Series 2, Number 168 November 2014

National Health and

Nutrition Examination Survey:

National Youth Fitness Survey

Estimation Procedures, 2012
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Suggested citation

Johnson CL, Dohrmann SM, Van de Kerckhove W, et al. National Health and

Nutrition Examination Survey: National Youth Fitness Survey estimation

procedures, 2012. National Center for Health Statistics. Vital Health Stat 2(168).

2014.

Library of Congress Cataloging-in-Publication Data

National Health and Nutrition Examination Survey. National Youth Fitness

Survey estimation procedures, 2012.

p. ; cm.— (Vital and health statistics. series 2, data evaluation and methods

research ; number 168) (DHHS publication ; no. (PHS) 2015–1368)

National Youth Fitness Survey estimation procedures, 2012

Includes bibliographical references and index.

ISBN 978–0-8406–0669-3 (alk. paper)—ISBN 0–8406-0669–9 (alk. paper)

I. National Center for Health Statistics (U.S.), issuing body. II. Title: National

Youth Fitness Survey estimation procedures, 2012. III. Series: Vital and health

statistics. Series 2, Data evaluation and methods research ; no. 168. IV. Series:

DHHS publication ; no. (PHS) 2015–1368. 0276–4733

[DNLM: 1. National Youth Fitness Survey (U.S.) 2. Health Surveys—United

States. 3. Physical Fitness—United States. 4. Adolescent—United States. 5.

Child—United States. 6. Motor Activity—United States. 7. Statistics as

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Series 2, Number 168

National Health and

Nutrition Examination Survey:

National Youth Fitness Survey

Estimation Procedures, 2012

Data Evaluation and Methods Research

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

Hyattsville, Maryland

November 2014

DHHS Publication No. 2015–1368
National Center for Health Statistics

Charles J. Rothwell, M.S., M.B.A., Director

Jennifer H. Madans, Ph.D., Associate Director for Science

Division of Health and Nutrition Examination Surveys

Kathryn S. Porter, M.D., M.S., Director
Contents
 

Acknowledgments ......................................................................................... iv 
 

Abstract ................................................................................................. 1 
 

Introduction .............................................................................................. 1 
 

Sample Design Summary ................................................................................... 2 
 

Sample Selection Stages .................................................................................. 2 
 

Sampling Rates ......................................................................................... 3 
 

Weighting Sample Data..................................................................................... 3 
 

Calculating Base Weights ................................................................................. 4 
 

Nonresponse Adjustment.................................................................................. 5 
 

Trimming .............................................................................................. 6 
 

Poststratification ........................................................................................ 6 
 

Computing Final Weights ................................................................................. 7 
 

Combined NHANES/NNYFS Weights...................................................................... 7 
 

Variance Estimation........................................................................................ 9 
 

Variance Estimation for Publicly Released NNYFS Data ........................................................ 10 
 

Variance Estimation for Combined NHANES/NNYFS Data in RDC .............................................. 10 
 

Cautionary Notes.......................................................................................... 10 
 

References ............................................................................................... 11 
 

Appendix I. Glossary....................................................................................... 12 
 

Appendix II. Supporting Tables .............................................................................. 15 
 

Appendix Tables

I.	 Target sample sizes, screening amounts, and sampling rates: NNYFS, 2012 ................................... 15 
 

II.	 Variables used to form nonresponse adjustment cells for weighting interview and examination samples: NNYFS,
 

2012.............................................................................................. 16 
 

III.	 Design effects, effective sample sizes, and final compositing factors used to combine annual samples: National
 

Health and Nutrition Examination Survey and NHANES National Youth Fitness Survey, 2011–2012................ 17 
 

iii
Acknowledgments
 

The authors gratefully acknowledge

the assistance of Ryne Paulose and

Michele Chiappa in the preparation of

this report.

iv
Background

The National Health and Nutrition

Examination Survey’s (NHANES)

National Youth Fitness Survey (NNYFS)

was conducted in 2012 by the Centers

for Disease Control and Prevention’s

National Center for Health Statistics

(NCHS). NNYFS collected data on

physical activity and fitness levels to

evaluate the health and fitness of

children aged 3–15 in the United

States. The survey comprised three

levels of data collection: a household

screening interview (or screener), an

in-home personal interview, and a

physical examination. The screener’s

primary objective was to determine

whether any children in the household

were eligible for the interview and

examination. Eligibility was determined

by preset selection probabilities for

desired sex-age subdomains. After

selection, the in-home personal

interview collected demographic, health,

physical activity, and nutrition

information about the child as well as

information about the household. The

examination included physical

measurements and fitness tests.

Objectives

This report provides background on

the NNYFS program and summarizes

the survey’s sample design

specifications. The report presents

NNYFS estimation procedures,

including the methods used to calculate

survey weights for the full sample as

well as a combined NHANES/NNYFS

sample for 2012 (accessible only

through the NCHS Research Data

Center). The report also describes

appropriate variance estimation

methods. Documentation of the sample

selection methods, survey content, data

collection procedures, and methods to

assess nonsampling errors are reported

elsewhere (available from: http://

www.cdc.gov/nchs/nhanes.htm).

Keywords: sampling • weighting • 

variance estimation

National Health and Nutrition

Examination Survey: National

Youth Fitness Survey

Estimation Procedures, 2012

by Clifford L. Johnson, M.S.P.H., National Center for Health

Statistics; Sylvia M. Dohrmann, M.S., Wendy Van de Kerckhove, M.S.,

Mamadou S. Diallo, M.Sc., Jason Clark, M.S., and Leyla K.

Mohadjer, Ph.D., Westat; and Vicki L. Burt, Sc.M., R.N., National

Center for Health Statistics

Introduction

The National Health and Nutrition

Examination Survey (NHANES) is one

of a series of health-related programs

conducted by the Centers for Disease

Control and Prevention’s (CDC)

National Center for Health Statistics

(NCHS). It provides information on the

health and nutritional status of the U.S.

population. In 2012, NCHS also

conducted the NHANES National Youth

Fitness Survey (NNYFS).

NNYFS was designed to collect

data on physical activity and fitness

levels for U.S. children aged 3–15 (1).

NNYFS was linked to NHANES by

using the same primary sampling units

and the same operational procedures as

NHANES. NNYFS data were collected

using an in-home personal interview;

fitness tests and a dietary recall were

done in a separate, single-trailer mobile

examination center (MEC). In addition,

results from questions and examination

components common to both NNYFS

and NHANES 2011–2012 were

collected using the same protocol so that

they could be combined for certain

analyses.

In October 2008, the federal

government issued the first Physical

Activity Guidelines for Americans to

provide science-based guidance on the

types and amounts of physical activity

that provide substantial health benefits

(2). Guidelines for children and

teenagers recommend 60 minutes or

more of aerobic, muscle-strengthening,

or other physical activity daily. The

inclusion of standardized tests of core

strength, upper and lower body strength,

cardiovascular capacity, and gross motor

skills in NNYFS for ages 3–15 provides

additional information with which to

evaluate the health and fitness of this

age group.

NNYFS provides information on the

noninstitutionalized resident population

of children aged 3–15 in the United

States. NNYFS excludes all children in

supervised care or custody in

institutional settings, children of

active-duty military personnel living

overseas, and any other U.S. citizens

residing outside of the 50 states and the

District of Columbia.

NNYFS included three levels of

data collection: a household screening

interview (or screener), an in-home

personal interview, and a standardized

physical examination conducted in the

MEC that included selected objective

measures of fitness. The primary

objective of the screener was to

determine whether any children in the

household were eligible for the in-home

personal interview and the MEC

examination. The personal interview

collected demographic, health, physical

activity, and nutrition information about

the child as well as information about

the household. The examination

included physical measurements, a

dietary interview, and fitness tests;

Page 1
Page 2 [ Series 2, No. 168

conducting the examinations in the

MEC helped to standardize their

administration.

NNYFS was conducted in parallel

with the 2012 data collection of the

2011–2012 survey cycle of NHANES.

Some questionnaire items and physical

measurements were collected in the

same manner in both surveys for 2012.

In some cases, this required a

modification of NHANES 2011

questionnaire items or physical measures

to create comparability for both surveys

in 2012. Thus, it is possible to combine

samples for the two surveys for 2012.

This is especially useful if the NNYFS

sample sizes are small for the specific

analysis of interest. Because single years

of NHANES data are not released as

public-use data files, the combined 2012

NHANES and NNYFS file is available

for analysis only in the NCHS Research

Data Center (RDC) (visit http://

www.cdc.gov/rdc/ for more details).

Specifically, sample weights were

created for use with this combined

NHANES and NNYFS sample of

children aged 3–15.

