Series 2, Number 159 August 2013

National Health and

Nutrition Examination Survey:

Estimation Procedures,

2007–2010
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Mirel LB, Mohadjer LK, Dohrmann SM, et al. National Health and Nutrition

Examination Survey: Estimation procedures, 2007–2010. National Center for

Health Statistics. Vital Health Stat 2(159). 2013.

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National Health and Nutrition Examination Survey : Estimation procedures,

2007–2010.

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

research ; number 159) (DHHS publication ; no. (PHS) 2013–1359)

Estimation procedures, 2007–2010

Includes bibliographical references.

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

procedures, 2007–2010. III. Series: Vital and health statistics. Series 2, Data

evaluation and methods research ; no. 159. IV. Series: DHHS publication ; no.

(PHS) 2013–1359.

[DNLM: 1. National Health and Nutrition Examination Survey (U.S.) 2. Health

Surveys—United States. 3. Body Weight—United States. 4. Nutrition Surveys—

United States. 5. Sampling Studies—United States. 6. Statistics as Topic—

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Series 2, Number 159

National Health and
 

Nutrition Examination Survey:
 

Estimation Procedures,
 

2007–2010
 

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

August 2013

DHHS Publication No. 2013–1359
National Center for Health Statistics

Charles J. Rothwell, M.S., Acting 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 
 

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

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

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

Trimming ............................................................................................... 5 
 

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

Computing Final Weights .................................................................................. 6 
 

Subsample Weights ....................................................................................... 7 
 

Combining 2-year Weights to Analyze Other Multiyear Samples .................................................. 7 
 

Variance Estimation......................................................................................... 7 
 

Variance Estimation for Publicly Released Data ................................................................ 8 
 

Variance Estimation in the Research Data Center ............................................................... 8 
 

References ............................................................................................... 9 
 

Appendix I. Glossary....................................................................................... 10 
 

Appendix II. Supporting Tables .............................................................................. 13 
 

Text Table

Table. Sampling subdomains classified by race and Hispanic origin, income, sex, and age: National Health and Nutrition
 

Examination Survey, 2007–2010 ........................................................................ 3 
 

Appendix Tables

I.	 Variables used in forming nonresponse adjustment cells for weighting interview samples: National Health and Nutrition
 

Examination Survey, 2007–2010 ....................................................................... 13 
 

II.	 Most common survey sample weights and their appropriate use: National Health and Nutrition Examination Survey,
 

2007–2010 ........................................................................................ 14 
 

III.	 Final sampling rates and base weights: National Health and Nutrition Examination Survey, 2007–2010 ............. 15 
 

IV. Variables used in forming nonresponse adjustment cells for weighting MEC examination samples: National Health and
 

Nutrition Examination Survey, 2007–2010 ............................................................... 17 
 

iii
Acknowledgments
 

The authors gratefully acknowledge

the assistance of Margaret Carroll,

Michele Chiappa, Rosemarie Hirsch,

Deanna Kruszon-Moran, and Ryne

Paulose-Ram in the preparation of this

report.

vi
National Health and Nutrition

Examination Survey: Estimation

Procedures, 2007–2010

by Lisa B. Mirel, M.S., National Center for Health Statistics; Leyla K.

Mohadjer, Ph.D., Sylvia M. Dohrmann, M.S., and Jason Clark, M.S.,

Westat; and Vicki L. Burt, Sc.M., R.N., Clifford L. Johnson, M.S.P.H.,

and Lester R. Curtin, Ph.D., National Center for Health Statistics

Background

Data collection for the National

Health and Nutrition Examination

Survey (NHANES), comprises three

levels: an initial household screening

interview (or ‘‘screener’’), an in-home

personal interview, and a physical

examination. The primary objective of

the screener is to determine whether

any household members are eligible for

the interview and examination. Eligibility

is determined by preset selection

probabilities for the desired

demographic subdomains. After an

eligible sample person is selected, the

in-home interview collects person-level

demographic, health, and nutrition

information, as well as information

about the household. The examination

includes physical measurements such

as blood pressure, a dental

examination, and the collection of blood

and urine specimens for laboratory

testing.

Objectives

This report provides background for

the NHANES program and summarizes

the sample design specifications for the

2007–2010 survey cycle. Estimation

procedures are then presented,

including the methods used to calculate

survey weights for the full sample and

for examination subsamples, as well as

guidelines for combining 2-year weights

for the analysis of multiyear data.

Finally, the appropriate variance

estimation methods are described. The

sample selection methods, survey

content, data collection procedures, and

methods for assessing nonsampling

errors are documented elsewhere.

Keywords: sampling • weighting •

variance estimation

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) to provide information on the

health and nutritional status of the U.S.

population. This information is used to

estimate the prevalence of various

diseases and conditions and to provide

information for use in planning health

policy.

NHANES provides information on

the noninstitutionalized civilian resident

population of the United States. It

excludes all persons in supervised care

or custody in institutional settings, all

active-duty military personnel, activeduty family members living overseas,

and any other U.S. citizens residing

outside the 50 states and the District of

Columbia. See the glossary in Appendix I

for further details on institutional and

noninstitutional group quarters.

Data collection for NHANES

comprises three levels: an initial

household screening interview (or

‘‘screener’’), an in-home personal

interview, and a standardized physical

examination in a specially equipped

mobile examination center (MEC),

including selected objective measures of

health status. The primary objective of

the screener is to determine whether any

household members are eligible for the

interview and examination. The in-home

interview collects person-level

demographic, health, and nutrition

information, as well as information

about the household. The examination

includes physical measurements such as

blood pressure, a dental examination,

and the collection of blood and urine

specimens for laboratory testing.

Conducting the examinations in the

MEC helps to standardize their

administration.

The practical considerations

surrounding the transportation of the

MECs across the country limit the

number of locations NHANES can visit

each year. As a result, parameter

estimates for single-year data are

relatively unstable due to large variance

estimates. To improve the statistical

reliability and stability of estimates with

larger variances, it is recommended that

analysts use combinations of 2-year

cycles. Combining data from 2-year

cycles is particularly appropriate for rare

events, for estimates pertaining to

detailed demographic subdomains, and

for measures that may have considerable

geographic variation. The differences in

the sample sizes and designs for all

cycles of NHANES should be

considered when comparisons are made

across various HANES surveys. More

details on the sample design for

NHANES 2007–2010, as well as the

sample design parameters for all six

NHANES surveys, are outlined in

Page 1
Page 2 [ Series 2, No. 159

‘‘National Health and Nutrition

Examination Survey: Sample Design,

2007–2010’’ (1).

