, the definition of 

the most appropriate nonresponse adjustment weighting cells followed these steps:

1. Conduct bivariate analysis to identify key predictors of school non-response and student 

non-response; 

2. Conduct multivariate logistic regression analysis, or response propensity models, including 

the subset of key predictors identified in 1), to identify significant predictors of nonresponse at both levels; 

3. Develop non-response adjustment weighting cells based on the significant predictors while 

incorporating information about correlations between predictors and resulting cell sizes.

Variables identified in the non-response bivariate and multivariate analyses included the 

following:

 Urban status;

 School type (public/private);

 A range of socio-economic status (SES) indicators and concentrations of minority students, 

all of which are mutually correlated.

These analyses are detailed in the nonresponse analysis report. The subset of variables selected for 

defining weight adjustment cells is effectively reduced by eliminating variables with high pairwise 

correlations and limiting to variables with adequate representation of responding schools. As a 

result, the variables used in creating nonresponse adjustment cells were school type, urban/rural 

designation of PSU, and categorization of schools as predominantly Hispanic (above or below 

median). Because of the small number of private schools included in the sample, they were 

included as their own category in the nonresponse adjustment cells. Nonresponse adjustment was 

conducted separately for middle schools and high schools.

The adjustment process uses the following equations for the adjustment factor:

Am =

∑ (Wlm

P

k,l∈schools sampled ∗ Wklm ∗ MOSklm)

∑ (Wlm

P

k,l∈schools with respondents ∗ Wklm ∗ MOSklm)

The student weight adjusted for nonresponse is then:

W3

s = W2s

∗ Am

The school response rates by weighting cells along with resulting nonresponse adjustments are 

presented in Exhibit 4-1.
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Methodology Report: 2015 National Youth Tobacco Survey

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EXHIBIT 4-1. RESPONSE RATES AND CORRESPONDING NON-RESPONSE ADJUSTMENT BY NON-RESPONSE 

ADJUSTMENT CELLS

Stratum (Non-Response) Sampled 

Schools

Responding 

Schools

Percent 

Responding

NonResponse 

Adjustment

Middle School

Private 15 6 40.00% 1.9

Public/Nonurban/Low Hispanic 34 27 79.41% 1.3

Public/Urban/Low Hispanic 20 16 80.00% 1.2

Public/Nonurban/High Hispanic 32 24 75.00% 1.4

Public/Urban/High Hispanic 39 30 76.92% 1.3

Total 140 103 73.57% .

High School

Private 15 8 53.33% 1.9

Public/Nonurban/Low Hispanic 29 26 89.66% 1.1

Public/Urban/Low Hispanic 22 15 68.18% 1.5

Public/Nonurban/High Hispanic 23 17 73.91% 1.4

Public/Urban/High Hispanic 26 16 61.54% 1.7

Total 115 82 71.30%

Overall Total 255 185 72.55%

For the 2015 NYTS, a total of 70 schools did not respond. The final analytic file contains student 

responses from schools in 53 PSUs, with approximately 72.6% of schools responding overall

(73.6% for middle schools and 71.3% for high schools). The largest adjustment was for private 

middle and high schools where only 6 of 15 and 8 of 15 schools, responded respectively. The

resulting nonresponse adjustment factor was 1.9 for both cells.

4.4 Post-stratification and Trimming

The final two steps in the weighting process include trimming and post-stratification. Trimming 

procedures are used to control the weight variability and reduce its impact on survey variances. 

Post-stratification methods ensure that weighted totals sum to population control totals and 

therefore, minimize the potential for biases due to non-response and non-coverage.

In previous NYTS cycles, extreme weights were first trimmed before being post-stratified to match 

public and private school enrollment counts. When trimming is performed prior to poststratification it can be less effective at controlling weight variability added back by poststratification step. If trimming follows post-stratification, on the other hand, then final weights 

would not sum to known population control totals, and the bias reduction benefits of poststratification would be diluted.
Methodology Report: 2015 National Youth Tobacco Survey

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The solution developed and used in the 2015 NYTS is an iterative approach that combines poststratification and trimming.

7,8 The methods incorporate a model-based approach to variable 

selection in weight trimming while controlling for extreme variability in weights across sampling 

units. Iterative post-stratification, or raking, approaches have long been used in weighting survey 

data to allow additional post-stratification variables and categories.9,10 By combining the two 

iterative methods in one approach, the rake-trim method ensures that trimmed weights retain their 

variance-reducing feature after post-stratification. Conversely, it also ensures that post-stratified 

weights add up to control totals. 

