METHODOLOGY REPORT 

OF THE 2020 NATIONAL 

YOUTH TOBACCO 

SURVEY

Recommended Citation

Office on Smoking and Health. 2020 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, 2020.

June 2020

0
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

June 2020

0
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 2020 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 ................................ 13

3.1 SURVEY INSTRUMENT .....................................................................................................13

3.2 EXTERNAL REVIEW AND APPROVALS...............................................................................14

3.3 DATA COLLECTION STAFFING .........................................................................................14

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

3.5 SURVEY ADMINISTRATION ..............................................................................................15

3.6 WEB-BASED DATA COLLECTION MANAGEMENT APPLICATION (DCMA) ...........................16

3.7 DATA SYNCING AND RECORDING.....................................................................................16

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

3.9 DATA MANAGEMENT ......................................................................................................17

CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA ........................................... 19

4.1 SAMPLING WEIGHTS .......................................................................................................19

4.2 NONRESPONSE ADJUSTMENTS .........................................................................................22

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

4.4 ESTIMATORS AND VARIANCE ESTIMATION .......................................................................30

APPENDICES

A. IMPLICATIONS OF COVID-19 CLOSURES FOR WEIGHTING THE 2020 NYTS DATA

B. QUESTIONNAIRE

C. STUDENT WEIGHT DETAIL

D. COMMON CORE OF DATA RACE/ETHNICITY DEFINITIONS
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-related indicators included in the NYTS are: tobacco use (ecigarettes, cigarettes, cigars, smokeless tobacco, hookahs, roll-your-own cigarettes, pipes, snus, 

dissolvable tobacco, bidis, and heated tobacco products); exposure to secondhand smoke and ecigarette aerosol; smoking cessation; minors’ ability to purchase or obtain tobacco products; 

knowledge and attitudes about tobacco; and familiarity with pro-tobacco and anti-tobacco media 

messages. Estimates based on 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. Similarly, future cycles of NYTS will provide measures and data for 

Healthy People 2030 objectives (USDHHS, 2020): TU-3, TU4, TU5, TU-6, and TU7.

First conducted during fall 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) 

collaborated to administer the NYTS.

1.2 OVERVIEW OF THE 2020 NYTS METHODOLOGY

The 2020 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 2020 cycle again was 

conducted electronically. Participants were provided with a tablet to complete the survey; data 

were collected offline using a programmed survey application; a single class period of 

approximately 35-45 minutes was allotted to complete the survey. Survey administrators later 

established secure WiFi connections to sync all locally stored tablet data to a central repository via 

encrypted transmissions. Absent students and whole classes unavailable on the day of survey 

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.

1
administration could participate in make-up surveys using a web-based version of the 

questionnaire programmed to mimic the tablet-based application. 

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 16, 2020 and was expected to extend until May 15, 

2020. However, data collection was ended early on March 16, 2020 due to widespread school 

closures as a result of the COVID-19 pandemic. The final sample consisted of 361 schools, of 

which 180 participated prior to school closures, yielding a school participation rate of 49.9%. A 

total of 14,531 student questionnaires were completed out of a sample of 16,634 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 43.6%. 

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. Appendix A describes the 

evaluation undertaken to determine the feasibility of weighting the 2020 NYTS given the overall 

participation rate was lower than historic levels. The evaluation showed that the sample of 14,531 

participating students from 180 schools was representative in terms of small potential bias and 

variances. The sample then was weighted with the procedures described in this report.

The remainder of this report provides detailed information on the methodology used in the 2020 

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

data (Chapter 4).

2
CHAPTER 2—NYTS SAMPLING METHODS

2.1 SAMPLE DESIGN

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 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 (nonHispanic white, non-Hispanic black, and Hispanic). Additional estimates also were supported for 

subgroups defined by grade, by sex, and by race/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. PSUs were stratified by racial/ethnic status 

and urban versus non-urban. 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

320 SSUs (school) 

selections: 240 large 

schools (2 per PSU), 

50 medium schools and 

30 small schools

3 Classes/students

2 classes per grade in

half of large schools ;1 

class per grade 

otherwise

3
As described in Section 1.2, the first stage of sampling selected Primary Sampling Units (PSUs) 

within each stratum for a total of 100 sample PSUs. At the second sampling stage, 240 large

schools, or SSUs, were selected from the sample PSUs. Two large schools were selected persample

PSU, one per level (middle or high). An additional large school for each level was selected in a 

subsample of 20 PSUs. An additional 50 medium SSUs and 30 small SSUs were selected from

subsample PSUs, for a total of 320 sample SSUs(320 = 240 + 50 + 30). 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 were 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=850 students per minority group by 

school level (1,700 total per group) correspond to prevalence estimates within +/- 5% for 

confidence intervals at 95% confidence for all key racial/ethnic subgroups when broken down by 

school level.

Sample sizes for Hispanics, a subgroup that has steadily increased in representation, meet even the 

more conservative targets. Sample sizes for blacks, which bordered on but did not reach the 

original targets in 2020 when broken down by school level, still lead to precise subgroup estimates. 

These conservative targets reflected expected design effects that were larger than those observed 

for the NYTS. 

These are evidenced in Tables 4-4 to 4-7 in Chapter 4, which show that for all key racial/ethnic 

subgroups, prevalence estimates were within +/- 5% for confidence intervals at 95% confidence 

(i.e., standard errors were less than 2.5%), as in the original design requirements. Standard errors 

were less than 2.5% for all estimates for Hispanic and black students at the middle school and high 

school level with one marginal exception.

3

2.2 SAMPLING FRAME

As in previous cycles, the 2020 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/ethnicity distributions within the school, district and county information, 

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

3 The marginal exception was for the ever use prevalence of electronic cigarettes for Hispanic high-school students 

where the standard error reached 2.54%. 

4
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.

4

Including schools sourced from the two NCES files resulted in a coverage 

increase among all public and non-public high schools of 6.6%. 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. 

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 3.8% 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 20.6% of schools (13.3% public and 42.8% private) which 

were eligible after the other exclusions. This exclusion of schools with fewer than 40 students led 

to the exclusion of only 1.06% of students of those in eligible schools. 

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 attending 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, secondary sampling units (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 1,258 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, 240 large schools, or SSUs, were

selected from the sample PSUs. Additionally, as described in Section 2.1, we selected 30 

small schools and 50 medium schools, resulting in a total of 320 sample SSUs(320 = 240

+ 50 + 30).

