2009 National Youth Tobacco Survey 

Methodology Report 
2 

Introduction 

Overview of the National Youth Tobacco Survey (NYTS) 

The National Youth Tobacco Survey (NYTS) was developed as an evaluation tool for the 

national tobacco prevention and control program (TCP) and as a benchmark for state TCPs.1 

The NYTS provides prevalence estimates for selected tobacco use behaviors among middle 

school (grades 6–8) and high school (grades 9–12) students regarding tobacco use (cigarettes, 

cigars, smokeless tobacco, tobacco pipes, bidis,*

 and kreteks†); exposure to secondhand smoke; 

smoking cessation; tobacco prevention school curriculum; minors’ ability to purchase or obtain 

tobacco products; and, knowledge and attitudes about tobacco and familiarity with pro-tobacco 

and anti-tobacco media messages. The NYTS data also serves to monitor the progress of Healthy 

People 20102

 indicator on exposure to pro-tobacco advertisement and promotions. 

First conducted during fall 19993 and again during spring 2000,4 2002,5 2004,6 2006,7 and 2009, 

NYTS data are representative of all middle school and high school students in the 50 states and 

the District of Columbia. 

Overview of the 2009 NYTS Methodology 

The 2009 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. In 2009, 

Non-Hispanic black and Hispanic students were oversampled. Sampling procedures were 

probabilistic and conducted without replacement at all stages, and entailed selection of: 

1) Primary Sampling Units (PSUs) (a county, or a group of small counties, or part of a very large 

county) within each created stratum. 

2) Schools within each selected PSU. 

3) Classes within each selected school. 

Participating students completed the self-administered survey via pencil and paper on a 

questionnaire booklet that could be scanned. 

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

level, participation was also anonymous. Schools used either passive or active permission forms 

at their discretion to fulfill requirements of the No Child Left Behind Act whereby parents must 

be provided with a means to opt out of their child’s participation. The overall school response 

rate was 92.3%, and the student response rate was 91.9%, resulting in an overall response rate of 

84.8% (the school response rate multiplied by the student response rate). 

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 

* Bidis (or “beedies”) are small brown cigarettes from India consisting of tobacco wrapped in a leaf and tied with a 

thread. 

† Kreteks (or “clove cigarettes”) are flavored cigarettes containing tobacco and clove extract.
3 

students in each grade matched national population proportions. Final adjusted weights were 

scaled to ensure that the weighted count of students was equal to the total sample size. 

 

The remainder of this report provides detailed information on the methodology used in 2009 

NYTS sample selection, data collection, and weighting of student response data. 

• Section I: NYTS Sampling Design 

• Section II: NYTS Sampling Methods 

• Section III: NYTS Data Collection 

• Section IV: Weighting of NYTS Response Data 
4 

SECTION I — NYTS SAMPLING DESIGN 

1.1 Sampling Design Overview 

The objective of the 2009 NYTS sampling design was to support estimation of tobacco-related 

knowledge, attitudes, and behaviors‡ in a national population of public, and secular and nonsecular private school students enrolled in grades 6 through 12 in the United States. More 

specifically, the study was designed to produce national estimates at ± 5% confidence level by 

school level (middle school defined as grades 6th – 8th, and high school defined as grades 9th – 

12th), by grade (6, 7, 8, 9, 10, 11, and 12), by sex, and by race/ethnicity for non-Hispanic white, 

non-Hispanic black, and Hispanic students.§

 

Additional estimates, such as cross-tabulations of grade by sex and of race/ethnicity by school 

level, were also supported; however, precision levels vary according to differences in subpopulation sizes. 

The universe for the study consisted of all public, Catholic, and other private school students 

enrolled in regular middle schools and high schools in grades 6 through 12 in the 50 states and the 

District of Columbia. Alternative schools, special education schools, Department of Defense 

operated 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. 

For the first time, in 2009 the NYTS was coordinated with the 2009 National Youth Risk 

Behavior Survey (YRBS). Both samples were drawn simultaneously; this necessitated 

modification of some design features of the NYTS to mirror the YRBS, as described in Section 

1.2. Schools could not be selected to participate in both surveys. Nevertheless, the design 

replicated key aspects of the 2006 NYTS. Interpretation of trends will not be affected by the 

design changes because trend calculations are based on weighted estimates for each survey year. 

The three-stage cluster sample was stratified by non-Hispanic black or Hispanic composition and 

urban versus rural status at the first (primary) stage. Primary Sampling Units (PSUs) were 

defined as a county, a group of smaller counties, or a portion of a very large county. PSUs were 

classified as "urban" if they are in one of the 54 largest metropolitan statistical areas (MSAs) in 

the United States; otherwise, they were classified as "rural" according to the Census Bureau’s 

classification.** Additional, implicit stratification was imposed by geography by sorting the PSU 

frame by state and by 5-digit Zip Code (within state). Within each stratum, PSUs were randomly 

sampled without replacement at the first stage. 

In subsequent sampling stages, a probabilistic selection of schools and students was made from 

the sample PSUs. Black and Hispanic students were oversampled using a modified weighted 

‡ See Section 3.1, survey instrument, for specific indicators measured.

§ Unless stated otherwise, subsequent use of the terms “white” and “black” pertain to non-Hispanic respondents 

only, and “Asian” is exclusive of Native Hawaiian/Other Pacific Islander (NOH/IPI). Additional definitions are 

provided in Appendix A.8 

** Office of Management and Budget (OMB) Bulletin 09-01, November 20, 2008, “Metropolitan Statistical Areas.”
5 

measure of size (MOS) that increased the probability of selection of PSUs and schools with 

disproportionately high minority student enrollments. The measure of size was adjusted to 

equalize the expected sample size by grade. 

The sampling stages may be summarized as follows. 

Stage I: Selection of PSUs 

At the first sampling stage, 80 PSUs, defined as a county, a group of smaller counties, or a 

portion of a very large county were selected from sixteen strata with probability 

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

within a PSU. 

Stage II: Selection of Schools 

At the second sampling stage, two large schools were selected from each PSU. An 

additional 20 medium schools and 20 small schools were selected from subsample PSUs. 

Small, medium, and large schools are defined below. Schools were selected with 

probability proportional to the total number of eligible students enrolled in a school using 

modified MOS as described in section 1.2. 

Stage III: Selection of Classes

At the third sampling stage, classes were selected from course schedules provided by each 

school that agreed to participate. Class lists were constructed such that all eligible students 

were both represented and represented one time only. Classes were then randomly selected 

from each list using a fixed interval with a random start, with the start set within the initial 

interval. According to the total number of eligible students enrolled in a school, the interval 

used varied. All students enrolled in selected classes were by default chosen for 

participation. 

Exhibit 1-1 presents a summary of the sampling design features. 

Exhibit 1-1 Key Sampling Design Features 

• Sampling Stage Sampling Units 

Sample Size 

(Approximate) Stratification Measure of Size 

1 

PSUs: Counties 

or groups of 

counties 

80 

Urban vs. nonurban (2 strata); 

Minority 

concentration (8 

strata) 

Aggregate school size in 

target grades 

2 Schools 

200 school 

selections: 160 

large schools (2 per 

PSU), 20 medium 

schools and 20 

small schools 

Small, Medium and 

Large; 

High-school vs. 

middle-school 

Weighted enrollment 

(increased for black, 

Hispanic groups) 

3 Classes/ students 

1 or 2 classes per 

grade (2 per grade 

in large highminority schools) 
6 

1.2 Modifications in the NYTS sampling design from 2006 to 2009 

The coordination of the 2009 NYTS with the 2009 national YRBS required some modification to 

the design features of the 2009 NYTS. The most salient modifications to the 2009 NYTS 

sampling design compared to the 2006 survey are outlined in Exhibit 1-2 below. Note that the 

changes may have affected precision for some subgroups (e.g., Asians), which were 

compensated for the enhanced precision for overall estimates. In addition, these modifications 

do not affect trend estimates.

 

Exhibit 1-2 Major Sampling Design Modifications for the 2009 NYTS

 

Design feature Modifications Benefits and reasons for 

changes

Stratification By minority concentration and 

urban status rather than by region 

and urban status as in the 2006 

NYTS stratification

Align design with the YRBS

Allocation of primary 

sampling units (PSU) 

to strata

More nearly proportional than the 

2006 NYTS allocation (more 

heavily disproportional in 2006)

Increased precision (smaller 

variances and design effects)

Measure of size Reduced oversampling for blacks 

and Hispanics, and no 

oversampling of Asians; the 2006 

NYTS had heavier oversampling of 

minority groups

Increased precision (smaller 

variances and design effects) 

School strata Size strata defined by small, 

medium and large schools instead 

of simply small and large schools 

as in the 2006 NYTS

Better control of sample size, 

and more effective sampling 

within schools

Sample sizes Smaller sample size at the first 

stage (PSUs) and overall; the 2006 

NYTS had larger sample sizes

More effective data collection 

and cost-efficient survey 

procedures

Stratification

 

For the 2009 NYTS, the first-stage stratification was similar to the YRBS (exhibit 1-2) to 

facilitate coordination of the PSUs between the two surveys. 
7 

Two modifications were made that reduce the extent of oversampling of minority groups. The 

rationale is that while this oversampling leads to more precise estimates for these racial/ethnic 

groups, it comes at a price for overall survey precision.

 

Allocation of primary sampling units (PSU) to strata

 

The first design change that reduced oversampling was in the allocation of the primary sample 

units which was made more nearly proportional to actual population distribution in both surveys 

(NYTS and YRBS), than it had been in 2006 pr prior surveys. 

 

Measure of Size (MOS)

 

The second modification was in the modified Measure of Size (MOS) such that the coefficients 

used to oversample minority students were made similar to actual population for black and 

Hispanic students; moreover, the oversampling for Asian students was eliminated. It may be 

emphasized that these changes led to lower variances and design effects for most survey 

estimates. In addition, as demonstrated in Section 4.5, estimates for black and Hispanic students 

still had excellent precision that comfortably met the required levels. Lower precision for blacks 

is a consequence of the reduced oversampling of black students. It is worth noting that the 

number of Hispanic students sampled was higher than in previous surveys even though the extent 

of oversampling was reduced.

 

School Strata

 

The introduction of three-school strata defined by size—small, medium and large—allowed for 

better calibration of the number of classes that needed to be selected to reach the desired sample 

size. The three-size strata are defined below: 

 

• Small schools — those schools with 25 or fewer students in one or more of the eligible 

grades for the level. 

 

• Medium schools — those schools with more than 25 students but fewer than 50 students 

in one or more of the eligible grades for the level. 

• Large schools — those schools with at least 50 students at each grade. 

 

The three-school size strata constitute a refinement of the previous NYTS stratification of schools 

into small schools, in which all students were included in the sample, and large schools, in which 

random classes were selected. The medium school stratum includes schools that do not contain the 

two classes per grade that would need to be selected in large schools, but that could not be 

considered small. Two classes were selected in some large, high minority schools; in all other 

schools1 class was selected. 

In large schools, an average of 1.75 classes per grade were selected by selecting 2 classes per grade 

in 75% of the selected large schools, and one class per grade in the remaining 25% of large schools. 

The double class sampling took place in the 60 sampled schools with the highest concentrations of 

Hispanics and black students for each level. Specifically, the sample included 60 high-minority, 
8 

large high-schools and 20 non-high minority, large high-schools. Similarly, the sample contains 60 

high-minority, large middle-schools and 20 non-high minority, large middle-schools. For the 2009 

NYTS design, thresholds were liberally set for the high concentration strata so that 75% of the large 

schools could be included in this stratum. 

1.3 Designed Sample Sizes 

The NYTS is designed to produce accurate estimation to within ± 5% for the following key 

subgroup estimates: 

• Middle and high school estimates (school level) —middle school students in total (grades 

6-8 combined) and high school students in total (grades 9-12 combined). 

• Grade estimates — Individual grades—6, 7, 8, 9, 10, 11, and 12, separately. 

• Sex group estimates—males and females in total, by school level (male middle school 

students, female high school students, etc.), and by individual grade (6th grade males, 6th

grade females, etc.). 

• Race/ethnicity group estimates (race/ethnicity)—in total and by school level (e.g., school 

level data by race for at least non-Hispanic whites, non-Hispanic blacks, and Hispanics). 

The design was developed to meet the required precision levels for these subgroups assuming a 

design effect (DEFF) of 2.0. Under this scenario, the design was developed to generate at least 

1,420 participating students by sex within each grade, and at least that many by minority group at 

each school level. As shown below, the design was projected to yield at least 1,583 minority 

students at each level. 

Exhibit 1-3 summarizes the sample sizes designed to generate approximately 2,840 participating 

students per grade. In this exhibit, the first two columns show the number of school selections and 

student selections per school while the last column shows the expected number of participating 

students. The numbers of large, medium and small schools were calibrated to generate the required 

numbers of students overall, by school level and by grade. The number of selections was based in 

part upon past response rates. Across the five previous cycles of the NYTS, the school participation 

has averaged 89%, with a low of 83%. Student participation has averaged 90% with a low of 88%. 

Slightly lower values were used in developing the sample design for the 2009 NYTS: 85% for 

schools and 85% for students. 
9 

Exhibit 1-3 Sample Size Projections for Participating Students on the 2009 NYTS 

School Type Number 

of Schools 

Selected 

Number of 

Students 

Selected per 

School 

Expected Number 

of Participating 

Students Across 

Schools 

Large, High Minority High 

Schools 60 200 8,670 

Large, Non-High Minority 

High Schools 20 100 1,445 

Medium High Schools 10 100 723 

Small High Schools 10 All available 

(assume 72) 

520 

Large, High Minority Middle 

Schools 60 150 6,502 

Large, Non-High Minority 

Middle Schools 20 75 1,084 

Medium Middle Schools 10 75 542 

Small Middle Schools 10 All available 

(assume 54) 390 

TOTAL 200 27,510 19,876 

Exhibit1-4 below shows that the sample design was expected to yield 1,763 and 1,583 black 

students for high school and middle school, respectively. The exhibit also shows that higher 

numbers were expected for Hispanic students, 1,991 for high schools and 1,753 for middle 

schools. 

Exhibit 1-4 Expected Minority Yields 

Part I: High Schools 

School Strata Number of Schools Expected Hispanics Expected Blacks 

Large HS, HighMinority 60 1561 1387 

Large HS, Non-High 

Minority 20 231 202 

Medium HS 10 116 101 

Small HS 10 83 73 

TOTAL 100 1991 1763 
10 

Part I: Middle Schools 

School Strata Number of Schools Expected Hispanics Expected Blacks 

Large MS, HighMinority 60 1431 1301 

Large MS, Non-High 

Minority 20 173 152 

Medium MS 10 87 76 

Small MS 10 62 55 

TOTAL 100 1753 1583 

For comparison purposes, Exhibit 1-5 presents the yields actually attained by race and ethnicity by 

school level in the 2009 NYTS and shows how the target sample sizes were exceeded for each 

subgroup. As pointed out earlier, the yields for Hispanics were well in excess of the target numbers. 

Exhibit 1-5 Sample Yields for Black and Hispanic Students by School Level 

Subgroup Target Sample Sizes Actual Participants 

Middle School Blacks 1583 1725 

Middle School 

Hispanics 1753 2816 

High School Blacks 1763 1850 

High School Hispanics 1991 3403 

The large sample size meant analysis by individual grade and by sex would be possible without 

any additional special considerations in the sampling plan. Additionally, grade and sex 

subgroups both typically are distributed evenly within schools. Males and females were expected 

to each constitute about half of the students selected, or an anticipated 12,250 students per sex 

group. Design effects will be relatively small for subgroups that are distributed evenly within 

schools; therefore, sex group estimates will have better precision than other groups than are less 

evenly dispersed across schools (e.g., racial/ethnic groups). Thus, precision can be within ± 3% 

for subgroup estimates, such as sex by school level. However, because the design yields a greater 

number of completed surveys from high school students than from middle school students, 

overall estimates will be more precise at the high school level than those at the middle school 

level. 
11 

The next paragraphs discuss special considerations in the design that were needed to achieve 

adequately precise estimates for subgroups defined by school level, grade, sex and race/ethnicity. 

Middle School and High School Estimates

Estimates by school level are required to support separate analysis of students across middle 

school grades (6, 7, and 8) and high school grades (9, 10, 11, and 12). However, schools tend to 

vary in their grade structures, an inconsistency that compromises the ability to easily and 

efficiently cluster schools for sampling purposes in a manner that also uniformly divides students 

by grade. For example, 9th grade students are served by both grades 7–9 junior high schools and 

by grades 9–12 high schools, also 6th grade students are served by K–6 elementary schools and 

6-8 middle schools. Approaches that require artificially slicing and melding schools (e.g., 

removing 9th grade enrollment counts from a junior high school and deeming them a high school 

eligible for selection at the high school level) were not considered viable options. Likewise, any 

approach that would require the creation of a rule classifying all schools as either “Middle 

School” or “High School” and then selecting one of each from every PSU would also result in 

certain grades being frequently found in both groupings. To counter such challenges, a simple 

sampling approach utilizing an implicit stratification scheme whereby the sampling frame was 

sorted by grade was ultimately chosen (e.g. if a 7–9 junior high school was selected as a middle 

school, only grades 7-8 would be samples). 

Grade Estimates 

NYTS estimates are typically not reported by grade level but rather by school level. Still, the 

design balances the sample sizes for grade level with at least 3,000 students targeted per grade, 

and thus also ensures that estimates at the grade (6th through 12th) level achieve the required 

precision levels. It is worth noting that this entails a larger student allocation to the high school 

stratum than to the middle school stratum as high schools have four grades versus three grades 

for middle schools. 

Sex Group Estimates 

The large sample size permitted analysis by sex without any special considerations in the sampling 

plan. During the class selection process, frames of eligible classes from co-educational schools in 

which classrooms were segregated by sex (i.e., an all male or all female class) were avoided if it 

was possible to do this without interfering with random selection of students. 

Racial Group Estimates 

In order to support separate analysis of the data for white, black, and Hispanic students, in total and 

by school level, adequate sample sizes were required for subgroups defined 1) by school level by 

racial grouping or 2) by sex by racial grouping. Sample sizes were not designed, however, to 

support detailed analyses by sex and school level within racial/ethnic subgroups (e.g., middle school 

Hispanic males). 