Similar to weighting, the raking and trimming methods were conducted separately for middle 

schools and high schools. In each iteration of the raking method used in the 2015 NYTS, poststratification is performed along two dimensions: a) school type (public or private)/ grade/ raceethnicity, and b) school type/ grade/ gender. These two classes are defined so that control totals 

are known and cells have reasonable size. In odd year iterations, public schools are raked to totals 

by grade and race-ethnicity while private schools are raked to grade totals. In even year iterations, 

public schools are raked to grade and race-ethnicity marginal totals while private schools are still 

raked to grade totals. Within the same iteration, this step is followed by the trimming step which 

truncates (or “caps”) the weight using the overall weight distribution (i.e. percentiles). The 

trimming method uses the interquartile range (IQR) as the basis for a threshold for weights that 

are excessively large. Specifically, any weights that exceed the median weight plus 4 times the 

IQR are trimmed.3 The excess weight is then distributed among the observations within each cell 

to ensure that effective post-stratification totals are preserved.

National estimates of race-ethnicity and gender percentages by school type and grade were 

obtained from two sources: 

 Private school enrollments by grade and five racial/ethnic groups were obtained from the 

Private School Universe Survey (PSS) School Year 2011-12, the most recent PSS dataset 

available;

 Public school enrollments by grade, sex, and five racial/ethnic categories were obtained 

from the Common Core of Data (CCD) Public Elementary/Secondary School Universe 

Survey: School Year 2013-14, the most recent CCD data file available.

 

7

Iachan R (2010, August). A new iterative method for weight trimming and raking. Paper presented at the American Statistical 

Association meeting. Vancouver. Canada

8

Izrael D, Battaglia MP, Frankel MR. (2009). Extreme survey weight adjustment as a component of sample balancing (a.k.a. 

Raking), Paper 274-2009, SAS Global Forum 2009.

9 Potter F. (1998). Survey of procedures to control extreme sampling weights. ASA Proceedings of the Section on Survey 

Research Methods, 446-457.

10 Oh HL, and Scheuren F. (1978). Some Unresolved Application Issues in Raking Ratio Estimation.

1978 Proceedings of the Section on Survey Research Methods, Washington, DC: American

Statistical Association, pp. 723-728.
Methodology Report: 2015 National Youth Tobacco Survey

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Both databases are produced by the National Center for Education Statistics (NCES). Raw schoollevel data files were downloaded and processed to mirror eligibility requirements imposed on the

sampling frame. Specifically, eligibility was defined in terms of a school type variable present on 

both files. Exhibit 4-2 provides the details of this categorization. In both cases, eligible schools 

were defined as “regular” schools—that is, those schools left after schools that either serve special 

populations or pull students from other eligible schools are removed. Furthermore, public schools 

were limited to those that had not closed since the time of the last CCD survey.

EXHIBIT 4-2. ELIGIBILITY CRITERIA FOR CONTROL TOTALS

Value of 

School Type Public School (CCD) Coding Private School (PSS) Coding Eligibility Status

1 Regular Regular Eligible

2 Special Education Montessori Not Eligible

3 Vocational Special Program Emphasis Not Eligible

4 Alternative/Other Special Education Not Eligible

5 -unused- Career / Technical / Vocational Not Eligible

6 -unused- Alternative / Other Not Eligible

7 -unused- Early Childhood Not Eligible

The private school and public school databases were then combined to produce the enrollments 

for all schools and to develop population percentages to use as controls in the post-stratification 

step. Exhibit 4-3 gives counts of schools and students by grade for private and public schools by 

school eligibility criteria.
Methodology Report: 2015 National Youth Tobacco Survey

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EXHIBIT 4-3. COUNTS OF SCHOOLS AND STUDENTS BY SCHOOL TYPE AND ELIGIBILITY STATUS

Type / Grade Ineligible Counts Eligible Counts Total (Raw File)

