METHODOLOGY REPORT 

OF THE 2021 NATIONAL 

YOUTH TOBACCO 

SURVEY 

August 2021

Recommended Citation

Office on Smoking and Health. 2021 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, 2021.
For questions about this report, please email Sean Hu at fik4@cdc.gov

Prepared for Centers for Disease Control and Prevention 

Prepared by ICF, Rockville, Maryland 

Centers for Disease Control and Prevention 

Office on Smoking and Health 

Atlanta, GA

August 2021 
TABLE OF CONTENTS

CHAPTER 1—NYTS SAMPLING DESIGN.......................................................................... 1

1.1 OVERVIEW OF THE NATIONAL YOUTH TOBACCO SURVEY (NYTS)........................................... 1 

1.2 OVERVIEW OF THE 2021 NYTS METHODOLOGY ......................................................................... 1 

CHAPTER 2—NYTS SAMPLING METHODS..................................................................... 3

2.1 SAMPLE DESIGN.............................................................................................................................. 3 

2.2 SAMPLING FRAME........................................................................................................................... 4 

2.3 SAMPLING UNITS AND MEASURE OF SIZE .................................................................................... 5 

2.4 PROJECTED SAMPLE SIZES............................................................................................................. 6 

2.5 FORMING SAMPLING UNITS ........................................................................................................... 7 

2.6 STRATIFICATION ............................................................................................................................. 8 

2.7 SAMPLE ALLOCATION AND SELECTION ........................................................................................ 9 

2.8 SAMPLE SIZES ATTAINED IN THE SURVEY.................................................................................. 10 

2.9 SAMPLE VALIDATION ................................................................................................................... 12 

CHAPTER 3—NYTS DATA COLLECTION AND PROCESSING ................................ 14

3.1 SURVEY INSTRUMENT .................................................................................................................. 14 

3.2 EXTERNAL REVIEW AND APPROVALS ......................................................................................... 15 

3.3 TECHNICAL ASSISTANCE PROVIDER (TAPS) STAFFING ............................................................ 15 

3.4 RECRUITMENT PROCEDURES ....................................................................................................... 15 

3.5 SURVEY ADMINISTRATION .......................................................................................................... 16 

3.6 WEB-BASED DATA COLLECTION MANAGEMENT APPLICATION (DCMA)............................... 17 

3.7 DATA RECORDING ........................................................................................................................ 17 

3.8 PARTICIPATION RATES ................................................................................................................. 17 

3.9 DATA MANAGEMENT ................................................................................................................... 18 

CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA ........................................... 20

4.1 SAMPLING WEIGHTS..................................................................................................................... 20 

4.2 NONRESPONSE ADJUSTMENTS..................................................................................................... 23 

4.3 POST-STRATIFICATION AND TRIMMING ...................................................................................... 25 

4.4 ESTIMATORS AND VARIANCE ESTIMATION ................................................................................ 29 

APPENDICES

A. QUESTIONNAIRE

B. COMMON CORE OF DATA RACE AND ETHNICITY DEFINITIONS

C. STUDENT WEIGHT DETAIL
1 

CHAPTER 1—NYTS SAMPLING DESIGN

1.1 OVERVIEW OF THE NATIONAL YOUTH TOBACCO SURVEY (NYTS)

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).1,2 Tobacco product-related indicators included in the NYTS are: tobacco 

product use (e.g., electronic cigarettes, cigarettes, cigars [including cigars, little cigars, and 

cigarillos], smokeless tobacco [chewing tobacco, snuff, or dip; snus, dissolvable tobacco 

products], hookahs, pipe tobacco, bidis, roll-your-own cigarettes, heated tobacco products, and 

nicotine pouches); exposure to secondhand smoke and e-cigarette aerosol; smoking cessation; 

minors’ access to tobacco products; knowledge and attitudes about tobacco; and familiarity with 

pro-tobacco advertisements and anti-tobacco media messages. National estimates based on NYTS 

data also serve as essential benchmarks against which TCPs can compare the extent of youth 

tobacco product use in their own states and communities. The NYTS provides multiple measures 

and data for seven of the 18 tobacco-related Healthy People 2030 objectives (USDHHS, 2020): 

TU-4, TU-5, TU-6, TU-7, TU-8, TU-9, and TU-22. 

First conducted during the fall of 1999 and again during the springs of 2000, 2002, 2004, 2006,

and 2009, then annually starting in 2011, 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. 

1.2 OVERVIEW OF THE 2021 NYTS METHODOLOGY

The 2021 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. 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 stratum; 2) Secondary Sampling Units (SSUs) (defined 

as schools or linked schools) within each selected PSU; and 3) students within each selected 

school. 

After being conducted via paper and pencil questionnaires since its inception in 1999, the NYTS 

began using electronic data collection methods starting in 2019. The 2019 and 2020 cycles were 

conducted using a tablet-based administration with offline data collection in schools, supported by 

trained survey administrators. Using this methodology during the 2020/2021 school year was not 

feasible given state and local emergency COVID-19 protocols (e.g., distance and hybrid learning 

models, restricted visitor access). Instead, the 2021 NYTS was administered as a 100% online 

survey, supported virtually by trained technical assistance providers (TAPs). Students participated 

in the survey while at school or at home during a designated class period as part of a classroom 

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 Centers for Disease Control and Prevention. Surveillance and Evaluation Data Resources for Comprehensive Tobacco Control Programs. 

Atlanta, GA: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on 

Smoking and Health; 2014.
2 

activity. Using a school-issued or personal internet-connected device, students logged into a secure 

website and watched a brief 2-minute instructional video before completing the survey. Students 

or whole classes unable to participate during the designated class period were asked to take the 

survey at the next possible opportunity. Due to the changes in the methodology for the 2021 

administration, results of the 2021 NYTS cannot be compared with previous NYTS survey results 

that were primarily conducted on school campuses. 

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

participation was anonymous. CDC’s Institutional Review Board (IRB) requires that parents be 

given the opportunity to opt their student out of participating in the survey. Schools used either 

passive or active permission forms at their discretion. 

Survey administration initiated on January 18, 2021 and concluded on May 21, 2021. The final 

sample consisted of 508 schools, of which 279 participated, yielding a school participation rate of 

54.9%. A total of 20,413 student questionnaires were completed out of a sample of 25,149 students, 

yielding a student participation rate of 81.2%. The overall participation rate, defined as the product 

of the school-level and student-level participation rates, was 44.6%. 

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 2021 

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

data (Chapter 4).
3 

CHAPTER 2—NYTS SAMPLING METHODS

2.1 SAMPLE DESIGN

The objective of the NYTS sampling design was to support estimation of tobacco product-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 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 and ethnicity 

(non-Hispanic White, non-Hispanic Black, and Hispanic; Appendix B). Additional estimates also 

were supported for subgroups defined by grade, by sex, and by race and ethnicity, each within 

school level domain; however, precision levels varied 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, Bureau of Indian Affairs 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 NYTS employed a repeat cross-sectional design. 

The sample was a stratified, three-stage cluster sample. Primary Sampling Units (PSUs) were 

stratified by racial/ethnic status and urban versus rural. PSUs were classified as "urban" if they 

were in one of the 54 largest Metropolitan Statistical Areas (MSAs) in the United States; otherwise, 

they were classified as "non-urban." Within each stratum, PSUs, defined as a county, a portion of 

a county, or a group of counties, were chosen without replacement. Table 2.1 presents key 

sampling design features.

Table 2.1 Key Sampling Design Features 

Sampling 

Stage Sampling Units Stratification Measure of Size 

(MOS)

Designed Sample Size

1 

PSUs: Counties, portions 

of a county, or groups of 

counties

Urban vs. Non-urban

(2 strata);

Minority concentration

(8 strata)

Aggregate school 

size in target 

grades

100 Counties, portions of 

a county, or groups of 

counties

2 Schools

Small, medium and large;

High school vs. middle

school

Aggregate eligible 

enrollment

450 SSUs (school) 

selections: 320 large 

schools, 80 medium 

schools and 50 small 

schools

3 Classes/students

2 Classes per grade in

half of large schools; 1 

class per grade otherwise

Acronyms: PSU = Primary Sampling Unit; SSU = Secondary Sampling Unit

As described in Section 1.2, the first stage of sampling selected PSUs within each stratum for a 

total of 100 sample PSUs. At the second sampling stage, a total of 450 Secondary Sampling Units 
4 

(SSUs), or schools, were selected from the sample PSUs, as follows: two large schools were selected

per sample PSU, one per level (middle or high); an additional large school for each level was 

selected in a subsample of 60 PSUs, for a total of 320 large SSUs. An additional 80 medium SSUs

and 50 small SSUs were selected from subsample PSUs, for a total of 450 sample SSUs (450 = 320 

+ 80 + 50). The PSU subsamples were selected with simple random sampling, and the schools 

were drawn with probability proportional to the total number of eligible students enrolled in a 

school. 

Depending on the average design effects, target subgroup sample sizes are between 1,200 and 

1,700. The NYTS design has experienced lower design effects with less oversampling over the 

last few cycles (due to proportional allocation and enrollment size measures). Compared to 

previous cycles, the NYTS sampling design has had both lower effects on unequal weighting and 

smaller clustering effects. These factors lead to lower design effects, particularly for subgroups. 

Smaller design effects have, in turn, led to smaller variances and improved precision. 

An appropriate sample size can enable generation of estimates with the required precision by 

grade, as well as by sex and school level. Therefore, the precision requirements generally focused

on racial/ethnic subgroups within school level. The targets of n=700 students per racial and ethnic 

minority group by school level (1,400 total per group) correspond to prevalence estimates within 

+/- 5% for confidence intervals at 95% confidence for all key racial and ethnic subgroups when 

broken down by school level. 

