Series 2, Number 155 May 2012

The National Health and

Nutrition Examination Survey:

Sample Design, 1999–2006
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Suggested citation

Curtin LR, Mohadjer L, Dohrmann S, et al. The National Health and Nutrition

Examination Survey: Sample design, 1999–2006. National Center for Health

Statistics. Vital Health Stat 2(155). 2012.

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National health and nutrition examination survey : sample design, 1999–2006.

p. ; cm.— (Vital and health statistics. Series 2, Data evaluation and methods

research ; no. 155) (DHHS publication ; no. (PHS) 2012–1355)

NHANES

‘‘April 2012.’’

Includes bibliographical references.

ISBN 0–8406–0649–4

1. National Center for Health Statistics (U.S.) II. Title: NHANES. III. Series:

Vital and health statistics. Series 2, Data evaluation and methods research ; no.

155. IV. Series: DHHS publication ; no. (PHS) 2012–1355.

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

Surveys—United States—Statistics. 3. Nutrition Surveys—United States—

Statistics. W2 A N148vb no.155 2012]

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Series 2, Number 155

The National Health and Nutrition

Examination Survey: Sample

Design, 1999–2006

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

Hyattsville, Maryland

May 2012

DHHS Publication No. (PHS) 2012–1355
National Center for Health Statistics

Edward J. Sondik, Ph.D., Director

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

Division of Health and Nutrition Examination Surveys

Clifford L. Johnson, M.S.P.H., Director
Contents
 

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

Background ............................................................................................... 1 
 

Introduction ............................................................................................... 2 
 

Design Specifications ....................................................................................... 3 
 

Survey Objectives ........................................................................................ 3 
 

Domain and Precision Considerations ........................................................................ 3 
 

Operational Requirements.................................................................................. 4 
 

Sample Design ............................................................................................ 4 
 

NHANES 1999–2001 Sample Design ........................................................................ 5 
 

NHANES 2002–2006 Sample Design ........................................................................ 7 
 

Weighting the Sample Data ................................................................................. 19 
 

Weighting the Sample Data for 1999–2002 ................................................................... 20 
 

Weighting the Sample Data for 2003–2006 ................................................................... 21 
 

Variance Estimation ....................................................................................... 24 
 

Variance Estimation for NHANES 1999–2002 ................................................................ 25 
 

Variance Estimation for NHANES 2003–2006 ................................................................ 25 
 

References ............................................................................................... 26 
 

Appendix I. Glossary....................................................................................... 28 
 

Appendix II. Tables and Figure .............................................................................. 32 
 

Figure

Major strata formed for selection of the 2002–2007 PSUs .................................................. 39 
 

Text Tables

A.	 Selected sample design parameters for the National Health and Nutrition Examination Surveys ..................... 2 
 

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

Examination Survey, 1999–2006 ........................................................................ 4 
 

C.	 Definitions of the Mexican-American density strata for National Health and Nutrition Examination Survey,
 

2002–2007.......................................................................................... 9 
 

D.	 Expected design effects for the analysis of annual samples due to oversampling in the high-density strata, for
 

Mexican-American sex-age sampling domains in which this oversampling was used .............................. 9 
 

E.	 Values of Tik and Aik used in the calculation of the primary sampling unit measure of size ....................... 11 
 

F.	 Descriptions of the major strata formed for selection of the 2002–2007 primary sampling units.................... 13 
 

G.	 Values of Aik* used in calculating measures of sizes, by race and Hispanic origin and density stratum for segment
 

selection in National Health and Nutrition Examination Survey, 2002–2006 .................................... 14 
 

H.	 Release group distribution for National Health and Nutrition Examination Survey, 2002–2006 ..................... 18 
 

J.	 Sampling rates used to sample pregnant women in National Health and Nutrition Examination Survey, 2002–2006 .... 19 
 

Appendix Tables

I.	 Derivation of expected screening requirements for National Health and Nutrition Examination Survey, 2002–2007 .... 32 
 

II.	 Final sampling rates and base weights for National Health and Nutrition Examination Survey, 2002–2006 ........... 35 
 

iii
III. Description of interview, examination, and laboratory subsamples in National Health and Nutrition Examination

Survey, 2003–2006 .................................................................................. 37

IV. Variables used in the formation of nonresponse adjustment cells for weighting all samples from 1999 to 2006........ 38

iv
Background

Data collection for the National

Health and Nutrition Examination

Survey (NHANES) comprises three

levels: a household screener, an

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 the preset selection

probabilities for the desired

demographic subdomains. After

selection as an eligible sample person,

the interview collects person-level

demographic, health, and nutrition

information as well as information about

the household. The examination

includes physical measurements, tests

such as eye and dental examinations,

and the collection of blood and urine

specimens for laboratory testing.

Objectives

This report will first describe the

broad design specifications for the

1999–2006 survey including survey

objectives, domain and precision

specifications, operational requirements,

sample design, and estimations

procedures. Details of the sample

design are divided into two sections.

The first section (NHANES 1999–2001

Sample Design) broadly describes the

sample design and various design

changes during the first three years of

the continuous NHANES (1999–2001).

The second section (NHANES

2002–2006 Sample Design) describes

the final sample design developed and

applied for 2002–2006. Weighting and

variance estimation procedures are

presented in the same manner;

however, to correspond to the public

data release cycles, the weighting and

variance sections are separated into

those used for 1999–2002, and those

used for 2003–2006. Much of this

report is based on survey operations

documents and sample design reports

prepared by Westat. Documentation of

the survey content, procedures, and

methods to assess nonsampling errors

are reported elsewhere.

Keywords: sampling • weighting •

variance estimation

The National Health and

Nutrition Examination Survey:

Sample Design, 1999–2006

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

Leyla K. Mohadjer, Ph.D., Westat; Sylvia M. Dohrmann, M.S., Westat;

Jill M. Montaquila, Ph.D., Westat; Deanna Kruszon-Moran, M.S.,

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

Center for Health Statistics; Margaret D. Carroll, M.A., M.S.P.H.,

National Center for Health Statistics; Rosemarie Hirsch, M.D.,

M.P.H., National Center for Health Statistics; Susan Schober, Ph.D.,

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

National Center for Health Statistics

Background

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 National

Center for Health Statistics (NCHS). A

unique feature of this survey is the

collection of physical examination data

for a nationally representative sample of

the resident civilian noninstitutionalized

U.S. population. The survey consists of

questionnaires administered in the home

followed by a standardized physical

examination in specially equipped

mobile examination centers (MECs).