Schools Students Schools Students Schools Students

Public

6 7,166 52,686 70,132 7,385,550 77,298 7,438,236

7 8,108 63,882 55,116 7,367,876 63,224 7,431,758

8 8,818 81,746 55,224 7,309,568 64,042 7,391,314

9 12,824 245,792 41,360 7,814,120 54,184 8,059,912

10 13,676 277,770 38,914 7,358,854 52,590 7,636,624

11 13,846 320,340 38,334 6,785,260 52,180 7,105,600

12 13,656 433,416 37,976 6,518,688 51,632 6,952,104

Total 78,094 1,475,632 337,056 50,539,916 415,150 52,015,548

Private

6 2,178 20,049 15,355 306,460 17,533 326,509

7 2,054 19,233 14,553 305,170 16,607 324,403

8 2,125 20,517 14,334 305,489 16,459 326,006

9 1,797 22,450 6,922 281,606 8,719 304,056

10 1,745 23,017 6,572 279,592 8,317 302,609

11 1,678 22,603 6,281 275,027 7,959 297,630

12 1,599 22,462 6,167 271,152 7,766 293,614

Total 13,176 150,331 70,184 2,024,496 83,360 2,174,827

Public and Private

6 9,344 72,735 85,487 7,692,010 94,831 7,764,745

7 10,162 83,115 69,669 7,673,046 79,831 7,756,161

8 10,943 102,263 69,558 7,615,057 80,501 7,717,320

9 14,621 268,242 48,282 8,095,726 62,903 8,363,968

10 15,421 300,787 45,486 7,638,446 60,907 7,939,233

11 15,524 342,943 44,615 7,060,287 60,139 7,403,230

12 15,255 455,878 44,143 6,789,840 59,398 7,245,718

Total 91,270 1,625,963 407,240 52,564,412 498,510 54,190,375

Overall, the total number of eligible students is 52,564,412. This total matches the sum of the 

adjusted weights prior to scaling. The control totals used in even and odd iterations of poststratification are included below in Exhibits 4-5a and 4-5b.
Methodology Report: 2015 National Youth Tobacco Survey

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EXHIBIT 4-4A. CONTROL TOTALS USED IN EVEN ITERATIONS OF THE RAKING AND TRIMMING METHOD

School Type Grade Race/Hispanic Origin Control 

Total

Private 6 Combined 290,654

Private 7 Combined 285,467

Private 8 Combined 283,706

Private 9 Combined 265,350

Private 10 Combined 260,393

Private 11 Combined 255,640

Private 12 Combined 252,489

Public 6 Non-Hispanic Asian and Pacific Islander 185,862

Public 6 Non-Hispanic Black 583,263

Public 6 Hispanic 964,949

Public 6 Non-Hispanic Native American 39,448

Public 6 Non-Hispanic White 1,911,126

Public 7 Non-Hispanic Asian and Pacific Islander 185,600

Public 7 Non-Hispanic Black 595,309

Public 7 Hispanic 967,634

Public 7 Non-Hispanic Native American 39,941

Public 7 Non-Hispanic White 1,955,941

Public 8 Non-Hispanic Asian and Pacific Islander 188,206

Public 8 Non-Hispanic Black 596,374

Public 8 Hispanic 943,113

Public 8 Non-Hispanic Native American 40,164

Public 8 Non-Hispanic White 1,972,189

Public 9 Non-Hispanic Asian and Pacific Islander 182,797

Public 9 Non-Hispanic Black 653,815

Public 9 Hispanic 981,536

Public 9 Non-Hispanic Native American 43,315

Public 9 Non-Hispanic White 2,032,458

Public 10 Non-Hispanic Asian and Pacific Islander 183,595

Public 10 Non-Hispanic Black 581,894

Public 10 Hispanic 883,635

Public 10 Non-Hispanic Native American 39,503

Public 10 Non-Hispanic White 1,963,264

Public 11 Non-Hispanic Asian and Pacific Islander 183,350

Public 11 Non-Hispanic Black 515,161

Public 11 Hispanic 783,554

Public 11 Non-Hispanic Native American 34,982

Public 11 Non-Hispanic White 1,872,334

Public 12 Non-Hispanic Asian and Pacific Islander 180,976

Public 12 Non-Hispanic Black 483,636
Methodology Report: 2015 National Youth Tobacco Survey

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School Type Grade Race/Hispanic Origin Control 