The prevalence estimates presented in Chapter 4 show that for all key racial and ethnic subgroups, 

prevalence estimates are within +/- 5% for confidence intervals at 95% confidence (i.e., standard 

errors are less than 2.5%). Standard errors are less than 2.5% for all estimates for Black and 

Hispanic students at the middle school and high school level. 

2.2 SAMPLING FRAME

As in previous cycles, the 2021 NYTS sample was based on a comprehensive sampling frame 

from multiple data sources to increase the coverage of schools nationally. The frame combined 

data files obtained from MDR Inc. (Market Data Retrieval Inc.) and from the National Center for 

Education Statistics (NCES). The MDR frame contained school information that included 

enrollments, grades, race and ethnicity distributions within the school, district and county 

information, and other contact information for public and non-public schools across the nation.

The NCES frame sources included the Common Core of Data for public schools and the Private 

School Survey for non-public schools. This dual-source frame build method was piloted first in 

2014 to build the frame for the NYTS.

3 Including schools sourced from the two NCES files 

resulted in substantial coverage increase among all public and non-public high schools.

4 Most of 

the added schools were smaller schools. Efforts were made to ensure that each school was 

represented only once in the final sampling frame, even if the school showed up in both source 

files. 

3 Redesigning National School Surveys: Coverage and Stratification Improvement using Multiple Datasets. William Robb, Kate 

Flint, Alice Roberts, Ronaldo Iachan, ICF International, FEDCASIC, March 2014

4 The coverage increase has ranged from 6.6% to 12.7% in recent sampling frames.
5 

Certain schools were removed from the frame prior to drawing the sample following a stepwise 

process. The first step excluded non-eligible schools by category to remove schools such as 

Department of Defense schools, vocational schools, and adult education schools. This resulted in 

the exclusion of 3.9% of schools (2.8% of public schools and 8.0% of private schools) and 1.1% 

of students. Lastly, schools were removed that had fewer than 40 students enrolled across eligible 

grades, resulting in the exclusion of 20.4% of schools (12.8% public and 42.6% private) which 

had been eligible after the other exclusions. This exclusion of schools with fewer than 40 students

led to the exclusion of only 1.03% of students of those in eligible schools.5

 

2.3 SAMPLING UNITS AND MEASURE OF SIZE

A three-stage cluster sample design was used to produce a nationally representative sample of 

students in grades 6–12 who attend public and private schools. The first-stage sampling frame 

consisted of PSUs made up of counties, groups of smaller, adjacent counties, or parts of larger 

counties. For the second stage of sampling, SSUs were defined as a physical school that can supply 

a full complement of students in grades 6 through 8 (middle school) or 9 through 12 (high school)

or a school created by linking component physical schools together to provide all grades for the 

level.

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

than one, one or two class selections per grade. Small SSUs contained fewer than 28 students at 

any grade level, and large SSUs contained at least 56 students at each grade level. The remaining 

schools were classified as medium sized.

The sampling stages may be summarized as follows:

• Selection of PSUs—One hundred PSUs (from approximately 1,257 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 320 large schools, or SSUs,

were selected from the sample PSUs. Additionally, as described in Section 2.1, we 

selected 80 medium schools and 50 small schools, resulting in a total of 450 sample SSUs

(450 = 320 + 80 + 50). 

• Selection of students—Students were selected via whole classes whereby all students 

enrolled in any one selected class were chosen for participation. Classes were selected 

from course schedules provided by each school so that all eligible students had only a 

single chance of selection.

The sampling approach utilized probability proportional to size (PPS) sampling methods with the 

measure of size (MOS) defined as the count of final-stage sampling units, students in intact 

classrooms. Coupled with the selection of a fixed number of units, the design resulted in an equal 

probability of selection for all members of the universe (i.e., a self-weighting sample). These 

5 Note that the two exclusions are additive so that 2.2% of students are excluded from the frame.
6 

conditions were approximated for the NYTS resulting in the attainment of a roughly self-weighting 

sample.

The MOS also was used to compute stratum sizes and PSU sizes. By assigning an aggregate 

measure of size to the PSU, the sample allocated to the PSU was in proportion to the student 

population. 

The third, and final, sampling stage selected classes within each grade of a sample SSU. We 

selected two classes per grade in large schools and one class per grade in the remaining schools. 

The threshold for double class sampling was based on a simulation study to ensure that the required 

numbers of students in specified racial and ethnic minority groups were achieved per school level.

All students in a selected class were then selected for the survey. 

2.4 PROJECTED SAMPLE SIZES

This section describes the planned sample sizes developed by the design, while Section 2.8 

discusses the sample sizes attained in the fielded survey. The NYTS sample size calculations were 

based on the following assumptions: 

• The main structure of the sampling design is consistent with the design used to draw the 

sample for prior cycles of the NYTS.

• The design included the selection of two large SSUs within each sample PSU, and an 

additional 120 large, 80 medium and 50 small schools from subsample PSUs. 

Across 16 previous cycles of the NYTS that had concluded prior to the 2021 NYTS design, school 

participation had averaged 80.8% with a low of 49.9%.6 Student participation had averaged 89.5% 

with a low of 85.9%. The combined response rate (student x school) averaged 72.4% with a low of 

43.6%. Historical participation rates at both school and student levels guided the sampling design 

and sample sizes. In calculating the sample sizes for the 2021 NYTS, we made our approach more

robust by assuming a conservative combined rate (student x school) of 42.5%, substantially lower

than the historical overallresponse rate. The main reason is to account for higher levels of anticipated 

school refusals due to COVID-19 precautions in the 2020/2021 school environment. A secondary 

reason is that the student participation rate needs to be adjusted to account for a growing number of 

ineligible students. The number of ineligible students needs to be subtracted from the net number of 

students available for selection in participating schools. Table 2.2 presents a detailed derivation of 

the sample sizes planned for the 2021 NYTS based on these assumptions.

6 Data collection during the 2020 NYTS was truncated due to school closures in response to COVID-19, resulting in lower school 

and student response rates than historical averages. 
7 

Table 2.2 Planned Sample Sizes for the 2021 NYTS

PSU Size # of 

SSUs

Number of 

Schools 

Sampled

# of 

Classes 

per 

School

# of 

Students 

per 

Class

# of Sampled 

Students 

prior to 

Attrition

# of 

Participating

Students 

Based on 

42.5% 

Response Rate

100

(with 60 

PSUs 

subsampled 

to provide 

additional

large 

schools)

Large HS 160 Double 

classes: 80

8 25 16,000 6,800

Single 

classes: 80

4 25 8,000 3,400

Large MS 160 Double 

classes: 80

6 25 12,000 5,100

Single 

classes:80

3 25 6,000 2,550

Large Total 320 42,000 17,850

40

(subsample)

Medium HS 40 40 4 25 4,000 1,700

Medium MS 40 40 3 25 3,000 1,275

Medium 

Total

80 7,000 2,975

25

(subsample)

Small HS 25 25 4 25 2,500 1,063

Small MS 25 25 3 25 1,875 797

Small Total 50 4,375 1,859

Overall Total 450 53,375 22,684

One-hundred PSUs were selected, with two large SSUs (“full” schools) selected from each PSU 

and one additional large SSU per level selected from 60 subsampled PSUs for a total of 320 large 

SSUs. The estimated sample yield from these large schools was 42,000 students before school and 

student non-response, leading to an expected total 17,850 participating students in large schools 

after accounting for non-response. 

To provide adequate coverage of students in small schools (those with an enrollment of less than 

28 students in any grade) 80 medium SSUs from a subsample of 40 PSUs, and 50 small SSUs from 

a subsample of 25 PSUs were selected. The expected yield was 7,000 from medium schools and 

4,375 students from small schools. In total, the expected number of participating students was 

22,684. 

Within each school, one class was selected from each grade to participate in the survey except in 

large schools with high racial and ethnic minority populations, where two classes per grade were 

selected. Note that the set of schools with high racial and ethnic minority populations defined for 

double class sampling is necessarily a subset of the large schools that can support such double 

class sampling. For the 2021 NYTS, we implemented double class selection for half of large 

schools (randomly selected) to ensure sufficient student yields. 

2.5 FORMING SAMPLING UNITS

2.5.1 Forming primary sampling units (PSUs) 

In defining PSUs, several issues were considered: 
8 

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

by grade, and small enough so as not to be selected with near certainty. 

• Each PSU should be compact geographically to control the number of school districts 

contacted and recruited. 

• Recent data should be available to characterize each PSU.

• PSUs are defined to contain at least four middle and five high schools. 

Generally, counties were equivalent to PSUs, with two exceptions: 

• Low population counties were combined to provide sufficient numbers of schools and 

students. 

• High population counties were divided into multiple PSUs so that the resulting PSUs would 

not be selected with certainty. 

The PSU frame was screened for PSUs that no longer met the above criteria. The frame was 

adjusted by re-combining small counties/PSUs as necessary to ensure sufficient size while 

maintaining compactness. Near-certainty PSUs were split using an automated procedure built into 

the sampling program.

2.5.2 Forming secondary sampling units (SSUs) 

Single schools represented their own SSU if they had students in each of grades 6 through 8 or in 

grades 9 through 12. Schools that did not have all eligible grades for the level were grouped 

together to form an SSU. Linked schools were treated as single schools during sampling. 

2.6 STRATIFICATION

The PSUs were organized into 16 strata, based on urban/non-urban location and proportion racial

and ethnic minority enrollment. 

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

Black students, then the PSU was classified as Hispanic. Otherwise, it was classified as 

Black. 

• If the PSU was within one of the 54 largest MSAs in the United States, it was classified as 

“urban,” otherwise it was classified as non-urban (or “rural,” for simplicity).

• Hispanic urban and Hispanic rural PSUs were classified into four density groupings 

depending upon the percentages of Hispanic students in the PSU. 