The design used selection with Probability Proportional to Size (PPS) sampling methods with a 

weighted MOS that increased the chances of selecting PSUs and schools with relatively high 
12 

minority enrollment, thereby increasing the likelihood of selecting classrooms with relatively 

high concentrations of minority students. 

1.4 Actual Sample Yield and Participation Rates 

Participation rates for this survey were high, and generally comparable to participation rates 

achieved in the prior cycles of NYTS. Of the 222 selected schools, 205 (92.3%) participated in 

the 2009 NYTS. Among the 24,666 selected students enrolled at the 205 participating schools, 

22,679 (91.9%) participated, resulting in an overall response rate of 84.8% (the school response 

rate multiplied by the student response rate). 
13 

SECTION II — NYTS SAMPLING METHODS

The sampling frame included public, Catholic, and other private schools in the 50 states and the 

District of Columbia with at least one grade in the 6th through 12th grade range. The sampling 

process employed a three-stage stratified cluster design whereby PSUs, schools within PSUs, and 

classrooms (i.e., students) within schools were probabilistically selected without replacement. 

The sampling approach utilized Probability Proportional to Size (PPS) sampling methods, in 

which the probability of a particular PSU being selected was proportional to the total number of 

eligible students enrolled in all eligible schools located within the PSU, and the probability of a 

particular school being selected proportional to the total number of eligible students enrolled in 

the school. A weighted MOS that increased the chances of selecting PSUs and schools with 

relatively high minority enrollment was also used, thereby increasing the likelihood of selecting 

classrooms with relatively high concentrations of minority students. 

2.1 Measure of Size (MOS) 

The sampling approach used PPS sampling methods to achieve over-sampling of blacks and 

Hispanics. In PPS sampling, when the MOS is defined as the count of final-stage sampling units, 

and a fixed number of units are selected in the final stage, the result is an equal probability of 

selection for all members of the universe. For the NYTS, these approximate conditions obtain a 

roughly-self weighting sample. This section describes the type of MOS employed for selecting 

PSUs and schools, including over-sampling of blacks and Hispanics. Note that in the 2009 cycle 

use of a total enrollment, rather than a per-grade average, for the MOS was done.** 

 

This oversampling method employs a function of the form— 

rhH + rbB + roO 

where the r's are the weighting factors for enrollment by three racial/ethnic groupings—Hispanic, 

black, and other (H, B, and O, respectively). The function increases the chances of schools with 

relatively large minority enrollments entering the sample, and also increases the probability of 

selection for high-minority PSUs. 

The effectiveness of a weighted MOS in achieving oversampling is dependent upon the 

distributions of blacks and Hispanics in schools. For example, if U.S. schools had identical 

percentages of minorities in every school, then the sample of students from any sample of 

schools would mirror the national percentages and use of a weighted measure of size would fail 

to oversample blacks and Hispanics. This is not the case as the distribution of high school 

students with respect to race and ethnicity follows that of the general population, where there is 

geographic clustering by race and ethnicity. As such, use of a weighted MOS, as has been 

employed in prior NYTS cycles, is an effective means of oversampling black and Hispanic 

students. 

 

** This moved the actual performance of the sample close to the ideal of a self-weighing sample, resulting in a 

modest precision gain. 
14 

As in previous NYTS cycles, the coefficients for the MOS weighting function were developed 

using simulation studies that ensure that target sample sizes are met for the two racial/ethnic 

groups of analytic interest (black and Hispanic students). These simulations are performed in 

iterative steps that— 

1) Adjust for racial/ethnic composition. 

2) Equalize the expected sample size by grade. 

Prior to full implementation, sampling design parameters are fine-tuned through simulated 

sample draws. The simulations involve the repeated selection of independent samples of schools 

(e.g., 1,000 samples) in an iterative application of the entire sampling process up to the selection 

of schools. 

The simulations verified that these parameters would also achieve the estimation goals of the 

NYTS, i.e., meet the target sample sizes by school level established for the two minority groups. 

As a result of these computations, the following weighting function was used for the measure of 

size: 

2 H + 2 B + O††

The measure of size was used also to compute stratum sizes and PSU sizes. Assigning an 

aggregate measure of size to PSU, the sample allocation oversamples PSU that have higher 

minority student concentrations. 

2.2 Stage 1 – Selection of PSUs

PSU Definition 

In defining PSUs, several issues are considered: 

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

students by grade, yet not so large as to be selected with near-certainty. 

• Each PSU should be compact geographically so that field staff can go from school to 

school easily. 

• There should be recent data available to characterize the PSUs. 

• PSU definitions should be consistent with secondary sampling unit (school) definitions. 

Generally, counties were equivalent to PSUs with two exceptions: (1) low population counties 

are combined to provide sufficient numbers of schools and students, and (2) counties that are 

†† Compared to previous cycles, this MOS used less extreme weighting of minorities to produce the same level of 

oversampling in terms of yields —leading to more precise estimates (smaller sampling errors.)
15 

very large were split to avoid becoming certainty or near-certainty PSUs. County population 

figures were aggregated from school enrollment data for the grades of interest. 

The 2009 NYTS frame of PSUs was constructed using 2007–2008 school year enrollment data 

and clustering methods that ensure that the compact PSUs have the correct number of schools 

and students. For the coordinated NYTS/YRBS surveys, new PSUs were constructed to 

accommodate the selection of the two samples, where one of the two samples (NYTS) contains 

middle schools as well as high schools. The new PSUs were constructed to contain at least 4 

middle schools and 5 high schools. The frame had 1,276 PSUs, 536 of which were comprised of 

one entire county. 

County population figures were aggregated from school enrollment data for the grades of 

interest. Enrollment data were obtained from the most recent Common Core of Data from the 

National Center for Education Statistics,9

 which are merged on a rolling basis into the current 

school and school district data files of Quality Education Data, Inc. (formerly QED, recently 

acquired by and now doing business as MDR, Market Data Retrieval, with more information 

available at www.schooldata.com).10

Stratification and Selection of PSUs 

The PSUs were organized into 16 strata, based on urban/rural status and minority enrollment. 

The approach involves the computation of optimum stratum boundaries using the cumulative 

square root of “f” method developed by Dalenius and Hodges.11,12The boundaries or cutoffs 

change as the frequency distribution (“f”) for the racial groupings change from one survey cycle 

to the next. These rules are summarized below. 

• If the percentage of Hispanic students in the PSU exceeded the percentage of black 

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

• If the PSU is within 1of the 54 largest MSA in the United States it is classified as 

“Urban,” otherwise it is classified as “Rural.” 

• Hispanic Urban and Hispanic Rural PSUs were classified into four density groupings 

depending upon the percentages of Hispanics in the PSU. The four minority density 

groupings, labeled 1 (high density) to 4 (low density), are formed within each urban/rural 

stratum according to the cumulative square root of the frequency rule developed by 

Dalenius and Hodges.11, 12. 

• Black Urban and black Rural PSUs were also classified into four groupings depending 

upon the percentages of blacks in the PSU, in the same manner as was done for Hispanic 

PSUs. 
16 

Allocation of the PSU sample 

After designing the PSUs, 80 were selected. The initial allocation was made proportional to 

student enrollment. Using simulations as in previous studies, adjustments were made to the 

initial allocation to meet minority targets. 

Exhibit 2-1 presents the allocation of the PSU sample to strata. Of the 80 selected PSU, two 

dropped out of the study due to complete school nonresponse; i.e., all sample schools in these 

two PSUs were non-responding schools. This resulted in 78 PSUs on the analytic file. 

Exhibit 2-1 Stratum Definition and PSU Allocation to Strata 

Predominant 

Minority 

Urban/ 

Rural 

Density 

Group 

Number 

Stratu

m Code 

Student 

Population 

(Frame 

Enrollment) 

Percent of 

Student 

Population 

Number 

of Sample 

PSUs 

Black 

Urban 

1 BU1 2,720,072 12.1% 8 

2 BU2 1,006,035 4.5% 6 

3 BU3 1,914,017 8.5% 2 

4 BU4 448,243 2.0% 2 

Rural 

1 BR1 4,385,688 19.6% 12 

2 BR2 2,073,147 9.2% 5 

3 BR3 1,267,720 5.7% 5 

4 BR4 431,890 1.9% 2 

Hispanic 

Urban 

1 HU1 2,984,133 13.3% 7 

2 HU2 1,335,499 6.0% 4 

3 HU3 1,914,392 8.5% 5 

4 HU4 1,951,665 8.7% 5 

Rural 

1 HR1 3,690,873 16.5% 9 

2 HR2 974,560 4.3% 3 

3 HR3 878,210 3.9% 3 

4 HR4 424,070 1.9% 2 

Selection of PSUs 

Within each first-stage stratum, the PSUs were sorted by five-digit zip code to attain a form of 

implicit geographic stratification. Implicit stratification and systematic sampling, coupled with 

the probability proportional to size (PPS) sampling method described below, ensures geographic 

sample representation. With PPS sampling, the selection probability for each PSU is proportional 

to the PSU’s measure of size. 

The following systematic sampling procedures were applied to the stratified frame to select a 

PPS sample of 80 PSUs: 
17 

• Select 80 PSUs with a systematic random sampling method within each stratum. The 

method applies within each stratum 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. 

• Subsample at random 9 of the sample PSUs for the medium school sample for each 

school level (middle school and high school). 

• Subsample at random 6 of the sample PSUs for the small school sample for each school 

level (middle school and high school). 

2.3 Stage 2 – Selection of Schools 

Schools were stratified by school level—middle schools and high schools—and by size (small, 

medium, and large). Middle schools were those that contained any of grades 6th – 8th, and high 

schools were those that contained grades any of grades 9th – 12th. Schools that contained a mix of 

high- and middle-school grades were split into two sampling units, one for each level. Three 

school size strata were constructed— small, medium and large—as defined in Section 1.1. 

Schools were classified as "whole" for high schools if 

they have all high-school grades 9–12, and whole for 

middle schools if they had all grades 6–8. Otherwise, 

they were considered a "fragment" school. Fragment 

schools formed component schools that were linked 

with other schools (fragment or whole) to form a cluster 

school that has all four grades. This process is 

illustrated in Figure 1, where fragment school A is 

linked with whole school B, to form a cluster school, or 

Secondary Sampling Unit (SSU) XXX. Schools were 

linked before sampling using an algorithm, developed 

for use in the national YRBS that links geographically 

proximate schools. Cluster schools were treated as a 

single school, or sampling unit, during sampling with 

selection performed at the grade level as described below. 

In addition, for each school level, large schools were classified as high-minority or low-minority. 

High-minority schools were identified as those containing more than 3.5% of black or Hispanic 

students. This threshold was set so that approximately 75% of the sample large schools would be 

selected to supply two classes per grade, rather than one. 

For large schools, one high school and one middle school were selected with PPS systematic 

sampling within a PSU. The schools were selected into the sample with probability proportional 

to the weighted measure of size. 

Small and medium schools were sampled independently from large schools; they were set in two 

separate strata sampled at lower rates. This approach was implemented by drawing a subsample 

of 12 PSUs for the sampling of small schools sampling and a subsample of 18 PSUs for medium 

Figure 1 – Cluster School Construction & 

Grade Sampling for High Schools 
18 

school sampling when the two school levels are combined. In other words, the subsamples 

consisted of 6 PSUs for small school sampling at each level, and 9 PSUs for medium school 

sampling at each level. 

Replacement of Schools/School Systems 

Refusing school districts, schools, classes, or students were not replaced. School and student 

nonresponse was accounted for in the design by inflating the sample sizes to account for the 

expected amount of nonresponse. With this approach, all schools could be contacted in a 

coordinated one-time recruitment effort, which would not have been possible for methods that 

allow for replacing schools. 

2.4 Stage 3 – Selection of Grades, Classes and Students 

Selection of Grades 

Except for cluster schools, all eligible grades were included in the class selection for each school. 

In cluster schools, grades were selected independently. One component school was selected to 

provide classes at each grade level, and grades within component schools were drawn with 

probability proportional to grade enrollment. This is illustrated in Figure 1, where grades 9 and 

10 were drawn into the sample from component school QED ID (QID) B, and grades 11 and 12 

were drawn into the sample from component school QID A. Thus, the SSU XXX is represented 

by two actual schools QID A and QID B, each supplying two of the four required grades. 

Selection of Classes and Students 

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

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

classes were chosen were constructed such 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 and implemented. 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 health also were considered core courses. In a small number of schools, however, it 

was extremely difficult to develop an appropriate frame using this particular approach. Therefore, in 

such schools, classes were selected by using a time of day (i.e., 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, school homerooms were used as 

the frame for class selection. Doing so is not ideal, though, as relatively few schools hold homeroom 

of duration sufficient enough for conducting the survey. 
19 

All students in a selected class who could complete the survey without special assistance were 

considered eligible and offered the opportunity to participate in the survey. Refusing students were 

not replaced. Students who were eligible but absent on the day NYTS was administered, were added 

to a make-up list and at a later time a make-up session was administered. Nonresponse at the student 

level was accounted for in the sample size using an average per class yield that assumed student 

response rates derived from historical experience with the NYTS. 

2.5 Probabilities of Selection 

This section describes the probabilities of selection associated with the various sampling stages 

including: 

• Probability of selecting PSUs. 

• Probability of selecting schools. 

• Probability of selection of grades. 

• Probability of selecting classes and students. 

These probabilities provide the basis for the sampling weights discussed in Section IV, Weighting 

of NYTS Response Data. 

The probability of selection for a student is the product of the probability of selection of the PSU, 

which is a group of schools, multiplied by the conditional probability of selecting the student's 

school, multiplied by the conditional probability of selecting the student's class. All students in a 

selected class are included in the sample. 

Probability of Selecting PSUs 

If MOSklm is the measure of size for school k in PSU l in stratum m and if Km is the number of 

PSUs to be selected in stratum m, then Pp

lm is the probability of selection of PSU l in stratum m: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

MOS

MOS

P = K

..m

.lm m P

lm

As noted below, 12 of the 80 sample PSUs were subsampled for the sampling of small schools, with 

6 PSUs subsampled for each school level. Similarly, 18 PSUs were subsampled for the sampling of 

medium schools with 9 for each school level. Thus, the subsample PSUs are assigned an additional 

sampling factor in their probability of selection for small schools (6/80) and for medium schools 

(9/80). 
20 

Probability of Selecting Schools 

The probability of selecting large school k in PSU l and stratum m, PLS

klm, was computed as 

follows: 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛

MOS

MOS

P = .lm

LS klm

klm

For small schools, one school was drawn from subsampled PSU, so the probability of selection of a 

small school, PSS

klm, then becomes: 

( ) ⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛

MOS

MOS

P = .lm

SS klm

klm 6 / 80

For medium schools, one school was drawn from subsampled PSU, so the probability of selection 

of a small school, PSS

klm, then becomes: 

( ) ⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛

MOS

MOS

P = .lm

MS klm

klm 9 / 80

Probability of Selection of Grades 

For most schools in the sample all eligible grades were selected so that the probability of selecting a 

grade was 1.0. In some cases, however, a school unit was really a set of several schools combined in 

the sampling process because they lacked one or more eligible grades. These units may be called 

cluster schools or melded schools. Some of the cluster schools contain more than one school that 

can be selected for a given grade. The selection of the school for that grade is made with PPS 

sampling. For example, suppose that there were two schools with 10th grades in a cluster, and their 

10th grade enrollments were in a 2-to-1 ratio. In this way, the larger school in the cluster had twice 

the probability of yielding a 10th grade to the sample than the smaller school. The school selections 

(from the school cluster) at each grade level were made independently. 

We denote by PG

jklm the probability of selecting grade-j in school-k, in PSU-l, stratum-m. 

Probability of Selecting Classes and Students 

A set of classes was identified for each school at each grade level such that every student in a given 

grade level was enrolled in exactly one of the classes in the set. For example, a required English 

course might be used. If the school's estimated black enrollment exceeded 40 percent or its 

estimated Hispanic enrollment exceeded 50 percent, then two classes were randomly selected, 

without replacement, from the list. Otherwise, one class was randomly selected. Selections were 

made at all eligible grade levels in the school. 
21 

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

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 (i.e. 1/Cjklm or 2/Cjklm). Note that the probability of student selection within a class does not 

vary by race, ethnicity, or sex. We denote this probability as PC

ijklm as the probability of selecting 

class-i in grade-j, school-k, PSU-l, and stratum-m. Because every student in a selected class is also 

selected, the probability of selecting any student in class-i, grade-j, school-k, PSU-l, stratum-k, is 

also equal to PC

ijklm. 

If ni is the number of PSUs to be selected from stratum i, Si is the size of stratum i, and Sij is the size 

of PSUi in stratum i (in all cases "size" refers to MOS), then the probability of selection of PSUi is 

niSij/Si. For the two schools to be selected in PSUi , Sijk is the size of school k in PSU j in stratum i,

and then the conditional probability of selection of the school given the selection of the PSU is 

2Sijk/Sij. If Cijk is the number of classes in school ijk and m is the number of classes to be selected, 

then the conditional probability of selection of a class is m/Cijk. Because all students were selected, 

the conditional probability of selection of a student given the selection of the class is unity. 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

C

m

S

S

S

n S

ij ijk

ijk

i

i ij (2)
22 

SECTION III — NYTS INSTRUMENT AND DATA COLLECTION 

3.1 Survey Instrument 

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

control outcome indicators. The 2009 survey instrument included a total of 81 questions, with the 

first seven collecting student demographic information and the remaining measuring a 

comprehensive set of tobacco-related topics (Appendix B). Specifically, areas covered by the 

survey included prevalence of tobacco use, knowledge of and attitudes towards tobacco use, proand anti-tobacco media and advertising, minors’ access to tobacco products, nicotine 

dependence, cessation attempts, exposure to second-hand smoke, and student’s exposure to 

tobacco use prevention school curricula. 

