METHODOLOGY REPORT 

OF THE 2018 NATIONAL

YOUTH TOBACCO 

SURVEY

August 2018

Recommended Citation

Office on Smoking and Health. 2018 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, 2018.

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

August 2018
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 2018 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 .............................................................................................................................7

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 .................................................. 12

3.1 SURVEY INSTRUMENT ..................................................................................................................12

3.2 EXTERNAL REVIEW AND APPROVALS .........................................................................................12

3.3 RECRUITMENT PROCEDURES .......................................................................................................12

3.4 DATA RECEIPT, PROCESSING, AND SCANNING.........................................................................133

3.5 PARTICIPATION RATES ...............................................................................................................133

3.6 DATA MANAGEMENT .................................................................................................................144

CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA......................................................... 155

4.1 SAMPLING WEIGHTS...................................................................................................................155

4.2 NONRESPONSE ADJUSTMENTS ...................................................................................................177

4.3 POSTSTRATIFICATION AND TRIMMING......................................................................................200

4.4 ESTIMATORS AND VARIANCE ESTIMATION ..............................................................................233

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 2018 NYTS METHODOLOGY

The 2018 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 310 schools, of which 238 participated, yielding a school 

participation rate of 76.8%. A total of 20,189 student questionnaires were completed out of a 

sample of 22,729 students, yielding a student participation rate of 88.8%. The overall participation 

rate, defined as the product of the school-level and student-level participation rates, was 68.2%. 

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 2018

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

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

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

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

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

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

they were classified as "rural." 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

270 SSUs (school) 

selections: 200 large 

schools (2 per PSU), 

30 medium schools and 

40 small schools

3 Classes/students

2 classes per grade in 

large schools in 8 black 

concentrated stratums; 

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 more than 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 nonHispanic blacks—at both the middle and high school levels. 

2.2 SAMPLING FRAME

As in previous cycles, the 2018 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 3.86% of schools (2.85% public and 7.94% private) and 0.64% of students. Next,

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

exclusion of 14.36% of schools (8.31% public and 38.93% 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—One hundred 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 200 large schools or SSUs

were selected from the 100 sample PSUs. The sample PSUs are subsampled to support 

the selection of small schools, 40 small schools from 20 subsample PSUs (one school for 

each level), and medium schools, 30 medium schools from 15 subsample PSUs (one 

school for each level). This resulted in a total of 270 SSUs (270 = 200+40+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, 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 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 30 medium and 40 small schools from subsample PSUs.

Across 11 previous cycles of the NYTS that had concluded at the time of the 2018 NYTS design, 

school participation had averaged 83.8% with a low of 72.5%. Student participation had averaged 

90.0% with a low of 87.4%. The combined response rate (student x school) averaged 75.43%

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 2018 NYTS based on 

these assumptions.

Table 2.2 Planned Sample Sizes for the 2018 NYTS

PSU Size # of 

SSUs

Number of 

Schools 

Sampled

# of 

Classes 

per 

School

# of 

Students 

per 

Class

# of Sampled 

Students 

prior to 

Attrition

# of 

Participated 

Students 

Based on 72% 

Response Rate

100 Large HS 100 Double 

classes: 50

8 25 10,000 7,200

Single 

classes: 50

4 25 5,000 3,600

Large MS 100 Double 

classes: 50

6 25 7,500 5,400

Single 

classes: 50

3 25 3,750 2,700

Large Total 200 26,250 18,900

15

(subsample)

Medium HS 15 15 4 25 1,500 1,080

Medium MS 15 15 3 25 1,125 810

Medium Total 30 2,625 1,890

20

(subsample)

Small HS 20 20 4 20 1,600 1,152

Small MS 20 20 3 20 1,200 864

Small Total 40 12,800 2,016

Overall Total 270 31,675 22,806

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

for a total of 200 large SSUs. The estimated sample yield from these large schools was 26,250

students before school and student non-response, leading to an expected total 18,900 participating 

students in large schools after accounting for non-response.
7

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

28 students in any grade) 30 medium SSUs from a subsample of 15 PSUs, and 40 small SSUs from 

a subsample of 20 PSUs were selected. The expected yield was 1,890 from medium schools and 

2,016 students from small schools. In total, the number of participating students was 22,806.