NHANES I, the first cycle of

NHANES, was conducted from 1971

through 1975. The second cycle

(NHANES II) was conducted from 1976

through 1980. Although NHANES I and

NHANES II each examined more than

20,000 individuals, the representation of

the minority population in the sample

was not large enough to permit adequate

estimates of the health status of

Mexican-American, Cuban-American, or

Puerto Rican persons, or even the three

groups combined. The objective of the

Hispanic Health and Nutrition

Examination Survey (HHANES),

conducted from 1982 through 1984, was

to produce estimates of health and

nutritional status for the three major

Hispanic subgroups that were

comparable with the estimates available

for the general population. NHANES III

was the seventh in a series of surveys

conducted by NCHS since 1960 using

health examination procedures, and the

third to include a nutrition component.

It was fielded from 1988 through 1994.

Details on the target populations, sample

designs, and data collection procedures

for these previous health examination

surveys have been described in previous

reports (6–13).

NHANES was again fielded in 1999,

and in the tradition of the past national

surveys it continues to be a keystone in

providing critical and unique

information on the health and nutritional

status of the U.S. population. This

information is essential for estimating

the prevalence of various diagnosed and

undiagnosed diseases and conditions,

examining the differences in disease

prevalence, and developing health

policy. NHANES provides information

to more than a dozen individual

agencies and reflects coordination within

the U.S. Department of Health and

Human Services (HHS) in the collection

of direct physical measurement data.

The differences in the sample sizes

and sample designs for the five cycles

of NHANES and for HHANES should

be considered when comparisons are

Page 1
Page 2 [ Series 2, No. 155

made across various NHANES surveys.

For example, note that until NHANES

III, the HANES surveys did not include

persons aged 75 and over and that

NHANES I and NHANES II did not

oversample Hispanic persons. The

sample design parameters for the five

NHANES surveys are compared in

Table A.

Introduction

Data collection for NHANES

comprises three levels: a household

screener, an 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 the preset selection

probabilities for the desired

demographic subdomains. After

selection as an eligible sample person,

the interview collects person-level

demographic, health, and nutrition

information as well as information about

the household. The examination includes

physical measurements, tests such as eye

and dental examinations, and the

collection of blood and urine specimens

for laboratory testing.

Beginning in 1999, the design and

operations for NHANES have taken a

new direction. The major difference

from previous cycles is that the current

NHANES is being implemented as a

continuous, annual survey of the

noninstitutionalized civilian resident

population of the United States.

NHANES 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. Each

calendar year of the NHANES

comprises a nationally representative

sample of this portion of the U.S.

population. This design facilitates

potential linkage to other health and

nutrition surveys that provide yearly

estimates and allows aggregate-level

national estimates from NHANES each

year or from combinations of years.

Because NHANES can visit only a

small number of locations each year,

variance estimates for single-year data

are relatively unstable. In addition,

releasing only 1 year of data increases

the possibility of disclosure of a sample

person’s identity. These two factors, plus

the need to provide timely national

estimates, resulted in the decision to

publicly release data in 2-year cycles.

Annual estimates may only be made

through limited access to the data in the

NCHS Research Data Center (RDC).

Although the annual samples are

nationally representative, annual

estimates should be produced only for

the nation as a whole, for the

recommended race and Hispanic origin

subdomain, or for very broad sex-age

subdomains within race and Hispanic

origin. It is also recommended that in

order to improve the statistical reliability

and stability of estimates with larger

variances, analysts use combinations of

2-year cycles. Combining data from

2-year cycles is particularly appropriate

for rare events, for preparing estimates

for very detailed demographic

Table A. Selected sample design parameters for the National Health and Nutrition Examination Surveys

Hispanic NHANES

Characteristic NHANES I NHANES II HANES NHANES III 1999–2006

Age of noninstitutionalized civilian

target population ............. 1–74 years 6 months–74 years 6 months–74 years 2 months and over 0 months and over

Geographic areas ............. United States United States Southwest for United States United States

(excluding (including Mexican Americans; (including (including Alaska

Alaska and Alaska and NY, NJ, CT for Alaska and and Hawaii)

Hawaii) Hawaii) Puerto Rican Hawaii)

persons;

Dade County, FL,

for Cuban persons

Average number of sample persons

per eligible household.......... 1 1 2–3 2–3 2

Number of survey locations ....... 100 64 17 in Southwest; 89 117

9 in NY, NJ, CT;

4 in Dade County, FL

Domains for oversampling ........ Low income: Low income: Dade County, FL: 52 subdomains 76 subdomains were

children aged children aged 6 months–19 years were predesignated predesignated

1–5 years; 6 months–5 years; and 45–74 years; consisting of consisting of sexwomen aged persons aged Southwest and sex-age groups age groups for

20–44 years; 60–74 years NY, NJ, and CT: for black, Mexican black and Mexicanpersons aged persons aged American, American persons

65 years 6 months– and all others; and income-sex-age

and over 19 years and target groups for all

45–74 years sample sizes other persons;

were established target sample sizes

for the subdomains were established

for the subdomains1

Total sample size ............. 28,043 27,801 15,931 39,695 50,939

Examined sample size .......... 20,749 20,322 11,672 30,818 39,352

Years covered ............... 1971–1974 1976–1980 1982–1984 1988–1994 1999–2006

1

In 1999, only 53 subdomains were predesignated for black, Mexican American, and others (no income used).
Series 2, No. 155 [ Page 3

subdomains, and for measures that may

have considerable geographic variation.

The study locations selected for

NHANES 1999–2004 were linked to the

National Health Interview Survey

(NHIS) at the county level (1). It was

later determined that only the locations

selected for 1999–2001 would be

fielded, and that a second sample would

be selected for NHANES 2002–2007

from a national frame utilizing the 2000

decennial census data. At that time, the

planned data release cycle was to

combine 3 years of data as 1999–2001,

2002–2004, and 2005–2007. During the

fielding of the 2002–2006 sample, the

NHANES data release cycles were set

as 2-year cycles due to the need for

more timely data release. As a result,

the 2-year data cycle 2001–2002 uses

the 2001 locations based on the linked

NHIS design and the 2002 locations

based on the independent design for

2002–2007. The locations designated for

2007 were discarded to permit 2-year

data releases through 2006, and a new

sample for 2007–2010 was selected,

again from a national frame, to produce

2-year estimates for 2007–2008 and

2009–2010. The 2007–2010 sample

design will be described in a separate

report.

This report will first describe the

broad design specifications for the

1999–2006 survey including survey

objectives, domain and precision

specifications, operational requirements,

sample design, and estimations

procedures. Details of the sample design

will be divided into two sections. The

first section (NHANES 1999–2001

Sample Design) will broadly describe

the sample design and various design

changes during the first three years of

the continuous NHANES (1999–2001).

The second section (NHANES

2002–2006 Sample Design) will

describe the final sample design

developed and applied for 2002–2006.