Aggregate-level national annual

estimates may be made for NHANES

1999–2010 through the NCHS Research

Data Center. Although the annual

samples are nationally representative,

annual estimates should be produced

only for the nation as a whole, for race

and Hispanic origin subdomains, or for

very broad sex-age subdomains within

race and Hispanic origin subdomains

because of limited sample sizes and

larger variances of point estimates

(where annual samples are not publicly

available).

The next section briefly summarizes

the sample design specifications for the

2007–2010 survey, and the remainder of

the report presents the estimation

procedures. For more detail on the

2007–2010 sample design, see the

Sample Design Report (1). The

‘‘Weighting the Sample Data’’ section

discusses the creation of weights for the

entire sample and subsamples, and the

‘‘Variance Estimation’’ section describes

the appropriate variance estimation

methods. Appendix I presents a glossary

of terms, and Appendix II contains

tables of supporting material.

Documentation of the survey content,

data collection procedures, and methods

for assessing nonsampling errors is

provided elsewhere (see http://

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

Sample Design

Summary

The NHANES sample represents

the total noninstitutionalized civilian

population residing in the 50 states and

the District of Columbia. As with

previous NHANES samples, a four-stage

sample design was used in NHANES

2007–2010. The first stage consisted of

selecting the primary sampling units

(PSUs) from a sampling frame of all

U.S. counties. The PSUs in the first

stage were mostly individual 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).

The second stage of selection for

the NHANES 2007–2010 sample

included a sample of area segments,

comprising census blocks or

combinations of blocks. However,

because these samples were based on

U.S. Census 2000 data, the measure of

size (MOS) used for sampling was

updated, if necessary, for PSUs that had

experienced large growth since 2000.

The sample was designed to

produce approximately equal sample

sizes per PSU. ‘‘Certainty PSUs’’—

those selected with certainty (with a

probability of one)—may have more

than or fewer than 24 segments, to

ensure appropriate representation in the

sample. ‘‘Noncertainty PSUs’’ have 24

segments. Additionally, some large

certainty PSUs were treated as multiple

study locations with varying numbers of

segments in each location, again to

ensure appropriate representation of the

PSU. The segments were also selected

with PPS. The MOS of the segments,

when combined with the subsampling

rates used within the segments, provided

approximately equal numbers of

sampled participants per segment.

The third stage of sample selection

consisted of dwelling units (DUs),

including noninstitutional group

quarters. In a given PSU, following the

selection of segments, a listing of all

DUs in the sampled segments was

prepared, and a subsample of these was

designated for screening to identify

potential sampled participants. The

screening rate was designed to produce

the desired number of sampled

participants for the most difficult

race-Hispanic origin-income-sex-age

domain (i.e., the domain sampled at the

highest rate).

The fourth stage of sample selection

consisted of persons within occupied

DUs, or ‘‘households.’’ All eligible

members within a household were listed,

and a subsample of individuals was

selected based on domains defined by

race and Hispanic origin, income, sex,

and age. The subsampling rates and

designation of potential sampled

participants within screened households

were arranged to provide approximately

self-weighting samples for each

subdomain while maximizing the

average number of sampled participants

per sample household.

The set of domains for which

specified reliability was desired in

NHANES 2007–2010 consisted of

sex-age groups for non-Hispanic black

persons and Hispanic persons, and

income-sex-age groups for the

remainder of the U.S. population. The

Table on page 3 provides the set of

sampling domains in NHANES

2007–2010. To increase the precision of

estimates for certain subdomains,

oversampling was carried out for

non-Hispanic white persons and others

aged 80 and over and for Hispanic

persons, non-Hispanic black persons,

and all other persons at or below 130%

of the federal poverty level. Although

data are released in 2-year cycles, at

least 4 years of data must be

accumulated to obtain an acceptable

level of reliability for the domains given

in the table. Thus, to create estimates

for smaller, 2-year samples (or any

annual estimates), some of the domains

must be collapsed to produce adequate

sample sizes for analysis.

Oversampling of population

subgroups was changed in the sample

design for the 2007–2010 survey period,

compared with the 1999–2006

NHANES. Adolescents were no longer

oversampled, and the supplemental

sample of pregnant women was

eliminated. The age domains 12–15 and

16–19 years were combined, and the

resulting domain sample size was

reduced. For the non-Hispanic black and

Hispanic domains, the 40–59 age group

was split in half into the 10-year age

domains 40–49 and 50–59, while the

total sample size for 40–59 stayed the

same. This led to an increase in the

number of participants aged 50–59 and

a decrease in those aged 12–19 when

compared with domains in previous

cycles.

The most significant change is that

all Hispanic persons were targeted for

oversampling, rather than just MexicanAmerican persons. In addition to

allowing estimates for the total group of

Hispanic persons, sampling fractions

were set so that the sample size for
Series 2, No. 159 [ Page 3

Mexican-American persons would not

be very different from previous years

and would be sufficient to produce

reliable estimates for this group. The

methodology for the oversampling of

Hispanic persons does not provide

sufficient sample sizes for calculating

estimates for other Hispanic subgroups.

The overall selection probability for

a person in race-Hispanic origin-incomesex-age subdomain k is

maxk{r

k

} rk Ph Phj maxk{r

k

} = rk Ph Phj [1]

where

h = the PSU

hj = segment within the hth PSU

k = race-Hispanic origin-incomesex-age subdomain

Ph = probability of selecting the hth

PSU

Phj = probability of selecting

segment j within the hth PSU

rk = sampling rate of persons in

the kth race-Hispanic

origin-income-sex-age

subdomain

maxk{rk} = maximum sampling rate

across all k race-Hispanic

origin-income-sex-age

subdomains.

Weighting the Sample

Data

The goal of NHANES is to produce

data representative of the civilian

noninstitutionalized U.S. population. The

weighting of sample data permits

analysts to produce estimates of the

statistics that would have been 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 features of the

survey: the differential probabilities of

selection for the sampling domains,

nonresponse to survey instruments, and

differences between the final sample and

the total population.

The NHANES samples were

weighted to meet the following

objectives:

1. Compensate for differential

probabilities of selection among

subgroups defined by race and

Hispanic origin, income, sex, and

age.

2.	 Reduce biases arising from the fact

that nonrespondents may be different

from respondents.
 

3.	 Fix weighted sample data to match

an independent U.S. Census Bureau

estimate of the target population

totals.

4. Compensate, to the extent possible,

for inadequacies in the sampling

frame (e.g., resulting from omission

of some housing units in the listing

of area segments and omission of

persons with no fixed address).

5.	 Reduce variances in the estimation

procedure by using auxiliary

information that is known with a

high degree of accuracy (Table I in

Appendix II).