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 2018 NYTS, we implemented double class 

selection for large schools in 8 black concentrated stratums to enhance the black student yields. 

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.

2.6 STRATIFICATION

The PSUs were organized into 16 strata, based on urban/rural location and proportion minority 

enrollment. 
8

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

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

black. 

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

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

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

depending upon the percentages of Hispanic students in the PSU. 

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

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

The density grouping bounds were computed using an optimization algorithm4that 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 2018

NYTS.

Table 2.3 Stratum Boundaries: Minority Percentage Cutoffs

Minority 

Concentration

Density 

Group

Bounds

Urban Rural

Black

1 0%-26% 0%-20%

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

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

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

Hispanic

1 0%-26% 0%-24%

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

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

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

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

2.7 SAMPLE ALLOCATION AND SELECTION

The 2018 NYTS was designed to select a sample of 100 PSUs. The PSUs were initially allocated 

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

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

the PSU sample to the 16 strata defined by 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.

 

4 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/Rural

Density 

Group 

Number

Stratum 

Code

Student 

Population

Number of 

Sample 

PSUs 

(Revised)

Non-Hispanic 

Black

Urban

1 BU1 2,227,951 8

2 BU2 1,540,898 5

3 BU3 471,727 2

4 BU4 507,056 2

Non-urban

1 BR1 2,698,983 9

2 BR2 1,308,136 5

3 BR3 1,006,800 4

4 BR4 485,551 2

Hispanic

Urban

1 HU1 3,424,131 12

2 HU2 2,477,544 8

3 HU3 2,455,318 8

4 HU4 1,912,601 7

Non-urban

1 HR1 5,036,843 16

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 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 the small school (20 PSUs) and medium school (15 PSUs) sampling 

of two schools per level in each subsample PSU.

2.8 SAMPLE SIZES ATTAINED IN THE SURVEY

The 2018 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 

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

measure, the sample led to more than 5,700 Hispanic students. It also led to 3,169 black students 

using the imputed variable and nearly 2,400 black students using the original variable. 
10

Table 2.5a Subgroup Sample Sizes: Number of Participating Students

What is your sex?

Q2 Frequency Percent

Cumulative

Frequency

Cumulative

Percent

Female 9920 49.63 9920 49.63

Male 10069 50.37 19989 100.00

Frequency Missing = 200

Table 2.5b Subgroup Sample Sizes: Number of Participating Students

Table 2.5c Subgroup Sample Sizes: Number of Participating Students

RECODE: Race/Eth - mult grp

RACE_M Frequency Percent

Cumulative

Frequency

Cumulative

Percent

NH-White 8941 46.42 8941 46.42

NH-Black 2414 12.53 11355 58.96

Hispanic 5761 29.91 17116 88.87

NH-Asian 726 3.77 17842 92.64

NH-AI/AN 304 1.58 18146 94.22

NH-NHOPI 100 0.52 18246 94.74

Multiple Races 1014 5.26 19260 100.00

Frequency Missing = 929

What grade are you in?

