METHODOLOGY REPORT 

OF THE 2017 NATIONAL

YOUTH TOBACCO 

SURVEY

December 2017

Recommended Citation

Office on Smoking and Health. 2017 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, 2017.

PRINT ONLY
For questions about this report, please email Sean Hu at shu@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

December 2017
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 2017 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 ....................................................................................4

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

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

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

2.7 SAMPLE ALLOCATION AND SELECTION ........................................................................................8

2.8 SAMPLE SIZES ATTAINED IN THE SURVEY....................................................................................9

2.9 SAMPLE VALIDATION ...................................................................................................................11

CHAPTER 3—NYTS DATA COLLECTION AND PROCESSING .................................................. 13

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

3.2 EXTERNAL REVIEW AND APPROVALS .........................................................................................13

3.3 RECRUITMENT PROCEDURES .......................................................................................................13

3.4 SURVEY ADMINISTRATION ..........................................................................................................13

3.5 WEB-BASED CASE MANAGEMENT SYSTEM ...............................................................................15

3.6 DATA RECEIPT, PROCESSING, AND SCANNING...........................................................................15

3.7 PARTICIPATION RATES .................................................................................................................16

3.8 DATA MANAGEMENT ...................................................................................................................16

CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA........................................................... 18

4.1 SAMPLING WEIGHTS.....................................................................................................................18

4.2 NONRESPONSE ADJUSTMENTS .....................................................................................................20

4.3 POSTSTRATIFICATION AND TRIMMING........................................................................................22

4.4 ESTIMATORS AND VARIANCE ESTIMATION ................................................................................26

APPENDICES

A. Questionnaire

B. Student Weight Detail

C. Common Core of Data Race/Ethnicity Definitions
1

Chapter 1—NYTS Sampling Design

1.1 OVERVIEW OF THE NATIONAL YOUTH TOBACCO SURVEY (NYTS)

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

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

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

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

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

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

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

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

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

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

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

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

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

TU-18 and TU-19.

First conducted during fall 1999 and again during the springs of 2000, 2002, 2004, 2006, and 2009, 

then annually starting in 2011, the NYTS provides data that are representative of all middle school 

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

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

have collaborated to administer the NYTS.

1.2 OVERVIEW OF THE 2017 NYTS METHODOLOGY

The 2017 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. Participating students completed the survey via pencil and paper using a self-administered, 

scannable questionnaire booklet.

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.

 

1 Centers for Disease Control and Prevention. (CDC) (2014). Best Practices for comprehensive tobacco control programs-2014. Atlanta, GA: US 

Department of Health and Human Services, Public Health Service, CDC.

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

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

Smoking and Health; 2014.
2

The final sample consisted of 241 schools, of which 185 participated, yielding a school 

participation rate of 76.8%. A total of 17,872 student questionnaires were completed out of a 

sample of 20,144 students, yielding a student participation rate of 88.7%. The overall participation 

rate, defined as the product of the school-level and student-level participation rates, was 68.1%. 

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

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

students in each grade matched national population proportions. 

The remainder of this report provides detailed information on the methodology used in the 2017

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

data (Chapter 4).
3

Chapter 2—NYTS Sampling Methods

2.1 SAMPLE DESIGN

The objective of the NYTS sampling design was to support estimation of tobacco-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 considerably according to differences in subpopulation sizes.

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

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

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

schools, Bureau of Indian Affairs schools, vocational schools that serve only pull-out 

populations, and students enrolled in regular schools unable to complete the questionnaire 

without special assistance, were excluded. The NYTS employed a repeat cross-sectional design.

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

stratified by race/ethnicity and urban versus non-urban (“rural”). PSUs were classified as "urban"

(U) if they were in one of the 54 largest Metropolitan Statistical Areas (MSAs) in the United States; 

otherwise, they were classified as “non-urban" (R). 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

85 Counties, portions 

of a county, or groups 

of counties

2 Schools

Small, medium and 

large;

High school vs. middle

school

Aggregate 

eligible 

enrollment

220 SSUs (school) 

selections: 170 large 

schools (2 per PSU), 

20 medium schools and 

30 small schools

3 Classes/students

2 classes per grade in 

large high-minority 

schools; 1 class per 

grade otherwise
4

The sample was designed to provide target sample sizes in the key analytic subgroups of interest. 

More specifically, the study was designed to produce national estimates at a 95% confidence level 

with a margin of error of 5% by school level (middle school and high school), by grade (6, 7, 8, 9, 

10, 11, and 12), by sex (male and female), and by race/ethnicity (non-Hispanic white, non-Hispanic 

black, and Hispanic). With the average design effects attained in the NYTS, the requirements 

translated to subgroup sample sizes of 1,200 or more. Sample sizes were sufficient to generate 

estimates with the required precision by grade as well as by sex and school level. Therefore, the 

precision requirements generally focus on racial/ethnic subgroups. As shown in Section 2.8, the 

requirements are met for the two key racial/ethnic subgroups—Hispanics and non-Hispanic 

blacks—at both the middle and high school levels. 