• 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 

4 Redesigning National School Surveys: Coverage and Stratification Improvement using Multiple Datasets. William 

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

5
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 

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 minority students were achieved per school level.

All students in a selected class then were 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 actual sample sizes attained in the survey. The NYTS sample size calculations were 

based on the following assumptions:

• The main structure of the sampling design was 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 40 large, 50 medium and 30 small schools from subsample PSUs.

Across 15 previous cycles of the NYTS that had concluded at the time of the 2020 NYTS design, 

school participation had averaged 82.9%. Student participation had averaged 89.7%. The combined 

response rate (student x school) averaged 74.3%. Historical participation rates at both school and 

student levels guided the sampling design and sample sizes. In calculating the sample sizes for the

2020 NYTS, we made our approach more robust by assuming a conservative combined rate (student

x school) of 63.8%, substantially lower than the historical overall response rate. These numbers are 

closer to the more recent experience at both levels. Table 2.2 presents a detailed derivation of the

sample sizes planned for the 2020 NYTS based on these assumptions.

6
Table 2.2 Planned Sample Sizes for the 2020 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 

63.75% 

Response Rate

100 Large HS 120 Double 

classes: 60

8 25 12,000 7,650

Single 

classes: 60

4 25 6,000 3,825

Large MS 120 Double 

classes: 60

6 25 9,000 5,738

Single 

classes:60

3 25 4,500 2,869

Large Total 240 31,500 20,081

25

(subsample)

Medium HS 25 30 4 25 2,500 1,594

Medium MS 25 30 3 25 1,875 1,195

Medium Total 50 4,375 2,789

15

(subsample)

Small HS 15 20 4 25 1,500 956

Small MS 15 20 3 25 1,125 717

Small Total 30 2,625 1,673

Overall Total 320 38,500 24,544

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

and one additional large SSU per level selected from 20 subsampled PSUs for a total of 240 large 

SSUs. The estimated sample yield from these large schools was 31,500 students before school and 

student non-response, leading to an expected total 20,081 participating students in large schools 

after accounting for non-response.

Additionally, 50 medium SSUs from a subsample of 25 PSUs were selected, yielding an expected 

sample size of 4,375 students. Finally, to provide adequate coverage of students in small schools 

(those with an enrollment of less than 28 students in any grade) 30 small SSUs from a subsample 

of 15 PSUs were selected. The expected yield was 2,625 students from small schools. In total, the 

number of participating students in large, medium, and small schools was 24,544.

For the 2020 NYTS, within each school, one class was selected from each grade to participate in 

the survey except in a portion of large schools where we implemented double class selection for 

half of large schools (randomly selected) to ensure sufficient student yields. Note that the set of 

the latter schools defined for double class sampling is necessarily a subset of the large schools that 

can support such double class sampling. 

2.5 FORMING SAMPLING UNITS

2.5.1 Forming primary sampling units (PSUs) 

In defining PSUs, several issues were considered: 

7
• 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 so that field staff could go from school to 

school easily. 

• PSUs should be consistent with school and school district definitions (i.e., should not cross 

or split districts).

• 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 recombining 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. For 

example, a school containing 6th grade but not 7th and 8th grades can be linked with another school 

with the latter grades at the middle school level. At the high school level, a school that contains 

only 9th but not the other high school grades can be linked with another containing the latter grades.

2.6 STRATIFICATION

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

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 nonHispanic 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.”

• Hispanic urban and Hispanic non-urban 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 non-urban PSUs were also classified 

into four groupings depending upon the percentages of black students in the PSU.

8
The density grouping bounds were computed using an optimization algorithm5

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 groupings changed 

from one survey cycle to the next. Table 2.3 presents the stratum boundaries used in the 2020

NYTS.

Table 2.3 Stratum Boundaries: Minority Percentage Cutoffs

Minority 

Concentration

Density 

Group

Bounds

Urban Non-urban

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

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 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.

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

9
Table 2.4 First-Stage Strata and Frame PSU Distribution

Predominant 

Minority

Urban/Nonurban

Density 

Group 

Number

Stratum 

Code

Student 

Population

Number of 

Sample 

PSUs 

(Revised)

Non-Hispanic 

Black

Urban

1 BU1 2,207,124 8

2 BU2 1,545,060 5

3 BU3 455,124 2

4 BU4 528,242 2

Non-urban

1 BR1 2,638,754 9

2 BR2 1,390,599 5

3 BR3 984,474 4

4 BR4 496,585 2

Hispanic

Urban

1 HU1 3,393,790 11

2 HU2 2,569,896 9

3 HU3 2,507,231 8

4 HU4 1,931,026 7

Non-urban

1 HR1 5,092,395 16

2 HR2 1,462,461 5

3 HR3 1,001,750 4

4 HR4 676,887 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 (20 PSUs), small school (15 PSUs) and 

medium school (25 PSUs) sampling of two schools per level in each subsample PSU.

2.8 SAMPLE SIZES ATTAINED IN THE SURVEY

The 2020 NYTS attained the target sample sizes in the key analytic subgroups of interest. Tables 

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

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

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

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

led to 4,355 Hispanic students and 1,602 black students using the single-race variable.

10
Table 2.5a Subgroup Sample Sizes: Number of Participating Students

What is your sex?

Q2 Frequency Percent

Cumulative

Frequency

Cumulative

Percent

Displayed, not answered5 39 0.27 39 0.27

Male 7153 49.23 7192 49.49

Female 7339 50.51 14531 100.00

Table 2.5b Subgroup Sample Sizes: Number of Participating Students

What grade are you in?

Q3 Frequency Percent

Cumulative

Frequency

Cumulative

Percent

Displayed, not answered5 16 0.11 16 0.11

6

th 2352 16.19 2368 16.30

7

th 2354 16.20 4722 32.50

8

th 2336 16.08 7058 48.57

9

th 1966 13.53 9024 62.10

10th 1882 12.95 10906 75.05

11th 1799 12.38 12705 87.43

12th 1806 12.43 14511 99.86

Ungraded or other grade 20 0.14 14531 100.00

Table 2.5c Subgroup Sample Sizes: Number of Participating Students

RECODE: Race/Eth - mult grp

RACE_M Frequency Percent

Cumulative

Frequency

Cumulative

Percent

<Missing> 365 2.51 365 2.51

NH-White 6521 44.88 6886 47.39

NH-Black 1529 10.52 8415 57.91

Hispanic 4355 29.97 12770 87.88

NH-Asian 821 5.65 13591 93.53

NH-AI/AN 204 1.40 13795 94.93

NH_NHOPI 54 0.37 13849 95.31

Multiple Races 682 4.69 14531 100.00

Note: This variable is named race_m (respondents could select more than one race) in the public use data set. The 

race/ethnicity categories are Hispanic, non-Hispanic (NH) white, non-Hispanic black, non-Hispanic Asian, nonHispanic American Indian or Alaskan Native (AIAN), and non-Hispanic Native Hawaiian or Pacific Islander 

(NHOPI). Please see the detailed definitions of race/ethnicity at Appendix D.