Less survey content overlap occurs

between NNYFS and 2011 NHANES

because the first year of the NHANES

2011–2012 survey cycle was fielded

before changes were implemented to

make the 2012 NHANES and the 2012

NNYFS more comparable.

A combined 2011–2012 NHANES

and NNYFS data file for children aged

3–15 is also available for use in RDC,

but the relatively small number of

questionnaire items and physical

measures comparably collected in

NHANES 2011, NHANES 2012, and

NNYFS limits the analytic usefulness of

these data despite the file’s increased

sample size, number of primary

sampling units (PSUs), and stability of

estimates.

The ‘‘Sample Design Summary’’

section briefly summarizes the sample

design specifications for NNYFS, with

the remainder of the report providing

the estimation procedures. The

‘‘Weighting Sample Data’’ section

describes the creation of weights for the

entire NHANES sample and the

combined NHANES and NNYFS

sample; the ‘‘Variance Estimation’’

section presents the appropriate variance

estimation methods; and the

‘‘Cautionary Notes’’ section presents

some analytic limitations of the

combined NNYFS and NHANES data.

General information related to the

planning, sample design, and field

operations of NNYFS, as well as

general analytical guidance, can be

found in ‘‘National Health and Nutrition

Examination Survey: National Youth

Fitness Survey Plan, Operations, and

Analysis, 2012’’ (1). Documentation of

the survey content, data collection

procedures, and methods to assess

nonsampling errors are reported

elsewhere (available from: http://

www.cdc.gov/nchs/nhanes.htm). For

more details on the 2011–2014

NHANES sample design, see ‘‘National

Health and Nutrition Examination

Survey: Sample Design, 2011–2014’’

(3).

Sample Design

Summary

The NNYFS sample represents the

total noninstitutionalized resident

population of children aged 3–15 in the

United States. The survey was

conducted in the same primary and

secondary sampling units as NHANES

during 2012. The four-stage sample

design used in NHANES was also used

in NNYFS. A description of the stages

of selection and the calculation of

sampling rates follows.

Sample Selection Stages

The first stage of the NHANES

2011–2014 sample design consisted of

selecting the PSUs from a sampling

frame of all U.S. counties. The PSUs in

the first stage were mostly counties; in a

few cases, adjacent counties were

combined to keep PSUs above a certain

minimum size. NHANES PSUs were

selected with probabilities proportionate

to a measure of size (PPS). After

selection, 15 PSUs were allocated to

each of the 4 years of the study period

randomly, so that each year contained a

nationally representative sample (an

NHANES design requirement). The

NHANES PSUs allocated to 2012 were

also the NNYFS PSUs.

The second stage of selection for

the 2012 NHANES and NNYFS

samples included a single sample of

area segments, comprising census blocks

or combinations of blocks. The sample

was designed to produce approximately

equal sample sizes per PSU for both

NHANES and NNYFS. PSUs selected

with certainty (with a probability of

one) may have more or fewer than 24

segments to ensure appropriate

representation in the sample.

Noncertainty PSUs have 24 segments.

The NNYFS segments were formed and

selected with PPS using the same

methods as in NHANES, with one

exception: The segments were enlarged

to ensure they would provide enough

samples of dwelling units (DUs) for

both surveys.

The third stage of sample selection

consisted of DUs, including

noninstitutional group quarters (i.e.,

group quarters that do not provide

formally authorized, supervised care or

custody in institutional settings). These

include college residence halls, group

homes intended for adults, residential

treatment facilities for adults, workers’

group living quarters and Job Corps

centers, and religious group quarters. In

a given PSU, following the selection of

segments, a listing of all DUs in the

sampled segments was prepared. A

subsample of these was selected, and a

random subset of these was designated

for screening to identify potential

sampled participants for NNYFS. All

other DUs were designated for screening

to identify potential sampled participants

for NHANES.

The fourth stage of sample selection

consisted of persons within occupied

DUs, or households. All eligible

children within a household were listed,

and a subsample was selected based on

domains defined by sex and age. The

sampling procedures for NNYFS were

generally the same as those used in

NHANES.
Series 2, No. 168 [ Page 3

Sampling Rates

NNYFS was conducted in

conjunction with NHANES, using some

of the same screening and interviewing

staff. The overall target number of

examinations for the study was 1,500,

with approximately equal sample sizes

within each sex and single year of age

combination. To meet this end, sampling

rates were developed for six sex-age

subdomains: males aged 3–5, 6–11, and

12–15, and females aged 3–5, 6–11, and

12–15. The subsampling rates and

designation of potential sampled

participants within screened households

were arranged to provide approximately

self-weighting samples for each

subdomain while simultaneously

maximizing the average number of

sampled participants per household.

The original target sample

distribution for NNYFS is shown in

Table I. To calculate sampling rates, an

overall 80% response rate was assumed.

This table also gives the projected

amount of screening required to a)

obtain one examined person in each

domain and b) attain the target number

of examined children in each domain.

The amount of screening needed for

NNYFS was originally 4,145

households, corresponding to the

number of occupied housing units

needed to identify the targeted number

of females aged 12–15. All screened

children in this subdomain were retained

in the sample. Screened children in the

other subdomains were subsampled to

bring the sampling rates for those

subdomains down to desired levels. The

extent of this subsampling is shown in

the last column of Table I.

A derivation of the NNYFS

maximum sampling rate (sampling rate

for the screening sample) is:

1.	 Screening sample size for 1 year =

4,145 households

2.	 A 50% reserve (additional 2,073

households) sample size for 1 year =

6,218 households

3.	 Projected total households in the

United States during 2011–2014 =

118,410,614 households

4.	 Therefore,

Maximum sampling rate =

6,218 /118,410,614 ≈ 1/19,044

The PSUs and segments for

NNYFS were selected as efficient

sampling units for NHANES, which,

unlike NNYFS, oversamples persons by

race and Hispanic origin. As a result, in

some areas more DUs were needed than

expected; although they contained a

sufficient number of minorities to meet

the NHANES sample targets, they did

not contain a sufficient number of

children. Consequently, NNYFS

selection probabilities changed twice

during 2012 to increase the sample yield

so that the overall annual target of 1,500

examinations could be reached.

The change was implemented after

DUs were selected for the first two

study locations. The selected 50%

reserve did not appear sufficient for

future study locations. The rates were

subsequently changed to include a 100%

reserve sample, changing the maximum

sampling rate to 8,291/118,410,614 ≈ 

1/14,282.

Children in different sampling

domains were originally given different

sampling rates so that approximately the

same number of examinations would be

obtained for each single year of age. For

the final six survey locations, in an

attempt to improve efficiency and obtain

more examinations, the rates for all

children were set to the maximum so

that all 3- to 15-year-olds living in

selected DUs would be selected for the

study.

Weighting Sample

Data

The goal of NNYFS was to produce

data representative of the

noninstitutionalized U.S. population of

children aged 3–15. The weighting of

sample data permits analysts to produce

estimates of statistics they would have

obtained if the entire sampling frame

had been surveyed. Sample weights can

be considered as measures of the

number of persons represented by the

particular sampled participant.

Weighting takes into account several

survey features: the differential

probabilities of selection for the

sampling domains, nonresponse to

survey instruments, and differences

between the final sample and the total

population.

NNYFS samples were weighted to

the following objectives:

1.	 Compensate for differential

probabilities of selection among

subgroups defined by sex and age.

2.	 Reduce biases arising from the fact

that nonrespondents may differ from

respondents.

3.	 Fix weighted sample data to match

an independent estimate from the

U.S. Census Bureau of the target

population totals.

4.	 Compensate, to the extent possible,

for inadequacies in the sampling

frame (resulting from omissions of

some housing units in the listing of

area segments, omissions of persons

with no fixed address, and others).

5.	 Reduce variances in the estimation

procedure by using auxiliary

information that is known with a

high degree of accuracy.

The sample weighting was carried

out in three steps. The first step

involved the computation of weights to

compensate for unequal probabilities of

selection (objective 1). The second step

adjusted for nonresponse (objective 2).

In the third step, the sample weights

were poststratified to census estimates

of the U.S. population to simultaneously

accomplish objectives 3–5.

These steps were performed for

respondents at each stage of the survey:

the screener, personal interview, and

examination. The weights described in

‘‘Calculating Base Weights’’ were the

starting point for the screener weight

calculation. Those weights were then

adjusted for nonresponse to the screener

and then poststratified. The resulting

weights became the starting point for

calculating the interview weights, which

were then adjusted for nonresponse to

the interview, inspected for extreme

weights, and again poststratified. Finally,

those poststratified interview weights

were the starting point for calculating

the examination weights, which were

adjusted for nonresponse to the
Page 4 [ Series 2, No. 168

examination, inspected for extreme

weights, and then poststratified.