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 Bureau

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 the

‘‘Calculating Base Weights’’ section

were the starting point for the screener

weight calculation. Those weights were

adjusted for nonresponse to the screener,

and then poststratified. The resulting

weights were the starting point for the

calculation of the interview weights,

which were then adjusted for

Table. Sampling subdomains classified by race and Hispanic origin, income, sex, and

age: National Health and Nutrition Examination Survey, 2007–2010

Non-Hispanic white and other

Non-Hispanic black Hispanic Non-low-income Low-income1

Males and females, age (years)

Under 1 Under 1 Under 1

1–2 1–2 1–2

3–5 3–5 3–5

Males, age (years)

6–11 6–11 6–11

12–19 12–19 12–19

20–39 20–39 20–29

. . . . . . 30–39

40–49 40–49 40–49

50–59 50–59 50–59

60 and over 60 and over 60–69

. . . . . . 70–79

. . . . . . 80 and over

Females, age (years)

6–11 6–11 6–11

12–19 12–19 12–19

20–39 20–39 20–29

. . . . . . 30–39

40–49 40–49 40–49

50–59 50–59 50–59

60 and over 60 and over 60–69

. . . . . . 70–79

. . . . . . 80 and over

Under 1

1–2

3–5

6–11

12–19

20–29

30–39

40–49

50–59

60–69

70–79
 

80 and over

6–11

12–19

20–29

30–39

40–49

50–59

60–69

70–79

80 and over

. . . Category not applicable.
 

1

Persons at or below 130% of the federal poverty level.
 
Page 4 [ Series 2, No. 159

nonresponse to the interview, inspected

for extreme weights, and again

poststratified. Finally, the poststratified

interview weights were the starting point

for calculation of the examination

weights. The weights were adjusted for

nonresponse to the examination,

inspected for extreme weights, and then

poststratified. Table II in Appendix II

lists the appropriate use of each set of

weights calculated for NHANES

2007–2010, including those calculated

for examination and laboratory

subsamples.

Note that extreme variability in the

weights results in reduced reliability

(increased sampling error) of some

survey estimates. The NHANES sample

was designed to minimize variability in

the weights, subject to operational and

analytic constraints. Additionally,

measures such as weight-trimming were

implemented to further reduce

variability. The impact of weight

variability is minimal when estimates

are for the demographic subdomains

used in the design; however, when

estimates are for domains that are

aggregated across design domains (for

example an estimate for the total

population), then the impact of weight

variability is greater.

Calculating Base Weights

The first-stage (or base) weight for

each sampled participant, wi(base), was

calculated as the reciprocal of the

sampled participant’s probability of

selection. These sampling rates are

provided in Table III (in Appendix II),

and their derivation is described in the

Sample Design Report (1).

The base weight for a sampled

participant is simply the reciprocal of

the sampling rate for the sampling

domain of the sampled participant

(denoted rk). For NHANES 2007–2010,

the base weight was adjusted further to

account for the following:

+	 Proportion of DUs released, fi(release)

+	 Proportion of deselected DUs,

fi(desel)

+	 Increase in sample size due to larger

segment sizes (2008–2010), fi(sizeinc)

+	 Number of years in the sample,

fi(year).

The final base weight was

calculated as

1 wi(base) = rk

(fi(release) fi(desel) fi(sizeinc) fi(year)) [2]

where subscript i indicates the sampled

participant. The following sections

briefly describe each component of this

calculation.

Adjustment for number of

sampled DUs released to the

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 NHANES. The

sampled participant base weight was

adjusted according to the proportion of

the total sample released to the field.

f was calculated as i(release)

1

fi(release) Di [3] =

where Di represents the proportion of

sampled DUs released for screening in

the location where sampled participant i

was selected. If response rates were

close to the predicted values and the

MOSs used during sampling were

current, the subsample factor would be

approximately 1.5 for study locations

fielded in 2007 to mid-2008 and 1.8 for

locations fielded afterward, given that

the overall DU sample size was

increased. That is, approximately

two-thirds of the sampled cases were

expected to be released for the earlier

locations, and approximately five-ninths

for later study locations. See the section

‘‘Adjustment for increase in segment

size (2008–2010)’’ for more details.

Deselection of released DUs

The sample yield monitoring and

evaluation methods used in NHANES

III, and subsequently in NHANES

2007–2010, occasionally suggested that

the expected number of sampled

participants from released DUs would

exceed the target sample size for the

study location. In these instances, DUs

from the set of DUs released but not yet

screened, to keep the sample size near

the target. To account for the

deselection, an adjustment factor was

applied to the base weight of sampled

participants who were identified in the

remaining units. In 2007–2010, the

expected number of sampled participants

exceeded the manageable sample size in

two study locations. The deselection

factors for those locations were 1.82 and

2.93. The factor, denoted by fi(desel), was

calculated as

1

fi(desel) = (1 – Di

) [4]

The denominator (1 – Di) represents the

proportion of released DUs deselected

from the sample. The deselection factor

for all remaining study locations was set

equal to 1.

Adjustment for increase in

segment size (2008–2010)

For study locations in 2008–2010,

the segment sizes and probability of DU

selection were increased so that it would

be easier to reach the target number of

identified sampled participants. To make

this adjustment, the overall selection

probability for a sampled participant

changed to

(1.2) maxk{r

k

} rk Ph Phj	 = 1.2 rk [5] Ph Phj maxk{r

k

}

Thus, a factor for these study locations,

denoted by fi(sizeinc), was calculated as

1

fi(sizeinc) 1.2 [6] =

Note that one study location was

small enough that the increase in the

probability of DU selection caused

every DU in the county to be selected

for the sample. For this location only,

the valuwere deselected or randomly

removed e of fi(sizeinc) was slightly

smaller.

Adjustment for number of

years in the sample

Because the selected sample of

study locations on which the sampling

rates were based was fielded over 4

years, the base weights calculated from

the original sampling rates also

correspond to a 4-year sample. In
Series 2, No. 159 [ Page 5

weighting subsets of those 4 years, the

following factor must be applied:

AWFi fi(year)
 = Number of years in sample [7]

where AWFi represents the factor that,

when applied to the weights, converts

the 4-year weights to annual weights.

For the 2007–2010 sample, AWFi is 4.

The divisor of fi(year) is simply the

number of years in the sample to be

weighted. For example, the divisor for

the records in the 2007–2008 sample

is 2.

Nonresponse Adjustment

If every selected household had

agreed to complete the screener, and

every selected person had agreed to

complete the interview and the

examination, weighted estimates using

the base weights described in the

‘‘Calculating Base Weights’’ section

would be approximately unbiased

estimates of characteristics for the

civilian 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 sampled 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

NHANES, the payment of cash

incentives and repeated callbacks for

refusal conversion are very effective in

reducing nonresponse, and thus

nonresponse bias. However, 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 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

NHANES to determine whether to

collapse cells: (a) a minimum of 30

respondents in each cell and (b) a

maximum adjustment factor of 1.35.