3.2 Recruitment and Scheduling of Schools 

The schools selected to participate in the 2009 NYTS fell across 34 different states. Recruitment 

began in May 2008 with calls to State Departments of Education and Health. Letters of support 

were obtained from various State agencies and used in mailings to districts and schools. 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 while also trying 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 an electronic calendar on a secure, shared drive to facilitate 

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

3.3 Mailings to Schools 

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

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

the school for data collection. Teacher packets contained the parental permission forms that had 

to be given out 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. Passive parental permission forms, or forms returned only if the parents do not want 

their child to participate, were sent approximately one week prior to the scheduled date of data 

collection. Active parental permission forms, forms that must be returned with the parent’s 

signature in order for the child to participate, were sent out at least two weeks prior to the 

scheduled date of data collection. Follow-up calls were made to the selected schools to answer 

any questions and to make sure materials were received and distributed to selected classes and 

students. 
23 

3.4 Hiring and Training Data Collectors 

Data collectors were recruited from a pool of previously trained data collectors as well as retired 

teachers associations, school health networks, and a variety of health education listservs. Data 

collector training was conducted on February 4–7, 2009. Initially, data collectors observed 

everything they would have to say or do as “experts” performed it. Then they acquired these 

skills through practice, demonstrated them to each other, and finally refined each other’s 

performance through constructive feedback. 

3.5 Management and Support of Data Collectors in the Field 

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

week, travel and logistics to get them where they need to be, and their must-read weekly bulletin. 

Weekly bulletins underlined key performance issues, corrected misconceptions, provided 

consistent direction on any procedural changes, and kept everyone abreast of the latest must-have 

information. In addition to these mailings, boxes of survey supplies were sent to data collectors 

either to the data collector’s home or hotel. These boxes contained all supplies necessary for 

completing the data collection, including questionnaires, data envelopes, field forms, and pencils. 

Data collectors were supplied with extra materials for emergency packs as well, which they 

carried with them at all times. 

In addition to receiving multiple mailings, supervisors remained in close contact with the data 

collectors by phone and email, including daily contact during the first week and no less than 

twice-weekly contact thereafter. Phone calls were specifically used to review performance, 

provide reminders, and give emotional support. Further, the calls gave data collectors the 

opportunity to ask questions, provided feedback from schools, and discussed difficult or 

rewarding data collection experiences. 

3.6 Survey Administration 

Survey administration in the schools began on February 9, 2009, immediately after data collector 

training and continued until June 12, 2009. Each data collector visited an average of three 

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

followed for every school: 

(1) Pre-contact call with the principal or lead contact prior to arrival at the school. 

(2) Entry meeting with the principal or lead contact. 

(3) Entry meeting with teacher or group of teachers prior to survey administration. 

(4) Survey administration. 

(5) Post-survey meeting with the teacher or teachers. 

(6) Post-survey meeting with the principal or lead contact prior to leaving the school. 
24 

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

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

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

participation was anonymous and voluntary. Students completed a pencil and paper selfadministered questionnaire booklet that could be scanned. 

3.7 Make-up Sessions 

If students in selected classes were eligible to participate but were absent on the day of the 

survey, a Make-Up List was completed. Copies of the Make-Up List were given to the applicable 

teacher and to the principal or lead contact; one also was kept by the data collector. Every effort 

was made to have the data collector return at least once to each visited school to conduct a makeup session if necessary. However, sometimes there was no opportunity for the data collector to 

return to a school prior to leaving for the next site; or, coming back while the data collector was 

still in the area was too soon for a make-up session. In these and other similar situations, teachers 

were asked to administer make-up surveys and send them back in the provided pre-paid business 

reply envelopes. 

Student anonymity was protected throughout the make-up process, with no means of connecting 

a make-up survey with a particular student. There were no student-level identifiers on the 

questionnaire, and school-level identifiers remain confidential and have not been released. Once 

data collection was completed, all Make-Up Lists were destroyed. 

The use of make-up sessions ensured comparability of data and protected respondent anonymity 

while at the same time maximizing response rates. After the completion of all data collection in a 

school, data collectors sent thank you letters to each of the participating teachers and the 

principal or lead contact. This allowed the data collector an opportunity to connect once more 

with the staff at the school, inquire as to the status of make-ups expected, and encourage further 

participation.
25 

SECTION IV — WEIGHTING OF NYTS RESPONSE DATA 

This section describes the procedures used to weight the data collected in NYTS 2009. The process 

involved the following steps: 

• Sampling weights. 

• Nonresponse adjustments. 

• Weight trimming. 

• Post-stratification to national estimates of racial totals by grade, sex, and school type. 

It is worth noting that like the YRBS, and unlike previous cycles of the NYTS, the 2009 NYTS 

survey weights were not adjusted to stratum totals prior to trimming; rather, post-stratification totals 

were used for population adjustments following the trimming process. 

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

student level response data was 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. 

4.1 Sampling Weights 

The sampling weight attached to each student response is the inverse of the probability of selection 

for that student. This basic weight can be adjusted to compensate for nonresponse, to alleviate 

excess weight variation, and to match the weighted data to known control totals. A convenient way 

of computing the basic weight is by inverting the probabilities of selection at each stage, to derive a 

partial weight or stage weight. The stage weights are then multiplied together to form the overall 

weight. 

4.1.1 Conditional Student Weights 

The 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, a nonresponse 

adjustment and a ratio adjustment to control to known school enrollment totals. These adjustments 

are explained further. 

Basic Weight. If KC

klm classes per grade were selected the conditional probability of selection of the 

student was KC

klm / Cjklm and the corresponding weight is the inverse of the following probability: 

 W1 = Cjklm / Kc

klm
26 

Nonresponse Adjustment. The conditional student weight is adjusted for nonresponse by 

multiplying the basic weight by the inverse of the proportion of students selected (this includes 

students that were absent the day that the NYTS was administered, given that at least one make-up 

session was done to capture the absent student’s data) that responded to the survey: 

W2 = W1×fNR 

where:

resp

select

n

n = = ×

∑

∑

1

Resp

1

Select

1

NR W

W

W

f 

Enrollment Ratio Adjustment. Next, the nonresponse adjusted student weights are ratio adjusted to 

conform to known school enrollment totals for each grade and sex: 

W3 = W2×fenroll 

where:

2 resp

enroll

Resp

2

enroll

enroll W n

N

W

N f

× = =

∑

Using the adjustment factors above, the ratio-adjusted student weight is equal to the ratio of the 

enrollment total to the number of students responding: 

 W3 = Nenroll/nresp. 

We form adjustment cells by grade and sex within each school. To avoid any excessively large 

weights, we apply a collapsing algorithm for nonresponse cells. We collapse if the nonresponse 

adjustment for the cell exceeds a cap, which we've set to be equal to: 

2 × enrollment/min (enrollment,10) 

If exceeded, we initially collapse males and females within grade within the school. If the 

adjustment still exceeds the cap, we collapse across grades. Finally, if necessary, we collapse all 

grades within the school. 

4.1.2 School Sampling Weights 

For large schools the partial school weight is 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 ⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛
27 

For small schools the partial school weight is: 

P

1 = MOS

MOS

W = SS

klm klm

SS .lm

klm ⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ (80 / 6)

For medium schools the partial school weight is: 

P

1 = MOS

MOS

W = MS

klm klm

MS .lm

klm ⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ ( 80 / 9)

Appendix D gives the school measure of size and the school component of the sampling weight. 

Note that for schools identified as small schools, only one school was selected per subsampled PSU. 

4.1.3 Grade Sampling Weights 

The partial weight for a grade, given the selection of the cluster school containing it, is simply the 

inverse of the probability of selection described in Chapter 2. In a non-cluster school the weight is 

1.0. We denote the grade weight as WG

jklm. Exhibit 4-1 displays selected component schools within 

cluster schools and resulting grade selection weights.‡‡

Exhibit 4-1 Grade Selection Weight for Cluster Schools 

SSU (Cluster School ID) QID (Component School ID) Grade Grade Selection Weight 

00008939M 00021982 06 2.491 

00008939M 00008939 07 1.000 

00008939M 00008939 08 1.000 

00236091M 00239321 06 1.380 

00236091M 00236091 07 1.000 

00236091M 00236091 08 1.000 

00245379H 00246623 09 2.076 

00245379H 00245379 10 1.000 

00245379H 00245379 11 1.000 

00245379H 00245379 12 1.000 

00258399M 00258399 06 2.046 

00258399M 00258399 07 1.000 

00258399M 00258399 08 1.000 

00424948M 00424948 06 2.466 

00424948M 00424948 07 1.000 

00424948M 00424948 08 1.000 

00708271H 02943699 09 1.852 

 

‡‡ Some schools do not have all four grades represented in the table due to nonresponse, and some schools have 

more than one component at a grade level due to data collection requirements. 
28 

SSU (Cluster School ID) QID (Component School ID) Grade Grade Selection Weight 

00708271H 00708271 10 1.000 

00708271H 00708271 11 1.000 

00708271H 00708271 12 1.000 

00737859M 00737859 07 5.104 

00737859M 00737859 07 5.104 

00737859M 00748022 08 1.206 

00752742M 00787629 06 1.588 

00752742M 00752742 07 1.000 

00752742M 00752742 08 1.000 

00765627M 90341308 06 4.353 

00921928M 90215398 06 1.282 

00921928M 90215398 07 1.282 

00921928M 00921928 08 1.000 

00931969H 00931969 09 3.373 

00931969H 00931969 10 1.000 

00931969H 00931969 11 1.000 

00931969H 00931969 12 1.000 

00953031M 00953031 06 1.356 

00953031M 00953031 07 1.000 

00953031M 00953031 08 1.000 

00972677H 90227951 09 5.670 

01203553H 01241409 09 5.571 

01203553H 01203553 10 1.000 

01203553H 01203553 11 1.000 

01203553H 01203553 12 1.000 

01262881M 01273703 07 1.272 

01262881M 01273703 07 1.272 

01262881M 01273703 08 1.272 

01360197M 01360197 06 1.267 

01360197M 01360197 07 1.000 

01360197M 01360197 08 1.000 

01479792M 01479792 06 4.143 

01479792M 01479792 07 1.000 

01479792M 01479792 08 1.000 

01531518M 01544356 06 1.761 

01531518M 01531518 07 1.000 

01531518M 01531518 08 1.000 

03215396M 03215396 06 1.329 

03215396M 03215396 07 1.000 

03215396M 03215396 08 1.000 
29 

SSU (Cluster School ID) QID (Component School ID) Grade Grade Selection Weight 

05441062M 05441062 06 1.786 

05441062M 05441062 07 1.000 

05441062M 05441062 08 1.000 

4.1.4 PSU Sampling Weights 

The weight of the PSU is 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 schools and medium schools, the PSU component of the weight was multiplied by the 

ratio of the number of PSUs to the number of PSUs selected to have a sample small school, or 

medium school; specifically, this ratio is 80/6 for small schools, and 80/9 for medium schools. 

Appendix C gives, for each sampled PSU, the PSU size measure (over large schools), the PSU-level 

sampling weight after removal of certainty PSUs, and the expected number of times the PSU was 

taken into the sample (probability of selection for non-certainty PSUs). The overall weights for a 

given PSU, school and grade combination were the product of the adjusted PSU, school and grade 

level weights. 

4.2 Nonresponse Adjustments 

Let WT1

hijklm be the product of the weights computed to this point, i.e.: 

W =W W W W R

hijklm

G

jklm

LS

klm

P

lm

T1

hijklm

This is the weight for students in large schools with a nonresponse/post-stratification adjustment 

made for nonresponding students in participating schools. The expression for small and medium 

school students is similar. This section describes how weights are adjusted for nonparticipation by 

entire schools, using strata as weighting classes. The adjustment factor for large schools in stratum 

m is: 

LS

klm

LS

klm

P

lm

k,l schools with respondents

LS

klm

LS

klm

P

lm

LS k,l schools sampled

m W W MOS

W W MOS

A =

∑

∑

ε

ε

The student weight, adjusted for nonresponse, is Alm WT1

hijklm. 

To avoid very large weight adjustment factors, which may lead to variance increases, weighting 

classes combined the top two sampling strata in terms of minority concentrations. These weighting 

cells were created for computing nonresponse adjustments only – the collapsed strata not kept on 
30 

the analytic file. Specifically, weighting cells combined the following pairs of strata: B4U and B3U; 

B3R and B4R; H3U and H4U; and H3R and H4R. School response rates by weighting class, and 

the resulting nonresponse adjustment factors, are detailed in Exhibit 4-2. Note that the weighting 

classes are defined using collapsed sampling strata. 

Exhibit 4-2 School Nonresponse Adjustment 

a) High Schools 

School Level 

Stratum 

(for NonResponse) 

Sampled 

Schools 

Responding 

Schools 

Percent 

Responding 

Nonresponse 

Adjustment 

HS BR1 13 10 76.9% 1.281 

HS BR2 5 5 100.0% 1.000 

HS BR3 7 7 100.0% 1.000 

HS BU1 10 10 100.0% 1.000 

HS BU2 7 5 71.4% 1.299 

HS BU3 3 2 66.7% 1.368 

HS HR1 9 8 88.9% 1.102 

HS HR2 4 4 100.0% 1.000 

HS HR3 5 5 100.0% 1.000 

HS HU1 8 6 75.0% 1.308 

HS HU2 4 4 100.0% 1.000 

HS HU3 11 11 100.0% 1.000 

HS Small 15 13 86.7% 1.097 

Total 101 90 89.1% 
31 

a) Middle Schools 

School Level 

Stratum 

(for NonResponse) 

Sampled 

Schools 

Responding 

Schools 

Percent 

Responding 

Nonresponse 

Adjustment 

MS BR1 16 16 100.0% 1.000 

MS BR2 6 6 100.0% 1.000 

MS BR3 9 9 100.0% 1.000 

MS BU1 9 9 100.0% 1.000 

MS BU2 8 4 50.0% 2.673 

MS BU3 6 6 100.0% 1.000 

MS HR1 11 11 100.0% 1.000 

MS HR2 5 5 100.0% 1.000 

MS HR3 5 5 100.0% 1.000 

MS HU1 10 10 100.0% 1.000 

MS HU2 5 5 100.0% 1.000 

MS HU3 10 8 80.0% 1.193 

MS Small 21 21 100.0% 1.000 

Total 121 115 95.0% 

4.3 Weight Trimming 

Extreme variation in sampling weights can cause inflated sampling variances, and offset the 

precision gained from a well-designed sampling plan. One strategy to compensate for this is to trim 

extreme weights and distribute the trimmed weight among the untrimmed weights. The method we 

used is based on a similar procedure done for the National Assessment of Educational Progress 

(NAEP).11, 12

The trimming is an iterative procedure. During each iteration, an optimal weight, Wo is calculated 

from the sum of the squared weights in the sample. Then, each weight Wi is marked and trimmed if 

it exceeds that optimal weight. The trimmed weight is summed within grade and spread out 

proportionally over the unmarked cases in the grade. This process is repeated until little or no 

weight is being trimmed. 

Wok is determined by the following: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ∑

n

w W = c

2

k n

k=1

2

1

ok

The constant c is arbitrary. Setting it to a low level will generate high levels of trimming; increasing 

it will reduce the level of trimming. For the current study, c has been set so that approximately 5% 

of the weight will be trimmed in the first iteration of the trimming algorithm. 

The results of the first iteration of the trimming operation are summarized in Exhibit 4-3. 
32 

