METHODOLOGY REPORT 

OF THE 2019 NATIONAL

YOUTH TOBACCO 

SURVEY

June 2019

Recommended Citation

Office on Smoking and Health. 2019 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, 2019.

PRINT ONLY
For questions about this report, please email Kimp Walton at kcw3@cdc.gov

Prepared for Centers for Disease Control and Prevention

Prepared by ICF, Rockville, Maryland

Centers for Disease Control and Prevention

Office on Smoking and Health

Atlanta, GA

June 2019
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 2019 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 .................................................. 13

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

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

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

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

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

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

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

3.8 RESPONSE RATES .........................................................................................................17

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

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

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

4.2 NONRESPONSE ADJUSTMENTS .........................................................................................21

4.3 POSTSTRATIFICATION AND TRIMMING .............................................................................25

4.4 ESTIMATORS AND VARIANCE ESTIMATION .......................................................................28

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 Surveys (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., cigarettes, cigars, smokeless tobacco, electronic 

cigarettes, hookahs, roll-your-own cigarettes, pipes, snus, dissolvable tobacco, bidis, and heated 

tobacco products), exposure to secondhand tobacco smoke and e-cigarette aerosol; smoking 

cessation; minors’ ability to purchase or obtain tobacco products; knowledge and attitudes about 

tobacco; and familiarity with pro-tobacco and anti-tobacco media messages. 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: TU-2, TU-3, TU-7, TU-11, TU-18 and TU-19.3

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 2019 NYTS METHODOLOGY

The 2019 NYTS employed a stratified, three-stage cluster sample design to produce a nationally 

representative sample of middle school and high school students in the United States. Sampling 

procedures were probabilistic and conducted without replacement at all stages and entailed 

selection of: 1) Primary Sampling Units (PSUs) (defined as a county, or a group of small counties, 

or part of a very large county) within each stratum; 2) Secondary Sampling Units (SSUs), (defined 

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

school. 

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

was administered in schools using electronic data collection method for the first time in 2019. 

 

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.

3 US Department of Health and Human Services. Healthy people 2020. Topics and objectives: tobacco use; 2019. Available at 

https://www.healthypeople.gov/2020/topics-objectives/topic/tobacco-use/objectives.
2

Participants were provided with a tablet to complete the survey; data were collected offline using 

a programmed survey application; a single class period of approximately 35-45 minutes was 

allotted to complete the survey. Survey administrators later established secure WiFi connections 

to sync all locally stored tablet data to a central repository via encrypted transmissions. Absent 

students and whole classes unavailable on the day of survey administration could participate in 

make-up surveys using a web-based version of the questionnaire programmed to mimic the tabletbased application. 

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

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

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

passive or active permission forms at their discretion.

Survey administration occurred from February 15, 2019 to May 24, 2019. The final sample 

consisted of 325 schools, of which 251 participated, yielding a school response rate of 77.2%. A 

total of 19,018 student questionnaires were completed (17,197 tablet-based; 1,821 web-based) out 

of a sample of 22,153 students, yielding a student response rate of 85.8%. The overall response

rate, defined as the product of the school-level and student-level response rates, was 66.3%. 

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 2019 

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 according to differences in subpopulation sizes.

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

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

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

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

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

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

The sample was a stratified, three-stage cluster sample. PSUs were stratified by racial/ethnic status 

and urban versus non-urban designation. PSUs were classified as "urban" if they were in one of 

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

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

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

features.

Table 2.1 Key Sampling Design Features

Sampling 

Stage Sampling Units Stratification

Measure of Size 

(MOS) Designed Sample Size

1

PSUs: Counties, 

portions of a county, or 

groups of counties

Urban vs. Non-urban

(2 strata);

Minority concentration

(8 strata)

Aggregate 

school size in 

target grades

100 Counties, portions 

of a county, or groups 

of counties

2 Schools

Small, medium and 

large;

High school vs. middle

school

Aggregate 

eligible 

enrollment

300 SSUs (school) 

selections: 200 large 

schools (2 per PSU), 

60 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

With the average design effects attained in the NYTS, the requirements translated to subgroup 

sample sizes of 1,700 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 minority racial/ethnic subgroups—Hispanics and nonHispanic blacks—at both the middle and high school levels. 

2.2 SAMPLING FRAME

As in previous cycles, the 2019 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 Market Data Retrieval, Inc. (MDR, Inc.) and from the National Center for 

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

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

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

frame sources included the Common Core of Data for public schools and the Private School Survey 

for non-public schools. This dual-source frame build method was piloted first in 2014 to build the 

frame for the National Youth Tobacco Survey.

4

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 removed ineligible schools that were classified as Department of Defense 

schools, vocational schools, and adult education schools. This resulted in the exclusion of 3.2% of 

schools (2.2% of public schools and 6.3% of private schools) and 0.7% of students. Lastly, schools 

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

exclusion of 22.6% of schools (8.29% public and 39.03% private) which were eligible after the 

other exclusions. This exclusion of very small schools led to the exclusion of only 0.8% of students

of those in eligible schools. 