Nonresponse adjustments were

carried out separately for screener

nonresponse, interview nonresponse, and

examination nonresponse. In general,

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,

the Chi-squared Automatic Interaction

Detector (CHAID) was used to identify

variables most highly related to response

propensity. See Tables I and IV in

Appendix II for the variables used to

form the nonresponse adjustment cells.

The nonresponse adjustment factors,

fi(NR), were calculated as

∑i

n

=1

as wi(base) =

∑i

n

=1

ar wi(base) [8]

fi(NR)

where wi(base) is the base weight for the

ith sampled participant in the ath cell,

nas is the total sample size in the ath

nonresponse adjustment cell, and nar is

the number of respondents in the ath

cell. The summation was carried out

separately for each cell. Thus, the

nonresponse-adjusted weights, wi(NR),

were calculated as

wi(NR) = wi(base) fi(NR) [9]

Trimming

Because nonresponse adjustments

can contribute to extreme weights,

trimming of the weights was considered.

Extreme weights may also occur when

units are sampled to yield fixed sample

sizes within a PSU, as was the case in

NHANES. 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, it is hoped that the

resulting reduction in variances will also

decrease the mean squared error.

The inspection method was used for

trimming weights in NHANES. This

method involves inspecting the

distribution of weights in the sample

and applies to samples (or subsets of

samples) that were originally designed

to be self-weighting.

The subdomains for trimming are

the race-Hispanic origin-income-sex-age

sampling domains. Because some groups

are oversampled, the weights within

domains may be quite variable. For this

reason, trimming thresholds were

dependent on the amount of

oversampling used in these 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.’’ Failure to preserve weighted

totals may lead to serious

understatements in estimated totals; thus,

preserving weighted totals is an
Page 6 [ Series 2, No. 159

important characteristic for a trimming

procedure.

The trimming factors, fi(TR), were

calculated as

n

∑ b

i=1ti

fi(TR) = n

∑ b

i=1wi(base) fi(NR) [10]

t

t

where nb is the sample size of the bth

race-Hispanic origin-income-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

wi(TR) = wi(NR) fi(TR) [11]

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 has several purposes. In most

household surveys, certain demographic

groups in the U.S. population (e.g.,

young black males) experience fairly

high rates of undercoverage in survey

efforts. Poststratification to census

estimates partially compensates for such

undercoverage and for any differential

nonresponse, and can help to reduce the

resulting bias in the survey estimates.

Poststratification can also help 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.

Because poststratification is a ratio

adjustment, this process will improve

he efficiency of estimates provided that

he 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.

All control totals were obtained

using undercount-adjusted weights from

the yearly March supplement to the U.S.

Census Bureau’s Current Population

Survey (CPS). For the 2-year samples,

the control totals were calculated as the

midpoint between the CPS estimates of

the poststrata population totals for the

two sample years. These CPS weights

have undergone poststratification to the

Census Bureau’s best estimates of the

total noninstitutionalized civilian

population of the United States,

including those not counted in surveys

or in the most recent decennial census.

Poststratification, therefore, brings the

weighted totals up to the level of the

presumed total noninstitutionalized civilian

population in the United States.

The poststratification factors, fi(PS),

were calculated as

Nc fi(PS) =

∑nc

i=1wi(TR) [12]

where Nc is the control total and nc is

the sample size of the poststratification

cell. Thus, the poststratified weights,

wi(PS), were calculated as

wi(PS) = wi(NR) fi(PS) [13]

Computing Final Weights

The final weight for each sampled

participant at each stage was calculated

as the product of the base weight and

the nonresponse adjustment, trimming,

and poststratification factors; that is,

wi = wi(base) fi(NR) fi(TR) fi(PS) [14]

More specifically, the final screening

weight was calculated as

wi(S) = wi(base) fi(NR,S) fi(TR,S) fi(PS,S) [15]

the final interview weight was

calculated as

wi(I) = wi(base) fi(NR,S) fi(TR,S) fi(PS,S) fi(NR,I)

fi(TR,I) fi(PS,I) [16]

and the final examination weight was

calculated as

wi(E) = wi(base) fi(NR,S) fi(TR,S) fi(PS,S) fi(NR,I)

fi(TR,I) fi(PS,I) fi(NR,E) fi(TR,E) fi(PS,E) [17]

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 were also assigned an examination

weight of zero. Their records were not

released to the public. Sampled

participants who did complete the

interview and were eligible for the

examination but did not respond were

assigned examination weights of zero

and their records were 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

exclusively for analyses of data from the

examination, or in conjunction with the

household interview data. This includes

data from the MEC interview and

examination, or laboratory data on the

entire examination sample (Table II).
Subsample Weights

Some NHANES respondents were

also asked to participate in survey

components that were statistically

defined (or random) subsamples of the

NHANES examined sample. Data

collected from these participants include

a variety of laboratory, nutrition and

dietary, environmental, audiometry, and

mental health components. Each

subsample was selected to be a

nationally representative sample.

For example, some but not all

participants were selected to give a

fasting blood sample on the morning of

their examination. The subsamples

selected for these components were

chosen at random with a specified

sampling fraction (e.g., one-half of the

total examined group), according to the

protocol for that component. Each

component subsample has its own

designated weight, which accounts for

the additional probability of selection

into the subsample component, as well

as any additional nonresponse to the

component. For some components,

subsample weights were calculated to

incorporate additional information

relevant to data collection (such as day

of the week for the dietary recall data).

The special survey weights were

included in the released data file.

Because these weights differ from the

examination weights for the whole

sample, the subsample-specific weights

must be used for statistical estimation of

measures collected only in that

subsample. (See Table II for a list of

these weights and information regarding

their appropriate use.) For example, no

sample weights are provided for the

overlap of the fasting subsample with an

environmental subsample. Subsample

weights from the same survey cycle are

not designed to be combined within the

data release cycle. In fact, many

subsamples are mutually exclusive. To

combine two or more subsamples,

random overlap would have to occur

between the subsamples, and appropriate

weights would need to be recalculated.

See the respective survey protocol and

documentation (available from:

http://www.cdc.gov/nchs/nhanes.htm) for

more specific information on each

subsample.

Combining 2-year Weights

to Analyze Other

Multiyear Samples

Only 2-year weights were calculated

for NHANES 2007–2008 and

2009–2010. To combine these cycles to

produce 4-year estimates, the 4-year

weights are calculated as one-half times

the 2-year weights. Six-year weights

may similarly be created for 2005–2010

estimates by multiplying the 2-year

weights by one-third. Future years of

data can continue to be added, using

these same methods for each sample.