Exhibit 4-3 Results of First Trimming Iteration 

-CVs (in %)- -Design EffectsTrimming 

Class 

Number 

Cases 

Trimming 

Factor 

Total 

Weight 

Percentage

Trimmed After Before After Before 

BR106 456 5.1 294,126 4.92006 77.18 90.47 1.5944 1.8166

BR107 493 1.8 424,310 4.48622 33.31 41.25 1.1107 1.1698

BR108 460 3.0 412,569 4.76107 52.00 64.40 1.2699 1.4138

BR109 339 2.1 678,128 4.81419 47.92 55.63 1.2289 1.3086

BR110 352 2.0 647,417 4.49599 48.86 58.52 1.2381 1.3415

BR111 365 1.7 624,426 4.93303 36.84 45.55 1.1353 1.2069

BR112 347 2.7 619,689 4.95949 57.86 67.43 1.3338 1.4534

BR206 224 1.9 176,670 4.62356 41.22 48.77 1.1691 1.2368

BR207 240 1.4 209,175 4.6034 15.25 27.35 1.0232 1.0745

BR208 253 1.4 222,524 3.82036 21.09 27.48 1.0443 1.0752

BR209 237 1.1 325,533 4.37476 12.00 17.66 1.0143 1.0311

BR210 224 1.8 316,329 4.64116 31.59 41.61 1.0993 1.1724

BR211 196 1.4 283,494 4.1263 24.39 30.18 1.0592 1.0906

BR212 249 1.8 273,175 4.70954 29.92 40.53 1.0892 1.1636

BR306 232 1.7 149,359 4.26989 52.33 56.15 1.2726 1.3139

BR307 264 1.4 147,977 4.58212 22.47 29.84 1.0503 1.0887

BR308 267 1.2 149,146 4.92345 9.35 19.58 1.0087 1.0382

BR309 217 1.5 161,124 4.1798 36.16 41.71 1.1302 1.1732

BR310 198 3.6 151,495 4.97754 46.16 60.83 1.2120 1.3682

BR311 232 1.2 132,803 4.71634 16.24 24.43 1.0263 1.0594

BR312 241 1.4 119,136 4.99761 19.47 28.39 1.0378 1.0803

BR406 98 1.0 33,518 3.15426 4.07 8.24 1.0016 1.0067

BR407 94 1.0 33,166 3.64053 1.37 9.12 1.0002 1.0082

BR408 97 1.0 33,572 3.08161 4.78 7.90 1.0023 1.0062

BR409 82 1.7 78,269 4.38292 27.89 35.69 1.0768 1.1258

BR410 95 1.1 63,943 4.34322 5.94 16.05 1.0035 1.0255

BR411 82 1.1 54,195 4.97495 6.07 15.60 1.0036 1.0240

BR412 94 1.1 52,392 4.94489 3.44 15.73 1.0012 1.0245

BU106 295 2.4 379,375 4.67858 42.23 53.13 1.1777 1.2814

BU107 352 3.7 375,157 4.79552 57.63 70.76 1.3312 1.4992

BU108 345 4.0 403,147 4.88429 52.49 67.39 1.2747 1.4528

BU109 378 4.6 440,205 4.79586 78.36 90.66 1.6124 1.8198

BU110 317 3.4 421,141 4.9992 51.00 65.82 1.2593 1.4319

BU111 325 3.9 419,306 4.73479 57.89 73.16 1.3341 1.5336

BU112 338 3.8 390,920 4.84746 58.23 71.68 1.3381 1.5123

BU206 245 4.1 251,498 4.94415 62.02 77.49 1.3830 1.5981

BU207 200 1.5 184,126 3.42367 36.29 40.91 1.1310 1.1666

BU208 213 1.3 169,852 3.6848 31.26 34.40 1.0973 1.1178

BU209 255 2.9 224,217 4.96765 61.78 71.46 1.3802 1.5086

BU210 239 4.5 218,248 4.96821 61.83 76.24 1.3808 1.5788

BU211 252 2.5 203,601 4.82176 56.31 64.33 1.3158 1.4123

BU212 218 3.1 189,970 4.87738 55.35 66.45 1.3049 1.4396

BU306 105 1.5 166,213 4.55928 39.66 48.83 1.1558 1.2362

BU307 117 1.6 171,716 4.33569 42.62 51.31 1.1801 1.2611

BU308 84 1.7 81,754 4.7507 27.77 37.78 1.0762 1.1410

BU309 8 0.0 177,969 0 0.00 0.00 1.0000 1.0000

BU310 49 1.0 190,082 1.5668 0.05 3.22 1.0000 1.0010

BU311 24 1.1 163,060 4.17001 6.07 14.59 1.0035 1.0204
33 

-CVs (in %)- -Design EffectsTrimming 

Class 

Number 

Cases 

Trimming 

Factor 

Total 

Weight 

Percentage

Trimmed After Before After Before 

BU312 18 1.0 213,376 4.45781 0.52 9.75 1.0000 1.0090

BU406 48 1.0 2,587 1.22041 0.03 2.55 1.0000 1.0006

BU407 99 1.8 77,098 4.65025 38.96 45.65 1.1502 1.2062

BU408 103 1.4 79,263 4.23322 23.72 29.75 1.0557 1.0876

BU409 35 1.5 46,585 3.84372 23.34 31.30 1.0529 1.0951

BU410 26 1.3 38,717 4.43317 13.59 22.88 1.0178 1.0503

BU411 58 1.0 33,058 3.23941 0.21 6.76 1.0000 1.0045

BU412 77 1.0 29,193 3.26081 0.21 6.82 1.0000 1.0046

HR106 285 7.2 598,992 4.82518 102.27 116.91 2.0422 2.3620

HR107 325 9.0 420,823 4.9268 90.88 109.85 1.8233 2.2029

HR108 300 3.7 402,208 4.89641 61.33 74.36 1.3749 1.5511

HR109 314 5.8 505,709 4.97881 71.56 87.90 1.5104 1.7701

HR110 306 5.1 543,104 4.92041 74.15 87.87 1.5480 1.7697

HR111 295 4.9 583,874 4.85425 81.56 94.09 1.6630 1.8824

HR112 274 3.9 458,644 4.99619 57.83 72.15 1.3333 1.5187

HR206 153 2.0 86,941 4.30162 51.92 58.68 1.2678 1.3421

HR207 159 1.4 73,024 4.30695 16.18 25.62 1.0260 1.0652

HR208 136 1.2 77,592 4.93673 8.91 20.55 1.0079 1.0419

HR209 142 1.1 180,522 3.32901 15.68 18.58 1.0244 1.0343

HR210 145 1.4 159,362 4.89744 32.20 37.65 1.1030 1.1407

HR211 153 1.2 153,875 4.14708 16.01 23.29 1.0255 1.0539

HR212 156 1.8 158,406 4.35678 34.01 41.47 1.1149 1.1708

HR306 91 1.7 65,722 4.77021 21.65 32.28 1.0464 1.1030

HR307 128 4.3 108,839 4.80465 49.88 65.49 1.2468 1.4255

HR308 119 1.6 116,576 3.94065 29.75 36.47 1.0878 1.1319

HR309 152 5.3 592,709 4.68174 164.60 169.69 3.6916 3.8606

HR310 162 7.5 553,139 4.76209 163.39 172.83 3.6531 3.9686

HR311 115 1.6 95,039 4.13824 32.59 38.36 1.1053 1.1459

HR312 134 3.8 93,476 4.93292 28.29 51.87 1.0795 1.2671

HR406 76 3.1 41,232 4.83269 67.24 75.83 1.4462 1.5675

HR407 83 1.7 42,250 3.80369 38.11 45.83 1.1435 1.2075

HR408 73 1.9 44,259 4.94079 45.20 52.12 1.2015 1.2680

HR409 92 1.1 57,895 3.59475 10.12 14.86 1.0101 1.0218

HR410 62 2.3 44,996 4.95317 50.34 61.42 1.2493 1.3712

HR411 105 1.4 43,676 4.37232 18.53 26.29 1.0340 1.0685

HR412 77 1.0 36,439 4.26497 1.92 9.51 1.0004 1.0089

HU106 385 2.6 552,640 4.85948 41.35 52.90 1.1705 1.2791

HU107 369 5.1 652,890 4.93958 56.13 73.27 1.3142 1.5354

HU108 283 7.1 520,751 4.96706 67.51 88.86 1.4542 1.7869

HU109 155 3.4 1,121,939 4.73841 112.52 117.47 2.2579 2.3709

HU110 152 3.0 1,257,205 4.82604 108.26 112.21 2.1643 2.2507

HU111 182 2.9 1,014,995 3.94705 89.36 97.72 1.7941 1.9497

HU112 160 3.0 1,077,581 4.80495 88.17 94.06 1.7725 1.8792

HU206 208 4.3 231,508 4.84239 76.52 87.81 1.5827 1.7674

HU207 196 6.6 215,756 4.91354 81.41 97.61 1.6594 1.9479

HU208 202 2.3 196,666 4.86206 65.37 72.95 1.4252 1.5295

HU209 215 1.8 220,358 4.23164 25.56 35.77 1.0650 1.1274

HU210 132 4.9 138,579 4.96755 68.13 82.60 1.4607 1.6772

HU211 145 9.2 128,692 4.91684 89.92 109.65 1.8029 2.1941

HU212 129 3.5 117,706 4.71922 74.54 83.29 1.5513 1.6883

HU306 202 2.7 451,031 4.57394 61.62 70.02 1.3778 1.4879
34 

-CVs (in %)- -Design EffectsTrimming 

Class 

Number 

Cases 

Trimming 

Factor 

Total 

Weight 

Percentage

Trimmed After Before After Before 

HU307 181 2.3 334,254 4.85287 62.89 69.86 1.3933 1.4853

HU308 157 2.6 248,540 4.56415 64.58 72.09 1.4144 1.5164

HU309 283 7.3 549,085 4.90977 153.97 169.53 3.3623 3.8640

HU310 276 7.8 472,354 4.80499 120.98 137.22 2.4582 2.8760

HU311 271 8.9 393,982 4.90389 118.95 134.51 2.4096 2.8027

HU312 336 12.0 415,124 4.87609 168.11 184.31 3.8176 4.3871

HU406 271 2.7 352,811 4.6649 50.50 61.85 1.2541 1.3811

HU407 266 1.8 381,446 4.74244 34.13 43.58 1.1160 1.1892

HU408 253 2.7 377,108 4.76599 47.99 59.45 1.2293 1.3520

HU409 232 1.8 265,816 4.86123 47.94 59.20 1.2288 1.3489

HU410 322 2.9 265,236 4.98256 52.12 63.46 1.2708 1.4015

HU411 258 1.9 219,179 4.82396 45.77 52.29 1.2086 1.2724

HU412 295 3.0 213,408 4.74783 47.03 58.72 1.2204 1.3436

Let Wik and Wok be the weight for the ith case and the optimum weight for the kth iteration, 

respectively, and define Tik as 1 if Wik is greater than or equal to Wok, and zero otherwise. 

Then the trimmed weight for the k + 1 iteration is defined as follows: 

⎪

⎪

⎪

⎩

⎪

⎪

⎪

⎨

⎧

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ ×

≥

∑

∑

if W < W

W (1- t )

W

t W

W 1-

W if W W

W =

ik ok

ik ik

n

i=1

ik

ik ok

ik

n

i=1

ok ik ok

i,k+1

4.4 Post-stratification to national estimates of racial totals by grade, sex, and school type 

To obtain accurate counts of high school students in schools considered eligible for the NYTS by 

grade, sex, and race for use in post-stratification, two school universe surveys conducted by the 

National Center for Education Statistics (NCES) were used. Raw school level data files were 

downloaded and processed to mirror eligibility requirements imposed on the sampling frame. 

National estimates of racial/ethnic percentages were obtained from the two sources. Private schools 

enrollments by grade and five racial/ethnic groups were obtained from the Private School Universe 

Survey (PSS), and public school enrollments by grade, sex, and five racial/ethnic categories were 

obtained from the Common Core of Data (CCD), both produced by the NCES. These databases 

were combined to produce the enrollments for all schools, and to develop population percentages to 

use as controls in the post-stratification step. 
35 

Specifically, population control totals for public school enrollments were taken from the most 

recent NCES Common Core of Data (CCD) Public Elementary/Secondary School Universe 

Survey: the most recent CCD data file available from the NCES. Records for special education, 

vocational, and other/alternative schools were deleted prior to computing control totals. Control 

totals for private school enrollments were taken from the NCES Private School Universe Survey 

(PSS), School Year 2007–2008 (most recent PSS data); this file was also restricted to “regular” 

schools. 

 

Exhibit 4-4 gives counts of schools and students by grade for public and non-public schools 

based on both the raw file and the resulting set of eligible schools. The latter set of totals was 

used as control totals for the post-stratification adjustments described next. 

Exhibit 4-4 Counts by Schools and Students by Type and Eligibility Status 

Raw (all) Eligible 

Type Grade Schools Students Schools Students 

Public 6 38,885 3,659,093 35,175 3,636,349 

 7 31,276 3,734,040 26,733 3,701,543 

 8 31,538 3,740,821 26,700 3,697,465 

 9 26,357 4,229,063 19,729 4,087,455 

 10 25,644 3,888,034 18,813 3,736,064 

 11 25,374 3,578,480 18,562 3,416,254 

 12 25,049 3,391,837 18,347 3,191,853 

 Total 204,123 26,221,368 25,466,983 

Private 6 17,574 342,675 15,541 324,372 

 7 16,560 339,249 14,662 320,914 

 8 16,415 337,350 14,469 317,943 

 9 8,513 312,502 6,863 290,626 

 10 8,124 303,841 6,525 281,885 

 11 7,771 294,436 6,233 273,818 

 12 7,551 286,063 6,096 266,841 

 Total 82,508 2,216,116 2,076,399 

Total 6 56,459 4,001,768 50,716 3,960,721 

 7 47,836 4,073,289 41,395 4,022,457 

 8 47,953 4,078,171 41,169 4,015,408 

 9 34,870 4,541,565 26,592 4,378,081 

 10 33,768 4,191,875 25,338 4,017,949 

 11 33,145 3,872,916 24,795 3,690,072 

 12 32,600 3,677,900 24,443 3,458,694 

 Total 286,631 28,437,484 27,543,382 
36 

The weighted data were then post-stratified to the joint national distribution of school type, grade, 

sex, and race/ethnicity. Exhibit 4-5 presents the post-stratification cells as well as the calculations 

involved in the post-stratification process. Within the Private School adjustment cells, sex was 

omitted, as enrollments by sex were not available for these schools. This is indicated by a 

“Combined” sex in Exhibit 4-5. Also within private schools, the non-white cells were collapsed to 

preclude small numbers of students in the adjustment classes. For the public schools, five 

racial/ethnic categories were used: white, black, Hispanic, Asian/ Pacific Islander, and Native 

American. 

Given a national estimate of student counts Ra and a weighted response total of Pa for poststratification adjustment class “a,” the post-stratification factor was the ratio of Ra to Pa. Exhibit 4-4 

gives the population control totals used in post-stratification adjustments side by side with the sum 

of the weights in each post-stratum cell, as well as the adjustment factors calculated as the ratio of 

these two totals. In other words, the adjustments in column G in this exhibit are computed as E/F, 

control total for the cell divided by the weight sum in the cell. 

Overall, as shown in Exhibit 4-4 and Exhibit 4-5, the control population total is 27,543,382§§ for 

the eligible student population, a total that matches the sum of the adjusted weights. Note that in 

Exhibit 4-5, column F contains a total representing the sum of unadjusted weights, prior to poststratification. Following post-stratification, adjusted weights sum to the control population totals. 

Because estimates are typically reported separately for middle schools and high schools, the 

weights were adjusted separately for both subpopulations. 

§§ Due to rounding, totals may not match exactly.
37 

Exhibit 4-5 Post-Stratification Adjustments 

School 

Type Grade Race/Hispanic Origin Sex 

(E) 

Control 

Total

(F) 

Weighted 

Estimate 

No. of 

Cases 

(G) Poststratification 

Adjustment 

Private 6 Other Combined 82,234 130,851 104 0.62845

Private 6 White Combined 242,138 382,462 221 0.63310

Private 7 Other Combined 81,533 124,206 111 0.65643

Private 7 White Combined 239,381 309,094 247 0.77446

Private 8 Other Combined 80,899 169,396 156 0.47757

Private 8 White Combined 237,044 220,007 189 1.07744

Private 9 Other Combined 70,068 160,917 128 0.43543

Private 9 White Combined 220,558 200,564 119 1.09969

Private 10 Other Combined 67,576 75,416 48 0.89604

Private 10 White Combined 214,309 154,475 127 1.38734

Private 11 Other Combined 64,739 47,856 22 1.35278

Private 11 White Combined 209,079 194,297 130 1.07608

Private 12 Other Combined 62,736 49,451 30 1.26865

Private 12 White Combined 204,105 104,159 106 1.95956

Public 6 Asian and Pacific Islander Female 86,864 85,141 72 1.02024

Public 6 Black Female 295,390 270,530 302 1.09189

Public 6 Hispanic Female 392,668 456,563 440 0.86005

Public 6 Native American Female 21,096 45,200 49 0.46672

Public 6 White Female 976,790 736,639 641 1.32601

Public 6 Asian and Pacific Islander Male 90,133 76,572 80 1.17710

Public 6 Black Male 305,423 265,043 286 1.15235

Public 6 Hispanic Male 409,960 471,998 416 0.86856

Public 6 Native American Male 22,056 49,764 49 0.44321

Public 6 White Male 1,035,970 863,461 714 1.19979

Public 7 Asian and Pacific Islander Female 86,116 92,542 85 0.93057

Public 7 Black Female 305,929 249,516 304 1.22609

Public 7 Hispanic Female 392,146 490,285 478 0.79983

Public 7 Native American Female 21,657 47,120 47 0.45961

Public 7 White Female 998,089 839,681 686 1.18865

Public 7 Asian and Pacific Islander Male 89,814 88,203 97 1.01826

Public 7 Black Male 315,814 249,729 265 1.26463

Public 7 Hispanic Male 410,745 476,322 499 0.86232

Public 7 Native American Male 22,355 29,731 30 0.75191

Public 7 White Male 1,058,878 855,578 717 1.23762

Public 8 Asian and Pacific Islander Female 84,553 59,361 57 1.42439

Public 8 Black Female 310,853 241,147 295 1.28906

Public 8 Hispanic Female 380,926 451,742 466 0.84324

Public 8 Native American Female 21,877 45,286 45 0.48309

Public 8 White Female 1,007,613 732,608 610 1.37538

Public 8 Asian and Pacific Islander Male 90,002 79,033 86 1.13879

Public 8 Black Male 315,355 277,845 313 1.13500

Public 8 Hispanic Male 396,279 446,100 418 0.88832

Public 8 Native American Male 22,538 48,029 45 0.46926
38 

School 

Type Grade Race/Hispanic Origin Sex 

(E) 

Control 

Total

(F) 

Weighted 

Estimate 

No. of 

Cases 

(G) Poststratification 

Adjustment 

Public 8 White Male 1,067,470 764,975 665 1.39543

Public 9 Asian and Pacific Islander Female 88,576 127,026 65 0.69731

Public 9 Black Female 362,787 347,068 215 1.04529

Public 9 Hispanic Female 417,711 737,257 449 0.56657

Public 9 Native American Female 24,527 51,027 32 0.48066

Public 9 White Female 1,079,878 1,374,390 746 0.78571

Public 9 Asian and Pacific Islander Male 96,601 128,326 67 0.75278

Public 9 Black Male 387,441 503,690 182 0.76921

Public 9 Hispanic Male 448,720 669,245 420 0.67049

Public 9 Native American Male 25,792 53,954 37 0.47803

Public 9 White Male 1,155,423 1,272,598 676 0.90792

Public 10 Asian and Pacific Islander Female 87,663 125,172 83 0.70034

Public 10 Black Female 315,178 444,007 235 0.70985

Public 10 Hispanic Female 357,917 685,899 441 0.52182

Public 10 Native American Female 22,526 56,245 25 0.40050

Public 10 White Female 1,055,125 1,335,917 714 0.78981

Public 10 Asian and Pacific Islander Male 92,952 103,760 59 0.89583

Public 10 Black Male 310,443 440,751 207 0.70435

Public 10 Hispanic Male 368,804 806,003 434 0.45757

Public 10 Native American Male 22,513 60,002 28 0.37521

Public 10 White Male 1,102,944 1,193,699 656 0.92397

Public 11 Asian and Pacific Islander Female 85,423 106,909 75 0.79902

Public 11 Black Female 273,924 364,522 226 0.75146

Public 11 Hispanic Female 303,910 569,379 407 0.53376

Public 11 Native American Female 19,799 36,905 22 0.53649

Public 11 White Female 1,020,240 1,106,160 711 0.92233

Public 11 Asian and Pacific Islander Male 88,764 125,940 85 0.70481

Public 11 Black Male 252,929 366,620 221 0.68989

Public 11 Hispanic Male 301,918 485,697 430 0.62162

Public 11 Native American Male 19,623 36,503 23 0.53757

Public 11 White Male 1,049,723 1,106,464 706 0.94872

Public 12 Asian and Pacific Islander Female 82,096 125,387 91 0.65474

Public 12 Black Female 249,192 408,100 259 0.61061

Public 12 Hispanic Female 269,235 587,502 447 0.45827

Public 12 Native American Female 18,311 30,486 17 0.60061

Public 12 White Female 990,258 1,141,071 702 0.86783

Public 12 Asian and Pacific Islander Male 84,447 120,795 100 0.69910

Public 12 Black Male 218,815 322,098 245 0.67934

Public 12 Hispanic Male 255,399 432,667 427 0.59029

Public 12 Native American Male 17,748 24,964 27 0.71096

Public 12 White Male 1,006,352 1,111,958 692 0.90503
39 

4.5 Analysis Strata and Variance Estimation 

Sampling variances for complex sampling designs can be estimated using one of several methods, 

including linearized estimators and balanced repeated replication. These methods may be 

implemented with a variety of software packages, including SUDAAN and WesVar, and with Stata 

and SAS using special sample survey procedures (such as Proc SurveyMeans in SAS Version 8). 