2.2 SAMPLING FRAME

As in previous cycles, the 2017 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 distributions within the school, district and county information, and other 

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

included the Common Core of Data for public schools and the Private School Survey for nonpublic schools. This dual-source frame build method was piloted first in 2014 to build the frame 

for the National Youth Tobacco Survey.

3

Including schools sourced from the two NCES files 

resulted in a coverage increase among all public and non-public high schools of 23%. 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 3.94% of schools (2.87% public and 8.16% private) and 0.68% of students. Next,

schools were removed that had fewer than 40 students in the eligible grades, resulting in the 

exclusion of 14.53% of schools (8.26% public and 39.06% private) and 1.35% of students.

2.3 SAMPLING UNITS AND MEASURE OF SIZE

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

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

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

counties. For the second stage of sampling, 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.

 

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

Robb, Kate Flint, Alice Roberts, Ronaldo Iachan, ICF International, FEDCASIC, March 2014
5

The sampling stages may be summarized as follows:

 Selection of PSUs—Eighty-five PSUs were selected from 16 strata with probability 

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

located within a PSU.

 Selection of schools—At the second sampling stage, a total of 170 large schools or SSUs

were selected from the 85 sample PSUs. The sample PSUs are subsampled to support the 

selection of small schools, 30 small schools from 15 subsample PSUs (one school for 

each level), and medium schools, 20 medium schools from 10 subsample PSUs (one 

school for each level). This resulted in a total of 220 SSUs (220=170+20+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.

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

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

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

single chance of selection.

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

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

grade level, medium SSUs contained between 28 and 55 per grade, and large SSUs contained at 

least 56 students at each grade level. Two classes per grade in large schools and one class per grade 

in the remaining schools were selected. The threshold for double class sampling was based on the 

simulation study to ensure that the required numbers of minority students (black and Hispanic 

students) were achieved per school level.

The sampling approach utilized 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. All 

students in a selected class were then selected for the survey.
6

2.4 PROJECTED SAMPLE SIZES

This section describes the planned sample sizes developed by the design, while Section 8 discusses 

the sample sizes actually attained in the survey. The NYTS sample size calculations were based 

on the following assumptions:

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

sample for prior cycles of the NYTS.

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

additional 20 medium and 30 small schools from subsample PSUs.

Across 11 previous cycles of the NYTS that had concluded at the time of the 2017 NYTS design, 

school participation had averaged 84.7%, with a low of 72.5%. Student participation had averaged 

82.8% with a low of 87.4%. The combined response rate (student x school) averaged 76.2%.

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

sample sizes. In calculating the sample sizes, a combined rate of 72% was conservatively assumed.

Table 2.2 presents a detailed derivation of the sample sizes planned for the 2017 NYTS based on 

these assumptions.

Table 2.2 Planned Sample Sizes for the 2017 NYTS

PSU Size # of 

SSUs

Number of 

Schools 

Sampled

# of 

Classes 

per 

School

# of 

Students 

per 

Class

# of Sampled 

Students 

prior to 

Attrition

Combined 

School X

Student 72% 

Response Rate

85 Large HS 85 Double 

classes: 39

8 28 8,758 6,306

Single 

classes: 46

4 28 5,141 3,701

Large MS 85 Double 

classes: 39

6 28 6,569 4,730

Single

classes: 46

3 28 3,856 2,776

Large Total 170 24,324 17,513

10

(subsample)

Medium HS 10 10 4 28 1,120 806

Medium MS 10 10 3 28 840 605

Medium Total 20 1,960 1,411

15 

(subsample)

Small HS 15 15 3.8 18.1 1,030 742

Small MS 15 15 2.8 11.2 475 342

Small Total 30 1,505 1,084

Overall Total 220 27,7894 20,008

Eighty-five PSUs were selected, with two large SSUs (“full” schools) selected from each PSU for 

a total of 170 large SSUs. The estimated sample yield from these large schools was 24,324 students 

 

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

was derived only from participating schools (and is thus considerably lower).
7

before school and student non-response, leading to an expected total 17,513 participating students 

in large schools after accounting for non-response.

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

28 students in any grade) 20 medium SSUs from a subsample of 10 PSUs, and 30 small SSUs from 

a subsample of 15 PSUs were selected. The expected yield was 1,411 from medium schools and 

1,084 students from small schools. In total, the number of participating students was 20,008.

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

high minority schools, where two classes per grade were selected. Note that the set of highminority schools defined for double class sampling is necessarily a subset of the large schools that 

can support such double class sampling. For the 2017 NYTS, double class selection in schools 

with higher enrollments of black students was implemented to enhance the black student yields. 

The cutoff used in the 2017 NYTS sampling was 3% percent black students or more in a school.

2.5 FORMING SAMPLING UNITS

2.5.1 Forming primary sampling units (PSUs) 

In defining PSUs, several issues were considered: 

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

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

the sampling program.