11
Table 2.5d Subgroup Sample Sizes: Number of Participating Students

RECODE: Race/Eth - no mult grp

RACE_S Frequency Percent

Cumulative

Frequency

Cumulative

Percent

<Missing> 365 2.51 365 2.51

NH-White 7071 48.66 7436 51.17

NH-Black 1602 11.02 9038 62.20

Hispanic 4355 29.97 13393 92.17

NH-Asian 879 6.05 14272 98.22

NH-AI/AN 205 1.41 14477 99.63

NH-NHOPI 54 0.37 14531 100.00

Note: This variable is named race_s (all respondents are ultimately categorized into a single race/ethnic group, 

including those who selected more than one race) in the public use data set. The race/ethnicity categories are 

Hispanic, non-Hispanic (NH) white, non-Hispanic black, non-Hispanic Asian, non-Hispanic American Indian or 

Alaskan Native (AIAN), and non-Hispanic Native Hawaiian or Pacific Islander (NHOPI). Please see the detailed 

definitions of race/ethnicity at Appendix D.

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 2019-2020 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

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

• Name and title of 2019-2020 school principal

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

• Grade levels served during 2019-2020 school year

• Approximate school enrollment

• At least a cumulative total 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 

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

Language Learners or Special Education students

12
13

CHAPTER 3—NYTS DATA COLLECTION AND PROCESSING

3.1 SURVEY INSTRUMENT

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

control outcome indicators. The 2020 survey instrument included 117 questions. The 2020 NYTS 

represented the second cycle the study was conducted using electronic data collection methods 

rather than the paper-and-pencil (PAPI) method used previously. The questionnaire application 

was programmed for offline data collection on an Android-based operating system. The survey 

application was written in HTML5 and JavaScript, and the final application was loaded onto a 

Samsung Tab A tablet. Each student was provided with a tablet for the purposes of taking the 

survey, and it was returned to the survey administrator at the conclusion of the survey session. 

Students logged into the application using a randomly-generated, randomly-distributed, five-digit 

access code that was unique to each user. 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. 

After survey administrators left the school, they established a WiFi connection and synced all 

locally stored data records to a central repository. Data were encrypted in transmission. 

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 1-2 

questions were 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 absent on the day of initial survey administration were asked to complete a 

make-up session upon returning to school. These students participated using a Web-based version 

of the questionnaire, which was programmed to mimic the tablet-based application in its look, feel, 

navigation functions, skip logic, and all other programming features. In addition to make-up 

sessions with absent students, approximately 9 classes used the Web-based survey for their initial 

administration due to scheduling preferences by the school to administer all classes during a 

common class period or conflicts that prevented a class from participating on the same day as other

selected classes in the same school. 

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 for 

tablet-based administration ranged from 19 seconds to 80 mins 15 seconds, with an average of 14 

mins 4 seconds. For Web-based administration, LOI ranged from 33 seconds to 79 mins 34 

seconds, with an average of 15 mins 41 seconds. After exclusion of outliers6, the average survey 

completion time for tablet and Web was about 14 mins 7 seconds.

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

measured a comprehensive set of tobacco-related topics (Appendix B). Specific areas covered by 

the survey included: prevalence of tobacco product use; knowledge of and attitudes toward tobacco 

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; 

exposure to tobacco product warnings; and tobacco use prevention school curricula. At the 

6 The average completion time was calculated after dropping outliers with survey duration lengths greater 

than 90 minutes (n=16). 
14

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 to this description and the images as 

needed as they answered related questions. The 2020 NYTS was the first cycle to include a sexual 

orientation question.

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 stakeholders, 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.

3.2 EXTERNAL REVIEW AND APPROVALS

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

elements, and sampling design of the 2020 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 2020 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 DATA COLLECTION STAFFING

To minimize the amount of data collector travel between home and school assignments, hiring was 

done geographically across the country, with greater numbers of data collectors in those areas with 

higher concentrations of sampled schools. Data collectors were recruited from a pool of previously 

trained data collectors and supplemented with candidates in desired geographic locations. Data 

collector training was conducted on January 13-17, 2020. 

Key components of the training included the following:

▪ Pre-contact activities with the schools

▪ Entry and exit meetings with school officials

▪ Data collection protocols 

▪ Recruitment visit protocols

▪ Follow-up activities

▪ Communication with headquarters staff
3.4 RECRUITMENT PROCEDURES

Schools in 34 states were selected to participate in the 2020 NYTS. Recruitment began in 

September 2019 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. Before public or diocesan schools were contacted, verbal or 

written agreement was first obtained by their district or diocese, respectively; private schools were 

approached directly. 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 academic 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. For a subset of participating schools, 

two data collectors needed to be present in order to ensure there were sufficient tablets available 

for students. Typically, two people were sent to the same school when class enrollments were 

greater than 35 or when two or more randomly selected classes occurred during the same period. 

Recruiters used a secure web-based calendar to facilitate communication and to avoid scheduling 

two schools for the same data collector on the same day.

3.5 SURVEY ADMINISTRATION

Survey administration in the schools began on January 16, 2020, in coordination with a 

comprehensive data collector training, and continued through March 16, 2020 when widespread 

school closures due to the COVID-19 pandemic interrupted fielding. Each data collector visited 

an average of three schools per week and traveled with a case of assigned tablets. While the details 

of each data collection varied, there were eight core steps followed for every school: 1) sync all 

tablets to access information relevant to an assigned school; 2) pre-contact call with the principal 

or lead contact prior to arrival at the school; 3) entry meeting with the principal or lead contact; 4) 

entry meeting with teacher or group of teachers prior to survey administration; 5) survey 

administration; 6) post-survey meeting with the teacher or teachers; 7) post-survey meeting with 

the principal or lead contact prior to leaving the school; 8) syncing local records to the central 

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

participation was anonymous and voluntary. Students completed the survey via an electronic, 

tablet-based survey application or a Web-based survey. 

3.5.1 FIELD PROCEDURES

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

each school received a packet of pre-survey 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. 