The 2009 NYTS data were prepared for estimating variances using the method of linearized 

estimators. Because estimates are typically reported separately for middle schools and high schools, 

analysis strata need to ensure that each stratum has two or more PSUs for variance estimation within 

each subpopulation (middle schools and high schools separately). 

Stratum codes used in sampling and weighting were converted to a numeric “analysis stratum” code 

for use in SUDAAN. All strata but one had at least two PSUs. Stratum BU3 was combined with 

BU4 for High Schools to result in an analysis stratum with at least two PSUs (analysis stratum 

coded 113). Thus, the analytic file contains 16 analysis stratum for middle schools and 15 for high 

schools. 

Exhibit 4-6 displays the correspondence between the sampling strata and the analysis strata. 

Exhibits 4-7 to 4-10 present key survey estimates and their sampling errors estimated using Taylor 

series linearization methods which are usually employed by 2009 NYTS data analysts, and 

implemented with SUDAAN or similar software (e.g., SAS Proc SurveyMeans). Specifically, the 

exhibits present standard error for prevalence estimates of current use and ever use of tobacco 

products separately for middle schools and high schools. 

Exhibit 4-6 Sampling and Analysis Stratum Coding Schemes 

High Black High Hispanic 

Sampling Stratum 

Code 

Analysis Stratum 

Code 

Sampling 

Stratum Code 

Analysis Stratum 

Code 

BU1 111 HU1 211 

BR1 101 HR1 201 

BU2 112 HU2 212 

BR2 102 HR2 202 

BU3 113 HU3 213 

BR3 103 HR3 203 

BU4 114(MS)/113(HS) HU4 214 

BR4 104 HR4 204 
40 

Exhibit 4-7 Ever Use Estimates for Tobacco Products among Middle School 

Students, National Youth Tobacco Survey, 2009 

Ever Use – Middle School Sex Race/ethnicity 

Product Estimate Overall Female Male Whites Blacks Hispanics Others 

Any tobacco Prevalence 22.21 19.68 24.57 19.09 29.17 28.05 19.56

 Standard Error 1.17 1.17 1.40 1.39 1.73 1.64 2.81

 

Cigarettes Prevalence 18.10 17.03 19.07 14.85 24.64 24.36 15.82

 Standard Error 1.13 1.09 1.32 1.33 1.55 1.65 2.30

 

Cigar Prevalence 10.19 7.80 12.41 8.16 11.84 15.11 10.11

 Standard Error 0.47 0.52 0.70 0.57 0.83 0.94 1.92

 

SLT Prevalence 5.99 3.59 8.22 6.30 4.07 6.09 9.05

 Standard Error 0.47 0.40 0.68 0.68 0.70 0.55 1.98

 

Pipe Prevalence 4.75 3.56 5.81 3.48 3.91 8.81 6.37

 Standard Error 0.41 0.47 0.46 0.43 0.51 0.95 1.35

 

Bidis Prevalence 3.11 2.38 3.79 2.08 3.62 5.36 4.54

 Standard Error 0.27 0.31 0.37 0.26 0.58 0.55 1.38

 

Kreteks Prevalence 1.84 1.25 2.40 1.42 1.93 2.71 2.59

 Standard Error 0.19 0.21 0.26 0.18 0.40 0.42 1.13
41 

Exhibit 4-8 Current Use Estimates for Tobacco Products among Middle School 

Students, National Youth Tobacco Survey, 2009 

Current Use – Middle School Sex Race/ethnicity 

Product Estimate Overall Female Male Whites Blacks Hispanics Others 

Any tobacco Prevalence 8.07 6.77 9.28 7.05 8.31 11.40 7.55

 Standard Error 0.52 0.47 0.69 0.72 0.73 0.93 1.98

 

Cigarettes Prevalence 5.02 4.80 5.22 4.34 5.17 6.85 5.84

 Standard Error 0.43 0.43 0.52 0.61 0.76 0.74 1.86

Cigar Prevalence 3.96 3.28 4.62 2.97 4.72 6.37 4.33

 Standard Error 0.31 0.38 0.40 0.34 0.66 0.56 1.36

SLT Prevalence 2.41 1.48 3.30 2.49 1.71 2.59 3.90

 Standard Error 0.26 0.26 0.35 0.34 0.37 0.40 1.34

Pipe Prevalence 2.25 1.81 2.65 1.45 1.86 4.81 3.37

 Standard Error 0.28 0.33 0.34 0.21 0.41 0.75 1.30

Bidis Prevalence 1.62 1.34 1.88 1.05 1.97 2.87 3.20

 Standard Error 0.20 0.26 0.25 0.19 0.44 0.44 1.21

Kreteks Prevalence 1.16 0.76 1.53 0.81 1.33 2.10 1.55

 Standard Error 0.17 0.20 0.23 0.15 0.34 0.38 0.81
42 

Exhibit 4-9 Ever Use Estimates for Tobacco Products among High School Students, 

National Youth Tobacco Survey, 2009 

Ever Use – High School Sex Race/ethnicity 

Product Estimate Overall Female Male Whites Blacks Hispanics Others 

Any tobacco Prevalence 48.53 44.29 52.67 50.44 38.74 54.18 36.29

 Standard Error 1.45 1.64 1.81 2.00 4.53 1.99 2.93

 

Cigarettes Prevalence 42.86 41.07 44.63 43.70 34.10 50.34 33.31

 Standard Error 1.67 1.62 1.87 2.37 4.15 2.15 2.86

 

Cigar Prevalence 28.69 21.88 35.42 32.43 16.10 29.41 16.82

 Standard Error 1.57 1.29 2.32 1.43 3.30 1.26 2.35

 

SLT Prevalence 14.24 5.69 22.72 17.54 7.46 9.33 10.88

 Standard Error 1.62 0.67 2.76 1.98 1.16 1.27 2.74

 

Pipe Prevalence 9.94 6.51 13.32 10.50 5.47 11.88 8.87

 Standard Error 0.80 0.48 1.32 1.03 0.64 1.31 1.48

 

Bidis Prevalence 5.21 4.34 6.00 4.54 5.40 7.52 4.48

 Standard Error 0.31 0.37 0.42 0.42 1.21 1.03 1.20

 

Kreteks Prevalence 4.75 3.52 5.90 5.10 2.95 4.87 4.73

 Standard Error 0.41 0.52 0.50 0.57 0.78 0.61 1.18
43 

Exhibit 4-10 Current Use Estimates for Tobacco Products among High School 

Students, National Youth Tobacco Survey, 2009 

Current Use – High School Sex Race/ethnicity 

Product Estimate Overall Female Male Whites Blacks Hispanics Others 

Any tobacco Prevalence 24.01 18.19 29.66 26.69 14.05 24.97 19.21

 Standard Error 1.38 1.14 2.02 1.69 1.93 1.69 2.24

 

Cigarettes Prevalence 17.37 14.82 19.86 19.44 7.43 19.36 15.42

 Standard Error 1.05 0.98 1.34 1.23 1.46 1.45 2.01

Cigar Prevalence 10.91 6.69 15.11 11.87 7.10 11.77 8.14

 Standard Error 1.00 0.69 1.43 1.10 1.79 1.13 1.23

SLT Prevalence 6.85 1.74 11.92 8.54 1.65 4.86 8.50

 Standard Error 1.00 0.30 1.75 1.25 0.78 0.84 2.67

Pipe Prevalence 3.98 2.50 5.43 3.27 3.59 6.71 5.03

 Standard Error 0.33 0.35 0.49 0.42 0.61 1.37 1.04

Bidis Prevalence 2.41 2.03 2.79 1.68 3.68 3.58 3.97

 Standard Error 0.26 0.25 0.36 0.26 0.97 0.56 1.67

Kreteks Prevalence 2.45 1.87 3.02 2.43 1.81 2.78 3.83

 Standard Error 0.22 0.39 0.38 0.28 0.47 0.50 1.11

Example specifications for applying the method with SUDAAN are provided below for computing 

prevalence estimates for current and ever use of any tobacco, cigarette and cigar at the high school 

level. 

1 PROC DESCRIPT 

DATA="C:\nyts09_sud_hs.ssd" 

 FILETYPE = SAS DESIGN=WR; 

2 NEST NSTRATUM NPSU/MISSUNIT; 

3 WEIGHT FINWGT; 

4 VAR ETOB ECIGT ECIGAR CTOB CCIGT CCIGAR; 

5 CATLEVEL 1 1 1 1 1 1; 

6 TITLE "NYTS09 High Schools, Overall"; 

7 SETENV LABWIDTH = 30 LINESIZE=120 PAGESIZE=59 COLWIDTH=11; 

8 PRINT NSUM="N" TOTAL="n" PERCENT=" %" SEPERCENT="SE" 

 

 / NSUMFMT=F8.0 TOTALFMT=F10.1 PERCENTFMT=F6.2 SEPERCENTFMT=F6.2 

 STYLE=NCHS; 
44 

References

1. CDC. Best Practices for comprehensive tobacco control programs. Atlanta, GA: US 

Department of Health and Human Services, Public Health Service, CDC; 1999. 

2. U.S. Department of Health and Human Services. Healthy People 2010. 2nd ed. With 

Understanding and Improving Health and Objectives for Improving Health. 2 vols. 

Washington, DC: U.S. Government Printing Office, November 2000. 

3. CDC. Tobacco use among middle and high school students—United States, 1999. MMWR

2000;49(10):49–53. 

4. CDC. Youth tobacco surveillance—United States, 2000. MMWR 2001;50(SS4). 

5. CDC. Youth tobacco surveillance—United States, 2002. MMWR 2006; 55(SS3). 

6. CDC. Tobacco use, access, and exposure to tobacco in media among middle and high school 

students—United States 2004. MMWR 2005;54(12):297-301. 

7. Ryan H, Arrazola R. 2006 National Youth Tobacco Survey and key prevalence indicators. 

http://www.cdc.gov/tobacco/data_statistics/surveys/nyts/pdfs/indicators.pdf. Last accessed 

October 2008. 

8. Office of Management and Budget, Revisions to the Standards for the Classification of 

Federal Data on Race and Ethnicity. 

http://www.whitehouse.gov/omb/fedreg_1997standards/. Last accessed October, 2008. 

9. Common Core of Data, National Center for Education Statistics http://nces.ed.gov/ccd/. School 

Year 2007–2008, version 1a. 

10. Quality Education Data. www.schooldata.com. Last access, October, 2009. 

11. *

Potter F, Survey of Procedures to Control Extreme Sampling Weights, in Proceedings of the 

Section on Survey Research Methods, American Statistical Association 1988;pp 453458. 

12. Potter F. A Study of Procedures to Identify and Trim Extreme Sample Weights, Proceedings 

of the Section on Survey Research Methods, American Statistical Association 1990;pp. 225-

230. 
45 

Appendix A 

 

Common Core of Data (CCD) 

Race/Ethnicity Definitions 
46 

Appendix A: Common Core of Data Race/Ethnicity Definitions 

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. 

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.

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. 

White—A person having origins in any of the original peoples of Europe, North Africa, or the 

Middle East.
47 

Appendix B 

2009 NYTS Survey Instrument 
48 

Appendix B: 2009 NYTS Survey Instrument 

OMB No.: 0920-0621 

Expiration Date: 

12/31/2011

National Youth 

Tobacco Survey (NYTS) 

2009 Questionnaire 

Thank You Very Much For Your Help. 

This survey is about tobacco. We would like to know about you and 

things you do that may affect your health. Your answers will be used for 

programs for young people like yourself. 

 DO NOT write your name on this survey. The answers you give will 

be kept private. 

 NO one will know what you write. Answer the questions based on 

what you really do and know. 

 Completing the survey is voluntary. Whether or not you answer the 

questions will not affect your grade in this class. Try to answer all the 

questions. If you do not want to answer a question, just leave it blank. There 

are no wrong answers. 

 The questions that ask about your background will only be used to 

describe the types of students completing this survey. The information will 

not be used to find out your name. No names will ever be reported. 

 Please read every question. Try to answer all the questions. Fill in the 

circles in the booklet completely. When you are finished, follow the 

instructions of the person giving you the survey. 

Public reporting burden for this collection of information is estimated to average 45 minutes 

per response, including time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of 

information. An agency may not conduct or sponsor, and a person is not required to respond to 

a collection of information unless it displays a currently valid OMB control number. Send 

comments regarding this burden estimate or any other aspect of this collection of information, 

including suggestions for reducing this burden to: CDC/ATSDR Reports Clearance Officer, 

1600 Clifton Road, NE, MS D-74, Atlanta, GA 30333, ATTN: PRA (0920-0621). 
 49 

DIRECTIOINS 

• Use a #2 pencil only. 

• Make dark marks. 

• Fill in a response like this: 

• If you change your answer, erase your old answer 

completely. 

The first questions ask for some background information 

about you. 

1. How old are you? 

a. 9 years old 

b. 10 years old 

c. 11 years old 

d. 12 years old 

e. 13 years old 

f. 14 years old 

g. 15 years old 

h. 16 years old 

i. 17 years old 

j. 18 years old 

k. 19 years old 

l. 20 years old 

m. 21 years old 

2. What is your sex? 

a. Female 

b. Male 

3. What grade are you in? 

a. 6th

b. 7th

c. 8th

d. 9th

e. 10th

f. 11th

g. 12th

h. Ungraded or other grade 

4. Are you Hispanic or Latino? 

a. Yes 

 b. No 

5. What race or races do you consider yourself to be? 

(Please select one or more than one category) Would 

you say: 

a. American Indian or Alaska Native 

b. Asian 

c. Black or African American 

d. Native Hawaiian or Other Pacific Islander 

e. White 

6. During an average week, how much money do you get 

from a job and other sources (allowance, etc.)? 

a. None 

b. Less than $1 

c. $1 to $5 

d. $6 to $10 

e. $11 to $20 

f. $21 to $50 

g. $51 to $100 

h. $101 to $150 

i. $151 or more 

7. During the past 30 days, how many days did you miss 

school for any reason, with or without permission? 

a. 0 days 

b. 1 day 

c. 2 to 5 days 

d. 6 to 10 days 

e. 11 or more days 

The next questions ask about your use of tobacco. 

The first group of questions is about cigarette smoking. 

8. Have you ever tried cigarette smoking, even one or two 

puffs? 

a. Yes 

b. No 

9. How old were you when you smoked a whole cigarette for 

the first time? 

a. I have never smoked a whole cigarette 

b. 8 years old or younger 

c. 9 

d. 10 

e. 11 

f. 12 

g. 13 

h. 14 

i. 15 

j. 16 

k. 17 years old or older 

10. About how many cigarettes have you smoked in your 

entire life? 

a. None 

b. 1 or more puffs but never a whole cigarette 

c. 1 cigarette 

d. 2 to 5 cigarettes 

e. 6 to 15 cigarettes (about 1⁄2 a pack total) 

f. 16 to 25 cigarettes (about 1 pack total) 

g. 26 to 99 cigarettes (more than 1 pack but less than 5 

packs) 

h. 100 or more cigarettes (5 or more packs) 
 50 

11. Now think back 12 months ago. At this time last year, 

about how much were you smoking? 

a. I have never smoked cigarettes 

b. I have smoked in my life, but I wasn’t smoking at this 

time last year 

c. I smoked on some days at this time last year 

d. I smoked on most days at this time last year 

e. I smoked every day at this time last year 

12. Have you ever smoked cigarettes daily, that is, at least 

one cigarette every day for 30 days? 

a. Yes 

b. No 

13. During the past 30 days, on how many days did you 

smoke cigarettes? 

a. 0 days 

b. 1 or 2 days 

c. 3 to 5 days 

d. 6 to 9 days 

e. 10 to 19 days 

f. 20 to 29 days 

g. All 30 days 

14. During the past 30 days, on the days you smoked, how 

many cigarettes did you smoke per day? 

a. I did not smoke cigarettes during the past 30 days 

b. Less than 1 cigarette per day 

c. 1 cigarette per day 

d. 2 to 5 cigarettes per day 

e. 6 to 10 cigarettes per day 

f. 11 to 20 cigarettes per day 

g. More than 20 cigarettes per day 

15. During the past 30 days, what brand of cigarettes did you 

usually smoke? (Choose only one answer)

a. I did not smoke cigarettes during the past 30 days 

b. I do not have a usual brand 

c. American Spirit 

d. Camel 

e. GPC, Basic, or Doral 

f. Kool 

g. Lucky Strike 

h. Marlboro 

i. Newport 

j. Parliament 

k. Virginia Slims 

l. Some other brand 

16. What type of cigarette did you usually smoke in the past 

30 days? 

a. I have never smoked 

b. I have smoked but not in the past 30 days 

c. I do not have a usual type 

d. Regular/Full flavor 

e. Light 

f. Ultra Light 

17. Are the cigarettes you usually smoke menthol cigarettes? 

a. I do not smoke cigarettes 

b. Yes 

c. No 

18. When was the last time you smoked a cigarette, even one 

or two puffs? 

a. I have never smoked even one or two puffs 

b. Earlier today 

c. Not today but sometime during the past 7 days 

d. Not during the past 7 days but sometime during the 

past 30 days 

e. Not during the past 30 days but sometime during the 

past 6 months 

f. Not during the past 6 months but sometime during the 

past year 

g. 1 to 4 years ago 

h. 5 or more years ago 

19. Where do you smoke cigarettes? 

(Choose one or more answers) 

a. I do not smoke now 

b. At home 

c. At school 

d. At work 

e. In the car 

f. At friends’ houses 

g. At sports events, parties, dances, raves, or other 

social events 

h. In public buildings (restaurants, fast food places, 

shopping malls, or other hangouts) 

i. Outdoors (sidewalks, parking lots, parks, or other 

places) 