Total

Public 12 Hispanic 727,482

Public 12 Non-Hispanic Native American 33,737

Public 12 Non-Hispanic White 1,845,218

EXHIBIT 4-4B. CONTROL TOTALS USED IN ODD ITERATIONS OF THE RAKING AND TRIMMING METHOD

School Type Grade Gender Control Total

Private 6 Combined 290,654

Private 7 Combined 285,467

Private 8 Combined 283,706

Private 9 Combined 265,350

Private 10 Combined 260,393

Private 11 Combined 255,640

Private 12 Combined 252,489

Public 6 Male 1,887,892

Public 6 Female 1,796,756

Public 7 Male 1,918,000

Public 7 Female 1,826,425

Public 8 Male 1,912,185

Public 8 Female 1,827,861

Public 9 Male 2,008,655

Public 9 Female 1,885,266

Public 10 Male 1,861,965

Public 10 Female 1,789,926

Public 11 Male 1,711,004

Public 11 Female 1,678,377

Public 12 Male 1,642,782

Public 12 Female 1,628,267

Comparison of New Nonresponse Adjustment and Post-Stratification Method with Previous Method

Comparisons with the previous method of nonresponse adjustment and post-stratification 

demonstrate that the new method had minimal impact on the weighted prevalence estimates but 

substantially reduced variance. The coefficient of variation for the weights (CV) decreased using 

the new method compared to the previous method. Consequently, the variance impact of unequal 

weighting has been substantially reduced with the new method. The design effect due to unequal 

weighting can be expressed as 1 + CV2. This reduction is demonstrated empirically next for a range 

of key prevalence estimates computed for the 2015 NYTS.
Methodology Report: 2015 National Youth Tobacco Survey

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Exhibit 4-5 provides an example comparison of weighted estimates and standard errors from the 

questions on current cigarette and flavored cigarette use, using new method and previous method. 

While the weighted estimates changed very little (in relative terms, 3 to 4%), the standard errors 

shrunk by approximately 25% among middle school students and 10-12% among high school 

students. 

EXHIBIT 4-5. PERCENT CHANGE IN WEIGHTED ESTIMATES AND STANDARD ERRORS COMPARING NEW METHOD AND 

PREVIOUS METHOD OF POST-STRATIFICATION IN THE NYTS

Weighted 

Estimate (%)

Percent 

Change in 

Estimate (%)

Standard Error

Percent 

Change in 

Standard 

Error (%)

Middle School

Current Cigarette Use

Previous Method 2.30 2.6 0.43 25.6

New Method 2.24 0.32

Flavored Cigarette Use

Previous Method 1.19 4.2 0.20 25.0

New Method 1.14 0.15

High School

Current Cigarette Use

Previous Method 9.77 4.0 0.86 10.5

New Method 9.38 0.77

Flavored Cigarette Use

Previous Method 3.35 2.7 0.32 12.5

New Method 3.44 0.28

4.5 Analysis Strata and Variance Estimation

Sampling variances for complex sampling designs can be estimated using one of several methods, 

including linearized estimators and balanced repeated replication. These methods are implemented 

with a variety of software packages, including SUDAAN, WesVar, Stata and SAS using special 

sample survey procedures (such as Proc SurveyMeans in SAS Version 9). The 2014 NYTS data 

were prepared for estimating variances using the linearized estimators method.

Because estimates are typically reported separately for middle schools and high schools, analysis 

strata need to ensure that each stratum has two or more PSUs for variance estimation within each 

subpopulation (middle schools and high schools separately). 
Methodology Report: 2015 National Youth Tobacco Survey

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As noted earlier, the allocation ensured that every stratum had at least two PSUs in the sample. 

This does not necessarily translate to two PSUs with valid student data for each school level 

(middle schools and high schools) in every stratum due to the effects of nonresponse at the school 

level. In particular, nonparticipating schools may lead to PSUs without student data for a given 

school level.

Exhibit 4-6 displays the correspondence between the sampling strata and the analysis strata, which 

are represented by two variables on the analysis file. As analyses are typically conducted separately

for middle and high schools, we ensure that each stratum is represented in the data file with at least 

two PSUs for each school level separately. All strata/level combinations but four had at least two 

PSUs, two combinations for middle schools and two for high school. As a result, two strata were 

collapsed for middle and high schools, as shown in this exhibit. Thus, the analytic file contains 14

values in the analysis strata variable for middle and high schools. In addition, stratum codes used 

in sampling and weighting were converted to a numeric “analysis stratum” code for use in 

SUDAAN, which requires numeric variables.