The timing of these pre-survey packet mailings 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. Follow-up calls were made to the 

15
selected schools to answer any questions and to make sure materials were received and distributed 

to selected classes and students.

Trained data collectors were issued a hard-shell rolling case with 30 tablets, a mobile hotspot 

device for syncing, charging equipment, and extra forms and emergency supply materials. On a 

rolling basis, data collectors received their assignments electronically for the coming week. In 

addition, weekly survey supplies that were specific to their assignments (i.e., student sign-in cards)

were sent to data collectors’ homes or hotels, if traveling. 

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

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

teams and field 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 SYNCING AND RECORDING

Preliminary student participation rates were recorded by the survey administrators into the 

DCMA described in Section 3.6. Field staff entered the number of eligible students in each 

selected class and the expected number of completed records based on their observation in the 

classroom. Once data were synced, the actual number of records received in the central 

repository was reflected in student participation reporting. If the number of expected records and 

the actual number of records differed, project staff verified the correct number and reconciled the 

discrepancy. As web-based make-ups were submitted by students, the DCMA automatically 

updated the number of actual records received and participation reporting was revised 

accordingly.

16
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, 361 schools were selected across 254 districts in 34 states. During sample 

validation, 29 schools were deemed to be ineligible and were replaced.

In total, 180 schools (49.9%) participated in the study. A total of 74 schools were scheduled for 

data collection after March 16, 2020 but were not completed due to widespread school closures 

related to the COVID-19 pandemic. An additional 54 schools were still in recruitment and a final 

disposition was not obtained. The remaining 53 schools were considered refusals. Of refusals, 33 

of them were due to their district refusing to grant access to their schools to discuss participation 

and 20 were due to refusals at the school level. The most common reasons given for a refusal at 

the district or school level were loss of instructional time and standardized testing. 

3.8.2 Student-level Participation Rates

Initial student-level participation rates were calculated from the field as data collectors completed 

survey administration each day. However, as data were received upon syncing and paperwork was 

received from the field, 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 schools from 

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

The final student participation rate for the 2020 NYTS was 87%. Overall, 16,634 eligible students 

from the 180 participating schools were invited to participate in the survey, and 14,531 did so. Of 

note, data collection for the 2020 NYTS was truncated due to school closures associated with 

COVID-19; data collection ended on March 16, 2020. Table 3.1 below shows the number of 

eligible students, participants, and participation rates for the NYTS.

Table 3.1 Overall NYTS 2020 Student Participation Rate

# Eligible # Completed Participation %

NYTS Participating Students 16,634 14,531 87.4%

The 2020 NYTS survey attained an actual school participation rate of 49.9% and a student 

participation rate of 87.4%. The overall participation rate, the product of the school-level and 

student-level participation rates, was 43.6%.

3.9 DATA MANAGEMENT

Records received via tablet and via web were included in a single national dataset. To take 

advantage of the electronic format of the NYTS, the dataset was designed to be self-cleaning based 

on programming logic. However, to ensure accuracy, CDC created a series of data-cleaning 

specifications that were applied to eliminate internal inconsistencies. These cleaning specifications 

also computed certain analytic variables and re-coded race/ethnicity values to match CDC-required 

17
18

classifications. Data “missingness” was categorized into one of four types: as a legitimate skip 

based on programmed logic (.S), as item-level refusal if a question was presented to a student onscreen but not answered (.N), as not answered because the student was never shown a question on 

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

is distinguished in the data set as follows:

• .E – Missing due to edit check

• .S – Legitimate skip

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

• .Z – Not displayed (partial complete)

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.
CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA

CDC followed a deliberate process in deciding to weight the 2020 NYTS data despite the 

challenges presented by the truncated fielding window due to widespread school closures 

following the spread of the COVID-19 virus. Appendix A briefly describes the deliberative 

process as well as its results and conclusions to proceed with the weighting. 

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.

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.

19
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.

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 20 subsampled PSUs, the partial school weight was:

20
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 WG

jklm. 

4.1.4 PSU Sampling Weights

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

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:

21
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 school. 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 2020 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 2020 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 2020 cycle, school type, proportion of students under poverty line and urban status 

were found to be predictive on nonresponse. Nonresponse adjustment cells were created using 

school level (high vs middle), school type (public vs non-public), proportion of students below 

poverty line (above median vs below median) and urban status (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.

22
The weights of students in participating schools were adjusted to account for nonparticipation by 

other schools. The adjustment factor (Am) 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:

Am =

∑ (Wlm

P

k,l∈sampled schools ∗ Wklm ∗ MOSklm)

∑ (Wlm

P

k,l∈participating schools ∗ Wklm ∗ MOSklm)

The student weight adjusted for nonresponse was then:

W3

s = W2s

∗ Am

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 and proportion of students below poverty line; public schools 

were divided by school level, proportion of students below poverty line and urban status. We 

defined two poverty groups in each category by looking at the median of the school-level variable 

“proportion of students below the poverty line” (schools above or below the median of this 

variable).

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, 

proportion of 

students below 

poverty is below 

median

188,964.37 3 608,771.86 7 42.857 3.222

High School, 

Non-public, 

proportion of 

students below 

poverty is above 

median

30,638.94 1 447,183.35 5 20.000 14.595

High School, 

public, 

proportion of 

students below 

poverty is below 

median, city

570,372.19 5 982,263.79 10 50.000 1.722

23
Weighting

Class

Weight Sum

Over

Participants

Responding 

School Count

Weight Sum 

over all Sample

Sample 

School 

Count

Response

Rate (%)

Adjustment 

Factor

High School, 

public, 

proportion of 

students below 

poverty is below 

median, noncity

3,613,229.58 30 8,509,251.98 71 42.254 2.355

High School, 

public, 

proportion of 

students below 

poverty is above 

median, city

980,355.27 9 3,000,505.79 32 28.125 3.061

High School, 

public, 

proportion of 

students below 

poverty is above 

median, noncity

3,060,009.25 31 3,905,323.39 41 75.610 1.276

Middle School, 

non-public, 

proportion of 

students below 

poverty is below 

median

102,541.31 2 611,631.48 11 18.182 5.965

Middle School, 

non-public, 

proportion of 

students below 

poverty is above 

median

187,216.45 4 659,747.18 11 36.364 3.524

Middle School, 

public, 

proportion of 

students below 

poverty is below 

median, city

173,725.19 2 907,141.50 10 20.000 5.222

24
Weighting

Class

Weight Sum

Over

Participants

Responding 

School Count

Weight Sum 

over all Sample

Sample 

School 

Count

Response

Rate (%)

Adjustment 

Factor

Middle School, 

public, 

proportion of 

students below 

poverty is below 

median, noncity

3,827,577.43 42 7,198,826.59 82 51.220 1.881

Middle School, 

public, 

proportion of 

students below 

poverty is above 

median, city

1,608,422.32 16 2,471,971.75 28 57.143 1.537

Middle School, 

public, 

proportion of 

students below 

poverty is above 

median, noncity

2,549,048.73 35 4,099,327.69 53 66.038 1.608

16892101.02 180 33401946.35 361

* 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 were school 

level (middle vs high school), school type (public vs non-public), proportion of students below poverty line (above 

median vs below median) and urban status (city vs non-city).