Q3 Frequency Percent

Cumulative

Frequency

Cumulative

Percent

6th 2903 14.47 2903 14.47

7th 3140 15.65 6043 30.12

8th 3012 15.01 9055 45.14

9th 2935 14.63 11990 59.76

10th 2664 13.28 14654 73.04

11th 2824 14.08 17478 87.12

12th 2568 12.8 20046 99.92

Ungraded or other grade 16 0.08 20062 100

Frequency Missing = 127
11

Table 2.5d Subgroup Sample Sizes: Number of Participating Students

Race Categories for Post-stratification

Imputed Race/Ethnicity Frequency Percent

Cumulative

Frequency

Cumula

tive

Percent

Asian/Native Hawaiian/Other PI 1005 4.98 1005 4.98

Black or African American 3169 15.70 4174 20.67

Hispanic/Latino 5910 29.27 10084 49.95

American Indian/Alaska Native 484 2.40 10568 52.35

White 9621 47.65 20189 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 2018-2019 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 2018-2019 school principal

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

 Grade levels served during 2018-2019 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

Chapter 3—NYTS Data Collection and Processing

3.1 SURVEY INSTRUMENT

The 2018 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 secondhand 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

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

elements, and sampling design of the 2018 NYTS: the Office of Management and Budget (OMB), 

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

3.3 RECRUITMENT PROCEDURES

The schools selected to participate in the 2018 NYTS were in 33 different states. Recruitment 

began in October 2017 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, districtand school-level recruitment began. 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.

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 
13

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. 

3.4 DATA RECEIPT, PROCESSING, AND SCANNING

The completed surveys 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. Survey booklets 

received as part of the make-up process were manually edited to capture an artificial date value of 

“January 00” in order to allow for separate analyses later among the subset of records completed 

as make-ups rather than part of the initial survey administration. All data were subsequently 

scanned using an optical scanner.

3.5 PARTICIPATION RATES

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

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

60%.

5

3.5.1 School-level Participation Rates

At the school level, 310 schools were selected across 212 districts in 33 states. During sample 

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

In total, 238 schools (76.8%) participated in the study. The remaining 72 schools were considered 

refusals. Of refusals, 52 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. 

3.5.2 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 

 

5 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.
14

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 2018 NYTS was 88.8%. Overall, 22,729 eligible students were invited to 

participate in the survey, and 20,189 did so. Table 3.1 below shows the number of eligible students, 

participants, and participation rates for the NYTS.

Table 3.1 Overall NYTS 2018 Student Participation Rate

# Eligible # Completed Participation %

NYTS Participating Students 22,729 20,189 88.8%

When the student participation rate is combined with the school participation rate, the combined 

overall study participation rate was 68.2%, thus considered sufficient for weighting purposes.

The 2018 NYTS survey attained an actual school participation rate of 76.8% and a student 

participation rate of 88.8%. The overall participation rate, the product of the school-level and 

student-level participation rates, was 68.2%.

3.6 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 

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.
15

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.
16

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 











(100 / 20)
17

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 











(100 /15)

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. 
18

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 2018 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 2018 cycle, NCES locale, poverty level and proportion of students in English language 

learning (ELL) classes were found to be predictive on nonresponse. Nonresponse adjustment cells 

were created using school type (public vs private), NCES locale and poverty level. 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. 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.

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. 
19

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 not partitioned into finer cells; public schools were divided by NCES Locale (4 

categories) and by poverty level.

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

1,954,035.05 26 2,464,465.08 35 74.286 1.261

Public Schools, 

Not Poor, City

120,231.75 2 1,115,113.64 11 18.182 9.275

Public Schools,

Poor*, City

5,264,177.89 52 6,975,007.40 68 76.471 1.325

Public Schools,

Not Poor, 

Suburb

3,824,189.26 35 6,678,452.89 59 59.322 1.746

Public Schools,

Poor, Suburb

5,055,217.96 48 5,581,908.73 53 90.566 1.104

Public Schools,

Not Poor, Town

1,414,109.53 9 1,715,253.77 13 69.231 1.213

Public Schools,

Poor, Town

2,410,126.69 20 2,410,126.69 20 100.00 1.000

Public Schools,

Not Poor, Rural

1,909,968.23 15 2,071,481.89 16 93.750 1.085

Public Schools,

Poor, Rural

3,202,155.08 31 3,973,121.25 35 88.571 1.241

25,154,211.43 238 32,984,931.32 310
20

* 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 two variables used in nonresponse adjustment cells are NCES Locale (4 categories) and a dichotomous poverty 

indicator. The poverty indicator for a school is based on a school-level variable, the proportion of 

student below the poverty. The indicator classifies as “poor” those above the median for this 

proportion, and as “non-poor” those at or below the median.