• Non-Hispanic Black urban and non-Hispanic Black rural PSUs were also classified into 

four groupings depending upon the percentages of Black students in the PSU. 

The density grouping bounds were computed using an optimization algorithm7 that is refreshed 

each cycle to reflect changes in the racial/ethnic distribution of the student population. The 

boundaries or cutoffs changed as the frequency distribution (“f”) for the racial and ethnic groupings 

changed from one survey cycle to the next. Table 2.3 presents the stratum boundaries used in the 

2021 NYTS.

7 The cumulative square root of “f” method developed by Dalenius and Hodges.
9 

Table 2.3 Stratum Boundaries: Minority Percentage Cutoffs 

Minority 

Concentration

Density 

Group

Bounds

Urban Rural

Black

1 0%-26% 0%-20% 

2 >26%-40% >20%-34% 

3 >40%-54% >34%-54% 

4 >54%-100% >54%-100% 

Hispanic 

1 0%-26% 0%-24% 

2 >26%-42% >24%-48% 

3 >42%-58% >48%-68% 

4 >58%-100% >68%-100% 

As described earlier, SSUs were stratified into three sizes for small, medium, and large schools. 

2.7 SAMPLE ALLOCATION AND SELECTION

The 2021 NYTS was designed to select a sample of 100 PSUs. The PSUs were initially allocated 

to strata proportional to student enrollment. For this cycle, a nearly proportional PSU allocation

was achieved, resulting in gains in sampling efficiency. Table 2.4 shows the actual allocation of 

the PSU sample to the 16 strata defined by racial and ethnic minority density and urban status, 

alongside a proportional allocation. The initial proportional allocation was slightly modified to 

ensure that all strata contained at least two PSUs to facilitate accurate variance estimation.
10

Table 2.4 First-Stage Strata and Frame PSU Distribution 

Predominant 

Minority Urban/Rural

Density 

Group 

Number

Stratum 

Code

Student 

Population 

Number of 

Sample 

PSUs 

(Revised)

Non-Hispanic 

Black

Urban

1 BU1 1,714,661 6 

2 BU2 1,853,137 8 

3 BU3 261,841 3 

4 BU4 507,699 5 

Non-urban 

1 BR1 2,017,742 7 

2 BR2 1,163,987 4 

3 BR3 974,876 6 

4 BR4 501,663 6 

Hispanic 

Urban

1 HU1 3,333,126 8 

2 HU2 2,948,746 6 

3 HU3 2,354,412 5 

4 HU4 2,375,099 6 

Non-urban 

1 HR1 5,501,371 18 

2 HR2 1,903,271 5 

3 HR3 972,022 4 

4 HR4 815,139 3 

The sample was selected with PPS methods at the first and second stages. With PPS sampling, the 

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

sampling procedures were applied to the stratified frame to select a PPS sample of PSUs:

• Selected 100 PSUs with a systematic random sampling within each stratum. The method 

applied within each stratum was a sampling interval computed as the sum of the measures 

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

stratum. 

• Subsampled PSUs for additional large schools (60 PSUs), medium schools (40 PSUs) and 

small schools (25 PSUs); sampling of two schools per level in each subsample PSU. 

2.8 SAMPLE SIZES ATTAINED IN THE SURVEY

The 2021 NYTS attained the target sample sizes in the key analytic subgroups of interest. Tables 

2.5a–d8,show the number of participating students in subgroups defined by gender, grade, and race 

and ethnicity. Table 2.5d about race and ethnicity distribution, is presented in two different ways: 

1) using the original variable allowing for multiple races and including missing data, and 2) using 

8

 Percents may not add to exactly 100% due to rounding. 
11

the variable whereby all respondents are categorized into a single race/ethnic group. The sample 

led to 5,056 Hispanic students and 3,446 Black students using the single-race variable. 

Table 2.5a Subgroup Sample Sizes: Number of Participating Students 

Table 2.5b Subgroup Sample Sizes: Number of Participating Students 

What is your sex? 

Q2 Frequency Percent

Cumulative 

Frequency

Cumulative 

Percent

Not Answered 121 0.59 121 0.59 

Not Displayed 5 0.02 126 0.62 

Male 10368 50.79 10494 51.41 

Female 9919 48.59 20413 100.00 

What grade are you in? 

Q3 Frequency Percent

Cumulative 

Frequency

Cumulative 

Percent

Not Answered 71 0.35 71 0.35 

Not Displayed 16 0.08 87 0.43 

6th 3371 16.51 3458 16.94 

7th 3198 15.67 6656 32.61 

8th 3194 15.65 9850 48.25 

9th 3097 15.17 12947 63.43 

10th 2542 12.45 15489 75.88 

11th 2595 12.71 18084 88.59 

12th 2281 11.17 20365 99.76 

Ungraded or other grade 48 0.24 20413 100.00 
12

Table 2.5c Subgroup Sample Sizes: Number of Participating Students 

RECODE: Race/Eth - multiple group 

RACE_M Frequency Percent

Cumulative 

Frequency

Cumulative 

Percent

<Missing> 631 3.09 631 3.09 

NH-White 9232 45.23 9863 48.32 

NH-Black 3280 16.07 13143 64.39 

Hispanic 5056 24.77 18199 89.15 

NH-Asian 851 4.17 19050 93.32 

NH-AI/AN 223 1.09 19273 94.42 

NH-NHOPI 62 0.30 19335 94.72 

Multiple Races 1078 5.28 20413 100.00 

Table 2.5d Subgroup Sample Sizes: Number of Participating Students 

RECODE: Race/Eth - no multiple group 

RACE_S Frequency Percent

Cumulative

Frequency

Cumulative

Percent

<Missing> 631 3.09 631 3.09 

NH-White 10104 49.50 10735 52.59 

NH-Black 3446 16.88 14181 69.47 

Hispanic 5056 24.77 19237 94.24 

NH-Asian 889 4.36 20126 98.59 

NH-AI/AN 225 1.10 20351 99.70 

NH-NHOPI 62 0.30 20413 100.00 

Note: This variable is named race_s in the public use data set. The multiple race categories are Hispanic, non-Hispanic (NH) White, nonHispanic Black, non-Hispanic Asian, non-Hispanic American Indian or Alaskan Native (AIAN), and non-Hispanic Native Hawaiian or Pacific 

Islander (NHOPI).

2.9 SAMPLE VALIDATION

Following the sample draw, each district and school were called to verify the correct information 

for each entity. 

District validation included confirmation of the following:

• District name

• Name and title of 2020-2021 district superintendent 

• District street address used for overnight deliveries, with city name and ZIP code 

School validation included confirmation of the following: 

• School is operational 
13

• School name and relationship to identified district (if applicable)

• Name and title of 2020-2021 school principal 

• School street address used for overnight deliveries, with city name and ZIP code 

• Grade levels served during 2020-2021school year 

• Approximate school enrollment 

• At least a cumulative enrollment of 40 students in the grades for which the school was 

selected

• School is a traditional “brick and mortar” school with traditional school-aged students who 

are not adults and who attend classes in person throughout the academic year 

• School has its own unique student body, meaning it does not draw its population from 

surrounding schools in order to provide specialized instruction 

• School does not exclusively serve a specialized student population such as English 

Language Learners or Special Education students 
14

CHAPTER 3—NYTS DATA COLLECTION AND PROCESSING

3.1 SURVEY INSTRUMENT

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

prevention and control outcome indicators. The 2021 survey instrument included 166 questions. 

The 2021 NYTS represented the third cycle the study was conducted using electronic data 

collection methods rather than traditional paper-and-pencil (PAPI) and the first cycle that was 

conducted 100% online. The web survey was created using ColdFusion9 and all data were stored 

in a MS SQL Server. To take the web survey, students navigated to a dedicated URL, nyts.cdc.gov, 

and entered a randomly generated, five-digit access code. Each access code was tied in a backend 

database to its associated school and classroom to facilitate tracking and calculate class and school 

response rates. 

The survey followed a skip-pattern logic based on the student’s responses to questions about ever 

and current tobacco product use behaviors. To improve students’ sense of privacy, only one 

question was displayed on each screen so that responses to prior questions were not susceptible to 

observation. Students were given one class period (approximately 35-45 minutes) to complete the 

survey. Students who could not take the survey on the planned date for administration were asked 

to take the survey at the next possible opportunity. 

The length of interview (LOI) was captured for each record and was calculated as the time lapse 

between the date/time of the first response and the date/time of the last response given. LOI ranged 

from 1 second to 23 days 59 minutes and 56 seconds, with an average of 26 minutes 16 seconds. 

After exclusion of outliers10, the average survey completion time was 22 minutes 10 seconds. 

The first five questions on the survey collected student demographic information, and the rest

measured 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 

product use; exposure to pro- and anti-tobacco media and advertising; minors’ access to tobacco 

products; nicotine dependence; cessation attempts; exposure to second-hand smoke; harm 

perceptions; and exposure to tobacco product warnings. At the beginning of each tobacco product 

section, a description of the product (with example brands) and generic images of specific tobacco 

products were provided to assist with product recognition and increase the accuracy of student 

data. Students could refer back to this description and the images as needed as they answered

related questions. The 2021 NYTS also included socio-demographic questions about family 

affluence, depression and anxiety, and sexual orientation and gender identity (SOGI). 

Historically, experts within CDC’s Office on Smoking and Health (OSH), Epidemiology Branch 

have taken the lead on the NYTS questionnaire design. Working in concert with a variety of local, 

state, and federal partners, including representatives from FDA, CDC reviews the questionnaire 

prior to each cycle to identify and remove redundancies, examine the most relevant indicators, and 

obtain guidance and suggestions for new items on the questionnaire. 