Weighting and variance estimation

procedures will be presented in the same

manner; however, to correspond to the

public data release cycles, the weighting

and variance sections are separated into

those used for 1999–2002, and those

used for 2003–2006. Much of this report

is based on survey operations documents

and sample design reports prepared by

Westat. Documentation of the survey

content, procedures, and methods to

assess nonsampling errors are reported

elsewhere (see http://www.cdc.gov/nchs/

nhanes.htm for more information).

Design Specifications

Survey Objectives

A necessary first step in designing a

survey is to define the survey’s primary

analytical objectives. As in the previous

NHANES, a primary purpose of

NHANES 1999–2006 is to produce a

broad range of descriptive health and

nutrition statistics for sex, race and

Hispanic origin, and age subdomains of

the population. These data can then be

used to measure and monitor the health

and nutritional status of the

noninstitutionalized population. Because

NHANES was designed to produce

cross-sectional data and because

respondents may be recontacted over

time for future interviews or

examinations, a set of cross-sectional

and longitudinal objectives was

developed. The analytic goals of

NHANES are to:

+	 estimate the number and percentage

of persons in the U.S. population

and in designated subgroups with

selected diseases and risk factors;

+	 monitor trends in the prevalence,

awareness, treatment, and control of

selected diseases;

+	 monitor trends in risk behaviors and

environmental exposures;

+	 analyze risk factors for selected

diseases;

+	 study the relationship between diet,

nutrition, and health;

+	 explore emerging public health

issues and new technologies;

+	 establish a national probability

sample of genetic material for future

genetic research;

+	 provide baseline health

characteristics for longitudinal

analysis when data are linked to

mortality outcomes through the

National Death Index; and

+	 provide health information that can

be linked, subject to confidentiality

concerns, to contextual and other

data sets such as the Centers for

Medicare & Medicaid Services.

Domain and Precision

Considerations

The set of domains for which

specified reliability was desired in

NHANES 1999–2006 consisted of sex

and age groups for black persons,

Mexican-American persons, and the

remainder of the U.S. population.

Table B provides the set of sampling

domains in NHANES 1999–2006. To

increase the precision of estimates for

certain subdomains, oversampling was

carried out for adolescents (aged 12–19),

older Americans (aged 60 and over),

Mexican-American persons, and the

black population. In addition, in

NHANES 2000–2006 pregnant women

and all others at or below 130% of the

poverty level were also oversampled.

Even though data are released in 2-year

cycles, the accumulation of at least 4

years of data is required to obtain an

acceptable level of reliability for the

domains given in Table B. Thus, to

create estimates for smaller 2-year

samples (or any annual estimates),

collapsing of some of the above

domains is necessary to produce

adequate sample sizes for analysis

purposes.

Two main requirements were

established for NHANES III when

considering the utility of a sample for

analysis purposes. These two conditions

were considered in the sample design of

NHANES 1999–2006 as well. They

included the following:

1. An estimated prevalence statistic

on the order of 10% in a sex-age

domain should have a relative

standard error of 30% or less;

and

2. Estimated (absolute) differences

between domains of at least 10%

should be detectable with a Type

I error rate (α) of ≤ 0.05 and a

Type II error rate (β) of ≤ 0.10.

To satisfy the first condition a

sample size of about 150 examined

persons was necessary. This assumed a

design effect of 1.5 resulting from the

variability in sampling rates across
Page 4 [ Series 2, No. 155

density strata necessary to accommodate

oversampling. The sample necessary to

satisfy the second condition was about

420 examined persons; therefore, the

second condition was the more stringent

one.

These were the general ideas used

in the sample design that provide

guidance on potential analytic

considerations. For example, for a very

small demographic group such general

considerations may indicate the need to

combine 4 years of NHANES data for a

specific variable and analysis; however,

the sample design effects for each

measured NHANES variable and for

specific demographic subdomains can be

quite different from the assumed general

design effect of 1.5. The issues of

precision and statistical power should be

addressed for each specific analysis.

Operational Requirements

A unique feature of NHANES is the

complete physical examination for each

respondent in the sample. To standardize

the administration, the examinations are

carried out in MECs. Three separate

MECs are in service at any given time.

Following a carefully designed schedule,

two MECS are in operation at study

locations, while the third is either

traveling or being prepared for operation

at a new location.

In order to maintain a cost-efficient

workload within each location while

considering the time and the cost

involved in moving a MEC between

survey locations, the maximum number

of study locations NHANES may visit

in each annual sample is 15. Taking this

into account, the number of sample

participants selected in each study

location should be between 300 and

600, with an average of approximately

450, to yield approximately 333

examined persons in each of the 15

locations visited that year.

Previous experience with NHANES

I–III and HHANES indicated that

response rates increased when a larger

sample of persons was selected within

households. One factor thought to be

responsible for the increased response

rates in multiple-sample participant

households was that each person was

given remuneration for his or her time

and participation. Thus, another

important factor considered in the final

design was to maximize the response

rates and reduce screening costs by

selecting as large an average number of

sample participants per household as

possible. Another factor affecting

response rates was the amount of travel

necessary for respondents to visit a

MEC. The primary sampling units

(PSUs) for NHANES are typically

defined as individual counties—rather

than combinations of counties as in

other area surveys—to increase the

likelihood of achieving high response

rates.

Sample Design

In 1996, a decision was made by

NCHS to field NHANES as a

continuous survey and to fully automate

data collection in the field. The plan

was to pilot test the content and

operations for new survey in 1997–

1998, and to field the survey starting in

1999 as a 6-year survey (with public

data release and published national

estimates based on two 3-year cycles)

similar to NHANES III.

Table B. Analytical

Survey, 1999–2006

subdomains classified by race and Hispanic origin, income, sex, and age: National Health and Nutrition Examination

Black Mexican American

Others1

Non-low income Low income (2000–2006 only)

Sex and age

Males and females under 1 year ....

Males and females 1–2 years......

Males and females 3–5 years......

Males 6–11 years .............

Males 12–15 years ............

Males 16–19 years ............

Males 20–39 years ............

. . .

Males 40–59 years ............

. . .

Males 60 years and over ........

. . .

. . .

Females 6–11 years ...........

Females 12–15 years...........

Females 16–19 years...........

Females 20–39 years...........

. . .

Females 40–59 years...........

. . .

Females 60 years and over .......

. . .

. . .

Males and females under 1 year

Males and females 1–2 years

Males and females 3–5 years

Males 6–11 years

Males 12–15 years

Males 16–19 years

Males 20–39 years

. . .

Males 40–59 years

. . .

Males 60 years and over

. . .

. . .

Females 6–11 years

Females 12–15 years

Females 16–19 years

Females 20–39 years

. . .

Females 40–59 years

. . .

Females 60 years and over

. . .

. . .