Note that extreme variability in the

weights results in reduced reliability

(increased sampling error) of some

survey estimates. The NNYFS sample

was designed to minimize variability in

the weights, subject to operational and

analytic constraints. Additionally,

measures such as weight trimming were

implemented to reduce variability in the

NNYFS weights. The impact of weight

variability is minimal when estimates

are applied to demographic subdomains

used in the design; however, when

estimates are instead designed for

domains aggregated across design

domains (for example, an estimate for

the total population), then the impact of

weight variability is greater.

Calculating Base Weights

The overall selection probability for

a person selected in sampling domain k

for NHANES is

where

Ph = Pr{select PSU h}

Phj = Pr{select segment hj | select

PSU h}

= Pr{select DU in segment

hj | select segment hj}

= Pr{domain (k) flagged for

selection in DU | DU in

segment hj selected}

rk

N = Sampling rate for NHANES

participant in sampling domain k

max{rk

N

} = Maximum sampling rate for

NHANES across all sampling

domains.

With the addition of NNYFS in the

PSUs and segments selected for

NHANES, the NHANES DU selection

probability was adjusted to account for

the selection of a sample large enough

for both surveys as well as the

additional stage of selection for

subsampling the larger DU sample in

the specific NHANES and NNYFS

samples. As a result, the expression for

the overall NHANES selection

probability was changed to

where RsrvAdj is the increase of the

DU sample size to meet the needs of

both the NHANES and NNYFS surveys

compared with the originally planned

NHANES DU sample size for 2012, and

1 is the subsampling rate for the

RsrvAdj

NHANES DU sample.

Consequently, the overall NNYFS

selection probability for a child in

sex-age group k is

where

⋅ RsrvAdj = Pr{select DU in

segment hj | select segment hj}

for inclusion in either the

NHANES or NNYFS sample

Pr{domain (k) flagged for selection in 

DU | DU in segment hj selected} 

for NNYFS 

r

Y

k = Sampling rate for NNYFS

participant in sampling domain k.

The base weight for a sampled child

is simply the reciprocal of the sampling

rate for the domain of the sampled

participant, r

Y

k. These sampling rates are

provided in Table I, and their derivation

is described in the preceding section.

For NNYFS, the base weight

was adjusted further to account for:

+	 Proportion of DUs released, fi(release)

+	 Within-segment adjustments to the

selection probabilities, fi(wsa)

+	 Release of an extra reserve sample,

fi(res)

The final base weight was

calculated as

where i indicates the sampled

participant. The following briefly

describe each component of this

calculation.

Adjustments for number of sampled

DUs released to field—The first

component, the release factor fi(release)

was introduced to reflect the procedures

used to obtain a relatively fixed sample

size within each study location in

NNYFS. The sampled child base weight

was adjusted according to the proportion

of the total sample released to the field.

The release factor was calculated as

where Di represents the proportion of

sampled DUs released for screening in

the location from which sampled

participant i was selected. If response

rates approached predicted values and

the measures of size used during

sampling were current, the subsample

factor would be approximately 1.5 for

the first two study locations and 2.0 for

all other locations. That is,

approximately two-thirds of the sampled

cases were expected to be released for

the earlier locations, and approximately

one-half of the sampled cases were

expected to be released for later study

locations (see ‘‘Adjustment for release

of extra reserve sample’’ for more

details).

Adjustment to increase sample size

within segments—Some study locations

had relatively small sample sizes after

DU selection, and the sample might not

have provided enough DUs to reach the

target number of identified sample

persons. For these study locations, the

DU sample size within each segment
Series 2, No. 168 [ Page 5

was increased, but the DU selection

probability was limited to 1.0. Most

segments had sample sizes increased by

the same percentage, but segments that

had most or all of the DUs already

sampled had smaller increases.

To accomplish this increase, the

combined NHANES and NNYFS DU

sample size was increased by an

adjustment factor (AdjFac) so that the

new DU selection probability was

To ensure that all of the increased

sample size was used for the NNYFS

sample, the new NNYFS DU

subsampling rate was calculated as

Solving algebraically, the percent

increase in the NNYFS DU sample

becomes

The factor to be applied to the base

weights, fi(wsa), is then calculated as

Adjustment factors other than 1.0 were

used in three study locations.

Adjustment for release of extra

reserve sample—Some study locations,

even after increasing the sample size

within segments, required a larger

sample to achieve the target number of

identified sample persons. For these

study locations, an extra reserve sample

was created, but it was not loaded into

the system unless the entire original

sample was released and more sampled

DUs were necessary.

Increasing the sample size in this

way—by increasing the sampling rate in

each segment—causes different factors

to be created for segments where the

sampling rate was raised to 100%.

Having different factors by segment can

add to variability in the weights, so this

extra reserve was released only if

necessary to achieve the target number

of identified sample persons.

The extra reserve sample was

created in the same way as the

adjustment to increase the sample size

within segments—by increasing the

combined sample by a certain factor for

each segment (RsrvFac). Applying this

extra reserve factor as above, the factor

to be applied to the base weights, fi(res),

is calculated as

The extra reserve sample was loaded in

only one study location. For all other

study locations, this factor is 1.0. Thus,

for one study location only, the

combined factor for increasing the

sample size is

The RsrvFac value differs by segment

within only one study location.

Nonresponse Adjustment

If every selected household had

agreed to complete the screener, and

every selected child had agreed to

complete the interview and the

examination, weighted estimates (using

the base weights described in

‘‘Calculating Base Weights’’) would be

approximately unbiased estimates of

characteristics for the noninstitutionalized U.S. population. But in reality,

some of the sampled participants who

were screened refused to be interviewed

(interview nonresponse), and some of

the interviewed participants refused the

examination (examination nonresponse).

Thus, nonresponse bias may result. Bias

in the survey estimates occurs when the

characteristics of nonrespondents are

very different from those of respondents.

The best approach to minimizing

nonresponse bias is to plan and

implement field procedures that

maintain high cooperation rates. For

NNYFS, the payment of cash incentives

and repeated callbacks for refusal

conversion are very effective in

reducing nonresponse and, thus,

nonresponse bias. Yet some nonresponse

occurs even with the best strategies;

therefore, adjustments are always

necessary to minimize potential

nonresponse bias.

A multistage procedure for

nonresponse adjustment was carried out

to adjust for nonresponse to the

screener, interview, and examination.

The nonresponse adjustment procedure

consists of computing adjustment factors

and applying these factors to the survey

weights separately by nonresponse cell.

Nonresponse adjustment reduces bias if

response rates and survey characteristics

vary from cell to cell, and if respondents

and nonrespondents sharing the same

characteristics are in the same cell. The

nonresponse adjustment factors are the

reciprocals of the weighted response

rates within the selected cells.

A negative effect of nonresponse

adjustment is that it increases the

variability of the weights, which in turn

increases the variance. When the

nonresponse cells contain a sufficient

number of cases and the adjustment

factors are not too large, the effect on

variances is modest. A large adjustment

factor in a cell is usually the result of

the small number of respondents in that

cell. To avoid having nonresponse

adjustments based on very small sample

sizes, or having large nonresponse

adjustment factors, cells are usually

collapsed to form larger cells. The

following criteria were used in NNYFS

to determine whether to collapse cells:

+	 Minimum of 30 respondents in each

cell

+	 Maximum adjustment factor of 1.35

Nonresponse adjustments were

carried out separately for screener

nonresponse, interview nonresponse, and

examination nonresponse. In general,
Page 6 [ Series 2, No. 168

nonresponse adjustment cells were

generated using variables with known

values for both respondents and

nonrespondents. A few variables with

low item nonresponse rates were

considered when creating nonresponse

adjustment cells. For the screener

nonresponse adjustment, cells were

defined by segments within each

location. For the interview and

examination nonresponse adjustments, a

logistic regression model was run to

determine which variables were

important in predicting response

propensity. Once the significant

variables were determined, a program

was run to classify cases into cells while

maximizing the difference in response

rates between groups. As with NHANES

weights, a classification program based

on the University of Michigan algorithm

Search was used. This software uses

measures based on a chi-squared

statistic; see Table II for the variables

used to form the nonresponse

adjustment cells.

The nonresponse adjustment factors,

fi(NR) , were calculated as

where

wi(base) = Base weight for i-th sampled

participant in a-th cell

nas = Total sample size in a-th

nonresponse adjustment cell.

nar = Number of respondents in a-th cell

The summation was carried out

separately for each cell. Thus, the

nonresponse-adjusted weights, wi(NR),

were calculated as

Trimming

Nonresponse adjustments can

contribute to extreme weights; therefore,

trimming of the weights was considered.