9 https://www.adobe.com/products/coldfusion-family.html 10 The average completion time was calculated after dropping outliers with survey duration lengths greater than 80 minutes 

(n=216). 
15

3.2 EXTERNAL REVIEW AND APPROVALS

Three bodies reviewed and approved the instrumentation, processes, privacy and security 

elements, and sampling design of the 2021 NYTS: the Office of Management and Budget (OMB), 

ICF’s Institutional Review Board (IRB) and CDC’s Institutional Review Board (IRB). 

With the transition to an electronic data collection format for the 2019 NYTS, the Security 

Assessment and Authorization (SA&A) approval and Enterprise Performance Life Cycle (EPLC) 

review remained valid for the 2021 NYTS cycle. The SA&A is a formal methodology for testing 

and evaluating the security controls of the system to ensure that it is configured properly to meet 

the security mandated by the Federal Information Security Management Act (FISMA). EPLC is a 

framework to enhance the Department of Health and Human Services (HHS) IT governance 

through rigorous application of sound investment and project management principals, in 

conjunction with industry’s best practices. 

3.3 TECHNICAL ASSISTANCE PROVIDER (TAPS) STAFFING

The role of the Technical Assistance Provider (TAP) was developed in response to anticipated 

complications due to COVID-19 that prohibited data collectors from conducting in-person survey 

administration. TAPs provided 100% virtual support to schools and teachers before, during, and 

after survey administration to 1) ensure teachers had received all the necessary materials to 

administer the survey, 2) answer any questions schools contacts and/or teachers may have prior to, 

during, or after survey administration, 3) ascertain that parental consent was properly obtained

prior to the scheduled survey administration date, and 4) provide remote IT support, if needed. To 

ensure schools in various time zones would be adequately supported during school hours, TAPs 

were hired geographically across the country so that every time zone with sampled schools had at 

least one TAP local to that part of the country. TAPs were recruited from a pool of previously 

trained data collectors. A remote training for TAPs was conducted on January 5-6, 2021. 

Key components of the training included the following: 

 Pre-and post-survey communications with the schools and teachers

 Protocols specific to the type of instructional model (e.g., in-person, exclusively distance 

learning, hybrid) expected to be in place at the time of survey administration 

 Orientation to student and teacher portals 

 IT troubleshooting 

 Communication with headquarters staff 

3.4 RECRUITMENT PROCEDURES

The schools selected to participate in the 2021 NYTS were located in 37 states. Recruitment began 

in October 2020 with calls to state departments of education and health to inform them of the 

survey effort and sampled schools in their state. After notification at the state level, district- and 

school-level recruitment began. For public or diocesan schools, verbal or written agreement was 

first obtained by their district or diocese, respectively, before contact was made with the school. 

However, private schools were approached directly. A date for survey implementation was 

selected that was convenient to the school, its academic calendar, and, in some instances, the 
16

anticipated date for return to in-person instruction. Recruiters and TAPs used a secure web-based

calendar to facilitate communication and adjust survey dates upon request by the school. 

3.5 SURVEY ADMINISTRATION

Survey administration began on January 18, 2021 after a comprehensive TAP training and 

continued through May 21, 2021. While the details of each data collection varied, there were six

core steps followed for every school: 

1) Conduct pre-contact call with the principal or lead contact to confirm instructional model

(e.g., in-person, exclusively distance learning, hybrid) 

2) Send tailored communications and survey materials to selected teachers in hard copy, 

electronically, or both, depending on instructional model 

3) Outreach directly to teachers to confirm receipt of materials, verify intentions to administer 

the survey on the scheduled date, confirm parental consent procedures were followed, and 

provide additional survey instructions 

4) Virtually monitor survey activities and respond to requests for technical support, as needed 

5) Follow-up with teachers regarding student response rates and class enrollment

6) Report final progress to school contact and thank them for their school’s participation 

Procedures were designed to protect students’ privacy by assuring that student participation was 

anonymous and voluntary. Using a school-issued or personal internet-connected device, students 

logged into a secure website, watched a brief 2-minute instructional video, and responded to a 

question regarding their location (e.g., classroom, home, other location) before completing the 

survey. All surveys were submitted directly to the secure SQL server. 

3.5.1 FIELD PROCEDURES

After schools had been recruited, classes selected, and a date for survey administration scheduled, 

each school with in-person or hybrid instructional models received a hard-copy mailing with presurvey materials containing instructions for the school contact and packets for the teacher of each 

selected class. Teacher packets contained the parental permission forms to be distributed to all 

students in the selected classes prior to data collection. School with exclusively distance learning 

models received their materials electronically. They, in turn, distributed the parental permission 

forms to students electronically following local established channels for teacher/parent 

communications. 

The timing of these pre-survey materials was determined in part by the type of permission form 

being used by the school; this decision was made by the school district or individual school. 

Passive parental permission forms (i.e., forms returned only if the parents do not want their child 

to participate) were sent approximately two weeks prior to the scheduled date of data collection in 

the majority of schools. Active parental permission forms (i.e., forms that must be returned with 

the parent’s signature for the child to participate) were sent out four weeks prior to the scheduled 

date of data collection for schools that require active consent. TAPs conducted follow-up calls and 

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

and distributed to selected classes and students. 
17

3.5.2 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 so 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 

held at this time. Lastly, in some schools, homerooms or advisory periods were used as the frame 

for class selection. 

3.6 WEB-BASED DATA COLLECTION MANAGEMENT APPLICATION (DCMA)

For multiple cycles of the NYTS, a web-based data collection management application (DCMA) 

has been utilized to help centralize the management of the study, facilitate information exchange 

with project staff, and allow all members of the project management, recruitment, supervisory 

teams, and remote staff access to information necessary to implement the study. The system is 

designed with differing levels of access depending on the user’s role on the study. The system’s 

main functions include generating invitation letters, tracking recruitment progress, scheduling 

data collection, registering student records submitted to the central repository, and tracking 

school and student response rates. 

3.7 DATA RECORDING

Preliminary student participation rates were calculated based on class enrollment numbers 

provided by teachers of selected classes and the number of surveys received in the central 

repository. If teachers reported a different number of expected completes than what was received 

in the central repository, a TAP followed up to resolve discrepancies and determine additional 

strategies to maximize student participation. As additional surveys were received after the initial 

survey administration date, the DCMA automatically updated the number of actual records 

received and participation reporting was revised accordingly. 

3.8 PARTICIPATION RATES

Participation rates for the NYTS were calculated at the school and student levels. 

3.8.1 School-level Participation Rates

At the school level, 508 schools were selected across 243 districts in 37 states. During sample 

validation, 30 schools were deemed to be ineligible and were replaced. 

In total, 279 schools (54.9%) participated in the study. The remaining 229 schools were considered 

refusals. Of refusals, 106 of them were due to district-level refusals to allow contact with schools
18

to discuss participation and 123 were school level refusals. The most common reasons given for a 

refusal at the district or school level were COVID-related concerns, loss of instructional time and 

burden to teachers/staff. 

3.8.2 Student-level Participation Rates

Initial student-level participation rates were calculated from the field as teachers reported 

enrollment information and submitted surveys registered in the central repository. In subsequent 

follow-ups between teachers and TAPs, further refinements were made to 1) revise the number of 

eligible students based on available documentation, 2) correct mathematical errors, 3) review 

counts of surveys received by the database, and 4) account for make-ups as they were received 

from students and classes that did not participate on the initial day of survey administration. 

The final student participation rate for the 2021 NYTS was 81.2%. Overall, 25,149 eligible 

students from the 279 participating schools were invited to participate in the survey, and 20,413 

did so. Table 3.1 below shows the number of eligible students, participants, and participation rates 

for the NYTS.

Table 3.1 Overall NYTS 2021 Student Participation Rate 

# Eligible # Completed Participation % 

NYTS Participating Students 25,149 20,413 81.2% 

The 2021 NYTS survey attained an actual school participation rate of 54.9% and a student 

participation rate of 81.2%. The overall participation rate, the product of the school-level and 

student-level participation rates, was 44.6%. 

3.9 DATA MANAGEMENT

To take advantage of the electronic format of the NYTS, the dataset was designed to be selfcleaning based on programming logic. However, to ensure accuracy, CDC created a series of datacleaning specifications that were applied to eliminate internal inconsistencies. These cleaning 

specifications also computed certain analytic variables and re-coded race and ethnicity values to 

match CDC-required classifications. Data “missingness” was categorized into one of four types: 

as a legitimate skip based on programmed logic, as item-level refusal if a question was presented 

to a student on-screen but not answered, as not answered because the student was never shown a 

question on screen (e.g., partial complete), or as recoded to missing due to edit checks. Missingness 

is distinguished in the data set as follows: 

• .S – Legitimate skip

• .N – Displayed, not answered (item-level refusal)

• .Z – Not displayed (partial complete)

• .E – Missing due to edit check 
19

The survey data file preparation for weighting involved a series of data file linking steps. These 

steps ensured that the data files merged the school information compiled during frame 

construction, sample selection, replacement of ineligible schools, recruitment, and data collection 

using a common school identifier. 
20

CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA

This chapter describes the procedures used to weight the NYTS data including: 

 

• Sampling weights 

• Nonresponse adjustments 

• Poststratification to national estimates by grade and weight trimming

This chapter 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 post-stratify the data 

to known sampling frame characteristics. This chapter closes with a description of the computation 

of weighted estimates and variance estimates. Details of the student weights are provided in 

Appendix C. 

Although the sample was designed to be approximately self-weighting, survey weights were

necessary to produce unbiased estimates. The basic weights, or sampling weights, were computed 

on a case-by-case basis as the reciprocal of the probability of selection of that case. Below is a 

simple presentation of the basic steps in weight computation. 