2.3 SAMPLING UNITS AND MEASURE OF SIZE

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

students in grades 6–12 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.

 

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

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

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

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

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

schools were classified as medium sized.

The sampling stages may be summarized as follows:

• Selection of PSUs—One hundred PSUs (from approximately 1,257 PSUs) were selected 

from 16 strata with probability proportional to the total number of eligible students 

enrolled in all eligible schools located within a PSU.

• Selection of schools—At the second sampling stage, a total of 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, 60 medium schools from 30 subsample PSUs (one 

school for each level). This resulted in a total of 300 SSUs (300 = 200+40+60). 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.

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 a simulation study to ensure that 

the required numbers of minority students were achieved per school level.

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

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

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

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

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

sample.

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

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

population.

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

students in a selected class then were 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 60 medium and 40 small schools from subsample PSUs.

Across 14 previous cycles of the NYTS that had concluded at the time of the 2019 NYTS design, 

school participation had averaged 83.3% with a low of 72.5%. Student participation had averaged 

89.9% with a low of 87.4%. The combined response rate (student x school) averaged 74.9%.

Historical response 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 2019 NYTS based on these 

assumptions.

Table 2.2 Planned Sample Sizes for the 2019 NYTS

PSU Size # of 

SSUs

Number of 

Schools 

Sampled

# of 

Classes 

per 

School

# of 

Students 

per 

Class

# of Sampled 

Students 

prior to 

Attrition

# of 

Participating

Students 

Based on 72% 

Response Rate

100 Large HS 100 Double 

classes: 40

8 25 8,000 5,760

Single 

classes: 60

4 25 6,000 4,320

Large MS 100 Double 

classes: 40

6 25 6,000 4,320

Single 

classes:60

3 25 4,500 3,240

Large Total 200 24,500 17,640

30

(subsample)

Medium HS 30 30 4 25 3,000 2,160

Medium MS 30 30 3 25 2,250 1,620

Medium Total 60 5,250 3,780

20

(subsample)

Small HS 20 20 4 25 2,000 1,440

Small MS 20 20 3 25 1,500 1,080

Small Total 40 3,500 2,520

Overall Total 270 33,250 23,940

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 24,500

students before school and student non-response, leading to an expected total 17,640 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) 60 medium SSUs from a subsample of 30 PSUs, and 40 small SSUs from 

a subsample of 20 PSUs were selected. The expected yield was 3,780 from medium schools and 

2,520 students from small schools. In total, the number of participating students was 23,940.

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 2019 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/non-urban 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.

• Hispanic urban and Hispanic non-urban PSUs were classified into four density groupings 

depending upon the percentages of Hispanic students in the PSU. 

• Non-Hispanic black urban and non-Hispanic black non-urban PSUs also were 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 2019 

NYTS.

Table 2.3 Stratum Boundaries: Minority Percentage Cutoffs

Minority 

Concentration

Density 

Group

Bounds

Urban Non-urban

Black

1 0%-26% 0%-20%

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

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

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

Hispanic

1 0%-26% 0%-24%

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

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

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

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

2.7 SAMPLE ALLOCATION AND SELECTION

The 2019 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.

 

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

Table 2.4 First-Stage Strata and Frame PSU Distribution

Predominant 

Minority

Urban/Nonurban

Density 

Group 

Number

Stratum 

Code

Student 

Population

Number of 

Sample 

PSUs 

(Revised)

Non-Hispanic 

Black

Urban

1 BU1 1,270,183 8

2 BU2 890,576 5

3 BU3 270,683 2

4 BU4 290,375 2

Non-urban

1 BR1 1,511,813 9

2 BR2 768,040 5

3 BR3 545,667 4

4 BR4 273,748 2

Hispanic

Urban

1 HU1 1,946,984 11

2 HU2 1,475,655 9

3 HU3 1,413,611 8

4 HU4 1,114,118 7

Non-urban

1 HR1 2,971,743 16

2 HR2 821,571 5

3 HR3 577,091 4

4 HR4 378,629 3

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

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

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

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

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

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

stratum. 

• Subsampled PSUs for the small school (20 PSUs) and medium school (30 PSUs) sampling 

of two schools per level in each subsample PSU.

2.8 SAMPLE SIZES ATTAINED IN THE SURVEY

The 2019 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 poststratification which includes complete data. By either measure, 

the sample led to more than 5,500 Hispanic students. It also led to 2,872 black students using the 

imputed variable and nearly 2,288 black students using the original variable. 
10

Table 2.5a Sample Sizes by Sex: Number of Participating Students

What is your sex?

Q2 Frequency Percent

Cumulative

Frequency

Cumulative

Percent

Male 9803 51.55 9803 51.55

Female 9099 47.84 18902 99.39

Displayed, not answered6 116 0.61 19018 100.00

Table 2.5b Sample Sizes by Grade Level: Number of Participating Students

 

6

“Displayed, not answered” is a type of missingness specific to an electronic administration that occurs when a 

student is presented with a question on screen, but that question is not answered; that is, item-level nonresponse.

What grade are you in?