20. Have you ever tried any of these tobacco products? 

(Choose one or more answers)

a. Eclipse cigarettes 

b. Omni 

c. Advance Lights 

d. Accord 

e. Ariva 

f. I have not tried any of these products 

21. During the past 30 days, how did you usually get your own 

cigarettes? (Choose only one answer)

a. I did not smoke cigarettes during the past 30 days 

b. I bought them in a store such as a convenience store, 

supermarket, or gas station 

c. I bought them from a vending machine 

d. I gave someone else money to buy them for me 

e. I borrowed them from someone else 

f. I stole them 

g. A person 18 years old or older gave them to me 

h. I got them some other way 
 51 

22. During the past 30 days, where did you buy the last pack 

of cigarettes you bought? (Choose only one answer)

a. I did not buy a pack of cigarettes during the past 30 

days 

b. A gas station 

c. A convenience store 

d. A grocery store 

e. A drugstore 

f. A vending machine 

g. I bought them over the Internet 

h. Other 

23. When you bought or tried to buy cigarettes in a store 

during the past 30 days, were you ever asked to show 

proof of age? 

a. I did not try to buy cigarettes in a store during the past 

30 days 

b. Yes, I was asked to show proof of age 

c. No, I was not asked to show proof of age 

24. During the past 30 days, did anyone ever refuse to sell 

you cigarettes because of your age? 

a. I did not try to buy cigarettes in a store during the past 

30 days 

b. Yes, someone refused to sell me cigarettes because 

of my age 

c. No, no one refused to sell me cigarettes because of 

my age 

25. During the past 30 days, on how many days did you 

smoke cigarettes on school property? 

a. 0 days 

b. 1 or 2 days 

c. 3 to 5 days 

d. 6 to 9 days 

e. 10 to 19 days 

f. 20 to 29 days 

g. All 30 days 

26. Do you want to stop smoking cigarettes? 

a. I do not smoke now 

b. Yes 

c. No 

27. Are you seriously thinking about quitting smoking? 

a. I have never smoked 

b. I do not smoke now 

c. Yes, within the next 30 days 

d. Yes, within the next 6 months 

e. Yes, but not within the next 6 months 

f. No, I am not thinking of quitting smoking 

g. Not sure 

28. How many times during the past 12 months have you 

stopped smoking for one day or longer because you were 

trying to quit smoking? 

a. I have not smoked in the past 12 months 

b. I have not tried to quit 

c. 1 time 

d. 2 times 

e. 3 to 5 times 

f. 6 to 9 times 

g. 10 or more times 

29. When you last tried to quit, how long did you stay off 

cigarettes? 

a. I have never smoked cigarettes 

b. I have never tried to quit 

c. Less than a day 

d. 1 to 2 days 

e. 3 to 7 days 

f. More than 7 days but less than 30 days 

g. 30 days or more but less than 6 months 

h. 6 months or more but less than a year 

i. 1 year or more 

30. During the past 12 months, did a medical doctor, dentist, 

or nurse ask you whether you smoke cigarettes? 

a. Yes 

b. No 

c. I don’t know/I can’t remember 

 

31. During the past 12 months, did a medical doctor, dentist, 

or nurse tell you to stop smoking? 

a. I have never smoked 

b. Yes 

c. No 

d. I don’t know/I can’t remember 

32. In the past 12 months, did you do any of the following to 

help you stop smoking? (Choose one or more answers)

a. I have never smoked 

b. I have not smoked in the past 12 months 

c. I did not try to quit in the past 12 months 

d. Attended a program in my school 

e. Attended a program in the community 

f. Called a help line or quit line 

g. Used nicotine gum 

h. Used nicotine patch 

i. Used any medicine to help quit 

j. Visited an internet quit site 

k. Got help from family or friends 

l. I tried to quit but did something else 

m. I tried to quit but did not do any of these things 

33. In the past 12 months, did you have to go to a stop 

smoking class because you were caught smoking? 

a. I have never smoked 

b. I have not smoked in the past 12 months 

c. Yes, I had to go to a stop smoking class 

d. No, I did not have to go to a stop smoking class 
 52 

34. Do you think you would be able to quit smoking cigarettes 

now if you wanted to? 

a. I do not smoke now 

b. Yes 

c. No 

35. How long can you go without smoking before you feel like 

you need a cigarette? 

a. I have never smoked cigarettes 

b. I do not smoke now 

c. Less than 1 hour 

d. 1 to 3 hours 

e. More than 3 hours but less than a day 

f. A whole day 

g. Several days 

h. A week or more 

36. How true is this statement for you? After not smoking for a 

while, I feel restless and irritable. 

a. I have never smoked cigarettes 

b. I don’t smoke now 

c. Not at all true 

d. Not very true 

e. Fairly true 

f. Very true 

37. How true is this statement for you? When I go without a 

smoke for a few hours, I experience craving. 

a. I have never smoked cigarettes 

b. I don’t smoke now 

c. Not at all true 

d. Not very true 

e. Fairly true 

f. Very true 

The next questions are about smokeless tobacco—

chewing tobacco, snuff, or dip. 

38. Have you ever used chewing tobacco, snuff, or dip, such 

as Redman, Levi Garrett, Beechnut, Skoal, Skoal Bandits, 

or Copenhagen? 

a. Yes 

b. No 

39. How old were you when you used chewing tobacco, snuff, 

or dip for the first time? 

a. I have never used chewing tobacco, snuff, or dip 

b. 8 years old or younger 

c. 9 

d. 10 

e. 11 

f. 12 

g. 13 

h. 14 

i. 15 

j. 16 

k. 17 years old or older 

40. During the past 30 days, on how many days did you use 

chewing tobacco, snuff, or dip? 

a. 0 days 

b. 1 or 2 days 

c. 3 to 5 days 

d. 6 to 9 days 

e. 10 to 19 days 

f. 20 to 29 days 

g. All 30 days 

41. During the past 30 days, on how many days did you use 

chewing tobacco, snuff, or dip on school property? 

a. 0 days 

b. 1 or 2 days 

c. 3 to 5 days 

d. 6 to 9 days 

e. 10 to 19 days 

f. 20 to 29 days 

g. All 30 days 

The next questions are about cigars and pipes. 

42. Have you ever tried smoking cigars, cigarillos, or little 

cigars, even one or two puffs? 

a. Yes 

b. No 

43. How old were you when you smoked a cigar, cigarillo, or 

little cigar for the first time? 

a. I have never smoked a cigar, cigarillo or little cigar 

b. 8 years old or younger 

c. 9 

d. 10 

e. 11 

f. 12 

g. 13 

h. 14 

i. 15 

j. 16 

k. 17 years old or older 

44. During the past 30 days, on how many days did you 

smoke cigars, cigarillos, or little cigars? 

a. 0 days 

b. 1 or 2 days 

c. 3 to 5 days 

d. 6 to 9 days 

e. 10 to 19 days 

f. 20 to 29 days 

g. All 30 days 

45. Have you ever tried smoking tobacco in a pipe, even one 

or two puffs? 

a. Yes 

b. No 
 53 

46. During the past 30 days, on how many days did you 

smoke tobacco in a pipe? 

a. 0 days 

b. 1 or 2 days 

c. 3 to 5 days 

d. 6 to 9 days 

e. 10 to 19 days 

f. 20 to 29 days 

g. All 30 days 

The next questions are about bidies and kreteks.Bidis (or 

“beedies”) are small brown cigarettes from India made of 

tobacco wrapped in a leaf tied with a thread. They can 

taste like vanilla, chocolate, cherry or other flavors. 

Kreteks (or “clove cigarettes”) are cigarettes containing 

tobacco and clove extract. 

47. Have you ever tried smoking any of the following: 

a. Bidis 

b. Kreteks 

c. I have tried both bidis and kreteks 

d. I have never smoked bidis or kreteks 

48. During the past 30 days, on how many days did you 

smoke bidis? 

a. 0 days 

b. 1 or 2 days 

c. 3 to 5 days 

d. 6 to 9 days 

e. 10 to 19 days 

f. 20 to 29 days 

g. All 30 days 

49. During the past 30 days, on how many days did you 

smoke kreteks? 

a. 0 days 

b. 1 or 2 days 

c. 3 to 5 days 

d. 6 to 9 days 

e. 10 to 19 days 

f. 20 to 29 days 

g. All 30 days 

The next questions ask about other people smoking and 

using tobacco around you. 

50. During the past 7 days, on how many days were you in the 

same room with someone who was smoking cigarettes? 

a. 0 days 

b. 1 or 2 days 

c. 3 or 4 days 

d. 5 or 6 days 

e. 7 days 

51. During the past 7 days, on how many days did you ride in 

a car with someone who was smoking cigarettes? 

a. 0 days 

b. 1 or 2 days 

c. 3 or 4 days 

d. 5 or 6 days 

e. 7 days 

52. Do you think the smoke from other people’s cigarettes is 

harmful to you? 

a. Definitely yes 

b. Probably yes 

c. Probably not 

d. Definitely not 

53. Does anyone who lives with you now smoke cigarettes? 

a. Yes 

b. No 

54. Does anyone who lives with you now use chewing 

tobacco, snuff, or dip? 

a. Yes 

b. No 

55. How many of your four closest friends smoke cigarettes? 

a. None 

b. One 

c. Two 

d. Three 

e. Four 

f. Not sure 

56. How many of your four closest friends use chewing 

tobacco, snuff, or dip? 

a. None 

b. One 

c. Two 

d. Three 

e. Four 

f. Not sure 

57. Which statement best describes the rules about smoking 

inside your home? 

a. Smoking is not allowed anywhere inside my home 

b. Smoking is allowed in some places or at some times 

c. Smoking is allowed anywhere in my home 

d. There are no rules about smoking in my home 

The next questions ask about your thoughts about 

tobacco. 

58. Do you think you will smoke a cigarette at anytime during 

the next year? 

a. Definitely yes 

b. Probably yes 

c. Probably not 

d. Definitely not 
 54 

59. Do you think you will be smoking cigarettes 5 years from 

now? 

a. I definitely will 

b. I probably will 

c. I probably will not 

d. I definitely will not 

60. If one of your best friends offered you a cigarette, would 

you smoke it? 

a. Definitely yes 

b. Probably yes 

c. Probably not 

d. Definitely not 

61. Do you think that you will try a cigarette soon? 

a. I have already tried smoking cigarettes 

b. Yes 

c. No 

62. During the past 12 months, has either of your parents (or 

guardians) told you not to smoke cigarettes? 

a. Mother (female guardian) only 

b. Father (male guardian) only 

c. Both 

d. Neither 

63. In the past 12 months, how often have your parents or 

guardians discussed the dangers of tobacco use with you? 

a. Never 

b. Rarely 

c. Sometimes 

d. Often 

e. Very often 

64. Do you think it is safe to smoke for only a year or two, as 

long as you quit after that? 

a. Definitely yes 

b. Probably yes 

c. Probably not 

d. Definitely not 

65. Do you think smoking cigarettes makes young people look 

cool or fit in? 

a. Definitely yes 

b. Probably yes 

c. Probably not 

d. Definitely not 

Some tobacco companies make items like sports gear, tshirts, lighters, hats, jackets, and sunglasses that people 

can buy or receive free. 

66. During the past 12 months, did you buy or receive 

anything that has a tobacco company name or picture on 

it?

a. Yes 

b. No 

67. Would you ever use or wear something that has a tobacco 

company name or picture on it such as a lighter, t-shirt, 

hat, or sunglasses? 

a. Definitely yes 

b. Probably yes 

c. Probably not 

d. Definitely not 

The next questions ask about events you may have 

attended or what you have seen or heard on the TV, radio, 

movies, billboards, signs, newspapers, magazines, stores, 

or the Internet. 

68. During the past 12 months, have you participated in any 

community activities to discourage people your age from 

using cigarettes, chewing tobacco, snuff, dip, or cigars? 

a. Yes 

b. No 

c. I did not know about any activities 

69. During the past 30 days, how many times did you see antismoking messages on TV? 

a. I did not watch TV in the past 30 days 

b. None 

c. 1 to 3 times in the past 30 days 

d. 1 to 3 times per week 

e. Daily or almost daily 

f. More than once a day 

70. During the past 30 days, how many times did you hear 

anti-smoking messages on the radio? 

a. I did not listen to the radio in the past 30 days 

b. None 

c. 1 to 3 times in the past 30 days 

d. 1 to 3 times per week 

e. Daily or almost daily 

f. More than once a day 

71. During the past 30 days, how many times did you see antismoking messages on the Internet? 

a. I did not use the Internet in the past 30 days 

b. None 

c. 1 to 3 times in the past 30 days 

d. 1 to 3 times per week 

e. Daily or almost daily 

f. More than once a day 

72. During the past 30 days, how many times have you seen 

anti-smoking messages on billboards or outdoor signs? 

a. None in the past 30 days 

b. 1 to 3 times in the past 30 days 

c. 1 to 3 times per week 

d. Daily or almost daily 

e. More than once a day 
 55 

73. During the past 30 days, how many times did you see antismoking messages in magazines or newspapers? 

a. I did not read magazines or newspapers 

b. None in the past 30 days 

c. 1 to 3 times in the past 30 days 

d. 1 to 3 times per week 

e. Daily or almost daily 

f. More than once a day 

74. When you watch TV or go to movies, how often do you 

see actors using tobacco? 

a. I don’t watch TV or go to movies 

b. Most of the time 

c. Some of the time 

d. Hardly ever 

e. Never 

75. When you are using the Internet, how often do you see 

ads for tobacco products? 

a. I do not use the Internet 

b. Most of the time 

c. Some of the time 

d. Hardly ever 

e. Never 

76. When you read newspapers or magazines, how often do 

you see ads or promotions for cigarettes and other 

tobacco products? 

a. I do not read newspapers or magazines 

b. All of the time 

c. Most of the time 

d. Some of the time 

e. Hardly ever 

f. Never 

77. When you go to a convenience store, supermarket, or gas 

station, how often do you see ads for cigarettes and other 

tobacco products or items that have tobacco company 

names or pictures on them? 

a. I never go to a convenience store, supermarket, or 

gas station 

b. All of the time 

c. Most of the time 

d. Some of the time 

e. Hardly ever 

f. Never 

The last questions ask if you were taught about tobacco in 

your classes. 

78. During this school year, did you practice ways to say “No” 

to tobacco in any of your classes (for example, by role 

playing)?

a. Yes 

b. No 

c. Not sure 

79. During this school year, were you taught in any of your 

classes the reasons why people your age smoke? 

a. Yes 

b. No 

c. Not sure 

80. During this school year, were you taught in any of your 

classes that most people your age do not smoke 

cigarettes? 

a. Yes 

b. No 

c. Not sure 

81. During this school year, were you taught in any of your 

classes about the dangers of tobacco use? 

a. Yes 

b. No 

c. Not sure 

Thank you for participating in 

this survey. 

DO NOT WRITE IN THIS BOX 

OFFICE USE ONLY 

SCHOOL ID

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2

3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4

5 5 5 5 5 5 5 5

6 6 6 6 6 6 6 6

7 7 7 7 7 7 7 7

8 8 8 8 8 8 8 8

9 9 9 9 9 9 9 9
 56 

Appendix C 

2009 NYTS 

Primary Sampling Unit (PSU) 

Sampling Weights
 57 

Appendix C: Primary Sampling Unit (PSU) Sampling Weights 

Stratum PSU 

Size 

Measure 

Expected Number 

of Draws 

Sampling 

Weight 

Subsampled for 

Small Schools 

BR1 050450 14234.26 0.03507 28.5104 0

BR1 131150 18493.25 0.04557 21.9444 0

BR1 191530 54988.11 0.13550 7.3802 0

BR1 210930 20076.60 0.04947 20.2138 0

BR1 261210 30213.94 0.07445 13.4317 0

BR1 290510 12300.10 0.03031 32.9935 0

BR1 340250 82818.96 0.20408 4.9001 0

BR1 360650 25643.75 0.06319 15.8255 1

BR1 401430 73000.49 0.17988 5.5592 0

BR1 420130 12516.36 0.03084 32.4235 0

BR1 550170 7042.77 0.01735 57.6228 0

BR1 551010 24172.60 0.05956 16.7886 0

BR2 170910 14770.15 0.02704 36.9862 0

BR2 340010 38533.45 0.07054 14.1771 1

BR2 371330 33854.81 0.06197 16.1363 0

BR2 401090 89760.43 0.16431 6.0861 0

BR2 450510 27051.82 0.04952 20.1943 1

BR3 010170 6225.38 0.01670 59.8797 0

BR3 011250 25174.66 0.06753 14.8075 0

BR3 120310 115711.54 0.31041 3.2216 0

BR3 280190 12168.12 0.03264 30.6353 0

BR3 450130 20183.20 0.05414 18.4695 0

BR4 010470 13974.34 0.03846 26.0034 0

BR4 450750 21674.57 0.05965 16.7653 0

BU1 090130 14878.46 0.03768 26.5425 0

BU1 121030 85447.33 0.21637 4.6217 0

BU1 180570 27103.03 0.06863 14.5707 0

BU1 211170 15879.06 0.04021 24.8699 0

BU1 240310 89742.84 0.22725 4.4005 1

BU1 261250 141350.00 0.35793 2.7939 0

BU1 270530 115881.29 0.29344 3.4079 0

BU1 471890 11853.09 0.03001 33.3171 0

BU2 130670 94983.32 0.41576 2.4052 1

BU2 171630 42427.70 0.18572 5.3846 1
 58 

Stratum PSU 

Size 

Measure 

Expected Number 

of Draws 

Sampling 

Weight 

Subsampled for 

Small Schools 

BU2 290950 89968.77 0.39381 2.5393 1

BU2 291890 138200.54 0.60494 1.6531 1

BU2 511530 63154.38 0.27644 3.6174 0

BU2 518100 57049.53 0.24972 4.0045 1

BU3 220710 40906.68 0.02799 35.7332 0

BU3 550790 130615.47 0.08936 11.1911 0

BU4 240330 152239.92 0.38413 2.6033 0

BU4 421010 231260.63 0.58352 1.7137 0

HR1 080410 71118.28 0.15712 6.3645 1

HR1 180710 15430.82 0.03409 29.3330 1

HR1 181690 15626.66 0.03452 28.9654 0

HR1 230170 8351.75 0.01845 54.1960 0

HR1 360570 10444.94 0.02308 43.3350 0

HR1 361110 20215.94 0.04466 22.3898 0

HR1 480350 7022.17 0.01551 64.4576 0

HR1 484850 23201.68 0.05126 19.5086 1

HR1 530190 7005.17 0.01548 64.6140 0

HR2 061110 126885.26 0.28476 3.5118 1

HR2 420770 49129.15 0.11026 9.0698 0

HR2 480930 8895.78 0.01996 50.0904 0

HR3 350010 107349.19 0.23449 4.2646 0

HR3 484090 16310.59 0.03563 28.0676 0

HR3 530770 42411.91 0.09264 10.7941 1

HR4 350070 13071.21 0.03284 30.4508 0

HR4 480610 98534.69 0.24756 4.0395 0

HU1 201030 7745.35 0.01515 66.0012 0

HU1 250170 148915.66 0.29130 3.4328 1

HU1 361030 188252.12 0.36825 2.7155 0

HU1 361190 122436.76 0.23951 4.1752 1

HU1 490350 112046.11 0.21918 4.5624 0

HU1 530110 44258.02 0.08658 11.5505 1

HU1 551330 44252.82 0.08657 11.5519 0

HU2 060850 213482.03 0.44597 2.2423 0

HU2 120570 158450.55 0.33101 3.0211 0

HU2 120950 170176.92 0.35551 2.8129 1

HU2 360810 238610.71 0.49847 2.0062 1
 59 

Stratum PSU 

Size 

Measure 

Expected Number 

of Draws 

Sampling 

Weight 

Subsampled for 

Small Schools 

HU3 040130 517172.33 0.88807 1.1260 0

HU3 060590 415121.13 0.71283 1.4029 0

HU3 360610 174384.81 0.29945 3.3395 1

HU3 481130 377086.68 0.64752 1.5443 0

HU3 482010 310073.38 0.53245 1.8781 1

HU4 060370 403839.51 0.60927 1.6413 1

HU4 060371 405838.27 0.61228 1.6332 0

HU4 060372 408036.34 0.61560 1.6244 1

HU4 060650 354803.57 0.53529 1.8682 0

HU4 120860 390028.33 0.58843 1.6994 1
 60 

Appendix D 

 