2.5.2 Forming secondary sampling units (SSUs)

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

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

together to form a SSU. Linked schools were treated as single schools during sampling.
8

2.6 STRATIFICATION

The PSUs were organized into 16 strata, based on urban/rural 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 

black. 

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

“urban,” otherwise it was classified as non-urban, or “rural”.

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

depending upon the percentages of Hispanic students in the PSU. 

 Non-Hispanic black urban and non-Hispanic black rural PSUs were also classified into 

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

The density grouping bounds were computed using an optimization algorithm5that 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 2017 

NYTS.

Table 2.3 Stratum Boundaries: Minority Percentage Cutoffs

Minority 

Concentration

Density 

Group

Bounds

Urban Non-Urban (Rural)

Black

1 0%-26% 0%-20%

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

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

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

Hispanic

1 0%-26% 0%-24%

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

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

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

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

2.7 SAMPLE ALLOCATION AND SELECTION

The 2017 NYTS was designed to select a sample of 85 PSUs. The PSUs initially were 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 

 

5 The cumulative square root of “f” method developed by Dalenius and Hodges (1959) “Minimum variance 

stratification”. J American Statistical Association, 54, 88-101.
9

proportional allocation. The initial proportional allocation was modified slightly to ensure that all 

strata contained at least two PSUs to facilitate accurate variance estimation.

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 2,720,181 7

2 BU2 975,490 4

3 BU3 908,299 4

4 BU4 516,712 5

Non-urban (R)

1 BR1 3,937,157 8

2 BR2 1,503,403 4

3 BR3 1,026,612 4

4 BR4 313,063 5

Hispanic

Urban

1 HU1 3,530,556 8

2 HU2 2,429,442 7

3 HU3 1,865,988 5

4 HU4 2,106,242 4

Non-urban (R)

1 HR1 4,427,215 12

2 HR2 1,284,402 3

3 HR3 988,655 3

4 HR4 523,491 2

The sample was selected with probabilities proportional to size (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 85 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 the small school (15 PSUs) and medium school (10 PSUs) sampling 

of two schools per level in each subsample PSU.

2.8 SAMPLE SIZES ATTAINED IN THE SURVEY

The 2017 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 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 
10

imputed variable developed for post-stratification which includes complete data. By either 

measure, the sample led to more than 4,500 Hispanic students. It also led to 3,757 black students 

using the imputed variable and nearly 3,000 black student using the original variable. 

Table 2.5a Subgroup Sample Sizes: Number of Participating Students

What is your sex?

Sex (Q2) Frequency Percent

Cumulative

Frequency

Cumulative

Percent

Male 8881 50.19 8881 50.19

Female 8815 49.81 17696 100.00

Frequency Missing = 176

Table 2.5b Subgroup Sample Sizes: Number of Participating Students

What grade are you in?

Grade (Q3) Frequency Percent

Cumulative

Frequency

Cumulative

Percent

6th 2524 14.20 2524 14.20

7th 2565 14.43 5089 28.63

8th 2473 13.91 7562 42.55

9th 2583 14.53 10145 57.08

10th 2637 14.84 12782 71.92

11th 2575 14.49 15357 86.41

12th 2391 13.45 17748 99.86

Ungraded or other grade 25 0.14 17773 100.00

Frequency Missing = 99
11

Table 2.5c Subgroup Sample Sizes: Number of Participating Students

RECODE: Race/Ethnicity6

RACE_M Frequency Percent

Cumulative

Frequency

Cumulative

Percent

NH-White 7532 44.09 7532 44.09

NH-Black 2983 17.46 10515 61.55

Hispanic 4614 27.01 15129 88.56

NH-Asian 728 4.26 15857 92.82

NH-AI/AN 230 1.35 16087 94.16

NH-NHOPI 96 0.56 16183 94.73

Multiple Races 901 5.27 17084 100.00

Frequency Missing = 788

Table 2.5d Subgroup Sample Sizes: Number of Participating Students7

Race Categories for Post-stratification

Imputed Race/Ethnicity Frequency Percent

Cumulative

Frequency

Cumula

tive

Percent

Asian/Native Hawaiian/Other PI 998 5.58 998 5.58

Black or African American 3757 21.02 4755 26.61

Hispanic/Latino 4706 26.33 9461 52.94

American Indian/Alaska Native 356 1.99 9817 54.93

White 8055 45.07 17872 100.00

Note: The multiple race 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).

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 2016-2017 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

 

6 This variable is based on the original race/ethnicity question in the survey. It allows for multiple races and has 

missing values.

7 There were 788 missing values for the Race/ethnicity variable in Table 2.5c. The variable in Table 2.5d imputed 

those values and also assigned a unique race/ethnic classification to multiple race respondents.
12

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

 Name and title of 2016-2017 school principal

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

 Grade levels served during 2016-2017 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
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 2017 survey instrument included 88 questions. The first five

questions collected student demographic information and the rest measured a comprehensive set 

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

of tobacco product use; knowledge of and attitudes toward tobacco use; 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.