Q3 Frequency Percent

Cumulative

Frequency

Cumulative

Percent

6

th 2944 15.48 2944 15.48

7

th 3024 15.90 5968 31.38

8

th 2869 15.09 8837 46.47

9

th 2790 14.67 11627 61.14

10th 2499 13.14 14126 74.28

11th 2502 13.16 16628 87.43

12th 2306 12.13 18934 99.56

Ungraded or other grade 27 0.14 18961 99.70

Displayed, not answered6 57 0.30 19018 100.00
11

Table 2.5c Sample Sizes by Race/Ethnicity (Multiple Selection): Number of Participating 

Students

RECODE: Race/Eth - mult grp

RACE_M Frequency Percent

Cumulative

Frequency

Cumulative

Percent

NH-White 8536 44.88 8536 44.88

NH-Black 2288 12.03 10824 56.91

Hispanic 5564 29.26 16388 86.17

NH-Asian 861 4.53 17249 90.70

NH-AI/AN 221 1.16 17470 91.86

NH-NHOPI 104 0.55 17574 92.41

Multiple Races 998 5.25 18572 97.65

Missing7 446 2.35 19018 100.00

Note: This variable is named race_m in the public use data set. The multiple race categories are Hispanic, nonHispanic (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). For respondents that identified as more 

than one race, they were categorized as “Multiple Races”.

Table 2.5d Sample Sizes by Race/Ethnicity (Single Selection): Number of Participating 

Students

RECODE: Race/Eth - no mult grp

RACE_S Frequency Percent

Cumulative

Frequency

Cumulative

Percent

NH-White 9351 49.17 9797 51.51

NH-Black 2430 12.78 12227 64.29

Hispanic 5564 29.26 17791 93.55

NH-Asian 898 4.72 18689 98.27

NH-AI/AN 225 1.18 18914 99.45

NH_NHOPI 104 0.55 19018 100.00

Missing7 446 2.35 446 2.35

Note: This variable is named race_s in the public use data set. For respondents that identified as more than one race, 

they were categorized into a single race with the following hierarchy: Hispanic, white, black, Asian, American 

Indian/Alaskan Native, and Native Hawaiian/Pacific Islander.

2.9 SAMPLE VALIDATION

 

7 Missing is defined as missing due to edit check; a legitimate skip; displayed, not answered; or not displayed.
12

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
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 2019 survey instrument included 104 questions. The 2019 NYTS 

represented the first time the study was conducted using electronic data collection methods rather 

than traditional paper-and-pencil (PAPI). The questionnaire application was programmed for 

offline data collection on an Android-based operating system. The survey application was written 

in HTML5 and JavaScript, and the final application was loaded onto a tablet device. Each student 

was provided with a tablet for the purposes of taking the survey, and it was returned to the survey 

administrator at the conclusion of the survey session. Students logged into the application using a 

randomly-generated, randomly-distributed, five-digit access code that was unique to each user. 

Each access code was tied in a backend database to its associated school and classroom to facilitate 

tracking and calculate class and school response rates. After survey administrators left the school, 

they established a WiFi connection and synced all locally stored data records to a central 

repository. Data were encrypted in transmission. 

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

and current tobacco product use behaviors. To improve students’ sense of privacy, only 1-2 

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

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

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

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

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

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

sessions with absent students, approximately 55 classes used the Web-based survey for their initial 

administration due to technical difficulties with the tablets, scheduling conflicts that prevented a 

class from participating on the same day as other selected classes in the same school, or a stated 

preference by the school to self-administer without a data collector present. 

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

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

for tablet-based administration ranged from 19 seconds to 15 hours, with an average of 

approximately 13 minutes. For Web-based administration, LOI ranged from 10 seconds to 23.5 

hours, with an average of approximately 14 minutes. After exclusion of outliers, the average 

survey completion time for tablet and Web was approximately 13 minutes.

8

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

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

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

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

nicotine dependence; cessation attempts; exposure to secondhand tobacco smoke and e-cigarette 

aerosol; harm perceptions; exposure to tobacco product warnings; and tobacco use prevention 

 

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

than 80 minutes (n<20). 
14

school curricula. At the beginning of each tobacco product section, a description of the product 

(with example brands) and generic images of specific tobacco products were provided to assist 

with product recognition and increase the accuracy of student data. Students could refer back to 

this description as needed as they answered related questions. 

Historically, subject matter 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 2019 NYTS: the Office of Management and Budget (OMB), 

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

With the transition to an electronic data collection format, a Security Assessment and 

Authorization (SA&A) application and Enterprise Performance Life Cycle (EPLC) review were

completed. The SA&A is a formal methodology for testing and evaluating the security controls of 

the system to ensure that it is configured properly to meet the security mandated by the Federal 

Information Security Management Act (FISMA). EPLC is a framework to enhance the 

Department of Health and Human Services (HHS) IT governance through rigorous application of 

sound investment and project management principals, in conjunction with industry’s best 

practices. 

3.3 DATA COLLECTION STAFFING

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

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

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

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

collector training was conducted on February 12 – 14, 2019. 