School Sampling Weights 
 61 

Appendix D: School Sampling Weights

Stratum PSU School ID 

School Size 

Class 

School Size 

Measure 

Expected 

Number of 

Draws 

Sampling 

Weight 

1N 090010 00232144 Large 1270.2 0.01704 58.685

1N 090010 00234549 Large 1970.4 0.02643 37.831

1N 090010 00242629 Large 6410.2 0.08599 11.629

1N 330170 00851594 Large 160.97 0.03897 25.66

1N 330170 00854143 Large 2128.09 0.51522 1.941

1N 330170 00858931 Large 1417.72 0.34323 2.913

1N 340010 00870868 Large 13626.73 0.59487 1.681

1N 340010 00903341 Large 2165.18 0.09452 10.58

1N 340010 01921483 Large 5573.91 0.24333 4.11

1N 360210 00936548 Large 258.39 0.09545 10.476

1N 360210 00952184 Large 567.31 0.20957 4.772

1N 360210 04137439 Large 585.74 0.21638 4.622

1N 360730 00947323 Large 1105.67 0.25927 3.857

1N 360730 00965182 Large 598.44 0.14033 7.126

1N 360730 02923719 Large 1070.31 0.25098 3.984

1N 420430 01214485 Large 1489.46 0.09112 10.975

1N 420430 01220466 Large 1009.34 0.06175 16.195

1N 420430 01895699 Large 1980.94 0.12118 8.252

1N 421030 01230441 Large 931.12 0.08903 11.232

1N 421030 01241726 Large 845.14 0.08081 12.374

1N 421030 01248776 Large 2280.43 0.21806 4.586

1U 090030 00236885 Large 5640.22 0.07183 13.921

1U 090030 00239106 Small 325.15 0.02052 48.725

1U 090030 00244502 Large 1170.55 0.01491 67.077

1U 090030 01893352 Large 4975.98 0.06337 15.779

1U 250250 00609306 Large 6611.12 0.08259 12.108

1U 250250 00609452 Small 892.82 0.03177 31.473

1U 250250 00609496 Large 3940.98 0.04923 20.312

1U 250250 90177412 Large 3422.42 0.04275 23.39

1U 340230 00877513 Large 10687.83 0.09696 10.314

1U 340230 00887893 Large 11721.12 0.10633 9.405

1U 340230 00900861 Large 3414.89 0.03098 32.28

1U 340390 00863431 Large 2651.74 0.04515 22.147

1U 340390 00883866 Large 2106.51 0.03587 27.879

1U 340390 00902971 Large 5747.25 0.09786 10.218

1U 360470 00973874 Large 15510.68 0.05496 18.195

1U 360470 02123122 Large 4460.07 0.0158 63.277

1U 360470 03127997 Large 323.5 0.00115 872.405

1U 360590 00939039 Large 342.78 0.00295 339.549

1U 360590 00959157 Large 3273.56 0.02813 35.554
 62 

Stratum PSU School ID 

School Size 

Class 

School Size 

Measure 

Expected 

Number of 

Draws 

Sampling 

Weight 

1U 360590 00968638 Large 2222.27 0.01909 52.374

1U 360610 00973965 Large 28420.12 0.15489 6.456

1U 360610 00974222 Large 12594.1 0.06864 14.569

1U 360610 00978191 Large 6898.86 0.0376 26.597

1U 360810 00926223 Large 6366.38 0.0233 42.91

1U 360810 02184550 Large 7682.02 0.02812 35.561

1U 360810 05492970 Large 2144.3 0.00785 127.399

1U 361030 00948699 Large 6274.74 0.05683 17.598

1U 361030 00967237 Large 3099.79 0.02807 35.622

1U 361030 00996723 Large 3841.13 0.03479 28.747

1U 421010 01185645 Large 3500.65 0.01956 51.129

1U 421010 01234876 Large 9006.44 0.05032 19.873

1U 421010 01235701 Large 2897.04 0.01619 61.782

2N 170070 00350805 Large 1354.23 0.05519 18.119

2N 170070 00407274 Large 1724.66 0.07029 14.227

2N 170070 00407296 Large 6131.77 0.24989 4.002

2N 180430 00429232 Large 656.63 0.17659 5.663

2N 180430 00444192 Large 1813.85 0.48781 2.05

2N 180430 00444341 Large 1322.06 0.35555 2.813

2N 190930 00458627 Large 1261.08 0.18675 5.355

2N 190930 00482354 Large 4287.31 0.63491 1.575

2N 190930 00482365 Large 2438.64 0.36114 2.769

2N 201730 00497757 Large 282.23 0.00717 139.484

2N 201730 00511944 Large 4595.02 0.11672 8.567

2N 201730 00512686 Large 7602.86 0.19313 5.178

2N 260730 00678052 Large 2341.46 0.44611 2.242

2N 260730 00678201 Large 811.91 0.15469 6.465

2N 260730 00687996 Large 654.69 0.12474 8.017

2N 261390 00669831 Large 3037.64 0.18121 5.519

2N 261390 00671738 Large 2218.49 0.13234 7.556

2N 261390 05950728 Large 4769.25 0.28451 3.515

2N 271090 00725027 Large 2325.55 0.25353 3.944

2N 271090 00725049 Large 5451.15 0.59429 1.683

2N 271090 00725071 Large 4035.39 0.43994 2.273

2N 291330 00782813 Large 1407.65 0.39973 2.502

2N 291330 00782824 Large 1286.25 0.36526 2.738

2N 291330 00782868 Large 1313.55 0.37301 2.681

2N 310190 00835415 Large 945.53 0.40084 2.495

2N 310190 00835426 Large 2165.08 0.91784 1.09

2N 310190 90072337 Large 639.92 0.27128 3.686

2N 390390 01075392 Large 1999.49 0.74158 1.348

2N 390390 01099777 Large 587.42 0.21787 4.59
 63 

Stratum PSU School ID 

School Size 

Class 

School Size 

Measure 

Expected Number 

of Draws 

Sampling 

Weight 

2N 390390 03253762 Large 390 0.14464 6.913

2N 391510 01115575 Large 2327.76 0.13788 7.253

2N 391510 01115746 Large 801.49 0.04747 21.064

2N 391510 01116273 Large 292.89 0.01735 57.643

2N 550090 01508613 Large 203.16 0.01244 80.397

2N 550090 01525399 Large 2661.04 0.16292 6.138

2N 550090 01525457 Large 5000.78 0.30616 3.266

2N 550090 02067948 Small 70.33 0.03978 25.14

2U 170310 00342146 Large 1692.64 0.01041 96.073

2U 170310 00405583 Large 1826.31 0.01123 89.041

2U 170310 00412852 Large 1565.09 0.00962 103.902

2U 170312 00367229 Large 3798.08 0.02435 41.074

2U 170312 00371563 Large 2011.12 0.01289 77.57

2U 170312 00412783 Large 10028.18 0.06428 15.557

2U 170430 00382859 Large 5815.51 0.06095 16.407

2U 170430 00397333 Large 10856.57 0.11379 8.788

2U 170430 00404681 Large 4780.04 0.0501 19.961

2U 170970 00396996 Large 2924.15 0.04198 23.819

2U 170970 00404207 Large 12802.38 0.18381 5.4405

2U 170970 00418708 Small 276.37 0.03844 26.0157

2U 261630 00661082 Large 6550.38 0.0423 23.6383

2U 261630 00670916 Large 1737.01 0.01122 89.1414

2U 261630 04125751 Large 1874.61 0.01211 82.5983

2U 291890 00768865 Large 1856.14 0.02538 39.3983

2U 291890 00768887 Large 3457.96 0.04729 21.1479

2U 291890 04126041 Large 4059.59 0.05551 18.0138

2U 390410 01073395 Small 408.77 0.0491 20.3671

2U 390410 01073759 Large 5004.11 0.06665 15.0033

2U 390410 01100988 Large 3970.65 0.05289 18.9083

2U 390410 04146567 Large 1194.06 0.0159 62.8763

2U 390610 01069367 Large 1395.75 0.0259 38.6149

2U 390610 01084636 Large 6051.57 0.11228 8.9063

2U 390610 01084716 Large 4361.39 0.08092 12.3577

2U 550890 01531846 Large 4660.61 0.05042 19.8348

2U 550890 01532158 Large 5446.64 0.05892 16.9723

2U 550890 01532533 Large 7254.96 0.07848 12.7419

3N 010170 00006997 Large 1532.68 0.38755 2.5803

3N 010170 00007006 Large 1530.81 0.38707 2.5835

3N 010170 00013927 Large 1012.99 0.25614 3.9041

3N 010730 00003216 Small 164.01 0.02538 39.4013

3N 010730 00005698 Large 4791.16 0.08596 11.6331

3N 010730 00011792 Large 2210.64 0.03966 25.2126
 64 

Stratum PSU School ID 

School Size 

Class 

School Size 

Measure 

Expected Number 

of Draws 

Sampling 

Weight 

3N 010730 00013439 Large 1401.83 0.02515 39.7594

3N 050010 00053744 Large 727.23 0.16744 5.9724

3N 050010 00056745 Large 3632.79 0.8364 1.1956

3N 050010 00056778 Large 1245.43 0.28674 3.4874

3N 100010 00250957 Large 1940.86 0.0383 26.1091

3N 100010 00251098 Large 3018.86 0.05957 16.7858

3N 100010 00251112 Large 5274.66 0.10409 9.6071

3N 100010 00251688 Small 163.99 0.04677 21.3821

3N 120730 00283042 Large 2186.88 0.14207 7.039

3N 120730 00283086 Large 3710.14 0.24102 4.149

3N 120730 06116950 Large 2628.03 0.17072 5.8574

3N 120990 00287943 Large 6062.14 0.06334 15.7871

3N 120990 00288029 Large 5102.06 0.05331 18.7578

3N 120990 00288051 Large 12336.98 0.12891 7.7574

3N 131330 00305975 Large 5954.44 0.3759 2.6603

3N 131330 00320144 Large 2109.17 0.13315 7.5104

3N 131330 90072767 Large 1786.59 0.11278 8.8665

3N 220010 00543149 Large 1416.81 0.32438 3.0828

3N 220010 00549027 Large 819.89 0.18771 5.3273

3N 220010 02028727 Small 76.46 0.07367 13.5736

3N 220010 06116483 Large 267.82 0.06132 16.3084

3N 280430 00736094 Large 363.42 0.09815 10.1887

3N 280430 00740329 Large 2855.87 0.77128 1.2965

3N 280430 00749547 Large 1561.25 0.42165 2.3717

3N 371330 01010426 Small 201.08 0.37142 2.6924

3N 371330 01032439 Large 2431.07 0.29187 3.4262

3N 371330 01032676 Large 3208.79 0.38524 2.5958

3N 371330 02944091 Large 1351.52 0.16226 6.1629

3N 400530 01149651 Large 518.63 0.12537 7.9761

3N 400530 01150292 Large 125.58 0.03036 32.9405

3N 400530 02880059 Large 264.59 0.06396 15.634

3N 450730 01276431 Large 1348.09 0.20306 4.9247

3N 450730 01276453 Large 904.05 0.13617 7.3435

3N 450730 01276464 Large 200.37 0.03018 33.1337

3N 470690 01306504 Large 3999.89 0.39923 2.5048

3N 470690 01306548 Large 1532.87 0.153 6.536

3N 470690 01309811 Large 1817.97 0.18145 5.511

3N 470690 01310725 Small 147.67 0.13974 7.1563

3N 480390 01333119 Large 6216.35 0.20544 4.8675

3N 480390 01338922 Large 3598 0.11891 8.4097

3N 480390 01387448 Large 11483.85 0.37953 2.6348

3N 482150 02905631 Large 8051.26 0.04375 22.858
 65 

Stratum PSU School ID 

School Size 

Class 

School Size 

Measure 

Expected Number 

of Draws 

Sampling 

Weight 

3N 482150 03238530 Large 16230.57 0.08819 11.3388

3N 482150 05962992 Large 9869.99 0.05363 18.646

3N 482430 01354707 Large 12136.21 0.05925 16.8771

3N 482430 01355096 Large 10185.51 0.04973 20.1094

3N 482430 05446027 Large 5636.98 0.02752 36.3359

3N 482950 01338401 Large 2548.22 0.54979 1.8189

3N 482950 01338412 Large 2461.83 0.53115 1.8827

3N 482950 01396361 Large 426.41 0.092 10.8695

3N 483110 01360572 Large 1612.75 0.08852 11.2965

3N 483110 01378105 Large 2479.18 0.13608 7.3486

3N 483110 01389219 Large 3956.84 0.21719 4.6043

3N 540110 01489224 Large 377.57 0.08508 11.7542

3N 540110 01491277 Large 682.27 0.15373 6.5048

3N 540110 01501389 Large 248.74 0.05605 17.8422

3U 110010 00255365 Large 4277.78 0.09399 10.6389

3U 110010 00255729 Large 1915.5 0.04209 23.7593

3U 110010 03244452 Large 5574.53 0.12249 8.1641

3U 120110 00269298 Large 5526.06 0.03086 32.4038

3U 120110 00270066 Large 8603.12 0.04804 20.814

3U 120110 00270215 Large 13874.11 0.07748 12.9064

3U 120860 00262601 Large 3216.13 0.01562 64.0203

3U 120860 00272518 Large 10512.22 0.05106 19.5865

3U 120860 00272711 Large 6458.28 0.03137 31.8812

3U 120970 02122683 Large 15785.5 0.12175 8.2137

3U 120970 02868065 Large 15792.11 0.1218 8.2103

3U 120970 02876532 Large 8299.48 0.06401 15.6224

3U 132550 00297587 Small 205.17 0.66978 1.493

3U 132550 00306527 Large 2365.84 0.06582 15.1939

3U 132550 00306571 Large 8861.62 0.24652 4.0564

3U 132550 03214643 Large 3428.75 0.09539 10.4838

3U 245100 00579277 Large 1320.8 0.02063 48.4747

3U 245100 00585473 Large 3759.03 0.05871 17.0324

3U 245100 00585473 Large 3759.03 0.05871 17.0324

3U 245100 00585509 Large 4338.91 0.06777 14.7561

3U 370590 01035054 Large 1020.89 0.13257 7.5434

3U 370590 01035236 Large 2039.93 0.26489 3.7751

3U 370590 01035688 Large 2495.42 0.32404 3.0861

3U 481130 01348522 Large 8330.02 0.02629 38.0377

3U 481130 01349297 Large 8728.97 0.02755 36.2992

3U 481130 01380679 Large 3487.84 0.01101 90.8455
 66 

Stratum PSU School ID 

School Size 

Class 

School Size 

Measure 

Expected Number 

of Draws 

Sampling 

Weight 

3U 482011 01366541 Large 11038.25 0.04099 24.3939

3U 482011 04125514 Large 4118.03 0.01529 65.387

3U 482011 05939329 Large 12130.95 0.04505 22.1966

3U 482310 01390643 Large 1088.17 0.20947 4.774

3U 482310 01624811 Large 4253.56 0.81878 1.2213

3U 482310 05734398 Large 218.02 0.04197 23.8278

3U 484730 01326994 Small 80.72 0.10238 9.7672

3U 484730 02881186 Large 17305.1 0.20774 4.8138

3U 484730 05513062 Large 4077.95 0.04895 20.4277

3U 484730 90069636 Large 6520.15 0.07827 12.7763

3U 484930 01384389 Large 9489.85 0.03397 29.4355

3U 484930 01385429 Large 20061.76 0.07182 13.9239

3U 484930 01395517 Large 6296.42 0.02254 44.3646

3U 510590 01441554 Large 9494.37 0.07691 13.0027

3U 510590 01441612 Large 10651.59 0.08628 11.59

3U 510590 01918856 Large 6435.78 0.05213 19.1821

4N 020680 00024939 Large 4356.6 0.16725 5.979

4N 020680 00025003 Large 3018.18 0.11587 8.6303

4N 020680 00025604 Large 8001.94 0.3072 3.2552

4N 060530 00125165 Large 5921.83 0.07931 12.6095

4N 060530 00186344 Large 6450.85 0.08639 11.5754

4N 060530 03267334 Large 9521.39 0.12751 7.8425

4N 060950 00118941 Large 4723.52 0.07649 13.0741

4N 060950 00191037 Large 6630.82 0.10737 9.3135

4N 060950 00192926 Large 13619.87 0.22054 4.5342

4N 061070 00193952 Large 16346.17 0.21947 4.5564

4N 061070 05447734 Large 8284.33 0.11123 8.9905

4N 061070 90069684 Large 11128.03 0.14941 6.693

4N 061110 00111742 Large 4846.35 0.0426 23.4724

4N 061110 00161432 Large 25884.49 0.22754 4.3947

4N 061110 00193227 Large 3191.72 0.02806 35.6408

4N 150030 00328041 Large 14993.66 0.09351 10.6945

4N 150030 00329952 Large 8262.5 0.05153 19.407

4N 150030 05486060 Large 10127.94 0.06316 15.8325

4N 350530 00910654 Large 7766.88 0.08555 11.6896