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

Two bodies reviewed and approved the instrumentation, processes, privacy and security elements, 

and sampling design of the 2017 NYTS: ICF’s Institutional Review Board (IRB) and CDC’s IRB.

Office of Management and Budget review and approval was not required for the 2017 cycle of 

NYTS because the existing approval was not set to expire until January 31, 2018.

3.3 RECRUITMENT PROCEDURES

The schools selected to participate in the 2017 NYTS were located in 32 different states. 

Recruitment began in June 2016 with calls to state departments of education and health. Letters of 

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

District- and school-level recruitment began in September 2016. Before public or diocesan schools 

were contacted, verbal or written agreement was first obtained by their district or diocese; 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 calendar were considered. Additionally, an effort was made to 

schedule groups of schools from the same school district or PSU around the same time to facilitate 

efficient travel to and survey implementation within selected schools. Recruiters used a secure

web-based calendar to facilitate communication and to avoid scheduling two schools for the same 

data collector on the same day.

3.4 SURVEY ADMINISTRATION

Survey administration in the schools began on February 13, 2017, immediately after data collector 

training, and continued until June 14, 2017. Each data collector visited an average of three schools 

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

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

entry meeting with the principal or lead contact; 3) entry meeting with teacher or group of teachers 
14

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

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

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

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

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

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

questionnaire booklet via pencil and paper.

3.4.1 Data Collection Staffing

In order 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. Data collector training was conducted on February 8-10, 

2017. 

Key components of the training included the following:

 Pre-contact activities with the schools

 Entry and exit meetings with school officials

 Data collection protocols

 Follow-up activities

 Communication with headquarters staff

3.4.2 Field Procedures

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

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

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

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

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

child to participate, were sent approximately one week 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 in order for the child to participate) were sent out at least two weeks prior to 

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

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

and distributed to selected classes and students.

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

week, travel, and logistics information. In addition to these mailings, boxes of survey supplies 

were sent to data collectors, either to the data collector’s home or hotel. These boxes contained all 

supplies necessary for completing the data collection, including questionnaires, survey envelopes,

field forms, and pencils. Data collectors were supplied with extra materials for emergency packs 

as well, which they carried with them at all times.
15

3.4.3 Classroom Selection

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

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

classes were chosen were constructed 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 were used as the frame for class selection. 

3.5 WEB-BASED CASE MANAGEMENT SYSTEM

For multiple cycles of the NYTS, a web-based case management system 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, and tracking school 

and student response rates.

3.6 DATA RECEIPT, PROCESSING, AND SCANNING

Data were shipped directly from the field to the contractor’s headquarters for processing and 

scanning. Shipments were immediately logged in as received and checked for completeness (i.e., 

data from all expected schools and classes were accounted for). The status of each school’s 

received data was logged into the case management system described in Section 3.5. Survey 

booklets were counted and discrepancies with the reported number of completed booklets 

reconciled. Booklets were also individually reviewed, page by page, to make sure bubbles were 

sufficiently filled in; stray pencil marks, comments, or illustrations that could interfere with 

scanning were erased; defaced booklets or those completed in something other than a No. 2 pencil 

were transcribed; and that at least one question had been answered. This process was repeated for 

each class within a school. As make-up surveys were received back from teachers, the process was 

again repeated and all accompanying documentation for that school was updated. Data were 

subsequently scanned using an optical scanner.
16

3.7 PARTICIPATION RATES

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

goal for weightable data was to have the product of the two participation rates equal to or greater 

than 60%.

8

3.7.1 School-level Participation Rates

At the school level, 242 schools were selected across 166 districts in 32 states. During sample 

validation, 15 schools were deemed to be ineligible and were replaced; one school was deemed 

ineligible but was not replaced. The remaining 241 schools were considered eligible for 

participation in the study.

In total, 185 schools (76.8%) participated in the study. The remaining 56 schools were considered 

refusals. Of these refusals, 21 of them were due to their district refusing to grant access to their 

schools to discuss participation and 35 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. 

3.7.2 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 reviewed, further refinements were made

to: 1) revise the number of eligible students based on available documentation, 2) correct

mathematical errors, 3) review counts of completed surveys, and 4) incorporate make-ups as they 

were received from schools from students and classes that did not participate on the initial day of 

survey administration.

After visual editing, incorporating make-ups, and receiving missing classes, the final student 

participation rate for the 2017 NYTS was 88.72%. Overall, 20,144 eligible students were invited 

to participate in the survey, and 17,872 did so. Table 3.1 below shows the number of eligible 

students, participants, and participation rates for the NYTS.

Table 3.1 Overall NYTS 2017 Student Participation Rate

# Eligible # Completed Participation %

NYTS Participating Students 20,144 17,872 88.7

The 2017 NYTS survey attained an actual school participation rate of 76.8% and a student 

participation rate of 88.7%. The overall participation rate, the product of the school-level and 

student-level participation rates, was 68.1%, thus considered sufficient for weighting purposes.