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, were used. 

25
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).7 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 also are 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/ethnicity. These dimensions reflect the 

iterations used in the raking procedures. 

7

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 1110 1,947,924.89

Public 6 Female 1154 1,856,189.11

Public 7 Male 1143 1,920,121.91

Public 7 Female 1082 1,827,816.09

Public 8 Male 1109 1,912,066.38

Public 8 Female 1088 1,823,161.62

Public 9 Male 895 2,002,535.13

Public 9 Female 941 1,879,501.87

Public 10 Male 868 1,893,367.40

Public 10 Female 916 1,810,372.60

Public 11 Male 813 1,783,595.77

Public 11 Female 889 1,738,184.23

Public 12 Male 832 1717515.72

Public 12 Female 875 1,690,406.28

Private 6 Combined 96 254,851.00

Private 7 Combined 135 252,753.00

Private 8 Combined 145 254,516.00

Private 9 Combined 130 254,889.00

Private 10 Combined 105 250,742.00

Private 11 Combined 98 249,096.00

Private 12 Combined 107 244,201.00

*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/Ethnicity Number of 

Records

Weight Sum = Control 

Total

Northeast 6 Non-Hispanic Other 300 415,523.32

Northeast 6 Non-Hispanic Black 25 86,745.53

Northeast 6 Hispanic 84 133,417.15

Northeast 7 Non-Hispanic Other 278 418,712.68

Northeast 7 Non-Hispanic Black 29 85,586.22

Northeast 7 Hispanic 115 128,800.10

Northeast 8 Non-Hispanic Other 311 429,840.68

Northeast 8 Non-Hispanic Black 27 84,693.89

Northeast 8 Hispanic 122 124,588.44

Northeast 9 Non-Hispanic Other 207 429,764.98

Northeast 9 Non-Hispanic Black 12 91,586.17

Northeast 9 Hispanic 74 129,617.85

Northeast 10 Non-Hispanic Other 191 421,659.41

Northeast 10 Non-Hispanic Black 13 86,719.70

Northeast 10 Hispanic 71 121,348.89

Northeast 11 Non-Hispanic Other 220 420,914.72

Northeast 11 Non-Hispanic Black 20 79,853.63

Northeast 11 Hispanic 91 107,284.65

Northeast 12 Non-Hispanic Other 218 422,603.90

Northeast 12 Non-Hispanic Black 16 77,933.10

Northeast 12 Hispanic 92 99,937.00

Midwest 6 Non-Hispanic Other 282 617,855.97

Midwest 6 Non-Hispanic Black 57 116,982.51

Midwest 6 Hispanic 76 112,706.51

Midwest 7 Non-Hispanic Other 247 617,522.21

Midwest 7 Non-Hispanic Black 44 111,331.97

Midwest 7 Hispanic 96 109,222.82

Midwest 8 Non-Hispanic Other 252 626,369.66

Midwest 8 Non-Hispanic Black 33 111,017.39

Midwest 8 Hispanic 103 107,145.94

Midwest 9 Non-Hispanic Other 298 638,225.42

Midwest 9 Non-Hispanic Black 32 118,931.71

Midwest 9 Hispanic 95 109,560.87

Midwest 10 Non-Hispanic Other 328 622,736.06

Midwest 10 Non-Hispanic Black 33 112,062.43

Midwest 10 Hispanic 110 103,957.51

Midwest 11 Non-Hispanic Other 308 609,640.87

Midwest 11 Non-Hispanic Black 30 104,628.70

Midwest 11 Hispanic 65 96,923.43

Midwest 12 Non-Hispanic Other 295 610,408.17

Midwest 12 Non-Hispanic Black 22 101,900.04

Midwest 12 Hispanic 84 89,502.78

South 6 Non-Hispanic Other 395 792,578.51

South 6 Non-Hispanic Black 243 363,665.63

South 6 Hispanic 258 423,494.86

South 7 Non-Hispanic Other 447 790,881.27

South 7 Non-Hispanic Black 270 351,680.50

South 7 Hispanic 339 409,341.22

28
Census Region Grade Race/Ethnicity Number of 

Records

Weight Sum = Control 

Total

South 8 Non-Hispanic Other 447 792,573.59

South 8 Non-Hispanic Black 286 344,856.86

South 8 Hispanic 298 399,009.55

South 9 Non-Hispanic Other 391 825,891.15

South 9 Non-Hispanic Black 176 377,787.74

South 9 Hispanic 265 426,616.12

South 10 Non-Hispanic Other 346 793,577.60

South 10 Non-Hispanic Black 122 349,946.57

South 10 Hispanic 255 390,866.83

South 11 Non-Hispanic Other 315 766,118.94

South 11 Non-Hispanic Black 148 324,368.04

South 11 Hispanic 241 355,490.02

South 12 Non-Hispanic Other 330 738,315.45

South 12 Non-Hispanic Black 116 307,965.35

South 12 Hispanic 235 315,158.20

West 6 Non-Hispanic Other 378 508,970.09

West 6 Non-Hispanic Black 22 48,124.99

West 6 Hispanic 240 438,899.92

West 7 Non-Hispanic Other 272 505,670.20

West 7 Non-Hispanic Black 27 47,309.12

West 7 Hispanic 196 424,632.68

West 8 Non-Hispanic Other 231 509,287.52

West 8 Non-Hispanic Black 24 47,017.07

West 8 Hispanic 208 413,343.41

West 9 Non-Hispanic Other 244 517,076.59

West 9 Non-Hispanic Black 17 48,771.84

West 9 Hispanic 155 423,095.57

West 10 Non-Hispanic Other 240 500,098.97

West 10 Non-Hispanic Black 22 47,541.52

West 10 Hispanic 158 403,966.51

West 11 Non-Hispanic Other 219 487,857.86

West 11 Non-Hispanic Black 15 44,757.57

West 11 Hispanic 128 373,037.57

West 12 Non-Hispanic Other 237 485,680.96

West 12 Non-Hispanic Black 15 45,316.44

West 12 Hispanic 154 357,401.60

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

data on race/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=88.55%

implies that the design-effect (DEFF) component due to unequal weighing effects is 1.78.