Exhibit 4-7 presents selected key survey estimates and their sampling errors estimated using Taylor 

series linearization method, which is usually employed by NYTS data analysts, and implemented with 

SUDAAN or similar software (e.g., SAS Proc SurveyMeans). Specifically, the Exhibit presents the 

percent and standard error of the percent for estimates of current use of selected tobacco products 

separately for high schools (Exhibit 4-7A) and middle schools (Exhibit 4-7B).
Methodology Report: 2015 National Youth Tobacco Survey

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EXHIBIT 4-6. SAMPLING AND ANALYSIS STRATUM CODING SCHEMES

HSMS Design 

Stratum

Design 

Stratum 

Count

Analysis 

Stratum

Analysis 

Stratum 

Count

HS BR1 9 BR1 9

HS BR2 5 BR2 5

HS BR3 3 BR3 3

HS BR4 2 BR4 2

HS BU1 6 BU1 6

HS BU2 2 BU2 2

HS BU3 2

BU3+BU4 3

HS BU4 1

HS HR1 11 HR1 11

HS HR2 2 HR2 2

HS HR3 3 HR3 3

HS HR4 2 HR4 2

HS HU1 6 HU1 6

HS HU2 7 HU2 7

HS HU3 1

HU3+HU4 5

HS HU4 4

MS BR1 7 BR1 7

MS BR2 3 BR2 3

MS BR3 3 BR3 3

MS BR4 2 BR4 2

MS BU1 6 BU1 6

MS BU2 3 BU2 3

MS BU3 2

BU3+BU4 3

MS BU4 1

MS HR1 12 HR1 12

MS HR2 2 HR2 2

MS HR3 2

HR3+HR4 3

MS HR4 1

MS HU1 8 HU1 8

MS HU2 6 HU2 6

MS HU3 4 HU3 4

MS HU4 5 HU4 5
Methodology Report: 2015 National Youth Tobacco Survey

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EXHIBIT 4-7A: CURRENT USE ESTIMATES FOR SELECTED TOBACCO PRODUCTS FOR HIGH SCHOOL STUDENTS

Product Overall % (SE) Female % 

(SE) Male % (SE) Non-Hispanic White % (SE)

Non-Hispanic 

Black % (SE)

Hispanic % 

(SE)

Electronic 

Cigarettes 16.0% (1.0%) 12.9% (1.0%) 19.0% (1.3%) 17.1% (1.3%) 9.5% (0.9%) 16.8% (1.2%)

Cigarettes 9.4% (0.8%) 7.9% (0.7%) 10.8% (1.0%) 10.1% (1.0%) 6.8% (1.9%) 9.5% (1.2%)

Cigars 8.8% (0.5%) 5.8% (0.5%) 11.7% (0.8%) 8.4% (0.7%) 12.6% (1.8%) 7.9% (0.8%)

Hookah 7.2% (0.5%) 7.0% (0.7%) 7.5% (0.5%) 6.5% (0.68%) 7.5% (1.3%) 9.0% (0.6%)

Smokeless

Tobacco 5.7% (0.7%) 1.8% (0.4%) 9.5% (1.1%) 7.6% (1.0%) 2.0% (0.6%) 4.4% (0.6%)

EXHIBIT 4-7B: CURRENT USE ESTIMATES FOR SELECTED TOBACCO PRODUCTS FOR MIDDLE SCHOOL STUDENTS

Product Overall % (SE) Female % 

(SE) Male % (SE) Non-Hispanic White % (SE)

Non-Hispanic 

Black % (SE)

Hispanic % 

(SE)

Electronic 

Cigarettes 5.3% (0.4%) 4.8% (0.4%) 5.9% (0.6%) 4.3% (0.5%) 4.5% (0.6%) 8.0% (0.8%)

Cigarettes 2.2% (0.3%) 2.2% (0.4%) 2.3% (0.4%) 2.0% (0.5%) 1.4% (0.3%) 2.6% (0.5%)

Cigars 1.5% (0.2%) 1.3% (0.2%) 1.7% (0.30%) 1.1% (0.3%) 2.3% (0.4%) 2.0% (0.4%)

Hookah 2.0% (0.3%) 2.1% (0.4%) 1.9% (0.29%) 1.5% (0.3%) 1.7% (0.5%) 3.2% (0.5%)

Smokeless

Tobacco 1.4% (0.4%) 0.8% (0.2%) 1.9% (0.7%) 1.2% (0.6%) 2.0% (0.5%) 1.7% (0.5%)
Methodology Report: 2015 National Youth Tobacco Survey

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Exhibit 4-8 provides example specifications for applying the method with both SAS and SUDAAN 

for computing weighted prevalence estimates and their estimated variances.