4.1 SAMPLING WEIGHTS

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

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

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

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

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

The NYTS computation of sampling weights began at the student sampling stage, and then moved 

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

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

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

began with the student weights within a given grade, school, and PSU, these weights are referred 

to as conditional. 

4.1.1 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 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.
21

The student selection weight is denoted as 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 weight was 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 was defined by a sequence of rules that depended on the number of 

responding students. This was to avoid large weights for classes with low numbers of respondents. 

This process operated entirely within schools. 

Initially, the weighting class was defined by grade and sex within each school. If the weight for 

the class exceeds a maximum value, C, then weighting classes are combined. This cap C was

computed using the following equation: 

min( ,10 )

2

N

N = C

cklm

cklm

cklm

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

weight were then recomputed. If the weight still exceeded the cap, grades were combined. The 

process was repeated, and if the student weight still exceeded the cap, the school was taken as the 

weighting class. 

This had the effect, within a 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 was collapsed to the school level. 

4.1.2 School Sampling Weights 

For large schools, the partial school weight was 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 those large schools belonging to the 60 subsampled PSUs, the partial school weight was: 
22

For small schools, the partial school weight was: 

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

The overall weights for a given PSU, school and grade combination were the product of the 

adjusted PSU, school and grade-level weights.

4.1.3 Grade Sampling Weights 

Grade selection occurred within linked schools where the grade was 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, was simply the inverse of the probability of selection 

described in Section 2.4. In a non-linked school, the weight was 1.0. The grade weight is denoted 

as WGjklm. 

4.1.4 PSU Sampling Weights 

The weight of the PSU was 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. 

This PSU subsampling component of the PSU weight was accounted for in the school selection 

probability and corresponding weight. 

4.1.5 Overall Sampling Weight

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

WT1, was then adjusted for nonresponse, trimmed, and post stratified to control totals, as described 

in the following sections. This weight was 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
23

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

equations are defined in the preceding sections. 

4.2 NONRESPONSE ADJUSTMENTS

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

from differences between responding and nonresponding students and schools included in the 

sample. 

4.2.1 Student Nonresponse Adjustment 

An adjustment for student nonresponse was made by sex and grade within schools. With this 

adjustment, the sum of the student weights over participating students within a school matched the 

total enrollment by grade and sex in the school collected during data collection. This adjustment 

factor was capped in extreme situations to limit the potential effects of extreme weights on the 

precision of survey estimates.

In the 2021 NYTS cycle, nonresponse adjustment cells were defined in a tailored and systematic 

approach stemming from the non-response analysis. These analyses are detailed in the 2021 NYTS 

Nonresponse Bias Analysis report. 

Specifically, the definition of the most appropriate nonresponse adjustment weighting cells 

followed these steps: 

1. Conduct bivariate analysis to identify key predictors of school nonresponse and student 

nonresponse. 

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

the subset of key predictors identified in Step. 1 to identify significant predictors of nonresponse at both levels. 

3. Develop nonresponse adjustment weighting cells based on the significant predictors while 

incorporating information about cell sizes and correlations between predictors. 

During the 2021 cycle, school type, school affluence (MDR index), and dichotomized NCES 

locale (city versus non-city) were found to be predictive on nonresponse. Nonresponse adjustment 

cells were created using school level (high vs middle), school type (public vs non-public), 

affluence (above median vs below median, or high vs low) and dichotomized NCES locale (city 

vs non-city).

Typically, with multiple variables associated with school nonresponse, the subset of variables 

selected for defining weight adjustment cells is effectively reduced in two ways: 1) by eliminating 

variables with high pairwise correlations, and 2) limiting to variables and cells with adequate 

representation of participating schools. Several weight adjustments were used to account for 

student and school nonresponse patterns. An adjustment for student nonresponse was made by sex 

and grade within school. With this adjustment, the sum of the student weights over participating 

students within a school matches the total enrollment by grade and sex in the school collected 

during data collection. This adjustment factor was capped in extreme situations to limit the 

potential effects of extreme weights on the precision of survey estimates. If enrollment by grade 

and sex is not available for certain schools, only adjustments by grade or school level were 

performed.
24

The weights of students in participating schools were adjusted to account for nonparticipation by 

other schools. The adjustment factor (AAkk) is the ratio of the sum of weighted MOS of all selected 

schools in the stratum over the sum of the weighted MOS for participating schools in a stratum.

The adjustment factor was computed and applied to public and non-public schools separately. 

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

AAkk = ∑ (WWkkkk

PP

kk,kk∈sssskksskkssss sssshooookkss ∗ WWkkkkkk ∗ MMMMMMkkkkkk)

∑ (WWkkkk

PP

kk,kk∈ssssppppppssppsssspppppppp sssshooookkss ∗ WWkkkkkk ∗ MMMMMMkkkkkk)

The student weight adjusted for nonresponse was then:

WW3

ss = WW2ss ∗ AAkk

Table 4.1 presents the nonresponse adjustment factors within each of the nonresponse adjustment 

cells. The adjustment cells were defined differently for public and non-public schools. Non-public 

schools were divided by school level; public schools were divided by school level, affluence, and 

dichotomized NCES locale. 

Table 4.1 Nonresponse Adjustment Factors in Each Adjustment Cell 

Weighting Class

Weight Sum 

Over 

Participants

Responding 

School 

Count

Weight Sum 

Over All 

Sample

Sample 

School 

Count

Response 

Rate

Adjustment 

Factor

High school, non-public 245,440.08 7 1,063,436.95 26 26.923 4.333

High school, public, 

low affluence, city

1,756,526.31 17 3,116,274.84 34 50.000 1.774

High school, public, 

low affluence, non-city

4,353,522.73 45 6,181,055.23 63 71.429 1.420

High school, public, 

high affluence, city

1,265,427.50 15 3,606,948.88 36 41.667 2.850

High school, public, 

high affluence, non-city

5,833,814.85 46 10081424.07 78 58.974 1.728

Middle school, nonpublic548,146.23 16 1,362,112.69 32 50.000 2.485

Middle school, low 

affluence, city

925,410.79 15 2,457,503.60 36 41.667 2.656

Middle school, low 

affluence, non-city

3,133,665.68 52 4,138,572.64 68 76.471 1.321

Middle school, 

affluence above 

median, city

319,192.58 8 2,385,137.98 34 23.529 7.472

Middle school, 

affluence above 

median, non-city

5,172,567.25 60 8,798,941.23 103 58.252 1.701

23553713.99 281 43191408.11 51011

The variables considered in the non-response analyses which led to non-response adjustment cells are more fully described in the 

non-response analysis report. The four variables used in non-response adjustment cells are school level (middle vs high school), 

school type (public vs non-public), affluence (low vs high affluence; from sampling frame (MDR) Index) and dichotomized NCES 

locale (city vs non-city). 

11 Two schools were span schools and are therefore counted twice as different sampling units, i.e., both as middle schools and high schools
25

4.3 POST-STRATIFICATION AND TRIMMING

The final steps in the weighting process include trimming and post-stratification. Extreme variation 

in sampling weights can inflate sampling variances and offset the precision gained from a welldesigned sampling plan. Nonresponse adjustments while minimizing bias can add additional 

variances. One strategy to compensate for these potential effects is to trim extreme weights and 

distribute the trimmed weight among the untrimmed weights. The trimming is an iterative 

procedure. It is possible to implement the iterative trimming in conjunction with the iterative poststratification, or raking, procedures described next. 

Post-stratification approaches capitalize on known population totals and percentages available for 

groups of schools and students. National estimates of racial/ethnic counts for poststratification 

were obtained from two sources described next. Private schools’ enrollments by grade and five 

racial/ethnic groups were obtained from the Private School Survey (PSS); public school 

enrollments by grade, sex, and five racial/ethnic categories were obtained from the Common Core 

of Data (CCD). Both are produced by the National Center of Education Statistics (NCES); the 

most recent versions, the 2017-18 CCD and the 2017-18 PSS was used. 

These databases were combined to produce the enrollments for all schools and to develop 

population counts to use as controls in the poststratification step. Iterative poststratification, or 

raking, methods allowed the use of additional poststratification variables and categories. The 

iterative approach allowed the simultaneous application of a trimming procedure (see, for example, 

Iachan, 2010).12 Trimming is designed to limit the variance increase that may follow from the biasreduction raking methods. The trimming method capped the weights at the median plus four times 

the interquartile range of the weight distribution. 

Tables 4.2 and 4.3 present the population control totals, which are also the sums of the weights in 

each post-stratum cell. Post-stratification variables, also shown in the two tables, are a) school type 

by grade and sex, and b) census region by grade and race and ethnicity. These dimensions reflect 

the iterations used in the raking procedures. 