Key components of the training included the following:

▪ Pre-contact activities with the schools

▪ Entry and exit meetings with school officials

▪ Data collection protocols

▪ Recruitment visit protocols

▪ Follow-up activities

▪ Communication with headquarters staff
15

3.4 RECRUITMENT PROCEDURES

The schools selected to participate in the 2019 NYTS were located in 33 different states. 

Recruitment began in September 2018 with calls to state departments of education and health to 

inform them of the survey effort and sampled schools in their state. After notification at the state 

level, district- and school-level recruitment began. Before public or diocesan schools were 

contacted, verbal or written agreement first was obtained through their district or diocese, 

respectively; private schools were approached directly. A date for survey implementation was 

selected to optimize the efficiency of data collection while accommodating school schedules. In 

selecting a date, convenience to the school and its academic calendar were considered. 

Additionally, an effort was made to schedule groups of schools from the same school district or 

PSU around the same time to facilitate efficient travel to and survey implementation within 

selected schools. For a subset of participating schools, two data collectors needed to be present in 

order to ensure there were sufficient tablets available for students. Typically, two people were 

sent to the same school when class enrollments were greater than 35 or when two or more randomly 

selected classes occurred during the same period. Recruiters used a secure web-based calendar to 

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

same day.

3.5 SURVEY ADMINISTRATION

Survey administration in the schools began on February 15, 2019, immediately after a 

comprehensive data collector training, and continued until May 24, 2019. Each data collector 

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

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

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

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

contact; 4) entry meeting with teacher or group of teachers prior to survey administration; 5) survey 

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

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

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

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

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

3.5.1 FIELD PROCEDURES

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

each school received a packet of pre-survey materials containing instructions for the school contact 

and packets for the teacher of each selected class. Teacher packets contained the parental 

permission forms to be distributed to all students in the selected classes prior to data collection. 

The timing of these pre-survey packet mailings was determined in part by the type of permission 

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

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

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

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

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

date of data collection for schools that require active consent. Follow-up calls were made to the 
16

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

to selected classes and students.

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

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

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

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

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

3.5.2 CLASSROOM SELECTION

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

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

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

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

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

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

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

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

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

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

be attending a class of one kind or another as the frame, and randomly selecting from all classes 

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

3.6 WEB-BASED DATA COLLECTION MANAGEMENT APPLICATION (DCMA)

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

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

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

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

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

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

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

student response rates.

3.7 DATA SYNCING AND RECORDING

Preliminary student response rates were recorded by the survey administrators into the DCMA

described in Section 3.5. Field staff entered the number of eligible students in each selected class

and the expected number of completed records based on their observation in the classroom. Once 

data were synced, the actual number of records received in the central repository was reflected in 

student participation reporting. If the number of expected records and the actual number of records 

differed, project staff verified the correct number and reconciled the discrepancy. As web-based 

make-ups were submitted by students, the DCMA automatically updated the number of actual 

records received and participation reporting was revised accordingly. 
17

3.8 RESPONSE RATES

Response 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 response rates equal to or greater than 60%.

9

3.8.1 School-level Response Rates

At the school level, 325 schools were selected across 224 districts in 33 states. During sample 

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

In total, 251 schools (77.2%) participated in the study. The remaining 74 schools were considered 

refusals. Of refusals, 42 of them were due to their district refusing to grant access to their schools 

to discuss participation and 32 were due to refusals at the school level. The most common reasons 

given for a refusal at the district or school level were loss of instructional time and standardized 

testing. 

3.8.2 Student-level Response Rates

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

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

received from the field, further refinements were made to: 1) revise the number of eligible students 

based on available documentation, 2) correct mathematical errors, 3) review counts of surveys

received by the database, and 4) account for make-ups as they were received from schools from 

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

The final student response rate for the 2019 NYTS was 85.8%. Overall, 22,153 eligible students 

were invited to participate in the survey, and 19,018 did so. Table 3.1 below shows the number of 

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

Table 3.1 Overall NYTS 2019 Student Response Rate

# Eligible # Completed Response %

NYTS Participating Students 22,153 19,018 85.8%

When the student response rate is combined with the school response rate, the combined overall 

study response rate was 66.3% thus considered sufficient for weighting purposes.

The 2019 NYTS survey attained an actual school response rate of 77.2% and a student response

rate of 85.9% The overall response rate, the product of the school-level and student-level response

rates, was 66.3%.

3.9 DATA MANAGEMENT

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

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

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

 

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

response rate needed for confidence in weighting.
18

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

also computed certain analytic variables and re-coded race/ethnicity values to match CDCspecified classifications. Data “missingness” or “blanks” was categorized as a legitimate skip 

based on programmed logic, as item-level refusal if a question was presented to a student on-screen 

but not answered, or not answered because the student was never shown a question on screen (e.g., 

partial complete). Missingness is distinguished in the data set as follows:

• .E – Missing due to edit check

• .S – Legitimate skip

• .N – Displayed, not answered

• .Z – Not displayed

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.
19

CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA

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

• Sampling weights

• Nonresponse adjustments 

• Poststratification 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.
20

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)
21

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 / 30)

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 post stratified to control totals, as described 

in the following sections. This weight was computed as:











W =W W W W

W =W W W W

W =W W W W

R

hijklm

G

jklm

SS

klm

P

lm

T1

hijklm

R

hijklm

G

jklm

MS

klm

P

lm

T1

hijklm

R

hijklm

G

jklm

LS

klm

P

lm

T1

hijklm

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. 
22

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 2019 NYTS cycle, nonresponse adjustment cells were defined in a tailored and systematic 

approach stemming from the non-response analysis. These analyses are detailed in the 2019 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 2019 cycle, census region and percentage of black students were found to be predictive 

on nonresponse. Nonresponse adjustment cells were created using -school level (high vs middle), 

census region (4 levels) and percentage of black students (below vs above median). 