Males and females under 1 year

Males and females 1–2 years

Males and females 3–5 years

Males 6–11 years

Males 12–15 years

Males 16–19 years

Males 20–29 years

Males 30–39 years

Males 40–49 years

Males 50–59 years

Males 60–69 years

Males 70–79 years

Males 80 years and over

Females 6–11 years

Females 12–15 years

Females 16–19 years

Females 20–29 years

Females 30–39 years

Females 40–49 years

Females 50–59 years

Females 60–69 years

Females 70–79 years

Females 80 years and over

Males and females under 1 year

Males and females 1–2 years

Males and females 3–5 years

Males 6–11 years

Males 12–15 years

Males 16–19 years

Males 20–29 years

Males 30–39 years

Males 40–49 years

Males 50–59 years

Males 60–69 years

Males 70–79 years

Males 80 years and over

Females 6–11 years

Females 12–15 years

Females 16–19 years

Females 20–29 years

Females 30–39 years

Females 40–49 years

Females 50–59 years

Females 60–69 years

Females 70–79 years

Females 80 years and over

. . . Category not applicable.
 

1

NHANES 1999 included only 23 subdomains for others. Separate subdomains for low-income others were not included until 2000.
 
Series 2, No. 155 [ Page 5

In response to a recommendation

from the National Academy of Sciences,

HHS submitted a plan to the Office of

Management and Budget proposing to

integrate and coordinate data collection

systems within HHS (1,5). Due to a

number of sample design factors,

linkage of NHIS and NHANES at the

sample participant level was determined

to be not feasible. The initial sample

design decision was that the NHANES

1999–2004 cycle of the continuous

survey would be linked to NHIS at the

county level. Instead of an independent

sampling frame based on all counties in

the United States, only the counties

selected in the 1995–2004 NHIS sample

design (2) were considered as the

sample frame for NHANES.

In early 2001, after 2 years of field

work, it was determined that the NHIS

PSUs were not a cost-efficient design

for the NHANES sample due to the

need to oversample race and Hispanic

origin domains. An independent national

design was reinstituted for future years

of the NHANES continuous survey. This

change required many modifications of

the original survey plans, including the

selection of PSUs and the creation of

sample weights and variance

calculations. As a result only the NHIS

PSUs selected for 1999–2001 were

fielded, and an independent sample was

selected for NHANES 2002–2007 from

a national frame utilizing the 2000

decennial census data.

Later in 2001, once data collection

and cleanup were finalized for

1999–2000, a decision was made to

release all NHANES survey data in

2-year cycles to be timelier, while still

protecting confidentiality and

maintaining reasonable sample sizes for

analytic purposes. Public-use data files

were eventually released for 1999–2000

and for 2001–2002. The 2001–2002 data

release combined the NHIS linked

design for 2001 with the independent

design for 2002. Based on the

independent design, data release cycles

for 2003–2004 and 2005–2006 were

planned and implemented. Rather than

combine the 2007 PSU with another

independent selection for 2008, the set

of PSUs designated for 2007 were

discarded, and a new sample for

2007–2010 was selected, again from a

national frame. This allowed the

2007–2008 data release to be based on

the same sample design. In summary,

the sample design for the ‘‘continuous’’

NHANES has evolved into the current

approach of allowing content changes

and releasing data every 2 years with

the survey sample design staying the

same for 4-year intervals (two 2-year

cycles, with independent sample designs

for the intervals 2003–2006, 2007–2010,

and 2011–2014).

Given this history, details of the

sample design for 1999–2006 will be

presented in two sections. The first

section (NHANES 1999–2001 Sample

Design) will broadly describe the

sample design and various design

changes during the first 3 years of the

continuous NHANES (1999–2001). The

second section (NHANES 2002–2006

Sample Design) will describe the final

sample design developed and

implemented during 2002–2006. The

impact of the sample design changes

over the 8-year period 1999–2006 for

estimation in 2-year data release cycles

(sample weighting and variance

estimation) will be described in the

‘‘Weighting the Sample Data’’ and the

‘‘Variance Estimation’’ sections of this

report.

NHANES 1999–2001

Sample Design

Because the PSU selection and

some aspects of the within-PSU

selection for NHANES 1999–2001

differ greatly from the later years, those

procedures are briefly reviewed in the

sections below. The detailed selection

procedures used for 2002–2006,

including the PSU selection procedures

for the NHANES 2002–2006 sample,

are described in the ‘‘NHANES

2002–2006 Sample Design’’ section.

PSU sample selection for

1999–2001

The NHANES 1999–2001 PSUs

were selected from a frame that

included all counties in two panels of

NHIS PSUs. Initially, a 6-year sample

of PSUs (for NHANES 1999–2004) was

selected from this frame; however, these

PSUs were used only for NHANES

1999–2001. The sample was redesigned

and a new sample of PSUs was selected

from a national frame for NHANES

2002–2007. NHIS is an ongoing survey

of the noninstitutionalized civilian

population that collects data on general

health-related issues such as health

status, health care utilization and access,

chronic diseases, and acute conditions. It

also collects demographic data, as well

as data on health insurance coverage,

income, and program participation. The

sample design for NHIS has undergone

periodic revisions since its inception in

1957; however, the basic design is a

multistage stratified area sample, with

metropolitan statistical areas (MSAs) or

groups of counties as the PSUs. The

NHIS PSUs are selected approximately

every 10 years by the U.S. Census

Bureau based upon the most current

decennial census information and in

conjunction with other household

surveys conducted by the Census

Bureau.

To facilitate linkage to other health

surveys, the PSUs selected in the

1995–2004 NHIS design were

distributed into four panels, each panel

considered to be a nationally

representative sample. Two NHIS panels

were allocated to linkage for the

Medical Expenditure Panel Survey,

conducted by the Agency for Health

Care Research and Quality. The

remaining two panels of NHIS PSUs

were made available for linkage to

NHANES.