3.8 DATA MANAGEMENT

Scanned data were converted from separate school-specific ASCII files to a single national SAS 

dataset. Because the NYTS is administered via a paper-and-pencil booklet, there is no restriction 

 

8 Note that the recruitment goal for the combined school x student participation rate is 70%. This is in excess of the combined 

participation rate needed for confidence in weighting.
17

for the participant (e.g., providing two or more responses to a single response question) that 

prevents inconsistencies in the dataset. Therefore, 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 

classifications.

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 (QID).
18

Chapter 4—Weighting of NYTS Response Data

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

 Sampling weights

 Nonresponse adjustments 

 Post-stratification to national estimates by grade and weight trimming

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

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

nonresponse patterns, to mitigate large variations in sampling weights, and to post-stratify the data 

to known sampling frame characteristics. The section also describes 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.

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.
19

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 small schools, the partial school weight was:

P

1

=

MOS

MOS

W =

SS

klm klm

SS .lm

klm 















(85 /15)
20

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

P

1

=

MOS

MOS

W =

MS

klm klm

MS .lm

klm 















(85 /10)

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:

P

1

=

MOS

MOS

K

1

W =

P

lm lm

.m

m

P

lm 











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

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

probability and corresponding weight.

4.1.5 Overall Sampling Weight

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

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

in the following sections. This weight was computed as:











W =W W W W

W =W W W W

W =W W W W

R

hijklm

G

jklm

SS

klm

P

lm

T1

hijklm

R

hijklm

G

jklm

MS

klm

P

lm

T1

hijklm

R

hijklm

G

jklm

LS

klm

P

lm

T1

hijklm

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. 
21

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 2017 NYTS cycle, the school nonresponse adjustment methods were refined to further 

minimize nonresponse bias potential. As opposed to the previous method, which created 

adjustment cells based on sampling strata, the new method defined nonresponse adjustment cells 

in a more tailored and systematic approach stemming from the non-response analysis. These 

analyses are detailed in the 2017 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 No. 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 2017 cycle, only the school type (public versus non-public) was found to be predictive 

on nonresponse. With only one predictive variable, multivariate analysis was not needed. 

Nonresponse adjustment cells were created using school type and region. Because of the small 

number of sample non-public schools, they were included as their own category in the nonresponse 

adjustment cells.

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. These steps were not needed in 2017 because only school 

type was significant. 

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.

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. 
22

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 defined by non-public school and public school by region. 

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

Non-public

Schools 1,218,137.13 14 2,850,776.31 25 56.000 2.340

Public Schools,

Region 1 4,281,074.24 31 4,597,158.84 35 88.571 1.073

Public Schools,

Region 2 6,785,317.73 35 8,014,359.24 44 79.545 1.181

Public Schools,

Region 3 8,629,885.92 71 11,288,173.40 92 77.173 1.308

Public Schools,

Region 4 5,811,827.49 34 7,821,973.30 46 73.913 1.345

26,726,242.50 185 34,572,441.08 242

4.3 POSTSTRATIFICATION AND TRIMMING

The final steps in the weighting process include trimming and poststratification. 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.

Poststratification 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 
23

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

most recent versions, the 2013–14 CCD and the 2013–14 PSS was used. 

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

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

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

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

Iachan, 2010).9 Trimming is designed to limit the variance increase that may follow from the biasreduction raking methods. The trimming method capped the weights at the median plus four times 

the interquartile range of the weight distribution.

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

each poststratum cell separately for public and non-public schools by grade and sex and by grade 

and race/ethnicity, respectively, to reflect the iterations used in the raking procedures. 

 