Extreme weights may also occur when

units are sampled to yield fixed sample

sizes within a PSU, which occurred in

NHANES and NNYFS. Even a few

unexpectedly large weights can seriously

inflate the variance of survey estimates.

Thus, weight trimming procedures may

be used to reduce the impact of any

such large sampled participant weights

on the estimates produced from the

sample.

Because trimming introduces a bias

in the estimates, the resulting reduction

in variances is also expected to decrease

the mean squared error. The inspection

method was used for trimming weights

in NNYFS. This method involves

inspecting the distribution of weights in

the sample, and it applies to samples (or

subsets of samples) that were originally

designed to be self-weighting.

The subdomains for trimming were

the age category (3–5, 6–11, and 12–15)

and sex sampling domains. Once the

weights to be trimmed had been

identified, the weights of the

nontrimmed cases were also adjusted so

that the weights for each sampling

domain summed to the corresponding

weighted sum prior to trimming. This is

referred to as ‘‘preserving weighted

totals,’’ an important feature because

failure to preserve weighted totals may

lead to serious understatements in

estimated totals.

The trimming factors, fi(TR) , were

calculated as

where nk is the sample size of the k-th

sex-age sampling domain, and ti is

equal to wi (base)fi(NR), provided that this

product does not exceed the threshold

and is set to be equal to the threshold

otherwise. The trimmed weights, wi(TR),

were calculated as

Poststratification

The final step in the weighting

procedure was poststratification to

known population totals, to compensate

for undercoverage or overcoverage of

certain demographic groups, and for any

residual differential nonresponse among

these groups. Poststratification of sample

weights to independent population

estimates is used for several purposes.

In most household surveys, certain

demographic groups in the U.S.

population (e.g., children aged 4 and

under) experience fairly high rates of

undercoverage in survey efforts. Besides

partially compensating for such

undercoverage and any differential

nonresponse, poststratification to census

estimates can help reduce resulting bias

in the survey estimates, reduce the

variability of sample estimates, and

achieve consistency with accepted U.S.

figures for various subpopulations.

Poststratification involves applying

a ratio adjustment to the survey weights.

Broad classes, called poststratification

cells or poststrata, are constructed using

auxiliary data, and a single ratio

adjustment factor is applied to all units

in a given poststratification cell. The

numerator of the ratio is a ‘‘control

total’’ obtained from a secondary source;

the denominator is a weighted total

obtained using the survey weights.

Therefore, at the poststratum level,

estimates obtained using the

poststratified survey weights will

correspond to the control totals used.

Since poststratification is a ratio

adjustment, this process improves the

efficiency of estimates, provided that the

variables used in constructing

poststratification cells are associated

with the analysis variables of interest.

Such gains in efficiency are most

evident in the case of linear estimates

such as means or totals; for ratio

estimates, the ratio adjustments cancel

each other out at the poststratum level,

and the overall gains in efficiency due

to poststratification tend to be small.

A major effect of poststratification

is that it implicitly imputes for

nonresponse of survey characteristics for

the missed persons. The assumption is

that these missed persons not covered

by the survey have the same distribution

of characteristics as interviewed persons

within the poststratification cells. This is

obviously an oversimplification; the

missed persons are likely to be different.

However, in the absence of any detailed

information on the characteristics of the

missed persons, poststratification

appears to be the only reasonable

technique available for reducing bias

due to undercoverage and nonresponse.

The control totals were obtained

using weights from the 2011 American
Series 2, No. 168 [ Page 7

Community Survey (ACS). These ACS

weights have undergone

poststratification to the latest census

estimates of the total U.S.

noninstitutionalized civilian population,

including those not counted in surveys

or the most recent decennial census.

Poststratification, therefore, brings the

weighted totals up to the level of the

presumed number of noninstitutionalized

children in the United States aged 3–15.

The poststratification factors, fi(PS),

were calculated as

where Nj is the control total and nj is

the sample size of the poststratification

cell. Thus, the poststratified weights,

wi(PS), were calculated as

Computing Final Weights

The final weight for each sampled

child at each stage was calculated as the

product of the base weight and the

nonresponse adjustment, trimming, and

poststratification factors; that is,

More specifically, the final screening

weight was calculated as

and the final interview weight was

calculated as

so that the final examination weight was

calculated to be

Only the interview and examination

weights were released to the public.

Any sampled participant who did

not respond to the interview was

assigned an interview weight of zero.

These sampled participants were

considered ineligible for the examination

and assigned an examination weight of

zero as well. Their records were not

released to the public. Sampled

participants who completed the

interview and were eligible for the

examination, but did not respond, were

assigned examination weights of zero,

and their records are included in the

public release.

The interview weight should be

used for analyses of data from the

household interview only. The

examination weights should be used for

analyses of data from the examination

exclusively, or in conjunction with the

household interview data.

Combined NHANES/

NNYFS Weights

The 2012 NNYFS was conducted

not only at the same time as the 2012

NHANES but also in the same PSUs

and segments. In addition, some

questionnaire items and some physical

measurement tests were collected in the

same manner in both surveys. Therefore,

it is possible to combine the samples to

increase the sample sizes for these

common items. The weights of the two

surveys were combined to form new

sample composite weights that could be

used to analyze the combined sample.

As noted earlier, a much smaller set

of 2011 NHANES questionnaire and

examination items were comparable to

those in the 2012 NHANES and 2012

NNYFS. This happened because the first

year of the NHANES 2011–2012 survey

cycle was fielded before changes were

implemented to make the 2012

NHANES and the 2012 NNYFS more

comparable. Regardless, it is possible to

combine the 2012 NNYFS with the

2011–2012 NHANES for these limited

items. Sample weights were also created

for this combined data set.

Only data from the NNYFS sample

are released to the public. Due to

confidentiality restrictions, data from

either of the combined files described

above are available only through RDC.

Combined 2012 weights

The process of compositing began

with the final interview and examination

weights for the separate 2012 NHANES

and NNYFS samples. These weights

were then combined into one file and

the weights adjusted within subgroups,

or compositing domains, defined by race

and Hispanic origin, sex, and age. The

method of calculating the adjustment

factors, known as compositing factors,

was designed so that estimates from the

combined sample would result in the

lowest variance for key statistics.

The combined interview and

examination weights were then each

poststratified to the same totals for the

civilian noninstitutionalized population

from the 2011 ACS used in the NNYFS

weighting.

Creation of compositing factors

Calculation of the compositing

factors was based on examination data

and weights from the two annual

samples. The compositing factor was

calculated as

N where neff,j represents the effective

sample size in compositing domain j Y from NHANES, and neff,j represents the

effective sample size in compositing

domain j from NNYFS. The effective

sample size is defined as the sample

size divided by the design effect. It

captures aspects of the sample design

that are likely to affect variance,

regardless of the choice of statistic. The

design effect for each sample (S) was

approximated as the product of the

design effect due to clustering (Deff S

C,j)

times the design effect due to unequal

weighting (Deff S

W,j), as in

Eighteen values of α were

calculated, one for each compositing

domain defined by race and Hispanic

origin (Hispanic, non-Hispanic black,

and non-Hispanic white and other), sex,

and age category (3–5, 6–11, and

12–15). These compositing factors,

along with the design effects and

effective sample sizes, are shown in

Table III.
Page 8 [ Series 2, No. 168

Adjusted weights

A total of 1,253 children aged 3–15

responded to the NHANES interview in

2012, and 1,640 children responded to

the NNYFS interview, resulting in a

final total of 2,893 children when the

two samples were combined. Each final

NHANES interview weight was adjusted

by the value of αj corresponding to the

sampled child’s compositing domain

shown in Table III; each final NNYFS

interview weight in compositing domain

j was adjusted by one minus the value

of αj

. That is, the initial combined C12 interview weights wi(base,I) were initially

set at

for child i in compositing

domain j in 2012 NHANES

sample

and

for child i in compositing

domain j in NNYFS

sample

For example, the NHANES

interview weights for non-Hispanic

black males aged 3–5 from that sample

were multiplied by 0.5715, while the

NNYFS weights for non-Hispanic black

males aged 3–5 from that sample were

adjusted by 1 – 0.5715 = 0.4285.

These initial weights were

poststratified to totals of the civilian

noninstitutionalized population from the

2011 ACS, which was also the source

for the 2012 NHANES and NNYFS

annual weights. The weights were

adjusted to the compositing domains,

which are the same as the 18 poststrata

used for poststratifying the NNYFS

sample. Although Asian persons were

oversampled for NHANES, the sample

size for that group in the combined

sample was not large enough to separate

them from non-Hispanic white and other

persons for poststratification.