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. 
23

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 school level (2 levels), 

census region (4 categories) and percent of black students (2 levels).

Table 4.1 Nonresponse Adjustment Factors in Each Adjustment Cell

Weighting 

Class

Weight Sum 

Over

Participants

Responding 

School Count

Weight Sum 

over all Sample

Sample 

School 

Count

Response

Rate (%)

Adjustment 

Factor

High School, 

Northeast, Percent 

of Black below 

Median

653,080.66 8 941,426.84 10 80.0 1.442

High School, 

Northeast, Percent 

of Black above 

Median

1,035,833.42 9 1,865,232.49 17 52.94 1.801

High School, 

Midwest, Percent 

of Black below 

Median

2,563,113.46 21 2,765,162.77 23 91.30 1.079

High School, 

Midwest, Percent 

of Black above 

Median

1,094,846.90 14 1,393,032.91 16 87.5 1.272

High School, 

South, Percent of 

Black below 

Median

1,163,120.47 10 1,631,635.98 14 71.43 1.403

High School, 

South, Percent of 

Black above 

Median

2,666,833.35 21 3,998,617.04 33 63.63 1.499

High School, 

West, Percent of 

Black below 

Median

2,444,469.04 22 2,917,864.85 25 88.0 1.194
24

Weighting 

Class

Weight Sum 

Over

Participants

Responding 

School Count

Weight Sum 

over all Sample

Sample 

School 

Count

Response

Rate (%)

Adjustment 

Factor

High School, 

West, Percent of 

Black above 

Median

866,199.81 7 1,324,862.62 11 63.63 1.53

Middle School, 

Northeast, Percent 

of Black below 

Median

988,596.15 13 1,309,670.64 16 81.25 1.325

Middle School, 

Northeast, Percent 

of Black above 

Median

827,396.95 12 1,074,522.13 14 85.71 1.299

Middle School, 

Midwest, Percent 

of Black below 

Median

1,558,816.87 18 1,993,281.03 21 85.71 1.279

Middle School, 

Midwest, Percent 

of Black above 

Median

701,752.15 10 1,187,344.32 16 62.5 1.692

Middle School, 

South, Percent of 

Black below 

Median

1,287,739.11 15 1,540,474.99 19 78.95 1.196

Middle School, 

South, Percent of 

Black above 

Median

2,311,484.38 28 3,879,420.47 43 65.12 1.678

Middle School, 

West, Percent of 

Black below 

Median

2,552,788.06 33 2,753,388.13 35 94.29 1.079

Middle School, 

West, Percent of 

Black above 

Median

711,926.50 10 919,436.37 12 83.33 1.291

23427997.3 251 31495373.6 325

* 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 three variables used in non-response adjustment cells are school 

level (high vs middle), census region (4 categories) and percentage of black student (below vs above median).
25

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

the interquartile range of the weight distribution.

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

each 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. 

 