The sampling domains created for

NHANES 1999–2006 are shown in

Table B. NHANES 1999 sampling

domains are the same as the NHANES

2000–2001 domains, except that there

are no breakouts by low-income status

for all others. The NHANES sample

was designed to yield a self-weighting

sample for each sampling domain while

producing an efficient workload for each

PSU. PSUs were selected with

probabilities proportionate to a measure

of size (MOS). The selection probability

of a PSU determines the maximum rate

at which persons residing in that

particular PSU can be selected. For
Page 6 [ Series 2, No. 155

NHANES 1999–2001, the PSU MOS

was defined to be

and

M ′ 

h = PSU MOS for 1999–2001

accounting for the NHIS

linkage;

Mh = basic PSU MOS;

Σ = summation;

Ak = coefficient of the race and

Hispanic origin group k;

h = the NHANES PSU;

i = the Mexican-American density

stratum;

k = the race and Hispanic origin

subdomain;

l = the sex-age subdomain;

πh = the selection probability for the

NHIS PSU containing NHANES

PSU h;

rikl = the sampling rate of persons in

density stratum i for the (k,l)-th

race and Hispanic origin-sex-age

subdomain;

′ Chk = the population estimate for the

year 1996 for race-ethnicity

subdomain k in PSU h

(Estimates for MexicanAmerican persons were not

available; therefore, estimates

from the 1990 census of the

proportion of Hispanic persons

who were Mexican were applied

to estimates of the Hispanic

population to obtain population

estimates for Mexican-American

persons.);

Tik = the proportion of the U.S.

population in the i-th density

stratum for the k-th race and

Hispanic origin subdomain;

C*

..kl = the most recent projection for

the year 2000 total population

count for race and Hispanic

M ′ h l M = = A C′ 

h , π k

h ∑k π hk h

where

C*

Ak = ∑i,lT r ..kl

ik ikl C*

..k.

origin-sex-age subdomain (k,l)

[Projections for MexicanAmerican persons were not

available; therefore, estimates

from the March 1994 Current

Population Survey (CPS) of the

proportion of Hispanic persons

who were Mexican were applied

to projections of the Hispanic

population to obtain population

projections for MexicanAmerican persons.]; and,

C* = the most recent projection for ..k.

the year 2000 total population

count for race and Hispanic

origin subdomain k.

When the MOS is defined in terms of

the sampling rates as well as population

counts, PSUs with larger populations for

oversampled subdomains have a greater

probability of being selected. This

reduces the amount of screening

required compared with a design in

which the MOS is a function of

population counts alone.

The probability of selection of an

NHANES PSU, conditional on the

NHIS PSU having been selected, is

Mh

′ Mh / πh Ph = k1 = k ′ 1 ∑hMh ∑h(Mh / πh)

where k1 is the number of PSUs

selected.

Within-PSU sample selection

for 1999–2001

Within each PSU, a sample of

segments was selected. In NHANES

1999–2001, two types of segments were

used: area segments, which were

typically census blocks or groups of

blocks; and new construction segments,

which were sets of building permits for

new residential construction. Two

approaches to segment selection were

used for NHANES 1999–2001. In all

NHANES 1999 study locations and the

first four study locations of NHANES

2000, dwelling units (DUs) constructed

after 1990 were sampled by forming

new construction segments containing

data gathered from census permit files

and field visits to the permit offices. As

a result of concerns about the continued

use of permit sampling, a double

sampling (or two-phase) approach was

developed to update the MOS of area

segments prior to segment selection.

This two-phase approach to segment

selection was used in the last 11 PSUs

of NHANES 2000, and in all the PSUs

in 2001.

To reduce the amount of screening

required, NHANES segments were

stratified according to the proportion of

the population that was Mexican

American. These strata are referred to as

‘‘density strata.’’ Higher sampling rates

were used to sample Mexican-American

persons within the density strata having

higher proportions of this population.

Segments were also selected with

probability proportionate to an MOS.

The segment MOS has the same form as

the PSU MOS. The MOS is based on

the sampling rates as well as the

population counts in order to give

segments with larger populations for

oversampled subdomains a greater

probability of being selected. Because

segments consist of census blocks or

combinations of blocks, the segment

MOS is calculated as a sum of block

sizes. Let Mhib denote the MOS of block

b, in density stratum i, in PSU h, where

C′

hk Mhib = ∑AikChibk , k Ch.k.

= ∑ C*

..kl A , ik rikl C* and
 l ..k.
 

rikl = the sampling rate of persons in

the (k,l)-th race and Hispanic

origin-sex-age-income (income

was not a subdomain in 1999)

subdomain in density stratum i;

Chibk = the 1990 decennial census

population count for race and

Hispanic origin k in block b in

density stratum i in PSU h;

Ch.k. = the 1990 decennial census

population count for race and

Hispanic origin k in PSU h; and

C′ = the population estimate for the hk

year 1996 for race and Hispanic

origin k in PSU h.

The MOS for the segments j were the

sums of the MOS of the block(s)

comprising each segment. These MOS

are denoted by Mhij.
Series 2, No. 155 [ Page 7

The conditional probability of

selection of segment j in stratum i

within PSU h is then

Mhij Phij = kh , ∑jeh Mhij

where kh is the number of segments

selected in PSU h (typically 24, but

there is a small amount of variation in

the number of segments in each PSU).

With two-phase segment selection,

the basic segment MOS, Mhij, was

calculated as described above. However,

the final MOS used in the first phase of

segment selection was obtained by

inflating Mhij to account for expected

growth in the segment due to new

construction. Ideally, Mhij would be

adjusted to account for expected

declines as well as expected growth, but

intercensal data on population declines

are not available at this level.

That is, the segment MOS for the

first-phase selection is

M*

hij(1) = ghj,EMhij ,

where ghj,E is the expected growth rate

(computed as the ratio of the 1990

population plus the estimated population

in new construction to the 1990

population) for the census-designated

place containing segment j.

Based on the DU counts obtained

for the first-phase sample, an actual

growth rate, ghj,A, was estimated for the

segment by computing the ratio of the

actual number of DUs counted to the

expected number of DUs in the segment

(based on 1990 census data). The MOS

used for the second-phase segment

selection was

ghj,A M* = . hij(2) gh,E

Within NHANES segments, the

sampling procedures for 1999–2001

were similar to those used in

2002–2006, as described in the

‘‘NHANES 2002–2006 Sample Design’’

section of this document. DUs were

selected with equal probability, at a rate

equal to the maximum within-segment

sampling rate required to attain the

subdomain sampling rates. (Note that

the sampling rates used in NHANES

1999–2001 were different than those

described in the ‘‘NHANES 2002–2006

Sample Design’’ section.) Persons were

selected within DUs using the ratio of

the subdomain sampling rate to the

maximum subdomain sampling rate. The

overall selection probabilities yield

approximately equal sample sizes for

each PSU.

NHANES 2002–2006

Sample Design

The survey locations in the

NHANES 2002–2006 sample were

selected as part of a larger sample

intended for 2002–2007 based on a

design much different from that used for

the locations visited in 1999–2001. As

mentioned earlier, once it was

determined that the data would be

released every 2 years (that is, 2003 and

2004 together, followed by 2005 and

2006), the locations intended to be

fielded in 2007 were never visited.

However, when referencing sample

design issues such as the determination

of sampling rates (see ‘‘Sampling rates

for 2002–2006’’) and PSU selection (see

‘‘Stratification and selection of PSUs for

2002–2006’’), the intended 2002–2007

6-year sample is often referenced.