The C12 poststratification factors, fi(PS,I) ,

were calculated as

where Nj is the control total and nj is

the sample size of the poststratification

cell. Thus, the final poststratified

interview weights for the combined

2012 NHANES/NNYFS sample were

calculated as

For NHANES, 1,186 of the 1,253

interview respondents aged 3–15

completed an examination in 2012. For

NNYFS, 1,576 of the 1,640 interview

respondents completed an examination.

This resulted in 2,762 examination

respondents in the combined sample.

The weights for the combined sample

were calculated in a manner similar to

that used for the interview weights.

Each final NHANES examination

weight in domain j was multiplied by

the value of αj corresponding to the

sampled child’s domain shown in

Table III; each final NNYFS

examination weight in domain j was

adjusted by 1 minus the value of αj

corresponding to the sampled child’s

domain. That is, the initial combined C12 examination weights wi(base,E)were

initially set at

for child i in compositing

domain j in 2012 NHANES

sample

and

for child i in

compositing domain j

in NNYFS sample

The combined NHANES/NNYFS

examination weights were then

poststratified to the same totals as the

interview weights, using the same

poststrata. The poststratification factors, C12 fi(PS,E) , were calculated as

where Nj is the control total and nj is

the sample size of the poststratification

cell. Thus, the final poststratified

examination weights for the combined

2012 NHANES/NNYFS sample were

calculated as

Combined 2011–2012

NHANES/2012 NNYFS weights

To produce the 2-year weights, the

final weights for children aged 3–15

from the 2011 NHANES and the final

composited weights for the 2012

NHANES/NNYFS were divided by 2

and combined. The combined weights

were then reviewed for extreme weights

that might require trimming. After

trimming, the combined interview and

examination weights were then each

poststratified to the same totals used in

the NNYFS weighting for the civilian

noninstitutionalized population from the

2011 ACS.

The 1,387 children aged 3–15 who

responded to the NHANES 2011

interview, 1,253 children who responded

to the NHANES 2012 interview, and

1,640 children who responded to the

NNYFS interview resulted in a total of

4,280 when the samples were combined.

The final interview weights for the 2011

NHANES sample and the final

composited interview weights for the

2012 NHANES/NNYFS were combined,

and the initial combined interview

weights wC11,12

i(base,I) were initially set at

for child i in 2011

NHANES sample

and

for child i in 2012

NHANES or NNYFS

samples

The combined weights were

reviewed for extreme values that might

need trimming. Trimming thresholds

were based on the mean weight for each

compositing domain. These were

defined by race and Hispanic origin

(Hispanic, non-Hispanic black, and

non-Hispanic white and other), sex, and

age category (3–5, 6–11, and 12–15).

Any weight exceeding five times the

domain mean was trimmed down to that

level. The excess weight was then

distributed within the same domains so

that the weights for each domain

summed to the weighted sum prior to

trimming, with the exception of the

subdomains in the non-Hispanic white

and other group. These were further
Series 2, No. 168 [ Page 9

divided by Asian/non-Asian and low

income/not low income, with the

children selected through the NNYFS

sample considered to be not low

income. Non-Hispanic non-black Asian

persons, and low-income white and

other persons, were oversampled in

2011–2012 NHANES. In the full

sample, 10 weights required trimming.

The C11,12 trimming factors, fi(TR,I) , were

calculated as

where nb is the sample size of the b-th

trimming domain and ti is equal to C11,12 w , provided that this product does i(base,I)

not exceed the threshold and is set to be

equal to the threshold otherwise. The

trimmed weights were calculated as

Once again, the adjusted weights

were poststratified to totals of the

civilian noninstitutionalized population

from the 2011 ACS, the same source

used for the 2011–2012 NHANES and

2012 NNYFS weights. The weights

were adjusted to the same poststrata

used for poststratifying the NNYFS and

combined 2012 NHANES/NNYFS

samples. Although non-Hispanic

non-black Asian persons were

oversampled for NHANES, again the

sample size for that group in the

combined sample was not large enough

to separate them from the non-Hispanic

white and other persons for this

adjustment. The poststratification C11,12 factors, fi(PS,I) , were calculated as

where Nj is the control total and nj is

the sample size of the poststratification

cell. Thus, the final poststratified

interview weights for the combined

2011–2012 NHANES/NNYFS sample

were calculated as

For NHANES 2011, 1,330 of the

1,387 interview respondents aged 3–15

completed an examination; for

NHANES 2012, 1,186 of the 1,253

interview respondents aged 3–15

completed an examination. For NNYFS,

1,576 of the 1,640 interview respondents

completed an examination. This resulted

in 4,092 examination respondents in the

combined sample. The final examination

weights for the 2011 NHANES sample

and the final composited examination

weights for the 2012 NHANES/NNYFS

were combined, and the initial combined C11,12 examination weights, wi(base,E), were

initially set at

for child i in the 2011

NHANES sample

and

for child i in the 2012

NHANES or NNYFS

sample

The combined weights were

reviewed for extreme values that might

need trimming. The methodology

described for the interview weights was

also used for the examination weights.

In the full sample, eight weights

required trimming.

The combined NHANES/NNYFS

examination weights were then

poststratified to the same totals as the

interview weights, using the same

poststrata, to arrive at the final

poststratified examination weights for

the combined 2011–2012 NHANES/

NNYFS C11,12 sample, wi(E) .

Variance Estimation

Sampling errors should be

calculated for all survey estimates to aid

in determining the statistical reliability

of those estimates. For complex sample

surveys, exact mathematical formulas

for variance estimates are usually not

available. Variance approximation

procedures are needed to provide

reasonable, approximately unbiased and

design-consistent estimates of variance.

Although the NNYFS sample is

nationally representative, it was selected

from only 15 PSUs, and the sample

sizes for some subdomains may be

small. The small number of PSUs also

poses challenges for variance estimation.

With a small number of PSUs, direct

design-based variance estimates may be

unstable for some measures. In addition,

because variance computations must

incorporate the NNYFS design, standard

statistical software routines (i.e.,

software packages that assume a simple

random sample) should not be used for

computing variances for NNYFS. This

section introduces design-based methods

of variance estimation for complex

sample survey data and describes the

creation of variables necessary for

variance estimation on the public- and

restricted-use data files for the NNYFS

sample.

Two variance approximation

procedures that account for the complex

sample design and allow the

computation of design effects are

replication methods and Taylor series

linearization.

Replication methods provide a

general means for estimating variances

for the types of complex sample designs

and weighting procedures usually

encountered in practice. The basic idea

behind the replication approach is to

select subsamples repeatedly from the

whole sample, to calculate the statistic

of interest (or replicates) for each of

these subsamples, and then to use the

variability among these replicate

statistics to estimate the variance of the

full-sample statistic. The jackknife and

balanced repeated replication (BRR)

methods are two common procedures

for deriving replicates from a full

sample. The jackknife procedure retains

most of the sample in each replicate,

whereas the BRR approach retains a

portion of the sample in each replicate.

For the linearization approach,

nonlinear estimates are approximated by

linear ones for estimating variance. The

linear approximation is derived by

taking the first-order Taylor series

approximation for the estimator.

Standard variance estimation methods

for linear statistics are then used to

estimate the variance of the linearized

estimator. Currently, NCHS recommends

using Taylor series linearization methods

for variance estimation in analyses of all
Page 10 [ Series 2, No. 168

NNYFS data. SUDAAN, Stata, R, and

SAS survey procedures can be used to

obtain variance estimated by this

method.

Variance Estimation for

Publicly Released NNYFS

Data

For the NNYFS sample, the 15

PSUs are considered to be from one

stratum in order to estimate sampling

error using the Taylor series

linearization approach. The small

number of PSUs in the NNYFS sample,

geographic data and other characteristics

of the area on the data files, and local

publicity campaigns while the survey is

in the field all pose a risk for data

disclosure. As a result, masked variance

units (MVUs) are provided for use with

the public-use file to reduce the chance

of an intruder being able to match PSUs

in the sample to PSUs in the population,

while minimizing the bias in the

variance caused by altering the PSU

structure. MVUs can be used as if they

were pseudo-PSUs to estimate sampling

errors, as in NHANES.

The MVUs, or pseudo-PSUs, on the

data file are not the ‘‘true’’ design PSUs.

They are instead a collection of

secondary sampling units (SSUs)

aggregated into groups for variance

estimation. They produce variance

estimates that closely approximate the

variances that would have been

estimated using the true design PSUs.

Many surveys swap data values

between cases for disclosure limitation.