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

 American Statistical Association meeting, Vancouver, Canada.
26

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

School Type Grade Sex*

Number of 

Records

Weight

Sum = Control Total

Public 6 Male 1362 1,887,823.87

Public 6 Female 1369 1,796,776.13

Public 7 Male 1411 1,918,137.18

Public 7 Female 1416 1,8266,32.82

Public 8 Male 1444 1,912,633.97

Public 8 Female 1224 1,828,214.03

Public 9 Male 1352 2,008,367.98

Public 9 Female 1244 1,884,892.02

Public 10 Male 1207 1,861,825.26

Public 10 Female 1100 1,789,842.74

Public 11 Male 1242 1,711,309.31

Public 11 Female 1118 1,678,618.69

Public 12 Male 1143 1,643,305.91

Public 12 Female 1022 1,628,871.09

Private 6 Combined 210 244,092.00

Private 7 Combined 231 241,805.00

Private 8 Combined 215 236,912.00

Private 9 Combined 196 232,776.00

Private 10 Combined 209 231,500.00

Private 11 Combined 154 226,218.00

Private 12 Combined 149 220,662.00

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

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 73 39,374.42

Public 6 Non-Hispanic Asian and Pacific Islander 149 197,794.42

Public 6 Non-Hispanic Black 398 581,340.65

Public 6 Hispanic 852 961,282.04

Public 6 Non-Hispanic White 1259 1,904,808.47

Public 7 Non-Hispanic Native American 93 40,001.74

Public 7 Non-Hispanic Asian and Pacific Islander 134 197,430.31

Public 7 Non-Hispanic Black 476 593,270.66

Public 7 Hispanic 871 964,625.76

Public 7 Non-Hispanic White 1253 1,949,441.54

Public 8 Non-Hispanic Native American 47 40,234.54

Public 8 Non-Hispanic Asian and Pacific Islander 140 200,029.94

Public 8 Non-Hispanic Black 451 594,425.34

Public 8 Hispanic 819 940,420.61

Public 8 Non-Hispanic White 1211 1,965,737.57

Public 9 Non-Hispanic Native American 44 43,149.08

Public 9 Non-Hispanic Asian and Pacific Islander 151 195,315.11

Public 9 Non-Hispanic Black 410 651,138.66

Public 9 Hispanic 737 978,398.96

Public 9 Non-Hispanic White 1254 2,025,258.20

Public 10 Non-Hispanic Native American 46 39,358.76

Public 10 Non-Hispanic Asian and Pacific Islander 137 194,973.41

Public 10 Non-Hispanic Black 321 579,675.39

Public 10 Hispanic 691 881,161.65

Public 10 Non-Hispanic White 1112 1,956,498.80

Public 11 Non-Hispanic Native American 41 34,874.72

Public 11 Non-Hispanic Asian and Pacific Islander 138 193,666.84

Public 11 Non-Hispanic Black 363 513,086.02

Public 11 Hispanic 663 781,658.91

Public 11 Non-Hispanic White 1155 1,866,641.52

Public 12 Non-Hispanic Native American 18 33,646.81

Public 12 Non-Hispanic Asian and Pacific Islander 147 190,497.87

Public 12 Non-Hispanic Black 308 482,280.82

Public 12 Hispanic 682 725,853.28

Public 12 Non-Hispanic White 1010 1,839,898.23

Private 6 Combined 210 244,092.00

Private 7 Combined 231 241,805.00

Private 8 Combined 215 236,912.00

Private 9 Combined 196 232,776.00

Private 10 Combined 209 231,500.00

Private 11 Combined 154 226,218.00

Private 12 Combined 149 220,662.00

*Race/Hispanic origin is combined for private schools due to small cell sizes.

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 
28

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=69.76%

implies that the design-effect (DEFF) component due to unequal weighing effects is 1.49.

11

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 estimators12 as implemented in survey 

procedures for statistical software (e.g., SAS, SUDAAN, Stata). 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.13

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

using the 2019 NYTS data. Sample SAS and SUDAAN code is provided in Exhibit 4.1.

 

11 The design effect due to unequal weighting may be expressed in terms of the cv of the weight as DEFF= 1 + 

cv**2.

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

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

stratum “113” had only one PSU when analyzed at both the middle and high school level.
29

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

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

exhaustive list of all tobacco products assessed in the NYTS 

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

(ever use, current use)* and Standard Errors

SAS:

Proc Surveymeans Data=nyts2019 mean;

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

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

Stratum v_stratum2;

Cluster psu2;

Weight finwgt;

Domain SCHOOLTYPE SCHOOLTYPE*Sex SCHOOLTYPE*Race_S;

Title “NYTS 2019, Tobacco Product Use Estimates by School Type, by School Type and Sex Cross-Classified, 

and by School Type and Race/Ethnicity Cross-Classified”;

run;

SUDAAN:

Proc Descript Data=nyts2019 Filetype= SAS Design=WR;

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

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

Nest v_stratum2 PSU2 / Missunit;

Weight finwgt;

Subgroup SCHOOLTYPE Sex Race_S;

Levels 2 2 3;

Tables SCHOOLTYPE SCHOOLTYPE*Sex SCHOOLTYPE*Race_S;

Title “NYTS 2019, Tobacco Product Use Estimates by School Type, by School Type and Sex Cross-Classified, 

and by School Type and Race Cross-Classified”;

Print Percent Sepercent / Style=NCHS;

run;
30

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

School Students14

Product

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

Electronic cigarettes 27.47% 

(1.10%)

27.36% 

(1.20%)

27.63% 

(1.32%)

32.45% 

(1.34%)

17.69% 

(1.71%)

23.18% 

(1.35%)

Cigars, little cigars, 

or cigarillos

7.65% 

(0.55%)

6.18% 

(0.54%)

9.00% 

(0.72%)

7.60% 

(0.77%)

12.27% 

(1.16%)

6.17% 

(0.64%)

Cigarettes 5.78% 

(0.69%)

4.06% 

(0.58%)

7.32% 

(0.92%)

7.09% 

(0.96%)

3.94% 

(0.76%)

3.79% 

(0.53%)

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

4.09% 

(0.60%)

1.13% 

(0.29%)

6.74% 

(0.94%)

5.70% 

(0.94%)

1.66% 

(0.56%)

1.86% 

(0.29%)

Hookah or waterpipe 3.38% 

(0.36%)

3.19% 

(0.39%)

3.55% 

(0.48%)

2.46% 

(0.36%)

6.42% 

(0.98%)

4.04% 

(0.62%)

Roll-your-own 

cigarettes

2.33% 

(0.31%)