However, as a result of sample design

changes initiated in 2007, it is important

to consider the following when

calculating estimates based on data from

the 6-year period 2005–2010. First,

because of the small sample numbers

and design inefficiency for nonMexican-American Hispanic persons

(and thus for total Hispanic persons) in

2005–2006, estimates should not be

created for total Hispanic persons for

the 2005–2010 data period. Second, for

the non-Hispanic white, non-Hispanic

black, and Mexican-American sample

domains, rescaling the sample weights

to create 6-year weights (as described in

the NHANES analytic guidelines) will

be sufficient for producing combined

6-year estimates for most analytic

variables.

With this reweighting, the target

population is the U.S. noninstitutionalized

population at the midpoint of the

combined interval, and the sum of

combined weights should be reasonably

close to an independent estimate of that

midpoint population. Although combining

years of data is useful for rare events,

users are cautioned that there is an

inherent assumption of no trend in the

estimate over the time period, or an

interpretation that the estimate is the

average over the time period.

s

Variance Estimation f

Sampling errors should be

calculated for all survey estimates, to

aid in determining the statistical

reliability of those estimates. For

Series 2, No. 159 [ Page 7

complex sample surveys, exact

mathematical formulas for variance

estimates are usually not available.

Variance approximation procedures are

required in order to provide reasonable,

approximately unbiased, and designconsistent estimates of variance.

Although each 2-year sample is

nationally representative, it was selected

from only 30 PSUs, and the sample

sizes for some specific race-Hispanic

origin-income-sex-age 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 NHANES design,

standard statistical software routines

(i.e., software packages that assume a

simple random sample) should not be

used for computing variances for

NHANES. 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

2007–2010 samples.

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 for each of these subsamples (or

‘‘replicates’’), and then to use the

variability among these replicate

tatistics to estimate the variance of the

ull-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.
Page 8 [ Series 2, No. 159

BRR was used for NHANES III.

Initially, the delete-one jackknife method

(a replication method) was used to

estimate variances based on data from

the NHANES 1999–2000 survey.

However, jackknife method replicate

weights were provided only for the

1999–2000 data release. If replication

methods are to be used for any other

survey years, replicate weights must be

computed by the analyst (2).

For the linearization approach,

nonlinear estimates are approximated by

linear estimates for the purpose of

variance estimation. 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 NHANES

data. SUDAAN, Stata, R, and SAS

survey procedures can be used to obtain

variance estimated by this method.

Variance Estimation for

Publicly Released Data

PSUs are selected from strata

defined by geography and proportions of

minority populations. In any 2-year

sample there are two PSUs from each

strata; these are used as variance strata

to estimate sampling error in the Taylor

Series Linearization approach. Within

each variance stratum, two variance

units are generally defined as the PSU.

However, because certainty PSUs are

not selected within strata, variance strata

for these PSUs are formed based on the

relative size of the PSU compared with

the other PSUs. As a result, some of

these variance strata may have one PSU

split into multiple variance units, while

other variance strata may have three

PSUs for variance estimation, depending

on the number and size of the certainty

locations that year.

The small number of PSUs in a

2-year NHANES 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 data files 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 (similar to past NHANES).

The MVUs or pseudo-PSUs on the

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

but are a collection of secondary

sampling units aggregated into groups

for the purpose of variance estimation.

They produce variance estimates that

closely approximate the variances that

would have been estimated using the

true design PSUs. 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 after the appropriate

adjustment described in ‘‘Combining

2-year Weights to Analyze Other

Multiyear Samples.’’

Many surveys swap data values

between cases to limit disclosure. Rather

than swapping individual values,

however, the procedure used in

NHANES 2007–2010, and described in

Park et al. (3), swapped entire segments

(secondary sampling units) between

PSUs. That is, for two similar segments

in different PSUs, the PSU and variance

stratum identifiers for all sampled cases

were swapped. Because any PSUs with

swapped segments are no longer

completely associated with a single real

PSU, 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 NHANES 2007–2010, 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 percentage of

Hispanic sampled participants,

prevalence of home ownership, and

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 of

swapping the pair on variance. The

distance measure was calculated as

- - q v(xl

|S

r

) – v(xl|S) D = ∑ [18] l=1| v(x­ | l

|S)

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(x

-

l | 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 both came

from at-risk study locations; swapping

of such pairs was minimized where

possible.

Further research by Park (4)

indicated that variance estimates

generally tend to increase as more

segments are 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 in the

Research Data Center

For the current sample design,

NHANES data are released to the public

in 2-year data cycles. In addition to

public-use data files, there are special
Series 2, No. 159

[ Page9

data sets that are available only through

the NCHS Research Data Center (RDC)

(see http://www.cdc.gov/rdc/index.htm).

These include data sets with (a) data

items that were collected for an odd

number of calendar years (1, 3, or5

years), (b) data linked geographically to

some other contextual data files (often

supplied by the data user), and (c) data

items that are determined to be too

sensitive or too detailed to be released

to the public due to confidentiality

restrictions.

Some of these data files have

special sample weights that should be

used when these nonpublic data sets are

analyzed within the confines of the

RDC environment. For example,

single-calendar-year data files have a single-year examination weight. This

single-year weight can be combined

with the examination weight provided

on the 2-year public-use file to createa

3-year examination weight. All

single-year sample weights were

calculated in the same manner as the

public-use 2-year weights described in

‘‘Weighting the Sample Design.’’ Ifa

special data file involves subsampling,

then special subsample weights were

created for that file that reflect the

number of calendar years in the data file

and the rate of subsampling. For all

special data files, appropriate

documentation is provided in the RDC

to describe the necessary sample

weights.

Special unmasked PSU and stratum

codes (which differ from the MVU

codes provided for the public-use files)

are provided for variance estimation for

data from those special files, using the

true PSU and stratum codes. These

unmasked design codes are necessary

given the need for true geographic

linkage with some data sets. Providing

the unmasked PSU and stratum codes

poses no disclosure risk because of the

restrictions of the RDC, such as the

prohibition of publication of PSU-level

estimates. Further, any subnational

estimate that is generated from an RDC

analysis must be reviewed and approved

by NCHS staff to protect the

confidentiality of sampled participants.

More information on the RDC, and

lists of special NHANES data files, are

available from the NCHS website at:

http://www.cdc.gov/rdc/. Information on

proposals for use of stored specimens is

available from: http://www.cdc.gov/nchs/

nhanes/genetics/stored_specimens.htm.

References

1.	 Curtin LR, Mohadjer LK, Dohrmann

SM, et al. National Health and

Nutrition Examination Survey: Sample

design, 2007–2010. National Center for

Health Statistics. Vital Health Stat

2(160). 2013. Available from:

http://www.cdc.gov/nchs/data/series/

sr_02/sr02_160.pdf.