9

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

American Statistical Association meeting, Vancouver, Canada.
24

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

School Type Grade Gender

Number of 

Records

Weight

Sum = Control Total

Public 6 Male 1233 1887889.41

Public 6 Female 1197 1796710.59

Public 7 Male 1195 1918191.20

Public 7 Female 1264 1826578.80

Public 8 Male 1200 1912595.69

Public 8 Female 1183 1828252.31

Public 9 Male 1180 2008314.37

Public 9 Female 1271 1884945.63

Public 10 Male 1197 1861769.58

Public 10 Female 1259 1789898.42

Public 11 Male 1239 1711214.99

Public 11 Female 1152 1678713.01

Public 12 Male 1116 1643345.33

Public 12 Female 1108 1628831.67

Private 6 Combined 106 260122.00

Private 7 Combined 125 259795.00

Private 8 Combined 112 259872.00

Private 9 Combined 160 250101.00

Private 10 Combined 191 247254.00

Private 11 Combined 205 243380.00

Private 12 Combined 179 237793.00
25

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

School Type Grade Race/Hispanic Origin

Number of 

Records

Weight Sum = 

Control Total

Public 6 Non-Hispanic Native American 92 39520.01

Public 6 Non-Hispanic Asian and Pacific Islander 107 184901.31

Public 6 Non-Hispanic Black 513 583490.27

Public 6 Hispanic 689 964836.55

Public 6 Non-Hispanic White 1029 1911851.86

Public 7 Non-Hispanic Native American 69 40144.14

Public 7 Non-Hispanic Asian and Pacific Islander 108 184802.31

Public 7 Non-Hispanic Black 485 595382.61

Public 7 Hispanic 660 968059.68

Public 7 Non-Hispanic White 1137 1956381.26

Public 8 Non-Hispanic Native American 44 40377.79

Public 8 Non-Hispanic Asian and Pacific Islander 108 187423.33

Public 8 Non-Hispanic Black 513 596541.72

Public 8 Hispanic 628 943768.85

Public 8 Non-Hispanic White 1090 1972736.31

Public 9 Non-Hispanic Native American 40 43307.51

Public 9 Non-Hispanic Asian and Pacific Islander 136 181737.57

Public 9 Non-Hispanic Black 561 653529.40

Public 9 Hispanic 641 981991.29

Public 9 Non-Hispanic White 1073 2032694.23

Public 10 Non-Hispanic Native American 39 39499.34

Public 10 Non-Hispanic Asian and Pacific Islander 138 182627.19

Public 10 Non-Hispanic Black 471 581745.81

Public 10 Hispanic 693 884308.87

Public 10 Non-Hispanic White 1115 1963486.79

Public 11 Non-Hispanic Native American 33 34995.73

Public 11 Non-Hispanic Asian and Pacific Islander 156 182576.00

Public 11 Non-Hispanic Black 538 514866.40

Public 11 Hispanic 672 784371.22

Public 11 Non-Hispanic White 992 1873118.65

Public 12 Non-Hispanic Native American 27 33758.82

Public 12 Non-Hispanic Asian and Pacific Islander 133 180238.60

Public 12 Non-Hispanic Black 499 483886.39

Public 12 Hispanic 605 728269.73

Public 12 Non-Hispanic White 960 1846023.46

Private 6 Combined 106 260122.00

Private 7 Combined 125 259795.00

Private 8 Combined 112 259872.00

Private 9 Combined 160 250101.00

Private 10 Combined 191 247254.00

Private 11 Combined 205 243380.00

Private 12 Combined 179 237793.00
26

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. For 

non-public schools, we did not poststratify by race/ethnic classifications – white and non-white. 

For public schools we used the full five categories.

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=86.7% 

implies that the design-effect (DEFF) component due to unequal weighing effects is 1.75.10

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 estimators11 as implemented in SAS 

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

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

weighting and for sample clustering and stratification. 

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

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

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

least two PSUs.12

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

using the 2017 NYTS data. Sample SAS code is provided in Exhibit 4.1.

 

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

DEFF= 1 + cv**2.

11 Skinner CJ, Holt D, and Smith TMF, Analysis of Complex Surveys, John Wiley & Sons, New York, 1989, 50.

12 Specifically, two strata (coded 202 and 203) were combined into one variance stratum (203) because the 

original stratum “202” had only one PSU when analyzed at the middle school level.
27

Exhibit 4.1: Example SAS and SUDAAN Code for Generating Weighted Estimates and Standard Errors

SAS:

Proc Surveymeans Data=nyts2017 mean;

Var ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Class ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Stratum v_stratum2;

Cluster psu2;

Weight wt;

Domain HSMS HSMS*Sex HSMS*Race_S;

Title “NYTS 2017, 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=nyts2017 Filetype= SAS Design=WR;

Var ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Catlevel 1 1 1 1 1;

Nest v_stratum2 PSU2 / Missunit;

Weight wt;

Subgroup HSMS Sex Race_S;

Levels 2 2 3;

Tables HSMS HSMS*Sex HSMS*Race_S;

Title “NYTS 2017, 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;
28

Table 4.4 Current Use Estimates13 for Selected Tobacco Products for High School Students

Product

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

CBIDIS 0.73% 

(0.11%)

0.61% 

(0.10%)

0.68% 

(0.17%)

0.60% 

(0.16%)

0.73% 

(0.23%)

1.10% 

(0.24%)

CCIGAR 7.74% 

(0.60%)

6.29% 

(0.69%)

8.98% 

(0.77%)

8.32% 

(0.79%)

8.46% 

(0.99%)

6.92% 

(0.88%)

CCIGT 7.66% 

(0.59%)

7.57% 

(0.73%)

7.57% 

(0.67%)

9.53% 

(0.80%)

3.82% 

(0.63%)

6.34% 

(0.96%)

CDISSOLV 0.68% 

(0.11%)

0.52% 

(0.13%)

0.82% 

(0.15%)

0.58% 

(0.15%)

0.47% 

(0.19%)

1.08% 

(0.21%)

CELCIGT 11.67% 

(1.02%)