8

8 The design effect due to unequal weighting may be expressed in terms of the cv of the weight as DEFF= 

1 + cv**2.

29
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 estimators9as 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.10

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

using the 2020 NYTS data. Sample SAS code is provided in Exhibit 4.1.

9

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 

SAS:

Proc Surveymeans Data=nyts2020 mean; 

Var eelcigt ecigt ecigar eslt ehookah celcigt ccigt ccigar cslt chookah; 

Class eelcigt ecigt ecigar eslt ehookah celcigt ccigt ccigar cslt chookah; 

Stratum v_stratum2; 

Cluster psu2; 

Weight finwgt; 

Domain 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/Ethnicity Cross-Classified”;

run; 

SUDAAN: 

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

Var eelcigt ecigt ecigar eslt ehookah celcigt ccigt ccigar cslt chookah; 

Catlevel 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; 

*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.

31
Table 4.4 Current (past 30-day) Use Estimates for Selected Tobacco Products for High 

School Students11

Product

Overall

%(SE)

N = 7453

Female

%(SE)

N = 3849

Male

%(SE)

N = 3594

White

%(SE)

N = 4037

Black

%(SE)

N = 670

Hispanic

%(SE)

N = 2255

Electronic 

cigarettes

19.58% 

(1.28%)

18.74% 

(1.43%)

20.42% 

(1.40%)

23.18% 

(1.34%)

9.10% 

(1.36%)

18.94% 

(2.09%)

Cigars, little 

cigars, or 

cigarillos

5.04% 

(0.53%)

4.68% 

(0.61%)

5.43% 

(0.65%)

4.16% 

(0.57%)

9.24% 

(1.27%)

5.63% 

(1.09%)

Cigarettes 4.62% 

(0.60%)

3.89% 

(0.57%)

5.37% 

(0.79%)

5.29% 

(0.71%)

2.84% 

(0.68%)

4.56% 

(0.85%)

Hookah or 

waterpipe

2.72% 

(0.34%)

2.86% 

(0.45%)

2.55% 

(0.35%)

1.75% 

(0.25%)

3.88% 

(0.87%)

4.41% 

(1.01%)

Smokeless

tobacco (chewing 

tobacco, snuff, or 

dip)

2.67% 

(0.41%)

0.78% 

(0.15%)

4.51% 

(0.73%)

3.66% 

(0.58%)

0.75% 

(0.41%)

1.75% 

(0.43%)

Roll-your-own 

cigarettes

1.44% 

(0.21%)

1.46% 

(0.27%)

1.43% 

(0.29%)

0.97% 

(0.19%)

1.49% 

(0.53%)

2.45% 

(0.59%)

Snus 0.99% 

(0.18%)

0.54% 

(0.16%)

1.40% 

(0.31%)

1.33% 

(0.30%)

0.27% 

(0.23%)

0.74% 

(0.16%)

Pipe tobacco 0.72% 

(0.15%)

0.43% 

(0.11%)

1.02% 

(0.26%)

0.90% 

(0.22%)

0.30% 

(0.19%)

0.71% 

(0.25%)

Dissolvable 

tobacco products

0.33% 

(0.10%)

0.22% 

(0.11%)

0.43% 

(0.15%)

0.46% 

(0.17%)

0.32% 

(0.33%)

0.12% 

(0.06%)

Bidis 0.23% 

(0.06%)

0.14% 

(0.06%)

0.33% 

(0.11%)

0.22% 

(0.08%)

0.08% 

(0.08%)

0.40% 

(0.17%)

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); and bidis (cbidis). 

11 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 

Students12

Product

Overall

%(SE)

N = 7042

Female

%(SE)

N = 3481

Male

%(SE)

N = 3539

White

%(SE)

N = 3025

Black

%(SE)

N = 930

Hispanic

%(SE)

N = 2087

Electronic cigarettes 4.65% 

(0.62%)

4.77% 

(0.80%)

4.54% 

(0.62%)

4.26% 

(0.57%)

2.61% 

(0.68%)

7.10% 

(1.12%)

Cigarettes 1.62% 

(0.26%)

1.99% 

(0.37%)

1.28% 

(0.22%)

1.27% 

(0.34%)

2.09% 

(0.53%)

2.20% 

(0.45%)

Cigars, little cigars, 

or cigarillos

1.50% 

(0.20%)

1.60% 

(0.29%)

1.42% 

(0.22%)

0.85% 

(0.24%)

3.12% 

(0.55%)

1.85% 

(0.42%)

Hookah or waterpipe 1.34% 

(0.23%)

1.51% 

(0.34%)

1.19% 

(0.20%)

0.70% 

(0.15%)

1.70% 

(0.55%)

2.41% 

(0.65%)

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

0.93% 

(0.15%)

0.61% 

(0.16%)

1.24% 

(0.25%)

1.06% 

(0.25%)

0.99% 

(0.34%)

0.76% 

(0.25%)

Roll-your-own 

cigarettes

0.86% 

(0.17%)

0.91% 

(0.21%)

0.80% 

(0.23%)

0.39% 

(0.13%)

1.08% 

(0.24%)

1.26% 

(0.37%)

Pipe tobacco 0.40% 

(0.10%)

0.38% 

(0.14%)

0.42% 

(0.14%)

0.26% 

(0.12%)

0.56% 

(0.34%)

0.44% 

(0.17%)

Snus 0.36% 

(0.08%)

0.42% 

(0.15%)

0.31% 

(0.10%)

0.38% 

(0.14%)

0.64% 

(0.26%)

0.24% 

(0.09%)

Bidis 0.17% 

(0.05%)

0.17% 

(0.07%)

0.18% 

(0.08%)

0.08% 

(0.04%)

0.50% 

(0.34%)

0.13% 

(0.07%)

Dissolvable tobacco 

products

0.15% 

(0.04%)

0.08% 

(0.04%)

0.22% 

(0.08%)

0.07% 

(0.03%)

0.46% 

(0.30%)

0.17% 

(0.09%)

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); and bidis (cbidis). 