2.	 Korn EL, Graubard BI. Analysis of

health surveys. New York, NY:

Wiley;29. 1999.

3.	 Park I, Dohrmann S, Montaquila J,

Mohadjer LK. 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;1761–7. 2006.

4.	 Park I. Primary sampling unit (PSU)

masking and variance estimation in

complex surveys. Surv Methodol

34(2):183–94. 2008.
Page 10 [ Series 2, No. 159

Appendix I. Glossary

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 those specific

demographic groups. For the National

Health and Nutrition Examination

Survey (NHANES), sampling domains

are defined by race and Hispanic origin,

income, age, and sex. See Sampling

domain.

Dwelling unit (DU)—Also ‘‘housing

unit.’’ A 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

individuals 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 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 such places as 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 an institutional setting,

such as correctional facilities,

nursing and skilled nursing

facilities, inpatient hospice facilities,

mental health or psychiatric

facilities, and group homes and

residential treatment centers for

juveniles. Institutional group

quarters are not included in the

NHANES sample.

Noninstitutional group quarters—

Group quarters that do not provide

formally authorized, supervised care

or custody in an institutional

setting. These include college and

university housing, group homes

intended for adults, residential

treatment facilities for adults,

workers’ group living quarters, Job

Corps centers, and religious group

quarters. Noninstitutional group

quarters are included in the

NHANES sample.

Household—The person or group of

persons living in an occupied DU.

Low-income—Beginning in 2000,

NHANES split the sampling domains

for white and other persons based on

their income status into low-income or

non-low-income. Low-income persons

were defined as those at or below 130%

of the federal poverty level. The poverty

threshold used in this determination was

based on the most recent poverty

guidelines published by the U.S.

Department of Health and Human

Services; 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 the purpose

of variance estimation and designed to

prevent disclosure of the identity of the

selected primary sampling units (PSUs).

For NHANES, rather than using the

units as sampled, some pseudounits 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

(maxk{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 to determine the

sample size and other sampling

parameters in NHANES.

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, the MOS is

actually a weighted average of estimates

of population counts for the raceHispanic origin-income groups of

interest.

National Center for Health

Statistics (NCHS)—As the nation’s

principal health statistics agency, NCHS

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, and data collected by the

National Health Interview Survey,

NHANES, the National Health Care

Surveys, and the National Survey of

Family Growth, among others. NCHS is

part of the Centers for Disease Control

and Prevention, an operating division of

the U.S. Department of Health and

Human Services.

Noninstitutionalized civilian

population—Includes all people living in

households, excluding those in

institutional group quarters and those on

active duty with the military. This is the

target population for NHANES.

Primary sampling unit (PSU)—The

first-stage selection unit in a multistage

area probability sample. In NHANES,

PSUs are counties or groups of counties

in the United States. Some PSUs are so

large 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’’).
Series 2, No. 159 [ Page 11

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 uses PPS sampling in the

selection of PSUs and secondary

sampling units.

Public-use data 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 respondent identities are

protected. NCHS disseminates these

files to encourage public use of the

survey data.

Race and Hispanic origin—This

phrase is used in this report as it was

used in NHANES sample selection. It

refers to Hispanic persons, non-Hispanic

black persons, and a third group

consisting of all other persons.

Replicates—Subsamples selected

repeatedly from a sample used in some

variance estimation approaches. With

these 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 (BRR) methods are two

common procedures for the derivation

of replicates from a full sample. The

BRR method was used in the creation of

replicate weights for most of the

NHANES 2007–2010 multiyear samples.

Respondent—A person selected into

a sample who agrees to participate in all

aspects of a survey. In NHANES,

persons agreeing to complete the

in-home interview are considered

‘‘interview respondents.’’ Persons

agreeing to complete both the in-home

interview and an examination in a

mobile examination center (MEC) are

considered ‘‘MEC respondents.’’

Response rate—The number of

survey respondents divided by the

number of persons selected into the

sample. The 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 data file—An

electronic data set of survey respondent

records, containing some information

that may, if released to the public, risk

disclosing individual survey

respondents. These data are available

only through the NCHS Research Data

Center. These include data sets with (a)

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); (c) data items determined

to be too sensitive or too detailed to be

released to the public due to

confidentiality restrictions; and (d)

surplus sera projects where past

biological samples have been stored and

subsequently used based on a formal

proposal submitted as a special study

(on either the full sample or a special

subsample).

Sample weight—The estimated

number of persons in the target

population that an NHANES respondent

represents. For example, if a man in the

sample represents 12,000 men in his

race-Hispanic origin-income-age

subdomain, then his sample weight is

12,000. The NHANES sample weights

were adjusted for different sampling

rates (of the race-Hispanic originincome-sex-age groups), different

response rates, and different coverage

rates among persons in the sample, so

that accurate national estimates could be

made from the sample. The product of

all of these adjustments is sometimes

called the ‘‘final’’ sample weight.

Sampling domain—NHANES

2007–2010 included 72 sampling

domains. See Domain.

Sampling rate—The rate at which a

unit is selected from a sampling frame.

For NHANES, the rates required for

sampling persons in the race-Hispanic

origin-income-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 NHANES, the screener consisted of

compiling a household roster and

collecting the income level of the

household and the race and Hispanic

origin, age, and sex of all household

members. In NHANES, only persons

aged 18 and over can answer the

screener.

Screening—The process of

conducting, or attempting to conduct,

the screener interview in selected DUs.

Occupied DUs (households) are

screened using the screener. Other units

can also be screened; the process for

these units is simply verification that

they are either vacant or not DUs. See

Screener.

Secondary sampling unit—The

second-stage selection unit in a

multistage area probability sample. For

NHANES, these are typically referred to

as ‘‘segments.’’ See Segment.

Segment—A group of housing units

located near each other, all of which

were considered for selection into the

sample. For NHANES, segments consist

of a census block, or groups of blocks,

and their selection makes up 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 each other out. NHANES is a

self-weighting sample of persons within

each sampling domain.

Stratification; Strata—The partitioning

of a population of sampling units into

mutually exclusive categories (strata).

Typically, stratification is used to increase

the precision of survey estimates for

subpopulations important to the survey’s

objectives. To select the PSUs fielded in

2007–2010, PSUs were stratified based on

region, metropolitan statistical area status,

and various population demographics.

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
Page 12 [ Series 2, No. 159

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 NHANES, the target

population is the resident civilian

noninstitutionalized population of the

United States, which excludes all

persons in supervised care or custody in

institutional settings, all active-duty

military personnel, active-duty family

members living overseas, and any other

persons residing outside the 50 states

and the District of Columbia.