Rather than swapping individual values,

however, the procedure used in NNYFS,

described by Park et al. (4), swapped

entire segments (SSUs) between PSUs.

That is, for two similar segments in

different PSUs, the PSU and variance

stratum identifiers for all sampled cases

were swapped. Any PSUs with swapped

segments are no longer completely

associated with a single real PSU; thus,

the chance of correctly matching a given

individual within the PSU is limited.

The point estimates of the overall

population means do not change under

this PSU masking, but the variance

estimates may change slightly.

To identify which segments to swap

in NNYFS, estimates were first

calculated for all of the segments in all

of the study locations for comparative

purposes. These estimates provide

general descriptions of the segments,

such as the percentage of sampled

participants with a particular race or

Hispanic origin or obesity prevalence

that should be similar for swapped

segments. Then study locations that

were the most at risk for data disclosure

(locations with smaller populations or in

rural areas) were identified.

Within each of these at-risk

locations, each segment was paired with

all segments from the other study

locations (including other at-risk

locations), and a distance measure was

calculated to determine the effect on

variance by swapping the pair. The

distance measure was calculated as

where q is the number of variables used

to calculate the estimates, l is an _ individual estimate, xl is the mean of _ that estimate, v(xl

|Sr) is the variance of _ the estimate after swapping, and v(xl

|S)

is the variance of the estimate before

swapping.

Within each at-risk location, the

segments were sorted by smallest

distance measure achieved, and some

segments were selected to be swapped.

Generally, pairs with the smallest

distances were swapped, but if any two

pairs included the same segment, one

pair was not used for swapping. In this

way, a single segment was swapped

only once. Consideration was also given

to pairs of segments that came from

at-risk study locations; swapping of such

pairs was minimized where possible.

Further research by Park (5)

indicated that variance estimates

generally tended to increase as more

segments were swapped, although the

variance for specific analysis variables

could also be underestimated after

swapping. For this reason, the amount

of swapping (i.e., the number of study

locations determined to be at risk and

the number of segments swapped per

location) is limited.

Variance Estimation for

Combined NHANES/

NNYFS Data in RDC

Special unmasked PSU and stratum

codes (which differ from the MVU

codes provided for public-use files)

were created and are available for use in

the RDC for variance estimation with

data from the combined 2012

NHANES/NNYFS and the combined

2011–2012 NHANES/NNYFS files.

These true (unmasked) design codes are

needed when true geographic linkage of

the above combined files with some

external data sets is required.

More information on the RDC and

lists of special NHANES data files are

available from the NHANES website:

http://www.cdc.gov/nchs/nhanes/

participant.htm. Information on RDC

proposals is also available from the

NHANES website.

Cautionary Notes

The purpose of compositing the

NHANES and NNYFS samples was to

join two surveys representing the same

population to form a single sample by

adjusting the weights so that the

combined sample may represent the

same population.

While the method used to combine

the NHANES and NNYFS annual

samples would produce unbiased

estimates for any set of compositing

factors, the optimum values for any

particular statistic are the ones that

result in the lowest variance for that

statistic. However, because only one set

of factors was created, the values will

not be optimal for a single given

variable. For some statistics, estimates

may be more precise if made on either

sample alone.

Despite the fact that the sample

sizes have increased in the combined

NHANES/NNYFS 2011–2012 compared

with NHANES 2011–2012 alone, the

variability of the weights has also

increased. This is essentially due to the

fact that there is an important weight

differential between NHANES 2011 and

NHANES/NNYFS 2012. These two

samples represent the same population,
Series 2, No. 168 [ Page 11

but the latter sample has more than

twice the number of cases as the former.

As a consequence, weights from

NHANES/NNYFS 2012 are much

smaller than the corresponding

NHANES 2011 weights, and the

average weight reduction by

compositing domain varies from 33.7%

to almost 79.0%. This weight

differential results in increased design

effects and other measures of precision.

For most cases, combining the two

samples improves the reliability of some

statistics, but this is not the case for all

situations. Other methods for combining

the NHANES and NNYFS samples are

available that may improve some results

but adversely affect others. Given the

sample design differences across the two

samples, in addition to the factors

mentioned in the ‘‘Weighting Sample

Data’’ section, the weights for the

combined sample are quite variable.

Note the potential influence that cases

with large weights can have on analyses,

especially when extreme weights are

associated with extreme data points.

Additionally, analysts should be aware

of potential differences between PSUs

as a cause of high variances for specific

analytic variables of interest.

In summary, awareness of these

analytic limitations is advised when

using either of the combined NNYFS

and NHANES data files. This is

especially true for the combined

NNYFS/NHANES 2011–2012 data set,

which has a very limited number of data

items that can be analyzed and increased

variability in sample weights.

References

1.	 Borrud L, Chiappa MM, Burt VL, et al.

National Health and Nutrition

Examination Survey: National Youth

Fitness Survey plan, operations, and

analysis, 2012. National Center for

Health Statistics. Vital Health Stat

2(163). 2014.

2.	 U.S. Department of Health and Human

Services. 2008 physical activity

guidelines for Americans. 2008.

Available from: http://www.health.gov/

PAGuidelines/pdf/paguide.pdf.

3.	 Johnson CL, Dohrmann SM, Burt VL,

Mohadjer LK. National Health and

Nutrition Examination Survey: Sample

design, 2011–2014. National Center for

Health Statistics. Vital Health Stat

2(162). 2014.

4.	 Park I, Dohrmann S, Montaquila J,

Mohadjer L, Curtin LR. Reducing the

risk of data disclosure through area

masking: Limiting biases in variance

estimation. Proceedings of the Survey

Research Methods Section, American

Statistical Association; p 1761–7. 2006.

5.	 Park I. Primary sampling unit (PSU)

masking and variance estimation in

complex surveys. Surv Methodol

34(2):183–94. 2008.
Page 12 [ Series 2, No. 168

Appendix I. Glossary

Centers for Disease Control and

Prevention (CDC)—One of the major

operating components of the U.S.

Department of Health and Human

Services.

Department of Health and Human

Services (HHS)—The U.S. government’s

principal agency for protecting the

health of all Americans and providing

essential human services, especially for

those least able to help themselves.

CDC, including the National Center for

Health Statistics, operates under HHS

authority.

Domain—A demographic group of

analytic interest (analytic domain).

Analytic domains may also be sampling

domains if a sample design is created to

meet goals for specific demographic

groups. For NHANES National Youth

Fitness Survey (NNYFS), sampling

domains are defined by sex and age. See

Sampling domain.

Dwelling unit (DU), housing unit—The

house, apartment, mobile home or

trailer, group of rooms, or single room

occupied as separate living quarters (see

Group quarters) or, if vacant, intended

for occupancy as separate living

quarters. Separate living quarters are

those in which the occupants live

separately from other persons in the

building and which have direct access

from outside the building or through a

common hall. In this report, the term

generally means those DUs that are

eligible for the survey (i.e., excluding

institutional group quarters), or that

could become eligible (e.g., vacant at

the time of sampling but which could be

occupied once screening begins).

Group quarters—A place where people

live or stay that is normally owned or

managed by an entity or organization

providing housing or services for the

residents. These services may include

custodial or medical care as well as

other types of assistance, and residency

is commonly restricted to those

receiving these services. People living in

group quarters usually are not related to

each other. Group quarters include

college residence halls, residential

treatment centers, skilled nursing

facilities, group homes, military

barracks, correctional facilities, workers’

dormitories, and facilities for people

experiencing homelessness. These are

generally grouped into two categories:

institutional group quarters and

noninstitutional group quarters.

Institutional group quarters—Group

quarters providing formally

authorized supervised care or

custody in institutional settings,

such as correctional facilities,

nursing and skilled nursing

facilities, inpatient hospice facilities,

mental health or psychiatric

hospitals, and group homes and

residential treatment centers for

juveniles. Institutional group

quarters and are not included in the

NHANES sample.

Noninstitutional group quarters—

Group quarters that do not provide

formally authorized supervised care

or custody in institutional settings.

These include college or university

housing, group homes and

residential treatment facilities for

adults, workers’ group living

quarters and Job Corps centers, and

religious group quarters.

Noninstitutional group quarters are

included in the NHANES and

NNYFS samples.

Household—The group of persons living

in an occupied dwelling unit.

Low income—Beginning in 2000,

NHANES split the sampling domains

for white and other persons based on

their income status into low income and

non-low income. Low-income persons

are those at or below 130% of the

poverty level. The poverty threshold

used in this determination was based on

the most recent poverty guidelines

published by HHS; these thresholds are

updated annually by the U.S. Census

Bureau.