Summary for 2002–2006

The NHANES sample represents

the total noninstitutionalized civilian

population residing in the 50 states and

District of Columbia. As with previous

NHANES, a four-stage sample design

was used in NHANES 2002–2006. The

first stage of the sample design

consisted of selecting the PSUs from a

frame of all U.S. counties, using the

2000 U.S. Census Bureau data. 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).

For most of the sample, the second

sampling stage consisted of area

segments comprising census blocks or

combinations of blocks. A regular

sample of area segments was selected

for the NHANES 2002–2006 samples.

However, because these samples were

based on the 2000 census data, the MOS

used for sampling was updated if

necessary for PSUs experiencing large

growth since 2000.

Within PSUs, an average of 24

segments was sampled. The sample was

designed to produce approximately

equal sample sizes per PSU; most PSUs

have exactly 24 segments. PSUs

selected with certainty (with a

probability of one) may have more or

fewer than 24 segments to ensure

appropriate representation in the sample.

Additionally, some large certainty PSUs

were treated as multiple-study locations

with varying numbers of segments in

each location to, again, 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 sample

participants per segment.

The segment sample can be viewed

as having two components. In most of

the United States segments were

selected with PPS at a uniform rate. The

sampling rate used in this component

was sufficient to satisfy the sample size

requirements for all the sex-age domains

except some of those for MexicanAmerican persons. The second

component was restricted to 2000

census block groups with moderate or

large proportions of Mexican-American

persons according to the 2000 decennial

census. A higher sampling rate was

designated in such areas. The block

groups were stratified according to the

proportion of the population in the

minority subdomains.

The third stage of sample selection

consisted of DUs, including

noninstitutional group quarters such as

dormitories. 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 were

designated for screening in order to

identify potential sample participants.

The subsampling rates were set up to

produce a national and approximately

equal probability sample of households

in most of the United States, with higher

rates for the geographic strata with high

minority concentrations. The screening

rate in each stratum was designed to

produce the desired number of sample
Page 8 [ Series 2, No. 155

participants for the most difficult race

and Hispanic origin-sex-age-income

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

highest rate) in the minority stratum.

Persons within the occupied DUs,

or households, were the fourth stage of

sample selection. All eligible members

within a household were listed, and a

subsample of individuals was selected

based on sex, age, race and Hispanic

origin, income, and pregnancy status.

The subsampling rates and designation

of potential sample participants within

screened households were arranged to

provide approximately self-weighting

samples for each subdomain within

minority density strata and

simultaneously to maximize the average

number of sample participants per

sample household.

A summary of the expected annual

sample sizes at the design stage is

shown below:

Number of study locations 15

Number of segments 360

Number of DUs to be screened

annually 12,500

Number of households to be

screened annually 11,000

Number of sampled persons each

year 6,525

Number of examined persons 5,000

The remainder of this section will

focus on the calculation of the sampling

rates (see ‘‘Sample rates for 2002–

2006’’), the selection of PSUs (see

‘‘Stratification and selection of PSUs for

2002–2006’’), and within-PSU selection

procedures (see ‘‘Selection of

segments’’). Again, since the sampling

rates and PSU sample were created

based on a 6-year sample design, much

of those sections will reference the

2002–2007 sample from which the

2002–2006 study locations were

selected.

Sampling rates for 2002–2006

The sampling rates used for the

2002–2006 sample were developed for a

sample intended to span the years

2002–2007. The rates required for

sampling race and Hispanic origin-sexage-income domains are the driving

force in all stages of sampling for

NHANES. The ‘‘Calculation of

sampling rates and screening to achieve

a self-weighting sample’’ section

describes the calculation of those

sampling rates and indicates the amount

of screening required to achieve a

self-weighting sample. The

‘‘Oversampling in Mexican-American

domains’’ section addresses the

increased efficiencies that can be gained

by oversampling Mexican-American

domains in high-Mexican areas. The

final overall sampling rates used for

PSU selection are given in the ‘‘Final

sampling rates’’ section. In the

‘‘Departures from a self-weighting

sample’’ section, the conditions under

which the sample may deviate from a

self-weighting design are presented.

Calculation of sampling rates and

screening to achieve a self-weighting

sample

NHANES is a multistage national

area probability survey with fixed

sample size targets for sampling

domains defined by race and Hispanic

origin, sex, age, and low-income status.

Thus, the first step in determining the

measures of size to be used for

sampling at each stage is to calculate

the sampling rate for each domain. The

sampling rate for a domain depends on

the target examination sample size, the

expected examination response rate, and

the estimated size of the population.

These sampling rates determine the

amount of screening that will be

required.

To calculate sampling rates,

expectations for response rates must be

set. In most cases, the domain-specific

NHANES 1999–2000 response rates

were assumed to be unchanged for

NHANES 2002–2007; for a few

domains for which the NHANES

1999–2000 response rate was 100%, a

response rate of 95% was assumed. The

response rates used in the calculations

ranged from 58% to 98%, with the

lowest response rates assigned to the

most challenging sampling domains,

such as older persons.

Several data sources were used to

obtain national estimates of the 2004

noninstitutionalized civilian population

by race and ethnicity for the NHANES

2002–2007 sample. At the time of PSU

selection, population projections were

not available for certain U.S.

subpopulations, such as MexicanAmerican persons or noninstitutional

civilian residents, so multiple data

sources were needed to create these

estimates. Age-sex distributions by race

were from the U.S. Census Bureau’s

projections of the total resident U.S.

population in 2004 for middle series

migration data. The proportion of

Hispanic persons who were Mexican

American (noninstitutionalized civilians)

came from the March 2000 CPS. These

data were used to adjust the counts of

Hispanic persons in order to estimate

the number of Mexican-American

persons in the 2004 population. The

proportion of the total resident

population that is civilian and

noninstitutionalized was calculated using

April 1, 2000, census projections for the

monthly postcensal resident population

and the monthly postcensal

noninstitutionalized civilian population.

National poverty estimates for others by

sex, age, and ratio of income to poverty

threshold for 1999 were found in

Table 2 of ‘‘Poverty in the United

States,’’ issued by the U.S. Census

Bureau.

Oversampling in Mexican-American

domains

The information in Table I in

Appendix II was used to determine the

overall sample size required to meet

each domain target in NHANES

2002–2006. Among black persons, the

domain requiring the most screening

contained males aged 16–19; it was

expected that 60,639 households must

be screened to meet the 6-year target for

this domain. The domain requiring the

most screening among others contained

low-income males aged 80 and over; it

was expected that 59,738 households

must be screened to meet the 6-year

target for this domain. The domain

requiring the most screening among

Mexican-American persons contained

males aged 60 and over; it was expected

that 115,176 households would need to

be screened to meet the 6-year target for

this domain.