Undercoverage—The result of

failing to include all the target

population within 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 a 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; these may

underestimate the sampling variance

because they do not incorporate any

effects of having taken a sample instead

of collecting data from every person in

the full population. Estimating the

variance in NHANES requires special

statistical software, as discussed in this

report.

Variance stratum—The cluster of

variance units used when forming a

replicate for variance estimation. For

NHANES, the PSU sampling strata

usually correspond to the variance strata.

Variance unit—A collection of

secondary sampling units aggregated

into groups and excluded when forming

a replicate for variance estimation. For

NHANES, an entire PSU usually

corresponds to a variance unit.

Weight—See Sample weight.
orting Tables

onresponse adjustment cells for weighting interview samples: National Health and Nutrition Examination Survey, 2007–2010

nse,

Order and categories of variables cross-classified to form nonresponse adjustment cells

2007–2008 2009–2010

n ........

.........

person .....

.........

.........

.........

.........

.........

.........

.........

.........

.........

.........

n ........

. . . . . . . . .

.........

.........

.........

n ........

.........

.........

.........

.........

.........

.........

.........

1.

2.

. .

3.

4.5.

. .

1.2.

. .

3.

4.

. .

. .

. .

1.2.

3.

4.

5.

. .

1.2.

3.

. .

. .

Non-Hispanic black, Hispanic, other

Areas with population over 3 million,

.

Male, female1–2, 3–4, 5–6, 7 or more

Northeast, Midwest, South, West

.

Male, female

1–5, 6 or more

.

Northeast, Midwest, South, West

Urban, suburban, rural

.

.

.

1–2, 3–4, 5–6, 7 or more

Urban, suburban, rural

Northeast, Midwest, South, West

Non-Hispanic black, Hispanic, other

Yes, no

.

Areas with population over 3 million,

Northeast, Midwest, South, West

1–2, 3–4, 5–6, 7 or more

.

.

other

other urban areas, suburban, rural

. . .

. . .

4. Non-Hispanic black, Hispanic, other

5. Male, female1. 1–4, 5–8, 9 or more

2. Northeast, Midwest, South, West

3. 1–3, 4, 5 or more

. . .

. . .

1. 1–4, 5–8, 9 or more

3. Northeast, Midwest, South, West

. . .

2. Yes, no

4. Non-Hispanic black, Hispanic, other

5. Male, female

1. 1–2, 3–4, 5–6, 7 or more

4. Areas with population over 3 million,

2. Northeast, Midwest, South, West

5. Non-Hispanic black, Hispanic, white,

...

3. Yes, no

2. Urban, suburban, rural

4. Northeast, Midwest, South, West

. . .

1. Yes, no

3. 1, 2 or more

other

other

Series 2, No. 159[ Page 13
Page 14 [ Series 2, No. 159

Table II. Most common survey sample weights and their appropriate use: National Health and Nutrition Examination Survey, 2007–2010

Weight	 Application

Interview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	 Use when analyzing data from the home interview only. Do not use if the analysis includes variables

that were also collected on examined persons in the mobile examination center (MEC).

Examination ............................ 	 Use when analyzing data from the MEC examination. Do not use if the analysis includes variables
 

collected as part of one of the dietary interviews or part of one of the subsamples (e.g., fasting

or environmental).

Dietary day 1 sample ....................... 	 Use when analyzing data from the day 1 24-hour dietary recall or the Flexible Consumer Behavior
 

Survey telephone follow-up module for examined persons who completed one or both of these

interviews.

Dietary day 2 sample ....................... 	 Use when analyzing data from the day 1 and day 2 24-hour dietary recalls for examined persons

who completed these interviews.

Fasting subsample ........................ 	 Use when analyzing the fasting glucose, insulin levels, triglycerides, and LDL cholesterol (lipids)

only for examined persons assigned to and meeting the criteria for the fasting subsample.

Oral glucose tolerance test (OGTT) subsample ....... 	 Use when analyzing only OGTT glucose levels or OGTT glucose levels with other data such as insulin

or fasting levels for examined persons assigned to and meeting the criteria for the OGTT.

Volatile organic compound subsample ............ 	 Use when analyzing data from volatile organic compound one-half laboratory subsample for

examined persons assigned to and meeting the criteria for this subsample.

Laboratory subsample A ..................... 	 Use when analyzing data from the one-third laboratory environmental subsample A for examined

persons assigned to and meeting the criteria for this subsample.

Laboratory subsample B ..................... 	 Use when analyzing data from the one-third laboratory environmental subsample B for examined

persons assigned to and meeting the criteria for this subsample.

Laboratory subsample C ..................... 	 Use when analyzing data from the one-third laboratory environmental subsample C for examined

persons assigned to and meeting the criteria for this subsample.

NOTE: LDL is low-density lipoprotein.
Series 2, No. 159 [ Page 15

Table III. Final sampling rates and base weights: National Health and Nutrition Examination Survey, 2007–2010

2007–2008 2009–20101

Numerator of Numerator of 2 3 3 Race and Hispanic origin-income-sex-age sampling domain sampling rate Base weight sampling rate Base weight

Non-Hispanic black

Male and female:

Under 1 .................................. 0.74 2,197.71 0.74 2,197.71

1–2 ..................................... 0.67 2,423.66 0.67 2,423.66

3–5 ..................................... 0.50 3,265.82 0.50 3,265.82

Male:
 

6–11..................................... 0.52 3,128.20 0.52 3,128.20
 

12–19.................................... 0.39 4,173.43 0.39 4,173.43
 

20–39.................................... 0.27 5,888.48 0.27 5,888.48
 

40–49.................................... 0.30 5,449.23 0.30 5,449.23
 

50–59.................................... 0.36 4,547.44 0.36 4,547.44
 

60 and over ................................ 0.91 1,786.37 0.91 1,786.37
 

Female:
 

6–11..................................... 0.54 3,007.61 0.54 3,007.61
 

12–19.................................... 0.36 4,550.57 0.36 4,550.57
 

20–39.................................... 0.22 7,215.89 0.22 7,215.89
 

40–49.................................... 0.24 6,860.13 0.24 6,860.13
 

50–59.................................... 0.28 5,729.55 0.28 5,729.55
 

60 and over ................................ 0.61 2,641.55 0.61 2,641.55
 

Hispanic

Male and female:

Under 1 .................................. 1.00 1,617.80 1.00 1,617.80

1–2 ..................................... 0.61 2,667.76 0.61 2,667.76

3–5 ..................................... 0.41 3,936.02 0.41 3,936.02

Male:
 

6–11..................................... 0.46 3,521.77 0.46 3,521.77
 

12–19.................................... 0.35 4,631.85 0.35 4,631.85
 

20–39.................................... 0.22 7,425.66 0.22 7,425.66
 

40–49.................................... 0.29 5,501.76 0.29 5,501.76
 

50–59.................................... 0.48 3,404.92 0.48 3,404.92
 

60 and over ................................ 1.00 1,617.80 1.00 1,617.80
 

Female:
 