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 

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

most recent versions, the 2013–14 CCD and the 2015-16 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).6 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. 

 

6

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

American Statistical Association meeting, Vancouver, Canada.
21

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 1218 1,887,823.87

Public 6 Female 1323 1,796,776.13

Public 7 Male 1313 1,918,137.18

Public 7 Female 1435 1,8266,32.82

Public 8 Male 1411 1,912,633.97

Public 8 Female 1250 1,828,214.03

Public 9 Male 1423 2,008,367.98

Public 9 Female 1371 1,884,892.02

Public 10 Male 1324 1,861,825.26

Public 10 Female 1231 1,789,842.74

Public 11 Male 1386 1,711,309.31

Public 11 Female 1349 1,678,618.69

Public 12 Male 1265 1,643,305.91

Public 12 Female 1175 1,628,871.09

Private 6 Combined 364 244,092.00

Private 7 Combined 410 241,805.00

Private 8 Combined 371 236,912.00

Private 9 Combined 169 232,776.00

Private 10 Combined 136 231,500.00

Private 11 Combined 124 226,218.00

Private 12 Combined 141 220,662.00
22

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 99 39374.42

Public 6 Non-Hispanic Asian and Pacific Islander 106 197794.42

Public 6 Non-Hispanic Black 384 581340.65

Public 6 Hispanic 766 961282.04

Public 6 Non-Hispanic White 1186 1904808.47

Public 7 Non-Hispanic Native American 91 40001.74

Public 7 Non-Hispanic Asian and Pacific Islander 104 197430.31

Public 7 Non-Hispanic Black 454 593270.66

Public 7 Hispanic 886 964625.76

Public 7 Non-Hispanic White 1213 1949441.54

Public 8 Non-Hispanic Native American 67 40234.54

Public 8 Non-Hispanic Asian and Pacific Islander 148 200029.94

Public 8 Non-Hispanic Black 433 594425.34

Public 8 Hispanic 831 940420.61

Public 8 Non-Hispanic White 1182 1965737.57

Public 9 Non-Hispanic Native American 69 43149.08

Public 9 Non-Hispanic Asian and Pacific Islander 129 195315.11

Public 9 Non-Hispanic Black 432 651138.66

Public 9 Hispanic 779 978398.96

Public 9 Non-Hispanic White 1385 2025258.20

Public 10 Non-Hispanic Native American 51 39358.76

Public 10 Non-Hispanic Asian and Pacific Islander 119 194973.41

Public 10 Non-Hispanic Black 405 579675.39

Public 10 Hispanic 727 881161.65

Public 10 Non-Hispanic White 1253 1956498.80

Public 11 Non-Hispanic Native American 50 34874.72

Public 11 Non-Hispanic Asian and Pacific Islander 142 193666.84

Public 11 Non-Hispanic Black 434 513086.02

Public 11 Hispanic 795 781658.91

Public 11 Non-Hispanic White 1314 1866641.52

Public 12 Non-Hispanic Native American 27 33646.81

Public 12 Non-Hispanic Asian and Pacific Islander 91 190497.87

Public 12 Non-Hispanic Black 399 482280.82

Public 12 Hispanic 705 725853.28

Public 12 Non-Hispanic White 1218 1839898.23

Private 6 Combined 364 244092.00

Private 7 Combined 410 241805.00

Private 8 Combined 371 236912.00

Private 9 Combined 169 232776.00

Private 10 Combined 136 231500.00

Private 11 Combined 124 226218.00

Private 12 Combined 141 220662.00

For poststratification purposes, a method of imputing at random with equal probabilities has been 

applied when the race variable is missing. So, a unique race/ethnicity was assigned to respondents 

with missing data on race/ethnicity, those with an “Other” classification, and those reporting 
23

multiple races. For non-public schools, we did not post-stratify by race/ethnic classifications. 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=74.3%

implies that the design-effect (DEFF) component due to unequal weighing effects is 1.55.