In NHANES, the screening cost per

household is only a small fraction of the

cost of the interview and examination,

and up to a certain point it has only a
minor effect on total cost; however,

when screening must be performed for

100,000 households or more, the

screening cost begins to account for a

substantial part of the total cost. In

addition, the workload associated with

screening so many households has a

major impact on recruitment,

supervision, and control of the entire

operation. To attain the target sample

sizes for these domains, areas with high

proportions of Mexican-American

persons were oversampled because it

was expected that over the course of 6

years a self-weighting national sample

of 60,639 households would attain the

target sample sizes for the black and

others domains.

Census blocks (the basis for the

formation of second-stage sampling

units, or segments) were stratified

according to the population percentage

of Mexican-American persons in the

block group. Table C gives the

definitions of the Mexican-American

density strata.

To determine the oversampling rates

to be used, the distribution of the total

population and of the MexicanAmerican population across the density

strata were examined. The concentration

of the Mexican-American population in

each density stratum determined the

operational efficiency of the

stratification scheme; the variation in

concentrations and sampling rates across

strata determined the statistical

efficiency.

Table D provides the expected

design effects due to oversampling in

the high-density strata for each of the

Mexican-American domains for which

this oversampling was intended in

NHANES 2002–2007. The expected

design effect is highest for MexicanSeries 2, No. 155 [ Page 9

American males aged 60 and over, the

domain that required the most

oversampling.

The following is a derivation of the

screening rate for the full 6-year basic

sample:

+	 Base screening sample size for 6

years = 60,639 households;

+	 Fifty percent add-on for reserve =

90,959 households;

+	 Projected total households in the

United States in 2000 = 107,492,953

households; and,

+	 Sampling rate for base sample =

90,959/107,492,953 ≈ 1/1,182.

The amount of additional screening

required as a result of oversampling

Mexican-American persons is the sum

of the amounts of additional screening

required in each density stratum in order

to attain the highest sampling rate for

the density stratum. In this case, a total

of 5,321 additional households were

estimated to be needed for this purpose.

Final sampling rates

Table II in Appendix II shows the

sampling rates used for the selection of

PSUs in NHANES 2002–2006 for each

of the sampling domains in the four

density strata. Despite no explicit target

sample sizes for the infant domains

(sample participants under age 1 year),

persons in these domains were sampled

at the maximum rate for their race and

ethnicity within the density stratum; that

is, the infant domains were ‘‘take all’’

domains. The sampling rates given in

Table I in Appendix II were designed to

provide a 50% reserve sample as well as

a provision for expected nonresponse in

each subdomain.

The sampling rates were calculated

using the approach described in the

‘‘Calculation of sampling rates and

screening to achieve a self-weighting

sample’’ and ‘‘Oversampling in

Mexican-American domains’’ sections.

In each density stratum, the maximum

sampling rate determined the screening

sample; that is, in each density stratum,

the sample of households to be screened

was to be selected at the highest

sampling rates that appeared for that

density stratum. All screened persons in

the subdomain having that maximum

rate were to be retained in the sample.

The screened persons in other

subdomains were to be subsampled to

bring the sampling rates for those

subdomains down to the desired levels.

The subsampling rates were designed to

minimize the variability in sampling

rates among strata while still achieving

the desired precision.

Departures from a self-weighting

sample

Calculating the sampling rates

required several assumptions relating to

population size and response rates. To

the extent that these assumptions were

not met, the actual screening required to

reach the target sample sizes differed

from the expected screening.

As stated in the ‘‘Calculation of

sampling rates and screening to achieve

a self-weighting sample’’ section,

several data sources were used to

develop the 2004 population projections

used in the sampling rate calculations.

These sources included the U.S. Census

Bureau’s July 2004 projections of the

resident population by age, sex, and race

and Hispanic origin, the 2000 postcensal

Table C. Definitions of the MexicanAmerican density strata for National Health

and Nutrition Examination Survey,

2002–2007

Percent
 

Mexican
 

American in
 

Density stratum stratum

1...................... 0–10
 

2...................... 10–30
 

3...................... 30–60
 

4...................... 60 or more 
 

Table D. Expected design effects for the analysis of annual samples due to oversampling

in the high-density strata, for Mexican-American sex-age sampling domains in which this

oversampling was used

Sex-age sampling domain Expected design effect for

(Mexican-American persons only) the analysis of annual samples

Male and female. . . . . . . . . . . . . . 
 

Male and female. . . . . . . . . . . . . . 
 

Male ..................... 
 

Male ..................... 
 

Male ..................... 
 

Female ................... 
 

Female ................... 
 

Female ................... 
 

Under 1 year

1–2 years

12–15 years

16–19 years

60 years and over

12–15 years

16–19 years

60 years and over

1.20

1.00

1.03

1.03

1.20

1.03

1.04

1.07
Page 10 [ Series 2, No. 155

projections of the resident and

noninstitutionalized civilian population,

and the March 2000 CPS estimates of

the proportion of Hispanic persons who

were also Mexican American. National

poverty estimates for white and others

originated with census data from 1999.

The population projections and resulting

expected screening requirement numbers

depended on the assumption that these

1999, 2000, and 2004 proportions

continued to hold in the years of data

collection.

Finally, as noted in the ‘‘Calculation

of sampling rates and screening to

achieve a self-weighting sample’’

section, the expected examination

response rates were set equal to

achieved examination response rates by

domain for earlier years of NHANES.

Screening requirements also varied from

expectations depending on how much

these earlier response rates differed from

the actual experience in 2002–2006.

Stratification and selection of

PSUs for 2002–2006

As mentioned previously, the

operational requirements for NHANES

are such that the amount of travel

necessary for a sample participant to

visit a MEC should be minimized to

increase the likelihood of achieving high

response rates. As a result, individual

counties were chosen as the PSUs for

NHANES. However, some counties

have such small populations that their

probabilities of selection would be lower

than what is required to attain the

sampling rates for some of the domains.

If selected for the sample, they would

introduce considerable variability into

the weights. Consequently, these small

counties were combined with one or

more adjacent counties to form more

efficient sampling units. For the same

reason, independent cities in Virginia

were combined with nearby counties.

The PSUs selected for the

2003–2006 sample were developed for a

sample intended to span from 2002 to

2007. The frame for the full NHANES

2002–2007 included all counties in the

entire country. From the approximately

3,100 counties and county equivalents in

the United States, 2,882 PSUs were

formed (most of which consisted of

individual counties), a sample of 90

study locations was selected, and 15 per

year were randomly allocated to each of

the years.

Calculation of PSU MOS

The NHANES sample was designed

to yield a self-weighting sample for

each sampling domain while producing

an efficient workload in each study

location. PSUs were selected with

probabilities proportionate to an MOS.

The selection probability of a PSU

determines the maximum rate at which

persons residing in that particular PSU

can be selected.