1.92% 

(0.26%)

2.73% 

(0.46%)

2.53% 

(0.46%)

2.43% 

(0.48%)

1.94% 

(0.32%)

Snus 1.66% 

(0.24%)

0.77% 

(0.17%)

2.48% 

(0.37%)

2.20% 

(0.35%)

0.64% 

(0.31%)

0.98% 

(0.22%)

Pipe tobacco 1.08% 

(0.18%)

0.58% 

(0.15%)

1.54% 

(0.30%)

1.25% 

(0.31%)

0.98% 

(0.28%)

0.72% 

(0.15%)

Dissolvable tobacco 

products

0.47% 

(0.09%)

0.30% 

(0.12%)

0.62% 

(0.15%)

0.50% 

(0.13%)

0.04% 

(0.04%)

0.53% 

(0.18%)

Bidis 0.38% 

(0.06%)

0.21% 

(0.05%)

0.53% 

(0.11%)

0.31% 

(0.09%)

0.25% 

(0.11%)

0.51% 

(0.13%)

Note: In the dataset, variables associated with current use of each tobacco product are as follows: 

electronic cigarettes (celcigt); cigars, little cigars, or cigarillos (ccigar); cigarettes (ccigt);

smokeless tobacco (cslt); hookah or waterpipe (chookah); roll-your-own cigarettes (crollcigts);

snus (csnus); pipe tobacco (cpipe); dissolvable tobacco products (cdissolv); and bidis (cbidis).

 

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

ranges from 1 to 3, and 2 (High School) if QN3 ranges from 4 – 7. Students who are missing QN3 are excluded 

from these tables.
31

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

Students15

Product

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

Electronic cigarettes 10.53% 

(0.62%)

10.77% 

(0.76%)

10.24% 

(0.79%)

10.32% 

(0.80%)

8.59% 

(1.14%)

13.14% 

(1.05%)

Cigars, little cigars, 

or cigarillos

2.35% 

(0.27%)

2.00% 

(0.35%)

2.70% 

(0.33%)

1.75% 

(0.31%)

3.95% 

(0.82%)

3.08% 

(0.51%)

Cigarettes 2.30% 

(0.28%)

2.45% 

(0.40%)

2.08% 

(0.27%)

2.14% 

(0.41%)

2.03% 

(0.42%)

3.08% 

(0.54%)

Hookah or waterpipe 1.55% 

(0.22%)

1.75% 

(0.35%)

1.33% 

(0.18%)

1.11% 

(0.27%)

2.07% 

(0.56%)

2.42% 

(0.51%)

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

1.47% 

(0.19%)

0.54% 

(0.14%)

2.31% 

(0.33%)

1.70% 

(0.32%)

0.91% 

(0.37%)

1.45% 

(0.29%)

Roll-your-own 

cigarettes

1.31% 

(0.17%)

1.11% 

(0.20%)

1.43% 

(0.21%)

0.74% 

(0.17%)

1.39% 

(0.40%)

2.60% 

(0.41%)

Snus 0.58% 

(0.10%)

0.22% 

(0.08%)

0.92% 

(0.18%)

0.57% 

(0.13%)

0.38% 

(0.19%)

0.83% 

(0.22%)

Pipe tobacco 0.49% 

(0.09%)

0.37% 

(0.13%)

0.60% 

(0.14%)

0.40% 

(0.11%)

0.39% 

(0.20%)

0.83% 

(0.26%)

Bidis 0.38% 

(0.07%)

0.28% 

(0.11%)

0.47% 

(0.11%)

0.30% 

(0.11%)

0.47% 

(0.20%)

0.60% 

(0.17%)

Dissolvable tobacco 

products

0.34% 

(0.07%)

0.14% 

(0.09%)

0.53% 

(0.12%)

0.21% 

(0.10%)

0.52% 

(0.22%)

0.60% 

(0.20%)

Note: In the dataset, variables associated with current use of each tobacco product are as follows: 

electronic cigarettes (celcigt); cigars, little cigars, or cigarillos (ccigar); cigarettes (ccigt);

smokeless tobacco (cslt); hookah or waterpipe (chookah); roll-your-own cigarettes (crollcigts);

snus (csnus); pipe tobacco (cpipe); dissolvable tobacco products (cdissolv); and bidis (cbidis).

 

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

ranges from 1 to 3, and 2 (High School) if QN3 ranges from 4 – 7. Students who are missing QN3 are excluded 

from these tables.
32

Table 4.6 Ever Use Estimates for Selected Tobacco Products for High School Students16

Product

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

Electronic cigarettes 46.95% 

(1.38%)

46.23% 

(1.42%)

47.73% 

(1.97%)

52.15% 

(1.39%)

33.81% 

(2.04%)

44.93% 

(1.92%)

Cigarettes 22.60% 

(1.70%)

19.18% 

(1.28%)

25.66% 

(2.64%)

25.89% 

(1.93%)

14.51% 

(1.84%)

21.08% 

(2.21%)

Cigars, little cigars, 

or cigarillos

20.75% 

(1.38%)

16.76% 

(0.97%)

24.45% 

(2.31%)