7

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 estimators8as 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.9

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

using the 2018 NYTS data. Sample SAS code is provided in Exhibit 4.1.

 

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

1 + cv**2.

8

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

9 Specifically, two strata (coded 113 and 114) were combined into one variance stratum (114) because the 

original stratum “113” had zero PSU when analyzed at the high school level.
24

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

SAS:

Proc Surveymeans Data=nyts2018 mean;

Var ebidis ecigar ecigt edissolv;

Class ebidis ecigar ecigt edissolv;

Stratum v_stratum2;

Cluster psu2;

Weight finwgt;

Domain HSMS HSMS*Sex HSMS*Race_S;

Title “NYTS 2018, 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=nyts2018 Filetype= SAS Design=WR;

Var ebidis ecigar ecigt edissolv;

Catlevel 1 1 1 1;

Nest v_stratum2 PSU2 / Missunit;

Weight finwgt;

Subgroup HSMS Sex Race_S;

Levels 2 2 3;

Tables HSMS HSMS*Sex HSMS*Race_S;

Title “NYTS 2018, 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;
25

Table 4.4 Current Use Estimates10 for Selected Tobacco Products for High School Students

Product:

Variable (name)

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

CBIDIS

(Bidis)

0.71% 

(0.08%)

0.53% 

(0.12%)

0.88% 

(0.14%)

0.76% 

(0.13%)

0.43% 

(0.18%)

0.88% 

(0.20%)

CCIGAR

(Cigar)

7.73% 

(0.48%)

6.11% 

(0.64%)

9.14% 

(0.48%)

7.85% 

(0.60%)

9.16% 

(1.40%)

7.69% 

(0.80%)

CCIGT

(Cigarette)

8.27% 

(0.56%)

7.39% 

(0.67%)

8.93% 

(0.65%)

9.97% 

(0.77%)

3.20% 

(0.57%)

7.59% 

(0.75%)

CDISSOLV

(Dissolvable 

tobacco product)

0.76% 

(0.09%)

0.54% 

(0.13%)

0.93% 

(0.15%)

0.84% 

(0.13%)

0.31% 

(0.18%)

0.93% 

(0.21%)

CELCIGT

(Electronic 

cigarette)

20.84% 

(1.02%)

18.86% 

(1.11%)

22.64% 

(1.06%)

26.83% 

(1.08%)

7.48% 

(1.17%)

15.10% 

(1.07%)

CHOOKAH

(Hookah)

4.22% 

(0.36%)

4.23% 

(0.52%)

4.10% 

(0.35%)

3.32% 

(0.37%)

3.94% 

(0.72%)

6.30% 

(0.76%)

CPIPE

(Pipe)

1.13% 

(0.14%)

0.82% 

(0.16%)

1.39% 

(0.18%)

1.13% 

(0.18%)

0.82% 

(0.38%)

1.50% 

(0.26%)

CROLLCIGTS

(Roll-your-own 

cigarette)

2.81% 

(0.27%)

2.29% 

(0.30%)

3.26% 

(0.35%)

2.70% 

(0.32%)

2.46% 

(0.55%)

3.78% 

(0.56%)

CSLT

(Smokeless 

tobacco)

4.51% 

(0.46%)

1.28% 

(0.24%)

7.55% 

(0.77%)

6.06% 

(0.71%)

1.18% 

(0.36%)

2.67% 

(0.38%)

CSNUS

(Snus)

2.44% 

(0.22%)

1.93% 

(0.25%)