4N 350530 00910734 Large 7081.64 0.078 12.8208

4N 350530 00911341 Large 13919.14 0.15331 6.5228

4N 410330 01163155 Large 1981.51 0.15438 6.4776

4N 410330 01165899 Large 2908.37 0.22659 4.4133

4N 410330 01170173 Large 1187.77 0.09254 10.8063

4N 410330 01173651 Small 169.64 0.0444 22.5235

4N 490490 01418398 Large 1887.48 0.09829 10.1742
 67 

Stratum PSU School ID 

School Size 

Class 

School Size 

Measure

Expected Number 

of Draws 

Sampling 

Weight

4N 490490 01419369 Large 4018.98 0.20928 4.7782

4N 490490 02122027 Large 2053.74 0.10695 9.3506

4U 040210 00038522 Large 2044.09 0.08959 11.162

4U 040210 00038919 Large 2915.43 0.12778 7.826

4U 040210 00039901 Large 4031.49 0.1767 5.659

4U 060010 00121047 Large 6386.21 0.02465 40.566

4U 060010 00121433 Large 1636.07 0.00632 158.343

4U 060010 00164422 Large 4251.44 0.01641 60.935

4U 060370 00124809 Large 3483.51 0.00901 111.012

4U 060370 00166317 Large 12743.62 0.03295 30.345

4U 060370 00171236 Large 7640.79 0.01976 50.611

4U 060371 00115372 Large 7866.33 0.02017 49.582

4U 060371 00136337 Large 7972.9 0.02044 48.919

4U 060371 00141799 Large 13198.65 0.03384 29.551

4U 060373 00101406 Large 17439.87 0.04412 22.664

4U 060373 00178038 Large 14590.99 0.03692 27.089

4U 060373 00196181 Large 16320.75 0.04129 24.218

4U 060374 00162265 Large 1282.74 0.0032 312.984

4U 060374 04142143 Large 4860.03 0.01211 82.608

4U 060374 05483477 Large 13010.29 0.03241 30.858

4U 060591 00075316 Small 324.43 0.00434 230.514

4U 060591 00123088 Large 11438.09 0.04726 21.158

4U 060591 00123827 Large 8566.98 0.0354 28.248

4U 060591 00191773 Large 15529.9 0.06417 15.583

4U 060650 00110363 Small 860.58 0.02147 46.581

4U 060650 00112098 Large 6523.57 0.02424 41.248

4U 060650 02114639 Large 11172.35 0.04152 24.085

4U 060650 03260248 Large 7710.32 0.02865 34.899

4U 060710 00110749 Small 874.63 0.01263 79.185

4U 060710 00168267 Large 10087.68 0.03087 32.39

4U 060710 00173732 Large 16716.02 0.05116 19.546

4U 060710 03268043 Large 25508.19 0.07807 12.809

4U 060850 00103221 Large 14242.23 0.04411 22.673

4U 060850 00120826 Large 8826.47 0.02733 36.584

4U 060850 00153385 Large 7893.65 0.02445 40.907

4U 320230 00849993 Large 1971.33 0.01186 84.283

4U 320230 03237343 Large 10096.91 0.06077 16.455

4U 320230 03237412 Large 8059.78 0.04851 20.615

4U 530330 01470712 Large 4343.6 0.0261 38.313

4U 530330 01482772 Large 8269.69 0.04969 20.124

4U 530330 01483142 Large 3969.32 0.02385 41.926
 68 

Appendix D 

 

School Sampling Weights 
 69 

Stratum PSU School ID School Size 

Class 

School Size 

Measure 

Expected 

Number of 

Draws 

Sampling 

Weight 

BR1 050450 00052037 Large 3358.95 0.53465 1.870

BR1 050450 05476594 Large 871.92 0.15559 6.427

BR1 131150 00312472 Large 397.44 0.04426 22.595

BR1 131150 03261300 Large 1442.75 0.18146 5.511

BR1 191530 00461582 Large 1519.94 0.07937 12.600

BR1 191530 00472754 Large 1035.35 0.03916 25.538

BR1 210930 00531519 Large 839.56 0.14701 6.802

BR1 210930 01891195 Large 409.75 0.03559 28.098

BR1 261210 00665407 Large 760.24 0.04701 21.271

BR1 261210 00678744 Large 372.49 0.03752 26.649

BR1 290510 00768592 Large 1179.75 0.35780 2.795

BR1 290510 00768639 Large 2323.23 0.30797 3.247

BR1 340250 00880876 Large 1496.24 0.03137 31.877

BR1 340250 00880967 Large 895.98 0.03250 30.773

BR1 360650 00931969 Large 548.62 0.03482 28.718

BR1 360650 00953031 Large 469.43 0.15838 6.314

BR1 360650 01904559 Small 26.78 1.00000 1.000

BR1 401430 01155348 Large 975.39 0.04302 23.242

BR1 401430 01156104 Large 1845.91 0.05457 18.325

BR1 420130 01221801 Large 687.39 0.24098 4.150

BR1 420130 01244942 Large 654.55 0.16464 6.074

BR1 550170 01520415 Large 1632.70 0.43462 2.301

BR1 550170 01520426 Large 1088.71 0.60147 1.663

BR1 551010 01538841 Large 1386.00 0.19656 5.088

BR1 551010 01543523 Large 805.46 0.05883 16.997

BR2 170910 00388282 Large 2028.48 0.47587 2.101

BR2 170910 00392812 Large 704.55 0.08522 11.735

BR2 340010 00881858 Large 2251.36 0.10296 9.713

BR2 340010 02865701 Large 2527.27 0.19606 5.100

BR2 371330 01032439 Large 1179.73 0.07884 12.684

BR2 371330 01032461 Large 2024.19 0.11378 8.789

BR2 401090 01148545 Large 1210.40 0.03697 27.050

BR2 401090 90219260 Large 430.26 0.00877 114.070
 70 

Stratum PSU School ID School Size 

Class 

School Size 

Measure 

Expected 

Number of 

Draws 

Sampling 

Weight 

BR2 450510 01262881 Large 346.65 1.00000 1.000

BR2 450510 01273623 Large 1244.29 0.10906 9.169

BR2 450510 01273714 Large 2318.26 0.15989 6.254

BR3 010170 00006986 Large 490.00 0.18988 5.267

BR3 010170 00006997 Large 1091.85 0.34084 2.934

BR3 011250 00021246 Large 2323.29 0.17967 5.566

BR3 011250 03265574 Large 1266.16 0.11992 8.339

BR3 120310 00258399 Large 297.34 0.00617 162.086

BR3 120310 00277196 Large 2933.41 0.05012 19.950

BR3 280190 00737859 Large 1358.28 0.36178 2.764

BR3 280190 00738138 Large 438.55 0.09163 10.914

BR3 450130 01267223 Large 1637.73 0.15312 6.531

BR3 450130 90173443 Large 932.69 0.11656 8.579

BR4 010470 00008804 Large 896.97 0.12765 7.834

BR4 010470 00008939 Large 603.50 0.19206 5.207

BR4 450750 01266864 Large 627.90 0.09759 10.247

BR4 450750 01277005 Large 1363.00 0.11884 8.415

BU1 090130 00236091 Large 473.16 0.14370 6.959

BU1 090130 00245379 Large 981.73 0.17628 5.673

BU1 121030 00289011 Large 2756.38 0.05575 17.939

BU1 121030 00289496 Large 1610.00 0.05077 19.696

BU1 180570 00442344 Large 169.66 0.01588 62.973

BU1 180570 00455488 Large 1655.61 0.12312 8.122

BU1 211170 00515745 Large 460.90 0.05136 19.472

BU1 211170 05741951 Large 773.53 0.15615 6.404

BU1 240310 00592094 Large 1054.90 0.02724 36.707

BU1 240310 01604737 Small 86.43 0.04591 21.782

BU1 240310 05947635 Large 1760.88 0.04122 24.258

BU1 261250 00655501 Large 1977.81 0.02520 39.681

BU1 261250 00693977 Large 1042.87 0.01928 51.862

BU1 270530 00708271 Large 2867.55 0.04894 20.432

BU1 270530 01923618 Large 199.30 0.00480 208.175

BU1 471890 01320879 Large 1185.50 0.23790 4.203

BU1 471890 01320926 Large 1956.17 0.30882 3.238

BU2 130670 05499909 Large 2378.90 0.05732 17.444
 71 

Stratum PSU School ID School Size 

Class 

School Size 

Measure 

Expected 

Number of 

Draws 

Sampling 

Weight 

BU2 130670 90175713 Large 2754.69 0.05693 17.566

BU2 171630 00393168 Large 188.25 0.05418 18.457

BU2 171630 00393634 Large 1005.33 0.04691 21.315

BU2 171630 00393645 Large 620.06 0.04663 21.446

BU2 171630 02906362 Large 143.00 0.17934 5.576

BU2 290950 00765627 Large 1238.46 0.04135 24.183

BU2 290950 00769916 Large 235.64 0.03594 27.823

BU2 290950 00772532 Large 205.05 0.03478 28.752

BU2 290950 90338689 Large 2128.45 0.04764 20.991

BU2 291890 00752742 Large 276.85 0.02207 45.307

BU2 291890 00759384 Large 356.04 0.00467 214.250

BU2 291890 00768865 Large 1301.04 0.02899 34.497

BU2 291890 00772962 Large 244.31 0.23966 4.173

BU2 511530 01448356 Large 2542.79 0.07612 13.137

BU2 511530 01452293 Large 1121.85 0.04128 24.224

BU2 518100 01431626 Large 111.11 0.17696 5.651

BU2 518100 01433838 Large 170.12 1.00000 1.000

BU2 518100 01457349 Large 2388.98 0.07311 13.678

BU2 518100 03269149 Large 2350.00 0.10142 9.860

BU3 220710 00554693 Large 1069.94 0.05247 19.058

BU3 220710 01605901 Large 307.01 0.02383 41.960

BU3 550790 01533457 Large 268.50 0.00813 122.929

BU3 550790 01535716 Large 2223.00 0.03567 28.036

BU4 240330 00594863 Large 4330.16 0.05161 19.376

BU4 240330 00594932 Large 1637.56 0.04931 20.280

BU4 421010 01235155 Large 3428.77 0.02855 35.030

BU4 421010 01236002 Large 1568.89 0.02163 46.225

HR1 080410 00228965 Large 1119.30 0.03194 31.312

HR1 080410 04205897 Small 265.72 0.19714 5.073

HR1 080410 05493951 Large 1222.11 0.04850 20.620

HR1 180710 00424948 Large 214.26 0.13352 7.489

HR1 180710 00449102 Large 934.25 0.11666 8.572

HR1 180710 00451166 Large 379.80 0.07433 13.454

HR1 180710 00453039 Large 157.56 0.60123 1.663

HR1 181690 00446597 Large 520.20 0.05399 18.522
 72 

Stratum PSU School ID School Size 

Class 

School Size 

Measure 

Expected 

Number of 

Draws 

Sampling 

Weight 

HR1 181690 00453651 Large 374.41 0.07454 13.415

HR1 230170 00570081 Large 1225.02 0.24156 4.140

HR1 230170 90355167 Large 306.11 0.18497 5.406

HR1 360570 00946068 Large 1601.28 0.37243 2.685

HR1 360570 00989476 Large 302.26 0.05857 17.073

HR1 361110 00965579 Large 2910.30 0.25754 3.883

HR1 361110 00994227 Large 1249.20 0.15646 6.391

HR1 480350 01344186 Large 450.91 0.16942 5.902

HR1 480350 01360925 Large 509.64 0.32741 3.054

HR1 484850 01326134 Small 67.37 0.04329 23.099

HR1 484850 01404523 Large 692.37 0.22725 4.400

HR1 484850 01407681 Large 2068.48 0.19542 5.117

HR1 530190 01473429 Large 306.03 0.16841 5.938

HR1 530190 01479792 Large 412.12 0.24980 4.003

HR2 061110 00169588 Large 1565.85 0.04025 24.847

HR2 061110 00185952 Large 3512.12 0.04866 20.551

HR2 061110 90355949 Small 44.85 0.06832 14.637

HR2 420770 01203462 Large 1896.94 0.11108 9.003

HR2 420770 01203553 Large 3570.22 0.12524 7.984

HR2 480930 01339063 Large 525.96 0.25602 3.906

HR2 480930 01352517 Large 485.76 0.12586 7.945

HR3 350010 00910574 Large 4273.91 0.07713 12.965

HR3 350010 00911501 Large 2059.71 0.05214 19.178

HR3 484090 01333527 Large 465.18 0.06942 14.406

HR3 484090 01362649 Large 1886.45 0.22538 4.437

HR3 530770 01484612 Large 2671.78 0.15138 6.606

HR3 530770 01487431 Small 442.50 0.12356 8.093

HR3 530770 01487974 Large 1343.29 0.09523 10.501

HR4 350070 00916011 Large 583.12 0.19684 5.080

HR4 350070 00917194 Large 653.47 0.14308 6.989

HR4 480610 01339788 Large 4900.61 0.09488 10.539

HR4 480610 02905562 Large 536.25 0.01363 73.378

HU1 201030 00492136 Large 492.51 0.19555 5.114

HU1 201030 00509913 Large 720.37 0.18986 5.267

HU1 250170 00614327 Large 4115.21 0.04841 20.658
 73 

Stratum PSU School ID School Size 

Class 

School Size 

Measure 

Expected 

Number of 

Draws 

Sampling 

Weight 

HU1 250170 00618092 Large 218.99 0.02637 37.923

HU1 250170 00630393 Large 862.05 0.01719 58.187

HU1 250170 90069805 Large 167.45 0.07135 14.016

HU1 361030 00955775 Large 1326.06 0.01843 54.261

HU1 361030 00965433 Large 1354.08 0.01285 77.822

HU1 361190 00921305 Small 89.15 0.03499 28.580

HU1 361190 01005427 Large 346.19 0.00540 185.353

HU1 361190 06117534 Large 1488.00 0.03112 32.132

HU1 490350 01416175 Large 893.52 0.02300 43.479

HU1 490350 01419992 Large 3527.53 0.06416 15.587

HU1 530110 01473644 Large 818.34 0.03852 25.962

HU1 530110 01625291 Large 1019.31 0.05471 18.277

HU1 530110 05442893 Small 601.65 0.64695 1.546

HU1 551330 01531518 Large 256.35 0.02109 47.421

HU1 551330 01534665 Large 1836.00 0.07963 12.558

HU2 060850 00120826 Large 1298.72 0.01750 57.150

HU2 060850 02920536 Large 8041.20 0.07103 14.079

HU2 120570 00280573 Large 2138.97 0.03114 32.110

HU2 120570 90172521 Large 4064.41 0.05092 19.639

HU2 120950 00286112 Large 2100.99 0.03021 33.097

HU2 120950 90198964 Large 5249.90 0.05996 16.677

HU2 120950 90219036 Small 47.57 0.02158 46.333

HU2 360810 00972677 Large 2415.75 0.01961 51.007

HU2 360810 04043335 Large 1791.75 0.02110 47.391

HU2 360810 90204063 Small 42.81 0.04799 20.840

HU3 040130 00041546 Large 5212.95 0.02186 45.744

HU3 040130 05441062 Large 952.19 0.00454 220.073

HU3 060590 00106969 Large 1701.06 0.01124 88.934

HU3 060590 00182215 Large 7522.02 0.03377 29.615

HU3 360610 00921928 Large 677.02 0.03642 27.454

HU3 360610 00973273 Large 3314.36 0.03751 26.660

HU3 360610 05746042 Large 452.99 0.00955 104.717

HU3 360610 90215388 Large 190.33 0.01373 72.852

HU3 481130 01350291 Large 3771.52 0.02043 48.943

HU3 481130 01360197 Large 1474.46 0.01084 92.218
 74 

Stratum PSU School ID School Size 

Class 

School Size 

Measure 

Expected 

Number of 

Draws 

Sampling 

Weight 

HU3 482010 01325141 Large 1043.73 0.06687 14.953

HU3 482010 01331315 Large 4411.56 0.02801 35.698

HU3 482010 05500869 Large 575.76 0.14537 6.879

HU3 482010 90198229 Large 2649.17 0.02060 48.549

HU4 060370 00083167 Small 97.19 0.01241 80.599

HU4 060370 00144166 Large 5005.13 0.02405 41.572

HU4 060370 02187700 Large 2722.22 0.01686 59.328

HU4 060371 90214996 Large 3480.57 0.02343 42.672

HU4 060371 90222744 Large 2215.84 0.01017 98.309

HU4 060372 00136891 Large 6748.48 0.03090 32.368

HU4 060372 00142012 Large 3119.80 0.02053 48.703

HU4 060650 02873870 Large 2814.76 0.01545 64.722

HU4 060650 03215396 Large 2292.93 0.01513 66.089

HU4 120860 00273384 Large 2312.40 0.01659 60.274

HU4 120860 00274242 Small 1818.27 0.25904 3.860

HU4 120860 00274355 Large 5491.25 0.02449 40.829