9.93% 

(0.97%)

13.30% 

(1.22%)

14.35% 

(1.06%)

5.83% 

(0.86%)

10.27% 

(1.82%)

CHOOKAH 3.43% 

(0.36%)

3.28% 

(0.37%)

3.41% 

(0.47%)

2.76% 

(0.42%)

3.53% 

(0.57%)

4.93% 

(0.75%)

CPIPE 0.87% 

(0.10%)

0.54% 

(0.11%)

1.05% 

(0.15%)

0.73% 

(0.14%)

0.31% 

(0.10%)

1.35% 

(0.31%)

CROLLCIGTS 2.72% 

(0.32%)

2.22% 

(0.31%)

3.02% 

(0.43%)

2.93% 

(0.53%)

1.76% 

(0.32%)

2.68% 

(0.35%)

CSLT 4.10% 

(0.65%)

1.31% 

(0.30%)

6.72% 

(1.05%)

5.71% 

(0.94%)

1.47% 

(0.42%)

2.54% 

(0.61%)

CSNUS 2.01% 

(0.28%)

1.92% 

(0.30%)

2.09% 

(0.33%)

2.69% 

(0.42%)

0.81% 

(0.23%)

1.49% 

(0.32%)

 

13 The estimates in tables 4.4 – 4.7 use the variable HSMS not provided on the public use file. Similar subgroup 

estimates can be produced using the variable Q3 which is included in that data file
29

Table 4.5 Current Use Estimates14 for Selected Tobacco Products for Middle School 

Students

Product

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

CBIDIS 0.31% 

(0.08%)

0.30%

(0.10%)

0.31% 

(0.11%)

0.19% 

(0.08%)

0.36% 

(0.18%)

0.66% 

(0.24%)

CCIGAR 1.72% 

(0.25%)

1.46% 

(0.27%)

1.93% 

(0.38%)

1.51% 

(0.37%)

1.66% 

(0.37%)

2.50% 

(0.43%)

CCIGT 2.32% 

(0.23%)

2.25% 

(0.29%)

2.31% 

(0.36%)

2.00% 

(0.31%)

1.99% 

(0.46%)

3.62% 

(0.46%)

CDISSOLV 0.32% 

(0.08%)

0.19% 

(0.06%)

0.41% 

(0.14%)

0.10% 

(0.08%)

0.23% 

(0.15%)

0.84% 

(0.32%)

CELCIGT 3.61% 

(0.28%)

2.98% 

(0.33%)

4.10% 

(0.40%)

3.78% 

(0.53%)

2.65% 

(0.48%)

4.29% 

(0.57%)

CHOOKAH 1.36% 

(0.20%)

1.10% 

(0.20%)

1.60% 

(0.33%)

0.63% 

(0.19%)

1.51%

(0.40%)

2.86% 

(0.49%)

CPIPE 0.45% 

(0.11%)

0.24% 

(0.08%)

0.63% 

(0.19%)

0.31% 

(0.12%)

1.04% 

(0.38%)

0.51% 

(0.19%)

CROLLCIGTS 1.36% 

(0.18%)

0.84% 

(0.16%)

1.75% 

(0.26%)

0.57% 

(0.15%)

1.45% 

(0.40%)

2.87% 

(0.46%)

CSLT 1.62% 

(0.24%)

0.81% 

(0.16%)

2.34% 

(0.42%)

1.61% 

(0.39%)

0.83% 

(0.26%)

2.43% 

(0.43%)

CSNUS 0.68% 

(0.12%)

0.66% 

(0.15%)

0.64% 

(0.17%)

0.57% 

(0.18%)

0.33% 

(0.16%)

1.25% 

(0.25%)

 

14 The estimates in tables 4.4–4.7 use the variable HSMS not provided on the public use file. Similar subgroup 

estimates can be produced using the variable Q3 which is available in that data file
30

Table 4.6 Ever Use Estimates15 for Selected Tobacco Products for High School Students

Product

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

EBIDIS 1.63% 

(0.18%)

1.37% 

(0.19%)

1.78% 

(0.25%)

1.56% 

(0.26%)

1.45% 

(0.31%)

1.98% 

(0.33%)

ECIGAR 20.03% 

(1.26%)

16.74% 

(1.15%)

23.04% 

(1.69%)

21.86% 

(1.35%)

21.18% 

(1.38%)

17.79% 

(2.11%)

ECIGT 23.38% 

(1.37%)

22.80% 

(1.44%)

23.56% 

(1.51%)

25.49% 

(1.55%)

17.79% 

(1.46%)

23.76% 

(2.54%)

EDISSOLV 1.53% 

(0.20%)

1.19% 

(0.20%)

1.79% 

(0.25%)

1.63% 

(0.30%)

0.37% 

(0.14%)

2.12% 

(0.37%)

EELCIGT 29.91% 

(1.55%)

28.28% 

(1.62%)

31.42% 

(1.70%)

33.84% 

(1.64%)