12 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 Students13

Product

Overall

%(SE)

N = 7453

Female

%(SE)

N = 3849

Male

%(SE)

N = 3594

White

%(SE)

N = 4037

Black

%(SE)

N = 670

Hispanic

%(SE)

N = 2255

Electronic 

cigarettes

39.38% 

(1.50%)

38.31% 

(1.71%)

40.46% 

(1.75%)

44.02% 

(1.27%)

22.18% 

(2.46%)

40.74% 

(2.54%)

Cigarettes 16.66% 

(1.29%)

15.60% 

(1.28%)

17.68% 

(1.61%)

18.41% 

(1.67%)

10.38% 

(1.70%)

17.27% 

(1.75%)

Cigars, little 

cigars, or 

cigarillos

14.20% 

(1.12%)

11.42% 

(1.20%)

16.95% 

(1.41%)

14.93% 

(1.33%)

15.65% 

(1.72%)

13.78% 

(1.99%)

Smokeless 

tobacco 

(chewing 

tobacco, snuff, 

or dip)

7.70% 

(1.07%)

3.18% 

(0.56%)

12.18% 

(1.63%)

10.74% 

(1.52%)

3.02% 

(0.80%)

4.07% 

(0.73%)

Hookah or 

waterpipe

7.60% 

(0.74%)

7.67% 

(0.93%)

7.49% 

(0.73%)

6.24% 

(0.55%)

9.51% 

(1.91%)

10.02% 

(1.83%)

Roll-your-own 

cigarettes

3.86% 

(0.41%)

3.64% 

(0.51%)

4.10% 

(0.46%)

3.78% 

(0.44%)

2.60% 

(0.65%)

4.68% 

(0.97%)

Snus 3.51% 

(0.50%)

2.53% 

(0.38%)

4.47% 

(0.72%)

4.67% 

(0.75%)

0.72% 

(0.42%)

2.73% 

(0.45%)

Pipe tobacco 2.71% 

(0.41%)

1.67% 

(0.30%)

3.75% 

(0.68%)

3.55% 

(0.66%)

0.82% 

(0.39%)

2.12% 

(0.36%)

Dissolvable 

tobacco 

products

1.48% 

(0.28%)

1.11% 

(0.27%)

1.86% 

(0.39%)

1.85% 

(0.38%)

0.91% 

(0.49%)

1.06% 

(0.25%)

Bidis 1.03% 

(0.17%)

0.72% 

(0.16%)

1.34% 

(0.27%)

1.03% 

(0.24%)

0.31% 

(0.17%)

1.32% 

(0.34%)

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); and bidis (ebidis). 

13 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 Students14

Product

Overall

%(SE)

N = 7042

Female

%(SE)

N = 3481

Male

%(SE)

N = 3539

White

%(SE)

N = 3025

Black

%(SE)

N = 930

Hispanic

%(SE)

N = 2087

Electronic 

cigarettes

10.79% 

(0.90%)

10.15% 

(1.00%)

11.39% 

(1.00%)

10.58% 

(0.93%)

7.54% 

(1.07%)

15.09% 

(1.49%)

Cigarettes 6.00% 

(0.66%)

6.19% 

(0.76%)

5.77% 

(0.67%)

5.45% 

(0.74%)

8.02% 

(1.02%)

6.72% 

(0.86%)

Cigars, little 

cigars, or 

cigarillos

3.50% 

(0.37%)

3.17% 

(0.48%)

3.81% 

(0.41%)

2.70% 

(0.30%)

7.38% 

(1.05%)

3.45% 

(0.67%)

Smokeless 

tobacco 

(chewing 

tobacco, snuff, 

or dip)

2.95% 

(0.35%)

2.04% 

(0.29%)

3.84% 

(0.56%)

3.52% 

(0.60%)

3.30% 

(0.67%)

2.15% 

(0.41%)

Hookah or 

waterpipe

2.87% 

(0.36%)

2.91% 

(0.47%)

2.82% 

(0.36%)

1.72% 

(0.30%)

4.24% 

(0.87%)

4.48% 

(0.86%)

Roll-your-own 

cigarettes

2.10% 

(0.27%)

2.15% 

(0.27%)

1.99% 

(0.38%)

1.39% 

(0.27%)

3.43% 

(0.67%)

2.75% 

(0.41%)

Pipe tobacco 1.25% 

(0.18%)

1.33% 

(0.23%)

1.17% 

(0.24%)

0.95% 

(0.19%)

1.10% 

(0.42%)

1.82% 

(0.34%)

Snus 1.00% 

(0.17%)

0.87% 

(0.18%)

1.14% 

(0.28%)

1.19% 

(0.28%)

0.83% 

(0.33%)

1.04% 

(0.27%)

Bidis 0.85% 

(0.14%)

0.69% 

(0.14%)

1.00% 

(0.25%)

0.63% 

(0.19%)

1.52% 

(0.56%)

1.17% 

(0.31%)

Dissolvable 

tobacco 

products

0.81% 

(0.13%)

0.78% 

(0.18%)

0.84% 

(0.18%)

0.60% 

(0.16%)

1.06% 

(0.52%)

1.27% 

(0.25%)

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); and bidis (ebidis). 

14 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. IMPLICATIONS OF COVID-19 CLOSURES FOR WEIGHTING THE 2020 NYTS DATA

This appendix discusses the checks performed to ensure that the 2020 NYTS survey data could 

be weighted after data collection was truncated due to widespread school closures as a result of 

the COVID-19 pandemic.

The first two steps checked that the sample was representative from the perspective of potential 

bias and that supports precise estimates for the key subgroups of interest. When the sample 

passes these initial checks, as shown below that the 2020 NYTS sample did, the weighting 

process itself provides additional checkpoints. 

The inflection points provided in the weighting process begin with a thorough nonresponse 

analysis that supports weight adjustments designed to minimize any potential bias. The 

weighting process further adjusts the survey data to ensure the weighted distribution matches the 

population data along demographic, geographic and socio-economic characteristics. 

Step 1: Is the participating school sample representative?

◼ Checks on the representation by region, school type, and level

◼ Checks on the representation of all design strata

We compared the distribution of the sample schools with the distribution of participating schools 

under the two definitions. The broader subset of 254 agreeing schools includes those 74 schools 

that were scheduled, but these were not included in the narrower subset of n=180 participating 

schools. We looked at the distributions by region (South, East, Midwest and West), by type 

(public and non-public), by level (middle school and high school) and by the 16 design strata. 