2.80% 

(0.33%)

2.93% 

(0.32%)

0.99% 

(0.30%)

2.03% 

(0.33%)

 

10 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
26

Table 4.5 Current Use Estimates11 for Selected Tobacco Products for Middle School 

Students

Product:

Variable (name)

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

CBIDIS

(Bidis)

0.30% 

(0.06%)

0.37% 

(0.12%)

0.24% 

(0.08%)

0.31% 

(0.11%)

0.00% 

(0.00%)

0.52% 

(0.13%)

CCIGAR

(Cigar)

1.57% 

(0.20%)

1.51% 

(0.24%)

1.61% 

(0.25%)

1.04% 

(0.20%)

2.89% 

(0.65%)

2.00% 

(0.34%)

CCIGT

(Cigarette)

1.68% 

(0.20%)

1.36% 

(0.24%)

1.98% 

(0.28%)

1.57% 

(0.31%)

1.32% 

(0.43%)

2.16% 

(0.31%)

CDISSOLV

(Dissolvable 

tobacco product)

0.28% 

(0.06%)

0.15% 

(0.06%)

0.41% 

(0.12%)

0.20% 

(0.07%)

0.30% 

(0.18%)

0.44% 

(0.15%)

CELCIGT

(Electronic 

cigarette)

4.84% 

(0.40%)

4.70% 

(0.46%)

4.98% 

(0.51%)

4.80% 

(0.50%)

3.02% 

(0.52%)

6.49% 

(0.85%)

CHOOKAH

(Hookah)

1.11% 

(0.16%)

0.86% 

(0.15%)

1.35% 

(0.25%)

0.75% 

(0.20%)

1.26% 

(0.58%)

1.96% 

(0.31%)

CPIPE

(Pipe)

0.35% 

(0.07%)

0.39% 

(0.10%)

0.33% 

(0.08%)

0.26% 

(0.09%)

0.29% 

(0.20%)

0.67% 

(0.15%)

CROLLCIGTS

(Roll-your-own 

cigarette)

1.19% 

(0.16%)

0.85% 

(0.14%)

1.50% 

(0.25%)

0.96% 

(0.23%)

1.37% 

(0.35%)

1.64% 

(0.32%)

CSLT

(Smokeless 

tobacco)

1.26% 

(0.19%)

0.32% 

(0.09%)

2.13% 

(0.35%)

1.26% 

(0.26%)

0.96% 

(0.41%)

1.45% 

(0.26%)

CSNUS

(Snus)

0.53% 

(0.10%)

0.54% 

(0.14%)

0.54% 

(0.13%)

0.56% 

(0.15%)

0.00% 

(0.00%)

0.89% 

(0.19%)

 

11 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
27

Table 4.6 Ever Use Estimates12 for Selected Tobacco Products for High School Students

Product:

Variable (name)

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

EBIDIS

(Bidis)

1.89% 

(0.21%)

1.74% 

(0.26%)

2.00% 

(0.28%)

2.23% 

(0.29%)

0.92% 

(0.35%)

2.10% 

(0.30%)

ECIGAR

(Cigar)

20.50% 

(0.84%)

16.79% 

(1.10%)

24.00% 

(0.88%)

22.24% 

(1.07%)

20.55% 

(2.49%)

18.93% 

(1.10%)

ECIGT

(Cigarette)

24.36% 

(0.95%)

22.55% 

(1.26%)

26.03% 

(1.04%)

26.61% 

(1.24%)

16.60% 

(2.01%)

24.73%

(1.30%)

EDISSOLV

(Dissolvable tobacco 

product)

1.79% 

(0.18%)

1.35% 

(0.19%)

2.13% 

(0.29%)

1.89% 

(0.21%)

1.19% 

(0.31%)

1.97% 

(0.32%)

EELCIGT

(Electronic 

cigarette)

36.64% 

(1.22%)