Masked variance units (MVUs)—A

collection of secondary sampling units

aggregated into groups for variance

estimation, designed to not reveal the

identity of the selected primary

sampling units (PSUs). For NHANES,

rather than using the units as sampled,

some pseudo-units are created by

swapping segments between PSUs. The

resulting units produce variance

estimates that closely approximate the

‘‘true’’ design variance estimates. MVUs

have been created for all 2-year survey

cycles, from NHANES 1999–2000

through 2009–2010. They can also be

used for analyzing any combined 4-, 6-,

or 8-year data set.

Maximum sampling rate (max{rk})

—The largest probability of selection

assigned to a demographic group within

a survey design. This value within

certain strata and demographic groups

was used in determining the sample size

and other sampling parameters in

NHANES and NNYFS.

Measure of size (MOS)—A value

assigned to every sampling unit in a

sample selection, usually a count of

units associated with the elements to be

selected. For NHANES and NNYFS, the

MOS used for PSU and segment

selection is actually a weighted average

of estimates of population counts for the

NHANES race-Hispanic origin-income

groups of interest.

National Center for Health Statistics

(NCHS)—The nation’s principal health

statistics agency, which designs,

develops, and maintains a number of

systems that produce data related to

demographic and health concerns. These

include data on registered births and

deaths collected through the National

Vital Statistics System, National Health

Interview Survey or NHIS, National

Health and Nutrition Examination

Survey or NHANES, National Health

Care Surveys, and National Survey of

Family Growth or NSFG, among others.

NCHS is one of 13 centers within CDC,

which is part of HHS.

Noninstitutional group quarters—See

listing under Group quarters.

Noninstitutionalized civilian

population—Includes all people living

in households, excluding institutional
Series 2, No. 168 [ Page 13

group quarters and those persons on

active duty with the military. This is the

target population for NHANES and

NNYFS.

Primary sampling unit (PSU)—The

first-stage selection unit in a multistage

area probability sample. In NHANES

and NNYFS, PSUs are counties or

groups of counties in the United States.

Some PSUs have such a large MOS that

they are selected into the survey with a

probability of one. These are referred to

as PSUs selected with certainty

(certainty PSUs); all other PSUs are

selected without certainty (noncertainty

PSUs).

Probability proportionate to size (PPS)

sampling—In this method, the

probability of selecting any unit varies

with the size of the unit, giving larger

units a greater probability of selection

and smaller units a lower probability.

NHANES and NNYFS use PPS

sampling in the selection of primary and

secondary sampling units (PSUs and

segments).

Public-use file—An electronic data set

containing respondent records from a

survey with a subset of variables

collected in the survey that have been

reviewed by analysts within NCHS to

ensure that the respondents’ identities

are protected. NCHS disseminates this

file to encourage widespread use of the

survey data.

Race and Hispanic origin—The term

used in this report as it was used in the

NHANES sample selection covering

four groups: Hispanic, non-Hispanic

black, non-Hispanic non-black Asian,

and a fourth group consisting of all

others.

Replicates—Subsamples selected

repeatedly from a sample used in some

variance estimation approaches. The

statistic of interest is calculated for each

subsample, and the variability among

the replicate statistics is used to estimate

the variance of the full-sample statistic.

The jackknife and balanced repeated

replication or BRR methods are two

common procedures for deriving

replicates from a full sample.

Respondent—A person selected into a

sample who agrees to participate in all

aspects of a survey. In NNYFS, persons

agreeing to complete the in-home

interviews are interview respondents.

Persons agreeing to complete the

in-home interviews and an examination

at a mobile examination center (MEC)

are MEC respondents.

Response rate—The number of survey

respondents divided by the number of

persons selected into the sample.

Response rates in this report are MEC

response rates, calculated as the number

of people receiving examinations in the

MEC divided by the total number of

people sampled.

Restricted-use file—An electronic data

set of survey respondent records

containing some information that may, if

released to the public, risk disclosing

individual survey respondents. The data

are available only through the NCHS

Research Data Center. These data sets

include a) NHANES data items

collected for an odd number of calendar

years (1, 3, or 5 years); b) data

geographically linked to other contextual

data files (often supplied by the data

user); and c) data items determined to

be too sensitive or detailed to be

released to the public due to

confidentiality restrictions.

Sample weight—For each NNYFS

respondent, the sample weight is the

estimated number of persons in the

target population that he or she

represents. For example, if a boy in the

sample represents 12,000 boys in his

age group, then his sample weight is

12,000. The NNYFS sample weights

were adjusted for different sampling

rates (of the sex-age groups), different

response rates, and different coverage

rates among persons in the sample, so

that accurate national estimates can be

made from the sample. Because it is the

product of all of these adjustments, it is

sometimes called the ‘‘final’’ sample

weight.

Sampling domain—NNYFS includes six

sampling domains, and Table I in this

report contains the specific sampling

domains; see also Domain.

Sampling rate—The rate at which a unit

is selected from a sampling frame. For

NNYFS, the rates required for sampling

persons in the sex-age domains were

designed to achieve the designated

number of MEC examinations in each

of those domains. The sampling rates

are the driving force in all stages of

sampling.

Screener—An interview (usually short)

containing a set of questions asked of a

household member to determine whether

the household contains anyone eligible

for the survey. In NNYFS, the screener,

or screening interview, consisted of a

household roster collecting the income

level of the household and the sex and

age of all members. In NNYFS, only

persons aged 18 and over could answer

the screener.

Screening—The process of conducting,

or attempting to conduct, the screening

interview in selected dwelling units.

Occupied dwelling units (households)

are ‘‘screened’’ through the screening

interview. Other units can also be

screened; the process for these units is

verification that they are either vacant or

not DUs. See Screener.

Secondary sampling unit (SSU)—The

second-stage selection unit in a

multistage area probability sample. For

NHANES and NNYFS, these are

typically referred to as ‘‘segments.’’

Segment—A group of housing units

located near each other, all of which

were considered for selection into the

sample. For NNYFS, segments consisted

of a census block or groups of blocks

selected at the second stage of sampling.

Within each segment, a sample of DUs

was selected.

Self-weighting sample—A sample for

which each elementary unit in the

population has the same nonzero chance

of selection into the sample; that is, they

are selected with the same constant

probability. Higher-stage sampling units

may be selected with differing

probabilities, but such differences in

selection probabilities at various stages

cancel out. NNYFS is a self-weighting

sample of persons within each sampling

domain.

Strata, stratification—The partitioning

of a population of sampling units into

mutually exclusive categories (strata).
Page 14 [ Series 2, No. 168

Typically, stratification is used to

increase the precision of survey

estimates for subpopulations important

to the survey’s objectives.

Study location—The set of segments

within a PSU that were fielded together,

with all MEC examinations conducted at

the same physical location. The

distinction between a PSU and a study

location is necessary because some large

certainty PSUs were divided into

multiple study locations and fielded at

different times.

Target population—The population to be

described by estimates from the survey.

In NNYFS, the target population was

the resident civilian noninstitutionalized

population of the United States, which

excluded all children in supervised care

or custody in institutional settings,

active-duty family members living

overseas, and any other persons residing

outside of the 50 states and District of

Columbia.

Undercoverage—The result of failing to

include all of the target population in

the sampling frame.

Variance—A measure of the dispersion

of a set of numbers. In this report, the

variance is specifically the sample

variance, which is a measure of the

variation of a statistic, such as a

proportion or mean, calculated as a

function of the sampling design and the

population parameter being estimated.

Many common statistical software

packages compute population variances

by default, which may underestimate the

sampling variance because they do not

incorporate any effects of having taken

a sample compared with collecting data

from every person in the full population.

Estimating the variance in NNYFS

requires special software, as discussed in

this report.

Variance stratum—The cluster of

variance units used when forming a

replicate for variance estimation.

Variance unit—A collection of SSUs

aggregated into groups and excluded

when forming a replicate for variance

estimation.

Weight—See Sample weight.
Series 2, No. 168 [ Page 15

Appendix II. Supporting Tables

Table I. Target sample sizes, screening amounts, and sampling rates: NNYFS, 2012

Age (years) and sex

Projected

population

in 2011–20141

Target

number

of NNYFS

examinations

Projected amount

of screening

required to

obtain one

examined person2

Projected amount

of screening

required to

obtain target

examinations

in self-weighting

area sample2

Numerator of

sampling rate3

Male Number of households

3–5 ....................................

6–11 ...................................

12–15 ..................................

6,418,335

12,983,222

8,519,830

173

346

231

23

11

17

3,991

3,946

4,009

0.9629

0.9520

0.9672

Female

3–5 ....................................

6–11 ...................................

12–15 ..................................