EXHIBIT 4-8: EXAMPLE: ESTIMATES, CURRENT USE BY SCHOOL TYPE

SAS:

SAS:

Proc Surveymeans Data=nyts2015 mean;

Var ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Class ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Stratum stratum2;

Cluster psu2;

Weight wt;

Domain Schooltype Schooltype*Sex Schooltype*Race_S;

Title “NYTS 2015, Estimates by School Type, by School Type and Sex Cross-Classified, and by School Type and 

Race/Ethnicity Cross-Classified”;

run;

SUDAAN:

Proc Descript Data=nyts2015 Filetype= SAS Design=WR;

Var ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Catlevel 1 1 1 1 1;

Nest Stratum2 PSU2 / Missunit;

Weight wt;

Subgroup School Sex Race_S;

Levels 2 2 3;

Tables School School*Sex School*Race_S;

Title “NYTS 2014, Estimates by School Type, by School Type and Sex Cross-Classified, and by School Type and 

Race Cross-Classified”;

Print Percent Sepercent / Style=NCHS;

run;
Appendix A

Questionnaire
APPENDIX A. QUESTIONNAIRE

Questionnaire only included in PDF version of this 

document. 
Appendix B

Student Weight Detail
Methodology Report: 2014 National Youth Tobacco Survey

Page 40

APPENDIX B. STUDENT WEIGHT DETAIL

Students are selected from schools via the selection of intact class sections as described in Section 

2.4.4. The student sampling weight is computed based on a ratio of enrolling to responding 

students described in Section 4.2.1. The purpose of this section is to show that the resulting student 

weight is equivalent to computing a student weight as the inverse of the selection probability—are 

the other stage sampling weights—followed by two adjustments, one for nonresponse, and another 

poststratifying to known enrollment totals.

For the purposes of clarity, we omit the subscripts denoting the sampling stages and weight class. 

The unsubscripted quantities presented are assumed to be within weight class c, as defined in 

section 4.2.1.

The probability of selection of a class when there are Cjklm classes at grade j in school k, PSUi, 

stratum m is just 1/Cjklm or 2/Cjklm, depending on whether 1 or 2 classes are taken in the school. All 

students in a selected class were chosen so the probability of selection of a student is the same as 

the class, as well as constant across students within student weighting class. The initial selection 

probability is taken to be the inverse of this sampling probability.

In our simplified notation, letting K represent the number of sampled class sections, we have:

K

C W 

Nonresponse Adjustment

The nonresponse adjustment inflates the weight of the responding students to equal that of the 

sampled students. The adjustment is calculated as the sum of the weights for sampled students to 

the sum of the weights for responding students;

R

n

F  





Responding

Selected

NR W

W

where n represents the number of sampled students and R represents the number of responding 

students in the student weight class. Note that the equation simplifies to a ratio that does not involve 

W, as W is constant within the class.

Enrollment Ratio Adjustment

Next, the nonresponse adjusted student weights are ratio adjusted to conform to known school 

enrollment totals for each grade and sex. The adjustment Fps is computed as

R W

N

W

N

F

 









ps
Methodology Report: 2014 National Youth Tobacco Survey

Page 41

where N is the number of enrolled students in the weight class, and 

W W FNR



 

The fully adjusted student weight is computed as:

W W FPS







Simplifying, we get

R

N

R W

N

W

W W FPS



 

 

  
Methodology Report: 2014 Youth Tobacco Survey

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Methodology Report: 2014 Youth Tobacco Survey

Page 43

Appendix C

Common Core of Data 

Race/Ethnicity Definitions
Methodology Report: 2014 Youth Tobacco Survey

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APPENDIX C. COMMON CORE OF DATA RACE/ETHNICITY DEFINITIONS

Non-Hispanic American Indian/Alaska Native—A person having origins in any of the original 

peoples of North and South America (including Central America) and who maintains cultural 

identification through tribal affiliation or community recognition.

Non-Hispanic Asian/Pacific Islander—A person having origins in any of the original peoples of 

the Far East, Southeast Asia, the Indian subcontinent, or the Pacific Islands. This area includes, for 

example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, Thailand, Vietnam, Guam, 

the Philippine Islands, Samoa, and other Pacific Islands.

Non-Hispanic Black—A person having origins in any of the black racial groups of Africa; African 

American.

Hispanic—A person of Mexican, Puerto Rican, Cuban, Central or South American, or other 

Spanish culture or origin, regardless of race.

Non-Hispanic White—A person having origins in any of the original peoples of Europe, North 

Africa, or the Middle East.

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