6–11..................................... 0.47 3,457.27 0.47 3,457.27
 

12–19.................................... 0.36 4,513.21 0.36 4,513.21
 

20–39.................................... 0.24 6,845.99 0.24 6,845.99
 

40–49.................................... 0.29 5,515.27 0.29 5,515.27
 

50–59.................................... 0.44 3,702.10 0.44 3,702.10
 

60 and over ................................ 0.95 1,706.07 0.95 1,706.07
 

Non-Hispanic white or other, low-income

Male and female:

Under 1 .................................. 1.00 1,617.80 1.00 1,617.80

1–2 ..................................... 0.65 2,473.22 0.68 2,384.89

3–5 ..................................... 0.46 3,534.90 0.49 3,291.11

Male:
 

6–11..................................... 0.28 5,850.60 0.34 4,786.85
 

12–19.................................... 0.19 8,298.50 0.29 5,601.49
 

20–29.................................... 0.19 8,564.47 0.27 6,034.06
 

30–39.................................... 0.33 4,896.73 0.38 4,216.63
 

40–49.................................... 0.26 6,310.83 0.31 5,148.31
 

50–59.................................... 0.31 5,161.38 0.36 4,444.52
 

60–69.................................... 0.37 4,377.29 0.41 3,991.06
 

70–79.................................... 0.68 2,374.67 0.70 2,300.46
 

80 and over ................................ 0.73 2,228.42 0.73 2,228.42
 

See footnotes at end of table.
Page 16 [ Series 2, No. 159

Table III. Final sampling rates and base weights: National Health and Nutrition Examination Survey, 2007–2010—Con.

2007–2008 2009–20101

Race and Hispanic origin-income-sex-age sampling domain2

Numerator of

sampling rate3 Base weight

Numerator of

sampling rate3 Base weight

Non-Hispanic white or other, low-income—Con.

Female:

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

12–19....................................

20–29....................................

30–39....................................

40–49....................................

50–59....................................

60–69....................................

70–79....................................

80 and over ................................

Non-Hispanic white or other, non-low-income

Male and female:

Under 1 ..................................

1–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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

Male:

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

12–19....................................

20–29....................................

30–39....................................

40–49....................................

50–59....................................

60–69....................................

70–79....................................

80 and over ................................

Female:

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

12–19....................................

20–29....................................

30–39....................................

40–49....................................

50–59....................................

60–69....................................

70–79....................................

80 and over ................................

0.27

0.19

0.14

0.20

0.23

0.25

0.27

0.33

0.38

0.37

0.20

0.14

0.14

0.09

0.09

0.10

0.08

0.09

0.13

0.21

0.40

0.14

0.09

0.09

0.09

0.07

0.07

0.11

0.20

0.30

6,033.45

8,592.04

11,981.17

7,894.48

7,032.79

6,530.81

5,902.28

4,961.80

4,310.49

4,346.57

8,108.34

11,763.13

11,263.77

17,699.38

17,977.79

15,962.95

20,314.70

18,720.29

12,555.30

7,534.69

4,056.66

11,690.30

17,930.75

18,359.86

17,654.24

21,643.66

22,132.77

14,603.62

8,063.03

5,447.32

0.33

0.28

0.24

0.30

0.31

0.30

0.33

0.38

0.40

0.37

0.20

0.14

0.14

0.09

0.09

0.10

0.08

0.09

0.13

0.21

0.40

0.14

0.09

0.09

0.09

0.07

0.07

0.11

0.20

0.30

4,936.46

5,799.63

6,632.44

5,438.42

5,190.87

5,327.77

4,945.16

4,272.66

4,049.25

4,346.57

8,108.34

11,763.13

11,263.77

17,699.38

17,977.79

15,962.95

20,314.70

18,720.29

12,555.30

7,534.69

4,056.66

11,690.30

17,930.75

18,359.86

17,654.24

21,643.66

22,132.77

14,603.62

8,063.03

5,447.32

1

Sample includes an additional target of 150 examinations of low-income non-Hispanic white persons or others older than 1 year, altering their sampling rates and base weights. All other rates and
 

base weights were unchanged.
 

2

Age in years. 
 

3

Corresponds to a 150% sample; sampling rates may be calculated by dividing the numerator by 1,618.
 
nonresponse adjustment cells for weighting MEC examination samples: National Health and Nutrition Examination Survey, 2007–2010

,

Order and categories of variables cross-classified to form nonresponse adjustment cells

2007–2008 2009–2010

....

....

....

....

....

....

....

n . . .

....

....

....

....

. . . .

....

....

....

....

....

....

n . . .

....

....

....

....

....

....

....

....

....

....

....

. . . .

1.

2.3.

4.

. .

. .1.

. .

. .

2.

3.

4.

. .

1.

2.

3.

4.

1.

2.3.

4.5.. .

. .

1.

2.3.

4.

5.

. .

. .

. .

Male, female

Urban, suburban, rural

1–2, 3–4, 5–6, 7 or more

Northeast, Midwest, South, West

.

.

1–2, 3–4, 5–6, 7 or more

.

.

Yes, no

Urban, suburban, rural

Northeast, Midwest, South, West

.

Less than high school or not known, high school,

Urban, suburban, rural

Northeast, Midwest, South, West

Male, female

Urban, suburban, rural

Northeast, Midwest, South, WestNon-Hispanic black, Hispanic, other

Male, female

1–2, 3–4, 5–6, 7 or more

.

.

Excellent/very good, good/fair, poor/unknown

Urban, suburban, ruralMale, female

Less than high school or not known, high school,

Northeast, Midwest, South, West

.

.

.

more

more

than

than

high

high

school

school

. . .. . .2. 1–2, 3–6, 7 or more

. . .

1. Excellent/very good/good/not known, fair/poor

3. One sampled person under age 16; one aged 16 or over; more than one,

all under age 16; more than 1, all aged 16 or over; more than one, mixed

ages

. . .

2. Non-Hispanic black, Hispanic, other

3. Male, female

...

. . .

. . .

4. 1–8, 9 or more

1. Less than high school or not known, high school, more than high school

. . .

. . .

. . .

. . .

. . .5. Non-Hispanic black, Hispanic, other

4. Male, female

1. 1–2, 3–6, 7 or more

2. Fair/more than fair/not known, poor

3. Yes, no

. . .

3. Areas with population over 3 million, other

. . .

. . .

. . .

1. Yes, no

2. Hispanic, not Hispanic

4. 1, 2–3, 4 or more

Series 2, No. 159[ Page 17
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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