18.86% 

(1.61%)

30.37% 

(3.00%)

EHOOKAH 11.23% 

(0.76%)

11.21% 

(0.91%)

11.14% 

(0.86%)

10.25% 

(0.90%)

11.65% 

(1.17%)

14.28% 

(1.39%)

EPIPE 2.83% 

(0.25%)

2.08% 

(0.30%)

3.43% 

(0.38%)

3.27% 

(0.34%)

0.95% 

(0.30%)

2.98% 

(0.48%)

EROLLCIGTS 5.95% 

(0.48%)

5.66% 

(0.57%)

6.01% 

(0.55%)

6.73% 

(0.66%)

3.64% 

(0.61%)

5.47% 

(0.80%)

ESLT 10.00% 

(1.32%)

4.43% 

(0.82%)

15.29% 

(1.88%)

13.89% 

(1.92%)

2.88% 

(0.57%)

6.16% 

(1.18%)

ESNUS 5.61% 

(0.75%)

4.73% 

(0.51%)

6.40% 

(1.20%)

7.09% 

(1.13%)

1.96% 

(0.38%)

4.72% 

(0.73%)

 

15 The estimates in tables 4.4–4.7 use the variable HSMS not provided on the public use file. Similar subgroup 

estimates can be produced using the variable Q3 which is available in that data file.
31

Table 4.7 Ever Use Estimates16 for Selected Tobacco Products for Middle School Students

Product

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

EBIDIS 0.73% 

(0.12%)

0.64% 

(0.17%)

0.80% 

(0.17%)

0.53% 

(0.17%)

0.82% 

(0.29%)

1.27% 

(0.33%)

ECIGAR 5.36% 

(0.51%)

4.41% 

(0.44%)

6.07% 

(0.71%)

4.49% 

(0.81%)

7.34% 

(0.98%)

6.80% 

(0.77%)

ECIGT 8.60% 

(0.62%)

7.44% 

(0.62%)

9.66% 

(0.88%)

8.27% 

(0.88%)

9.32% 

(1.11%)

9.81% 

(0.79%)

EDISSOLV 0.95% 

(0.17%)

0.54% 

(0.16%)

1.27% 

(0.27%)

0.81% 

(0.21%)

0.79% 

(0.36%)

1.43% 

(0.35%)

EELCIGT 11.16% 

(0.64%)

9.78% 

(0.77%)

12.39% 

(0.78%)

11.04% 

(0.90%)

10.75% 

(1.21%)

13.38% 

(0.94%)

EHOOKAH 3.33% 

(0.29%)

2.64% 

(0.29%)

3.92% 

(0.46%)

2.03% 

(0.37%)

4.39% 

(0.57%)

5.94% 

(0.59%)

EPIPE 1.08% 

(0.19%)

1.10% 

(0.28%)

1.06% 

(0.27%)

1.04% 

(0.31%)

0.91% 

(0.30%)

1.37% 

(0.38%)

EROLLCIGTS 3.00% 

(0.30%)

2.34% 

(0.34%)

3.43% 

(0.42%)

2.24% 

(0.36%)

3.22% 

(0.69%)

4.57% 

(0.60%)

ESLT 3.29% 

(0.47%)

1.65% 

(0.26%)

4.76% 

(0.81%)

3.85% 

(0.75%)

1.23% 

(0.29%)

3.53% 

(0.54%)

ESNUS 1.92% 

(0.32%)

1.62% 

(0.27%)

2.20% 

(0.43%)

2.03% 

(0.48%)

1.12% 

(0.30%)

2.27% 

(0.36%)

 

16 The estimates in tables 4.4–4.7 use the variable HSMS not provided on the public use file. Similar subgroup 

estimates can be produced using the variable Q3, which is available in that data file.
APPENDIX A. QUESTIONNAIRE

Questionnaire only included in PDF version of this document. 
APPENDIX B. STUDENT WEIGHT DETAIL

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

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

students described in Section 4.1.1. The purpose of this section is to show that the resulting student 

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

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

another poststratifying to known enrollment totals.

For the purposes of clarity, subscripts denoting the sampling stages and weight class are omitted. 

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

Section 4.1.1.

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

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

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

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

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

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

K

C W 

Nonresponse Adjustment

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

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

to the sum of the weights for responding students,

R

n

F  





Responding

Selected

NR W

W

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

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

W, as W is constant within the class.

Enrollment Ratio Adjustment

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

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

R W

N

W

N

F

 









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

W W FNR



 

The fully adjusted student weight is computed as:

W W FPS







The simplified equation is as follows:

R

N

R W

N

W

W W FPS



 

 

  
APPENDIX C. COMMON CORE OF DATA RACE/ETHNICITY DEFINITIONS

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

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

identification through tribal affiliation or community recognition.

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

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

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

the Philippine Islands, Samoa, and other Pacific Islands.

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

American.

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

Spanish culture or origin, regardless of race.

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

Africa, or the Middle East.

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