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

Association meeting, Vancouver, Canada.
26

Table 4.2 Sum of Final Weights vs. Control Total - by Public Flag, Grade and Sex 

School Type Grade Sex*

Number of 

Records

Weight 

Sum=Control Total

Public 6 Male 1503 1,947,924.89 

Public 6 Female 1492 1,856,189.11 

Public 7 Male 1575 1,920,121.91 

Public 7 Female 1404 1,827,816.09 

Public 8 Male 1536 1,912,080.43 

Public 8 Female 1430 1,823,155.03 

Public 9 Male 1424 2,002,535.13 

Public 9 Female 1484 1,879,501.87 

Public 10 Male 1234 1,893,367.30 

Public 10 Female 1235 1,810,373.17 

Public 11 Male 1187 1,783,595.77 

Public 11 Female 1285 1,738,184.23 

Public 12 Male 1130 1,717,522.26 

Public 12 Female 1094 1,690,411.26 

Private 6 Combined 385 254,851.00 

Private 7 Combined 256 252,753.00 

Private 8 Combined 255 254,508.54 

Private 9 Combined 213 254,889.00 

Private 10 Combined 84 250,741.54 

Private 11 Combined 142 249,096.00 

Private 12 Combined 65 244,189.48 

*Sex is combined for private schools due to small cell sizes.
27

Table 4.3 Sum of Final Weights vs. Control Total - by Region, Grade and Race 

Census Region Grade Race/Hispanic Origin Number of Records Weight Sum = Control Total 

Northeast 6 Combined13 162 415,523.32 

Northeast 6 Non-Hispanic Black 39 86,745.53 

Northeast 6 Hispanic 75 133,417.15 

Northeast 7 Combined 151 418,712.68 

Northeast 7 Non-Hispanic Black 59 85,586.22 

Northeast 7 Hispanic 67 128,800.10 

Northeast 8 Combined 110 429,840.68 

Northeast 8 Non-Hispanic Black 49 84,693.89 

Northeast 8 Hispanic 53 124,588.44 

Northeast 9 Combined 279 429,764.98 

Northeast 9 Hispanic 172 221,204.02 

Northeast 10 Combined 236 421,659.41 

Northeast 10 Hispanic 135 208,068.59 

Northeast 11 Combined 212 420,914.72 

Northeast 11 Non-Hispanic Black 81 79,853.63 

Northeast 11 Hispanic 95 107,284.65 

Northeast 12 Combined 147 422,603.90 

Northeast 12 Non-Hispanic Black 79 77,933.10 

Northeast 12 Hispanic 85 99,937.00 

Midwest 6 Combined 777 617,855.97 

Midwest 6 Non-Hispanic Black 99 116,982.51 

Midwest 6 Hispanic 119 112,706.51 

Midwest 7 Combined 705 617,522.21 

Midwest 7 Non-Hispanic Black 97 111,331.97 

Midwest 7 Hispanic 115 109,222.82 

Midwest 8 Combined 832 626,369.66 

Midwest 8 Non-Hispanic Black 106 111,017.39 

Midwest 8 Hispanic 114 107,145.94 

Midwest 9 Combined 570 638,225.42 

Midwest 9 Non-Hispanic Black 77 118,931.71 

Midwest 9 Hispanic 89 109,560.87 

Midwest 10 Combined 509 622,736.06 

Midwest 10 Non-Hispanic Black 60 112,062.43 

Midwest 10 Hispanic 85 103,957.51 

Midwest 11 Combined 543 609,640.87 

Midwest 11 Non-Hispanic Black 86 104,628.70 

Midwest 11 Hispanic 73 96,923.43 

Midwest 12 Combined 434 610,408.17 

Midwest 12 Non-Hispanic Black 100 101,900.04 

13 For Northeast region grade 9 and grade 10, Non-Hispanic Black and Hispanic were collapsed due to cell size. The 

“Combined” category includes American Indian + Asian + White.
28

Census Region Grade Race/Hispanic Origin Number of Records Weight Sum = Control Total 

Midwest 12 Hispanic 76 89,502.78 

South 6 Combined 651 792,578.51 

South 6 Non-Hispanic Black 394 363,665.63 

South 6 Hispanic 365 423,494.86 

South 7 Combined 599 790,881.27 

South 7 Non-Hispanic Black 415 351,680.50 

South 7 Hispanic 323 409,341.22 

South 8 Combined 576 792,573.59 

South 8 Non-Hispanic Black 433 344,856.86 

South 8 Hispanic 312 399,009.55 

South 9 Combined 526 825,891.15 

South 9 Non-Hispanic Black 438 377,787.74 

South 9 Hispanic 325 426,616.12 

South 10 Combined 427 793,577.60 

South 10 Non-Hispanic Black 335 349,946.57 

South 10 Hispanic 213 390,866.83 

South 11 Combined 407 766,118.94 

South 11 Non-Hispanic Black 320 324,368.04 

South 11 Hispanic 202 355,490.02 

South 12 Combined 330 738,315.45 

South 12 Non-Hispanic Black 355 307,965.35 

South 12 Hispanic 170 315,158.20 

West 6 Combined 338 508,970.09 

West 6 Non-Hispanic Black 49 48,124.99 

West 6 Hispanic 312 438,899.92 

West 7 Combined 368 505,670.20 

West 7 Non-Hispanic Black 54 47,309.12 

West 7 Hispanic 282 424,632.68 

West 8 Combined 311 509,287.52 

West 8 Non-Hispanic Black 41 47,017.07 

West 8 Hispanic 284 413,343.41 

West 9 Combined 352 517,076.59 

West 9 Non-Hispanic Black 20 48,771.84 

West 9 Hispanic 273 423,095.57 

West 10 Combined 255 500,098.97 

West 10 Non-Hispanic Black 12 47,541.52 

West 10 Hispanic 286 403,966.51 

West 11 Combined 285 487,857.86 

West 11 Non-Hispanic Black 14 44,757.57 

West 11 Hispanic 296 373,037.57 

West 12 Combined 239 485,680.96 

West 12 Non-Hispanic Black 18 45,316.44 

West 12 Hispanic 256 357,401.60 
29

For poststratification purposes, a unique race and ethnicity was assigned to respondents with 

missing data on race and ethnicity, those with an “Other” classification, and those reporting 

multiple races. 

The raking and trimming method ensured that final weights sum to the population control totals in 

each cell while also limiting the coefficient of variation (CV) of the weights. The CV=90.45% 

implies that the design-effect (DEFF) component due to unequal weighing effects is 1.82.14

4.4 ESTIMATORS AND VARIANCE ESTIMATION

Weighted estimates of means, percentages and totals can be computed using the final weights 

included in the analysis file. If wi is the weight of case i (the inverse of the probability of selection 

adjusted for nonresponse and poststratification adjustments) and xi is a characteristic of case i (e.g., 

xi=1 if student i smokes, but is zero otherwise), then the mean of characteristic x is estimated as (Σ 

wixi)/(Σ wi). A weighted population total estimate is computed similarly as (Σ wixi). The weighted 

population estimates can be computed with the Statistical Analysis System (SAS) as well as with 

other statistical software.

These estimates are accompanied by measures of sampling variability, or sampling error, such as 

variances and standard errors, that account for the complex sampling design. These measures 

support the construction of confidence intervals and other statistical inference such as statistical 

testing (e.g., subgroup comparisons or trends over successive NYTS cycles). Sampling variances 

can be estimated using the method of general linearized estimators15 as implemented in SAS 

survey procedures. These software packages must be used because they permit estimation of 

sampling variances for multistage stratified sampling designs. They also account for unequal 

weighting and for sample clustering and stratification. 

The final weight files also include PSU and strata variables which support the analysis of clustered 

survey data and accurate variance estimation. As in previous cycles, a variable for “variance 

strata,” was added which may differ from the design strata, to ensure that all variance strata had at 

least two PSUs.16

Tables 4.4–4.7 present weighted estimates and estimated standard errors for key outcome measures 

using the 2021 NYTS data. Sample SAS code is provided in Exhibit 4.1. 

14 The design effect due to unequal weighting may be expressed in terms of the cv of the weight as DEFF= 1 + cv**2. 15 Skinner CJ, Holt D, and Smith TMF, Analysis of Complex Surveys, John Wiley & Sons, New York, 1989, 50.
30

Exhibit 4.1: Example SAS and SUDAAN Code for Generating Weighted Tobacco Product Use Estimates 

(ever use, current use)* and Standard Errors 

*Example SAS and SUDAAN code will generate estimates of ever use and current (past 30-day use) of e-cigarettes, 

cigarettes, cigars, smokeless tobacco products (chewing tobacco, snuff, or dip), and hookah tobacco. This is not an 

exhaustive list of all tobacco products assessed in the NYTS 

Example SAS and SUDAAN code will generate estimates of ever use and current (past 30-day use) of e-cigarettes, cigarettes, cigars, smokeless 

tobacco products (chewing tobacco, snuff, or dip), hookah tobacco, roll-your-own cigarettes, pipe tobacco, snus, dissolvable tobacco, bidis, heated 

tobacco products, and nicotine pouches. 

SAS:

Proc Surveymeans Data=nyts2021 mean;

Var eelcigt ecigt ecigar eslt ehookah erollcigts epipe esnus edissolv ebidis ehtp epouch

 celcigt ccigt ccigar cslt chookah crollcigts cpipe csnus cdissolv cbidis chtp cpouch; 

Class eelcigt ecigt ecigar eslt ehookah erollcigts epipe esnus edissolv ebidis ehtp epouch

 celcigt ccigt ccigar cslt chookah crollcigts cpipe csnus cdissolv cbidis chtp cpouch; 

Stratum v_stratum2; 

Cluster psu2;

Weight finwgt;

Domain SCHOOLTYPE SCHOOLTYPE*Sex SCHOOLTYPE*Race_S;

Title “NYTS 2021, Tobacco Product Use Estimates by School Type, by School Type and Sex Cross-Classified, 

and by School Type and Race and ethnicity Cross-Classified”;

run; 

SUDAAN:

Proc Descript Data=nyts2020 Filetype= SAS Design=WR;

Var eelcigt ecigt ecigar eslt ehookah erollcigts epipe esnus edissolv ebidis ehtp epouch

 celcigt ccigt ccigar cslt chookah crollcigts cpipe csnus cdissolv cbidis chtp cpouch; 

Catlevel 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; 

Nest v_stratum2 PSU2 / Missunit;

Weight finwgt;

Subgroup SCHOOLTYPE Sex Race_S;

Levels 2 2 3;

Tables SCHOOLTYPE SCHOOLTYPE*Sex SCHOOLTYPE*Race_S;

Title “NYTS 2020, Tobacco Product Use 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;
31

Table 4.4 Current (past 30-day) Use Estimates for Selected Tobacco Products for High 

School Students17 

Product

Overall 

%(SE)