22.02% 

(1.64%)

24.55% 

(1.58%)

17.91% 

(1.92%)

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

10.29% 

(1.26%)

4.39% 

(0.79%)

15.64% 

(1.83%)

14.15% 

(1.69%)

4.45% 

(0.96%)

5.21% 

(0.88%)

Hookah or waterpipe 9.90% 

(1.14%)

9.42% 

(0.91%)

10.38% 

(1.90%)

8.09% 

(1.18%)

14.81% 

(1.97%)

11.79% 

(1.56%)

Roll-your-own 

cigarettes

6.39% 

(1.07%)

4.57% 

(0.55%)

8.09% 

(1.86%)

7.33% 

(1.33%)

4.18% 

(0.79%)

5.40% 

(1.09%)

Snus 5.02% 

(0.74%)

2.92% 

(0.48%)

6.96% 

(1.15%)

6.28% 

(0.95%)

1.81% 

(0.52%)

4.03% 

(0.88%)

Pipe tobacco 3.84% 

(0.73%)

2.04% 

(0.25%)

5.50% 

(1.33%)

4.53% 

(0.92%)

2.20% 

(0.45%)

2.94% 

(0.68%)

Dissolvable tobacco 

products

1.61% 

(0.22%)

1.04% 

(0.18%)

2.15% 

(0.39%)

1.45% 

(0.23%)

1.19% 

(0.33%)

2.03% 

(0.57%)

Bidis 1.50% 

(0.33%)

0.82% 

(0.16%)

2.14% 

(0.62%)

1.43% 

(0.39%)

0.90% 

(0.28%)

1.79% 

(0.45%)

Note: In the dataset, variables associated with ever use of each tobacco product are as follows: 

electronic cigarettes (eelcigt); cigars, little cigars, or cigarillos (ecigar); cigarettes (ecigt); 

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

snus (esnus); pipe tobacco (epipe); dissolvable tobacco products (edissolv); and bidis (ebidis).

 

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

ranges from 1 to 3, and 2 (High School) if QN3 ranges from 4 – 7. Students who are missing QN3 are excluded 

from these tables.
33

Table 4.7 Ever Use Estimates for Selected Tobacco Products for Middle School Students17

Product

Overall

%(SE)

Female

%(SE)

Male

%(SE)

White

%(SE)

Black

%(SE)

Hispanic

%(SE)

Electronic cigarettes 19.90% 

(0.81%)

19.87% 

(0.96%)

19.87% 

(1.06%)

19.26% 

(1.01%)

18.44% 

(1.60%)

23.92% 

(1.25%)

Cigarettes 8.37% 

(0.62%)

8.03% 

(0.77%)

8.66% 

(0.64%)

8.18% 

(0.79%)

8.39% 

(1.17%)

9.18% 

(0.89%)

Cigars, little cigars, 

or cigarillos

6.29% 

(0.59%)

5.53% 

(0.72%)

6.99% 

(0.60%)

5.15% 

(0.54%)

9.63% 

(1.74%)

7.70% 

(0.76%)

Smokeless tobacco 

(chewing tobacco, 

snuff, or dip)

3.83% 

(0.47%)

2.13% 

(0.41%)

5.43% 

(0.67%)

4.89% 

(0.73%)

1.94% 

(0.54%)

3.18% 

(0.52%)

Hookah or waterpipe 3.54% 

(0.38%)

3.77% 

(0.46%)

3.29% 

(0.41%)

2.66% 

(0.39%)

4.17% 

(0.73%)

4.90% 

(0.70%)

Roll-your-own 

cigarettes

3.08% 

(0.33%)

3.03% 

(0.44%)

3.06% 

(0.32%)

2.52% 

(0.38%)

2.71% 

(0.56%)

4.81% 

(0.68%)

Snus 1.56% 

(0.19%)

0.81% 

(0.15%)

2.26% 

(0.30%)

1.66% 

(0.24%)

0.85% 

(0.25%)

2.01% 

(0.42%)

Pipe tobacco 1.55% 

(0.16%)

1.39% 

(0.20%)

1.69% 

(0.24%)

1.41% 

(0.25%)

1.98% 

(0.40%)

1.89% 

(0.32%)

Dissolvable tobacco 

products

1.09% 

(0.16%)

0.82% 

(0.17%)

1.36% 

(0.24%)

0.75% 

(0.19%)

1.35% 

(0.44%)

1.77% 

(0.34%)

Bidis 1.08% 

(0.15%)

1.03% 

(0.23%)

1.10% 

(0.17%)

0.86% 

(0.18%)

1.33% 

(0.36%)

1.68% 

(0.36%)

Note: In the dataset, variables associated with ever use of each tobacco product are as follows: 

electronic cigarettes (eelcigt); cigars, little cigars, or cigarillos (ecigar); cigarettes (ecigt); 

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

snus (esnus); pipe tobacco (epipe); dissolvable tobacco products (edissolv); and bidis (ebidis).

 

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

ranges from 1 to 3, and 2 (High School) if QN3 ranges from 4 – 7. Students who are missing QN3 are excluded 

from these tables.
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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