6,418,335

12,477,784

8,239,995

173

346

231

23

12

18

3,991

4,106

4,145

0.9629

0.9906

1.0000

Total.................................... 55,057,503 1,500 . . . . . . . . .

. . . Category not applicable.
 

1Population projection created for NHANES 2011–2014 primary sampling unit (PSU) selection.
 

2Estimated number of occupied households is 118,410,614, as created for NHANES 2011–2014 PSU selection.
 

3For a sample including a 50% reserve; denominator is 19,044.
 

NOTE: NNYFS is National Health and Nutrition Examination Survey’s (NHANES) National Youth Fitness Survey.
 
Page 16 [ Series 2, No. 168

Table II. Variables used to form nonresponse adjustment cells for weighting interview and examination samples: NNYFS, 2012

Categories of variables cross-classified

Variables considered for nonresponse Interview Examination

Race and ethnicity of sampled person ......... Non-Hispanic black, Non-Hispanic non-black Asian, Hispanic, other . . .

State grouping based on health characteristics of

population (used in PSU stratification)1 ......... Healthy states, California, somewhat healthy states, fairly healthy states,

poorly healthy states ...

Urbanicity ........................... Urban areas with population over 3 million, all other urban areas,

suburban areas, rural areas . . .

Sex of household reference person ........... Male, female . . .

Age (years) of sampled person .............. 3–5, 6–11, 12–15 . . .

Age (years) of household reference person ...... Under 30, 30–39, 40–49, 50 and over . . .

Census region ........................ Northeast, Midwest, South, West Northeast, Midwest, South, West

Number of sampled persons in household ....... . . . 1, 2, 3, 4 or more

. . . Category not applicable.
 

1State health-related variables used to derive health ranking are death rate, infant mortality rate, percentage of adults with high blood pressure, percentage of adults overweight or obese, percentage of
 

adults with poor nutrition, and percentage of adults who smoke. PSU is primary sampling unit.
 

NOTE: NNYFS is National Health and Nutrition Examination Survey’s (NHANES) National Youth Fitness Survey.
 
Series 2, No. 168 [ Page 17

Table III. Design effects, effective sample sizes, and final compositing factors used to combine annual samples: National Health and

Nutrition Examination Survey and NHANES National Youth Fitness Survey, 2011–2012

Design effect Effective sample size

Domain ( j ), by race and ethnicity, sex, and age (years) NHANES NNYFS NHANES NNYFS αj

Non-Hispanic black

Male:

3–5 ..................................

6–11 ..................................

12–15 .................................

Female:

3–5 ..................................

6–11 ..................................

12–15 .................................

1.14

1.17

1.09

1.08

1.18

1.11

1.11

1.31

1.23

1.18

1.27

1.21

42.18

67.46

41.45

29.62

79.93

34.20

31.62

67.93

49.58

34.79

75.87

42.87

0.5715

0.4983

0.4554

0.4599

0.5130

0.4438

Hispanic

Male:

3–5 ..................................

6–11 ..................................

12–15 .................................

Female:

3–5 ..................................

6–11 ..................................

12–15 .................................

1.12

1.23

1.10

1.11

1.19

1.13

1.14

1.32

1.19

1.21

1.25

1.17

44.84

79.90

39.09

36.13

66.14

45.90

54.28

87.75

55.28

49.65

89.06

56.39

0.4524

0.4766

0.4142

0.4212

0.4262

0.4487

Non-Hispanic white and other

Male:

3–5 ..................................

6–11 ..................................

12–15 .................................

Female:

3–5 ..................................

6–11 ..................................

12–15 .................................

1.87

1.81

2.08

1.61

1.91

1.91

1.23

1.29

1.26

1.25

1.33

1.30

30.94

66.83

34.55

36.08

67.57

26.19

66.63

118.38

97.32

57.70

125.87

95.73

0.3171

0.3609

0.2620

0.3847

0.3493

0.2148

NOTE: NHANES is National Health and Nutrition Examination Survey, and NNYFS is NHANES National Youth Fitness Survey.
Vital and Health Statistics

Series Descriptions

ACTIVE SERIES

Series 1.	 Programs and Collection Procedures—This type of report

describes the data collection programs of the National Center

for Health Statistics. Series 1 includes descriptions of the

methods used to collect and process the data, definitions, and

other material necessary for understanding the data.

Series 2.	 Data Evaluation and Methods Research—This type of

report concerns statistical methods and includes analytical

techniques, objective evaluations of reliability of collected

data, and contributions to statistical theory. Also included are

experimental tests of new survey methods, comparisons of

U.S. methodologies with those of other countries, and as of

2009, studies of cognition and survey measurement, and final

reports of major committees concerning vital and health

statistics measurement and methods.

Series 3.	 Analytical and Epidemiological Studies—This type of

report presents analytical or interpretive studies based on vital

and health statistics. As of 2009, Series 3 also includes

studies based on surveys that are not part of continuing data

systems of the National Center for Health Statistics and

international vital and health statistics reports.

Series 10.	 Data From the National Health Interview Survey—This

type of report contains statistics on illness; unintentional

injuries; disability; use of hospital, medical, and other health

services; and a wide range of special current health topics

covering many aspects of health behaviors, health status, and

health care utilization. Series 10 is based on data collected in

this continuing national household interview survey.

Series 11.	 Data From the National Health Examination Survey, the

National Health and Nutrition Examination Surveys, and

the Hispanic Health and Nutrition Examination Survey—

In this type of report, data from direct examination, testing,

and measurement on representative samples of the civilian

noninstitutionalized population provide the basis for (1)

medically defined total prevalence of specific diseases or

conditions in the United States and the distributions of the

population with respect to physical, physiological, and

psychological characteristics, and (2) analyses of trends and

relationships among various measurements and between

survey periods.

Series 13.	 Data From the National Health Care Survey—This type of

report contains statistics on health resources and the public’s

use of health care resources including ambulatory, hospital,

and long-term care services based on data collected directly

from health care providers and provider records.

Series 20.	 Data on Mortality—This type of report contains statistics on

mortality that are not included in regular, annual, or monthly

reports. Special analyses by cause of death, age, other

demographic variables, and geographic and trend analyses

are included.

Series 21.	 Data on Natality, Marriage, and Divorce—This type of

report contains statistics on natality, marriage, and divorce

that are not included in regular, annual, or monthly reports.

Special analyses by health and demographic variables and

geographic and trend analyses are included.

Series 23.	 Data From the National Survey of Family Growth—These

reports contain statistics on factors that affect birth rates,

including contraception and infertility; factors affecting the

formation and dissolution of families, including cohabitation,

marriage, divorce, and remarriage; and behavior related to

the risk of HIV and other sexually transmitted diseases.

These statistics are based on national surveys of women and

men of childbearing age.

DISCONTINUED SERIES

Series 4.	 Documents and Committee Reports—These are final

reports of major committees concerned with vital and health

statistics and documents. The last Series 4 report was

published in 2002. As of 2009, this type of report is included

in Series 2 or another appropriate series, depending on the

report topic.

Series 5.	 International Vital and Health Statistics Reports—This

type of report compares U.S. vital and health statistics with

those of other countries or presents other international data of

relevance to the health statistics system of the United States.

The last Series 5 report was published in 2003. As of 2009,

this type of report is included in Series 3 or another series,

depending on the report topic.

Series 6.	 Cognition and Survey Measurement—This type of report

uses methods of cognitive science to design, evaluate, and

test survey instruments. The last Series 6 report was

published in 1999. As of 2009, this type of report is included

in Series 2.

Series 12.	 Data From the Institutionalized Population Surveys—

The last Series 12 report was published in 1974. Reports

from these surveys are included in Series 13.

Series 14.	 Data on Health Resources: Manpower and Facilities—

The last Series 14 report was published in 1989. Reports on

health resources are included in Series 13.

Series 15.	 Data From Special Surveys—This type of report contains

statistics on health and health-related topics collected in

special surveys that are not part of the continuing data

systems of the National Center for Health Statistics. The last

Series 15 report was published in 2002. As of 2009, reports

based on these surveys are included in Series 3.

Series 16.	 Compilations of Advance Data From Vital and Health

Statistics—The last Series 16 report was published in 1996.

All reports are available online, and so compilations of

Advance Data reports are no longer needed.

Series 22.	 Data From the National Mortality and Natality Surveys—

The last Series 22 report was published in 1973. Reports

from these sample surveys, based on vital records, are

published in Series 20 or 21.

Series 24.	 Compilations of Data on Natality, Mortality, Marriage, and

Divorce—The last Series 24 report was published in 1996.

All reports are available online, and so compilations of reports

are no longer needed.

For answers to questions about this report or for a list of reports published

in these series, contact:

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