Table A-1: Regional Distribution of Sample: Agreeing, and Participating Schools

Region Sample Schools Set of Agreeing 

Schools (n=254)

Final subset 

Participating Schools

(n=180)

Midwest 21.33% 17.32% 20.00%

Northeast 15.24% 18.11% 17.78%

South 38.23% 37.80% 41.11%

West 25.21% 26.77% 21.11%

Table A-2: Distribution by School Type: Sample, Agreeing, and Participating Schools

Region Sample Schools Set of Agreeing 

Schools (n=254)

Final subset 

Participating 

Schools (n=180)

Non-public 9.42% 5.91% 5.56%

Public 90.58% 94.09% 94.44%

i
Table A-3: Distribution by School Level: Sample, Agreeing, and Participating Schools

Region Sample Schools Set of Agreeing 

Schools (n=254)

Final subset 

Participating 

Schools (n=180)

High School 45.98% 45.28% 43.89%

Middle School 54.02% 54.72% 56.11%

Table A-4: Distribution by Strata: Sample, Agreeing, and Participating Schools 

Region Sample Schools Set of Agreeing 

Schools (n=254)

Final subset 

Participating 

Schools (n=180)

BR1 7.76% 9.45% 7.78%

BR2 3.88% 5.12% 7.22%

BR3 3.60% 3.54% 4.44%

BR4 2.22% 3.15% 4.44%

BU1 8.31% 8.66% 9.44%

BU2 4.16% 2.36% 3.33%

BU3 2.22% 1.57% 1.67%

BU4 1.11% 0.79% 0.00%

HR1 16.07% 16.93% 17.22%

HR2 4.71% 5.12% 5.00%

HR3 4.16% 5.51% 6.11%

HR4 3.32% 4.33% 3.33%

HHU1 11.63% 8.66% 8.33%

HU2 9.97% 8.27% 10.56%

HU3 8.59% 7.48% 6.11%

HU4 8.31% 9.06% 5.00%

The tables show that the distributions of the subsets of participating schools are generally similar 

to the original sample distributions.15

Additional non-response analyses were performed under Step 3 described below as part of the 

weighting process.

Step 2: Can precise estimates be computed?

Step 2 included the three interrelated actions discussed below, which depend on sample sizes and 

design effects (DEFFs) to gauge the expected precision of subgroup estimates. Unlike the 

empirical tests described in Step 4 below, which involve weighted estimates following the 

15 One of the sixteen design strata had no participating schools. This does not present any statistical or analytical 

challenges as the substrata can be collapsed for the analyses.

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computation of the weights, the three actions in Step 2 can take place before weighting to signal 

that weights can and should be computed. 

◼ Check key subgroup sample sizes (n’s) that suggest precise subgroup estimates (e.g., for

minority students)

◼ Estimate expected precision for subgroup estimates and overall, using projected n’s and

DEFFs

◼ Estimate expected precision for subgroup estimates, and overall, using unweighted data

We projected the expected numbers of black and Hispanic students in the participating schools. 

using the percentages of minority students in each school available in the sampling frame data, 

and then validated them using the survey data. As discussed in Section 2.1, the numbers exceed 

historical targets but not the most recent targets of 1,700 per subgroup (falling short for the 

subgroup of blacks by school level); nevertheless, the precision is still better than the required 

levels. As shown in Tables 4-4 to 4-7 in the full report, standard errors for subgroup estimates 

are all less than 2.5% so that 95% confidence intervals are all within +/- 5 percentage points. 

That is true even for subgroups defined by racial/ethnic groups within school level groupings.

These numbers showed that precise subgroup estimates by race/ethnicity and by sex for the 2020 

NYTS weighted data could confidently be computed, as shown in Chapter 4.

Step 3: Compute survey weights

As a typical prelude to the weighting process, additional analyses were conducted to compare

non-participating schools and participating schools. These analyses were even more critical for 

the 2020 NYTS in light of the expanded pool of non-participating schools. Specifically, these 

analyses were central to the need for a) demonstrating that the bias potential is minimal as nonparticipating schools did not differ substantially from participating schools, and b) ensuring that 

non-response weight adjustments minimized whatever potential bias that might result from nonresponse.

Most of these comparisons were based on school characteristics such as location (region, urban 

vs non-urban), school type, and school size. Some of these comparisons include school-level 

student characteristics such as the percentage of black and Hispanic students and socio-economic 

variables.

Significant differences between participating and non-participating schools were observed in 

bivariate analyses for the following school characteristics:

• School type (Public, Non-public)

• NCES Locale (City, Suburb, Town, Rural)

• Region (Northeast, Midwest, South, West)

• Enrollment Shift

• Percent of students’ college bound

• Proportion of Students below Poverty Line

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Multivariate logistic regression models then identified a subset of these variables as significant: -

• School Type

• NCES Locale (two categories, City versus non-City); and

• Poverty Level

These were the variables entered in the logistic regression model (i,e., those highlighted as 

significant in the bivariate analyses) that had significant coefficients and odds ratios (p< 0.05). 

To minimize bias potential, weighting classes for school non-response weight adjustments were 

based on these characteristics. As described in Chapter 4, nonresponse adjustment cells were 

created using school level (high vs middle), school type (public vs non-public), proportion of 

students below poverty line (above median vs below median) and urban status (city vs non-city).

Step 4: Compute weighted estimates and compare with previous cycles

We computed weighted estimates for key NYTS measures as summarized in Tables 4-3 to 4-7 in 

the full report. As stated above, standard errors for subgroup estimates are all less than 2.5% so 

that 95% confidence intervals are all within +/- 5 percentage points. 

We also compared the weighted estimates and variances to similar estimates computed for the 

2019 NYTS overall and by key subgroups defined by school level, gender and race/ethnicity. 

While weighted estimates were generally similar, notable declines in prevalence were observed 

for key measures of cigarette and electronic cigarette use. Sample sizes were large enough, and 

variances small enough, to detect this remarkable decline in nearly all measures, but particularly 

evident for those products with relatively higher prevalence in 2019 (e.g., electronic cigarettes). 

This trend was also consistent across all subgroups.

On the other hand, variances and standard errors were generally similar in magnitude despite the 

smaller sample sizes, overall and for key subgroups, due in part to the smaller magnitudes of 

prevalence rates overall.

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APPENDIX B. QUESTIONNAIRE

Questionnaire only included in PDF version of this document. 

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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: 

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, 

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

students in the student weight class. Note thatthe equation simplifiesto 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

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

The fully adjusted student weight is computed as:

The simplified equation is as follows:

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APPENDIX D. 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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