N=10,515 

Female

%(SE)

N=5,172 

Male 

%(SE)

N=5,312 

White

%(SE)

N=5,334 

Black

%(SE)

N=1,857 

Hispanic

%(SE)

N=2,684 

Bidis 0.41% 

(0.08%) 

N=51

0.52% 

(0.14%) 

N=30

0.32% 

(0.09%) 

N=21

0.37% 

(0.10%) 

N=22

0.81% 

(0.31%) 

N=14

0.35% 

(0.14%) 

N=13

Cigars, little cigars, 

or cigarillos

2.11% 

(0.19%) 

N=224 

1.53% 

(0.23%) 

N=96

2.62% 

(0.28%) 

N=127 

2.06% 

(0.23%) 

N=103 

4.45% 

(0.65%) 

N=79

1.17% 

(0.20%) 

N=31

Cigarettes 1.89% 

(0.20%) 

N=198 

1.76% 

(0.25%) 

N=83

1.99% 

(0.30%) 

N=114 

2.25% 

(0.26%) 

N=122 

0.97% 

(0.35%) 

N=18

1.63% 

(0.26%) 

N=46

Dissolvable 

tobacco products 

0.19% 

(0.06%) 

N=22

0.22% 

(0.09%) 

N=12

0.16% 

(0.07%) 

N=10

0.18% 

(0.07%) 

N=8

0.31% 

(0.21%) 

N=5

0.15% 

(0.08%) 

N=7

Electronic 

cigarettes

11.27% 

(0.80%) 

N=1116 

11.86% 

(0.99%) 

N=576 

10.72% 

(0.85%) 

N=537 

14.45% 

(1.08%) 

N=760 

5.87% 

(0.99%) 

N=87

7.60% 

(0.90%) 

N=214 

Hookah or 

waterpipe

1.21% 

(0.18%) 

N=128 

1.29% 

(0.30%) 

N=69

1.15% 

(0.16%) 

N=59

0.76% 

(0.13%) 

N=41

3.18% 

(0.78%) 

N=49

1.33% 

(0.37%) 

N=34

Pipe tobacco 0.45% 

(0.10%) 

N=45

0.41% 

(0.12%) 

N=19

0.48% 

(0.15%) 

N=25

0.50% 

(0.14%) 

N=24

0.58% 

(0.31%) 

N=7

0.40% 

(0.12%) 

N=14

Heated tobacco

products 

0.82% 

(0.12%) 

N=81

0.74% 

(0.18%) 

N=39

0.93% 

(0.19%) 

N=42

0.92% 

(0.18%) 

N=50

0.84% 

(0.34%) 

N=12

0.73% 

(0.24%) 

N=14

Roll-your-own 

cigarettes

0.58% 

(0.09%) 

N=68

0.67% 

(0.14%) 

N=41

0.51% 

(0.13%) 

N=27

0.59% 

(0.12%) 

N=35

0.80% 

(0.32%) 

N=14

0.57% 

(0.17%) 

N=18

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

0.96% 

(0.17%) 

N=106 

0.26% 

(0.10%) 

N=18

1.59% 

(0.27%) 

N=88

1.28% 

(0.23%) 

N=75

0.25% 

(0.14%) 

N=5

0.49% 

(0.19%) 

N=16

Snus 0.46% 

(0.10%) 

N=49

0.26% 

(0.09%) 

N=13

0.63% 

(0.15%) 

N=36

0.57% 

(0.15%) 

N=30

0.01% 

(0.01%) 

N=1

0.26% 

(0.10%) 

N=12

Nicotine pouches 1.10% 

(0.19%) 

N=101 

0.55% 

(0.15%) 

N=25

1.60% 

(0.31%) 

N=76

1.41% 

(0.31%) 

N=68

0.61% 

(0.26%) 

N=8

0.66% 

(0.21%) 

N=18

Note: In the dataset, variables associated with current use of each tobacco product are as follows: electronic cigarettes (celcigt); cigars, little 

cigars, or cigarillos (ccigar); cigarettes (ccigt); smokeless tobacco (cslt); hookah or waterpipe (chookah); roll-your-own cigarettes (crollcigts); 

snus (csnus); pipe tobacco (cpipe); dissolvable tobacco products (cdissolv); bidis (cbidis); heated tobacco products (chtp); and nicotine pouches 

(cpouch). 

17 The estimates in tables 4.4–4.7 use the variable SCHOOLTYPE, which is coded as 1 (Middle School) if QN3 ranges from 1 to 

3, and 2 (High School) if QN3 ranges from 4 – 7. Students who are missing QN3 are excluded from these tables.
32

Table 4.5 Current Use Estimates for Selected Tobacco Products for Middle School 

Students18

Product

Overall 

%(SE)

N=9,763 

Female

%(SE)

N=4,699 

Male 

%(SE)

N=5,015 

White

%(SE)

N=4,739 

Black

%(SE)

N=1,575 

Hispanic

%(SE)

N=2,360 

Bidis 0.17% 

(0.04%) 

N=22

0.19% 

(0.07%) 

N=10

0.16% 

(0.06%) 

N=12

0.01% 

(0.01%) 

N=2

0.49% 

(0.16%) 

N=9

0.33% 

(0.13%) 

N=10

Cigars, little cigars, or 

cigarillos

0.59% 

(0.10%) 

N=76

0.54% 

(0.13%) 

N=32

0.65% 

(0.12%) 

N=44

0.46% 

(0.11%) 

N=25

1.37% 

(0.35%) 

N=24

0.62% 

(0.18%) 

N=21

Cigarettes 1.04% 

(0.16%) 

N=117 

1.17% 

(0.25%) 

N=63

0.89% 

(0.19%) 

N=53

1.05% 

(0.20%) 

N=59

1.09% 

(0.31%) 

N=21

1.18% 

(0.29%) 

N=26

Dissolvable tobacco 

products 

0.13% 

(0.05%) 

N=16

0.13% 

(0.06%) 

N=8

0.14% 

(0.07%) 

N=8

0.05% 

(0.03%) 

N=5

0.28% 

(0.15%) 

N=5

0.23% 

(0.14%) 

N=5

Electronic cigarettes 2.77% 

(0.30%) 

N=310 

3.18% 

(0.45%) 

N=166 

2.35% 

(0.28%) 

N=141 

2.61% 

(0.38%) 

N=154 

2.33% 

(0.62%) 

N=39

3.93% 

(0.61%) 

N=93

Heated tobacco

products 

0.42% 

(0.09%) 

N=56

0.39% 

(0.12%) 

N=22

0.43% 

(0.12%) 

N=31

0.25% 

(0.08%) 

N=19

0.59% 

(0.21%) 

N=15

0.69% 

(0.22%) 

N=18

Hookah or waterpipe 0.39% 

(0.10%) 

N=41

0.53% 

(0.18%) 

N=21

0.25% 

(0.10%) 

N=19

0.15% 

(0.05%) 

N=12

0.94% 

(0.30%) 

N=12

0.57% 

(0.18%) 

N=13

Pipe tobacco 0.18% 

(0.05%) 

N=19

0.21% 

(0.09%) 

N=9

0.16% 

(0.07%) 

N=10

0.18% 

(0.09%) 

N=8

0.10% 

(0.07%) 

N=3

0.27% 

(0.13%) 

N=6

Nicotine pouches 0.30% 

(0.08%) 

N=35

0.44% 

(0.15%) 

N=20

0.17 

(0.05) 

N=15

0.14% 

(0.05%) 

N=10

0.50% 

(0.23%) 

N=7

0.56% 

(0.21%) 

N=16

Roll-your-own 

cigarettes

0.36% 

(0.07%) 

N=50

0.36% 

(0.09%) 

N=26

0.33% 

(0.10%) 

N=23

0.19% 

(0.07%) 

N=16

0.67% 

(0.23%) 

N=13

0.68% 

(0.21%) 

N=20

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

0.47% 

(0.09%) 

N=62

0.27% 

(0.10%) 

N=13

0.67% 

(0.14%) 

N=49

0.45% 

(0.13%) 

N=28

0.25% 

(0.13%) 

N=7

0.56% 

(0.18%) 

N=17

Snus 0.16% 

(0.04%) 

N=26

0.11% 

(0.06%) 

N=7

0.20% 

(0.06%) 

N=19

0.13% 

(0.05%) 

N=13

0.10% 

(0.07%) 

N=3

0.27% 

(0.12%) 

N=8

Note: In the dataset, variables associated with current use of each tobacco product are as follows: electronic cigarettes (celcigt); cigars, little 

cigars, or cigarillos (ccigar); cigarettes (ccigt); smokeless tobacco (cslt); hookah or waterpipe (chookah); roll-your-own cigarettes (crollcigts); 

snus (csnus); pipe tobacco (cpipe); dissolvable tobacco products (cdissolv); bidis (cbidis); heated tobacco products (chtp); and nicotine pouches 

(cpouch).