34.87% 

(1.46%)

38.32% 

(1.19%)

43.54% 

(1.24%)

18.44% 

(1.58%)

32.33% 

(1.51%)

EHOOKAH

(Hookah)

11.16% 

(0.65%)

11.28% 

(0.80%)

10.98% 

(0.73%)

10.15% 

(0.69%)

9.26% 

(1.17%)

14.96% 

(1.09%)

EPIPE

(Pipe)

3.40% 

(0.28%)

2.87% 

(0.33%)

3.79% 

(0.41%)

3.71% 

(0.39%)

1.44% 

(0.51%)

4.38% 

(0.58%)

EROLLCIGTS

(Roll-your-own 

cigarette)

6.19% 

(0.47%)

5.59% 

(0.54%)

6.71% 

(0.56%)

6.99% 

(0.60%)

3.72% 

(0.57%)

6.26% 

(0.73%)

ESLT

(Smokeless tobacco)

10.06% 

(0.74%)

4.51% 

(0.46%)

15.46% 

(1.16%)

13.50% 

(1.10%)

2.76% 

(0.54%)

5.68% 

(0.66%)

ESNUS

(Snus)

5.98% 

(0.46%)

4.53% 

(0.45%)

7.40% 

(0.69%)

7.61% 

(0.69%)

1.22% 

(0.46%)

5.42% 

(0.62%)

 

12 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.
28

Table 4.7 Ever Use Estimates13 for Selected Tobacco Products for Middle School Students

Product:

Variable (name)

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

EBIDIS

(Bidis)

0.69% 

(0.10%)

0.77%

(0.16%)

0.62% 

(0.15%)

0.62% 

(0.17%)

0.36% 

(0.19%)

1.03% 

(0.20%)

ECIGAR

(Cigar)

5.19% 

(0.52%)

4.25% 

(0.53%)

6.07% 

(0.60%)

3.77% 

(0.52%)

9.87% 

(1.50%)

6.38% 

(0.66%)

ECIGT

(Cigarette)

7.82% 

(0.59%)

7.37% 

(0.73%)

8.21% 

(0.64%)

6.70% 

(0.78%)

10.33% 

(1.33%)

9.86% 

(0.74%)

EDISSOLV

(Dissolvable tobacco 

product)

0.73% 

(0.11%)

0.54% 

(0.15%)

0.92% 

(0.17%)

0.63% 

(0.14%)

0.52% 

(0.22%)

1.05% 

(0.22%)

EELCIGT

(Electronic 

cigarette)

11.78% 

(0.64%)

11.05% 

(0.79%)

12.54% 

(0.71%)

11.45% 

(0.89%)

10.13% 

(0.88%)

14.81% 

(1.26%)

EHOOKAH

(Hookah)

2.61% 

(0.31%)

2.46% 

(0.34%)

2.73% 

(0.39%)

1.67% 

(0.29%)

4.01% 

(0.82%)

4.00% 

(0.64%)

EPIPE

(Pipe)

0.84% 

(0.13%)

0.88% 

(0.21%)

0.82% 

(0.18%)

0.63% 

(0.16%)

0.66% 

(0.37%)

1.14% 

(0.25%)

EROLLCIGTS

(Roll-your-own 

cigarette)

2.43% 

(0.20%)

2.09% 

(0.29%)

2.67% 

(0.26%)

1.85% 

(0.32%)

2.71% 

(0.50%)

3.45% 

(0.41%)

ESLT

(Smokeless tobacco)

3.11% 

(0.33%)

1.27% 

(0.23%)

4.88% 

(0.57%)

3.95% 

(0.51%)

1.38% 

(0.44%)

2.44% 

(0.35%)

ESNUS

(Snus)

1.57% 

(0.21%)

1.42% 

(0.22%)

1.74% 

(0.29%)

1.77% 

(0.35%)

0.47% 

(0.23%)

1.85% 

(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 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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