18 The estimates in tables 4.4–4.7 use the variable SCHOOLTYPE, which is coded as 1 (Middle School) if QN3 ranges from 1 to 3, and 2 (High 

School) if QN3 ranges from 4 – 7. Students who are missing QN3 are excluded from these tables.
33

Table 4.6 Ever Use Estimates for Selected Tobacco Products for High School Students19

Product

Overall 

%(SE)

N=10,515 

Female

%(SE)

N=5,172 

Male 

%(SE)

N=5,312 

White

%(SE)

N=5,334 

Black

%(SE)

N=1,857 

Hispanic

%(SE)

N=2,684 

Bidis

0.96% 

(0.13%) 

N=108 

1.06% 

(0.19%) 

N=58

0.88% 

(0.20%) 

N=50

0.78% 

(0.15%) 

N=44

1.45% 

(0.46%) 

N=22

1.06% 

(0.24%) 

N=35

Cigars, little cigars, or 

cigarillos

7.63% 

(0.50%) 

N=757 

4.93% 

(0.38%) 

N=273 

10.03% 

(0.75%) 

N=482 

8.72% 

(0.78%) 

N=437 

8.67% 

(0.90%) 

N=163 

5.37% 

(0.60%) 

N=126 

Cigarettes

11.16% 

(0.63%) 

N=1095 

10.81% 

(0.77%) 

N=517 

11.51% 

(0.82%) 

N=576 

13.01% 

(0.89%) 

N=655 

6.36% 

(0.94%) 

N=126 

9.53% 

(1.01%) 

N=255 

Dissolvable tobacco 

products 

0.52% 

(0.10%) 

N=53

0.54% 

(0.12%) 

N=30

0.51% 

(0.14%) 

N=23

0.62% 

(0.14%) 

N=29

0.31% 

(0.21%) 

N=5

0.44% 

(0.15%) 

N=15

Electronic cigarettes

28.86% 

(1.23%) 

N=2887 

30.16% 

(1.43%) 

N=1502 

27.70% 

(1.39%) 

N=1375 

33.85% 

(1.59%) 

N=1747 

16.93% 

(1.31%) 

N=306 

25.04% 

(1.39%) 

N=694 

Heated tobacco

products 

2.27% 

(0.16%) 

N=191 

2.45% 

(0.29%) 

N=97

2.11% 

(0.20%) 

N=93

2.30% 

(0.22%) 

N=99

2.13% 

(0.59%) 

N=28

2.36% 

(0.43%) 

N=52

Hookah or waterpipe 

3.85% 

(0.34%) 

N=419 

4.22% 

(0.47%) 

N=237 

3.45% 

(0.36%) 

N=180 

2.93% 

(0.36%) 

N=149 

6.81% 

(0.84%) 

N=126 

4.00% 

(0.51%) 

N=116 

Pipe tobacco

1.45% 

(0.20%) 

N=136 

1.14% 

(0.25%) 

N=54

1.73% 

(0.28%) 

N=81

1.79% 

(0.26%) 

N=88

0.80% 

(0.34%) 

N=12

1.14% 

(0.25%) 

N=31

Nicotine pouches

2.97% 

(0.30%) 

N=269 

1.74% 

(0.28%) 

N=75

4.09% 

(0.46%) 

N=194 

4.13% 

(0.44%) 

N=205 

1.11% 

(0.36%) 

N=13

1.52% 

(0.28%) 

N=40

Roll-your-own 

cigarettes

2.49% 

(0.23%) 

N=258 

2.47% 

(0.27%) 

N=129 

2.51% 

(0.31%) 

N=128 

2.56% 

(0.33%) 

N=145 

2.05% 

(0.45%) 

N=36

2.47% 

(0.34%) 

N=59

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

4.08% 

(0.48%) 

N=419 

1.70% 

(0.35%) 

N=89

6.23% 

(0.68%) 

N=330 

5.73% 

(0.64%) 

N=314 

1.41% 

(0.43%) 

N=25

1.68% 

(0.34%) 

N=52

Snus 

1.92% 

(0.26%) 

N=183 

0.81% 

(0.18%) 

N=38

2.93% 

(0.41%) 

N=145 

2.88% 

(0.36%) 

N=145 

0.01% 

(0.01%) 

N=1

0.74% 

(0.21%) 

N=25

Note: In the dataset, variables associated with ever use of each tobacco product are as follows: electronic cigarettes (eelcigt); cigars, little cigars, 

or cigarillos (ecigar); cigarettes (ecigt); smokeless tobacco (eslt); hookah or waterpipe (ehookah); roll-your-own cigarettes (erollcigts); snus 

(esnus); pipe tobacco (epipe); dissolvable tobacco products (edissolv); bidis (ebidis); heated tobacco products (ehtp); and nicotine pouches 

(epouch).

19 The estimates in tables 4.4–4.7 use the variable SCHOOLTYPE, which is coded as 1 (Middle School) if QN3 ranges from 1 to 3, and 2 (High 

School) if QN3 ranges from 4 – 7. Students who are missing QN3 are excluded from these tables.
34

Table 4.7 Ever Use Estimates for Selected Tobacco Products for Middle School Students20

Product

Overall 

%(SE)

N=9,763 

Female

%(SE)

N=4,699 

Male 

%(SE)

N=5,015 

White

%(SE)

N=4,739 

Black

%(SE)

N=1,575 

Hispanic

%(SE)

N=2,360 

Bidis

0.40% 

(0.09%) 

N=45

0.50% 

(0.14%) 

N=23

0.30% 

(0.08%) 

N=22

0.19% 

(0.10%) 

N=10

0.71% 

(0.20%) 

N=13

0.72% 

(0.23%) 

N=20

Cigars, little cigars, or 

cigarillos

2.09% 

(0.23%) 

N=227 

2.03% 

(0.31%) 

N=99

2.17% 

(0.26%) 

N=128 

1.66% 

(0.27%) 

N=88

4.30% 

(0.66%) 

N=72

2.47% 

(0.54%) 

N=53

Cigarettes

4.08% 

(0.46%) 

N=451 

4.24% 

(0.60%) 

N=221 

3.92% 

(0.47%) 

N=229 

3.84% 

(0.53%) 

N=209 

5.67% 

(1.13%) 

N=94

4.06% 

(0.75%) 

N=102 

Dissolvable tobacco 

products 

0.40% 

(0.10%) 

N=40

0.43% 

(0.15%) 

N=17

0.37% 

(0.10%) 

N=23

0.39% 

(0.14%) 

N=19

0.43% 

(0.17%) 

N=9

0.55% 

(0.24%) 

N=11

Electronic cigarettes

7.28% 

(0.60%) 

N=765 

7.50% 

(0.73%) 

N=384 

7.02% 

(0.68%) 

N=373 

7.51% 

(0.68%) 

N=385 

6.97% 

(1.21%) 

N=110 

8.36% 

(1.08%) 

N=205 

Heated tobacco

products 

1.12% 

(0.15%) 

N=111 

0.98% 

(0.19%) 

N=51

1.25% 

(0.21%) 

N=57

0.83% 

(0.19%) 

N=41

1.02% 

(0.38%) 

N=17

1.75% 

(0.48%) 

N=39

Hookah or waterpipe 

1.31% 

(0.23%) 

N=138 

1.53% 

(0.33%) 

N=79

1.07% 

(0.19%) 

N=57

0.63% 

(0.15%) 

N=49

2.67% 

(0.50%) 

N=36

2.14% 

(0.60%) 

N=37

Pipe tobacco

0.49% 

(0.09%) 

N=60

0.45% 

(0.14%) 

N=24

0.53% 

(0.13%) 

N=36

0.37% 

(0.10%) 

N=24

0.43% 

(0.21%) 

N=10

0.69% 

(0.19%) 

N=18

Nicotine pouches

0.57% 

(0.11%) 

N=69

0.60% 

(0.18%) 

N=30

0.54% 

(0.12%) 

N=39

0.41% 

(0.10%) 

N=29

0.18% 

(0.10) 

N=5

0.99% 

(0.30%) 

N=26

Roll-your-own 

cigarettes

1.12% 

(0.17%) 

N=131 

1.18% 

(0.23%) 

N=67

1.04% 

(0.21%) 

N=63

0.84% 

(0.18%) 

N=55

1.10% 

(0.35%) 

N=23

1.87% 

(0.44%) 

N=44

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

1.87% 

(0.29%) 

N=204 

1.60% 

(0.31%) 

N=73

2.15% 

(0.37%) 

N=131 

1.82% 

(0.27%) 

N=107 

1.54% 

(0.51%) 

N=27

2.03% 

(0.46%) 

N=45

Snus 

0.35% 

(0.07%) 

N=52

0.29% 

(0.09%) 

N=16

0.40% 

(0.10%) 

N=36

0.30% 

(0.08%) 

N=26

0.10% 

(0.07%) 

N=3

0.44% 

(0.16%) 

N=15

Note: In the dataset, variables associated with ever use of each tobacco product are as follows: electronic cigarettes (eelcigt); cigars, little cigars, 

or cigarillos (ecigar); cigarettes (ecigt); smokeless tobacco (eslt); hookah or waterpipe (ehookah); roll-your-own cigarettes (erollcigts); snus 

(esnus); pipe tobacco (epipe); dissolvable tobacco products (edissolv); bidis (ebidis); heated tobacco products (ehtp); and nicotine pouches 

(epouch).

20 The estimates in tables 4.4–4.7 use the variable SCHOOLTYPE, which is coded as 1 (Middle School) if QN3 ranges from 1 to 3, and 2 (High 

School) if QN3 ranges from 4 – 7. Students who are missing QN3 are excluded from these tables.
35

APPENDIX A. QUESTIONNAIRE

Questionnaire only included in PDF version of this document. 
APPENDIX B. COMMON CORE OF DATA RACE AND 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.
APPENDIX C. STUDENT WEIGHT DETAIL

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

Section 2.3. The student sampling weight was computed based on a ratio of enrolling to responding 

students described in Section 4.1.1. 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—as 

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, subscripts denoting the sampling stages and weight class are omitted. 

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

Section 4.1.1. 

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 a student weighting class. The initial selection 

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

A simplified notation, letting K represent the number of sampled class sections, would look like: 

K

C W =

Nonresponse Adjustment 

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

sampled students. The adjustment was 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 

WR

N

W

N F

∗ ′ =

′ =

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

FWW NR ′ ∗=

The fully adjusted student weight is computed as: 

FWW PS ′′ = ′∗

The simplified equation is as follows:

R

N

WR

N W

FWW PS

=

∗ ′ = ′∗

′′ = ′∗