The expression used to define the

PSU MOS is similar to that used in

NHANES III. The MOS of PSU h,

denoted by Mh, is a weighted average of

estimated populations by race and

ethnicity and was calculated as follows:

Mh = ∑kAkChk ,

..kl A , k = ∑i,lTikrikl

C

C

*

*

..k.

where

i = the within-PSU MexicanAmerican density stratum;

k = the race and Hispanic origin and

income subdomain;

l = the sex-age subdomain;

Chk = the 2000 census population

estimate for race and Hispanic

origin and income subdomain k

in PSU h (see below);

Tik = the proportion of the U.S.

population in the i-th density

stratum for the k-th race and

Hispanic origin and income

subdomain;

rikl = the sampling rate of persons in

density stratum i for the (k,l)-th

race and Hispanic origin and

income-sex-age subdomain;

C*

..kl = the most recent projection of the

2004 total population count for

race and Hispanic origin and

income-sex-age subdomain (k,l);

and

C*

..k. = the most recent projection of

year 2004 total population count

for race and Hispanic origin and

income subdomain k.

Because single counties rather than

larger areas made up of groups of

counties are optimal as NHANES PSUs,

the Mh was first calculated with h

representing a single county.

Population estimates for the total,

black, and Hispanic populations were

obtained from the ‘‘Census 2000

Redistricting Data.’’ Because countylevel data for Mexican-American

population data were not available, a

ratio of Mexican-American to Hispanic

persons was calculated using 1990

decennial census data for the county.

Under the assumption that this ratio has

not changed significantly, it was applied

to the county’s 2000 Hispanic

population count to estimate the 2000

Mexican-American population, as

follows:

1990

=

ChM ChM 1990 ChH , ChH
 

1990 where C and C1990 are the 1990 hM hH

Mexican-American and Hispanic

populations in county h, ChM is the

2000 Hispanic population in county h,

and ChM is the estimated MexicanAmerican population for county h. The

population for the ‘‘other’’ race and

Hispanic origin domain was calculated

by subtracting the 2000 MexicanAmerican population estimate and the

black population from the total 2000

population for the county.

In order to arrive at estimates of the

civilian, noninstitutional population for

each racial and ethnic group used in the

PSU MOS, these estimates were further

adjusted by the percentage of the total

population in the PSU that was

institutionalized according to the 2000

decennial census because more detailed

estimates were not available. (Note that

this is not included notationally above.)

The factors Aik shown in Table E

are the weights used to assign the

relative contribution from each racial

and ethnic group in the computation of

the MOS. Population concentration in

each density stratum, Tik must be

estimated for Mexican-American

persons because the sampling rates vary

across density strata for some domains.
Series 2, No. 155 [ Page 11

These concentrations were estimated

using 1990 census data at the block

group level and are also shown in

Table E. Estimates of the factors Tik are

not necessary for the other race and

Hispanic origin and income domains.

For these groups it can be shown that

Aik = Ak because rikl = rkl and Σi

Tik = 1.

Minimum measures of size

The selection probability of a PSU

determines the maximum rate at which

persons residing in that particular PSU

can be selected for NHANES while

retaining the self-weighting nature of the

sample. If the MOS of a PSU is too

small, the required sampling rates for

some subdomains cannot be achieved.

Consequently, special weighting

procedures would be required for such

PSUs, and the resulting variability in

weights would increase sampling errors.

To ensure that all required sampling

rates could be achieved, counties with

very small MOS were combined with

other adjacent counties.

The condition that determines the

minimum MOS of a PSU is

Ph ≥ ˆrk for all h and k,

where

Ph = the probability of selecting PSU

h; and,

ˆrk = the maximum sampling rate

among the sampling domains

for race and Hispanic origin k.

For certainty PSUs, this condition

always holds, because ˆrk ≤ 1 and

Ph = 1. For noncertainty PSUs, the

probability of selecting PSU h is

where

Mh Ph = cNC ∑heNCMh

NC = the set of noncertainty counties;

cNC = the number of noncertainty

PSUs to be selected; and,

Mh = the MOS for PSU h.

Thus, the condition that determines

the minimum MOS is equivalent to

∑heNCMh Mh ≥ ˆrk . cNC

For each county, it was necessary to

check whether the MOS of the county

met the ‘‘minimum MOS’’ condition.

Because the right-hand side is a

constant, the first step in this check was

to compute this product. The number of

noncertainty locations, cNC, was 72.

The second term on the right-hand

side of the expression was found to be

∑heNCMh = 578.39

cNC

Many counties have very low

Mexican-American or black populations.

For these counties, extreme measures

were taken to ensure that the sampling

rates for Mexican-American or black

domains could be met. For this reason,

the value of ˆrk used in the calculation

varied depending on the MexicanAmerican or black population

proportions in a given PSU. The values

of ˆrk used in the calculation of the

minimum PSU MOS are noted below:

+	 If at least 3% of the PSU population

distribution was Mexican American,

then ˆrk = ˆrMexican American

+	 If less than 3% of the PSU

population distribution was Mexican

American, but at least 3% black,

then ˆrk = ˆrBlack

+	 If less than 3% of the PSU

population distribution was Mexican

American and less than 3% black,

then

ˆrk= ˆrWhite/other

The same rule was used in the

selection of PSUs in 1999–2001. Cutoffs

of at least 3% Mexican-American or at

least 3% of black persons have

traditionally been used in NHANES for

this purpose and were again used here.

Based on this minimum MOS

criterion, 256 counties were found to

have MOS that were too small. These

counties were combined with

neighboring noncertainty counties. The

neighboring counties had to be adjacent,

and the maximum distance between any

two points in the combined-county area

had to be less than 125 miles. Also,

unless there were no alternatives that

met the aforementioned criteria, counties

combined were from the same state.

Twenty counties that failed to be

combined either had no surrounding

counties that fit this qualification, or

adjacent counties already belonged to a

combination. These counties were

manually combined by slightly

loosening the mileage restrictions where

necessary. Three boroughs in Alaska

were not combined either by computer

or manually because the distances

between them and areas under

consideration for combining were too

great; in some cases, the areas were

separated by water, making travel

difficult between the areas.

After the necessary county

combinations were made, the PSU

MOS, Mh was recalculated with h

representing the combined counties as a

single PSU.

. . . Category not applicable.

Table E. Values of Tik and Aik used in the calculation of the primary sampling unit measure of size

Race and Hispanic origin Density Tik Aik

Mexican American ........................... 1 0.30 0.000614

2 0.23 0.000614

3 0.20 0.000614

4 0.27 0.000614

Black ................................... . . . . . . 0.000405

Other, low income ........................... . . . . . . 0.000227

Other, non-low income ........................ . . . . . . 0.000118
Page 12 [ Series 2, No. 155

Selection of certainty PSUs

Some counties had an MOS large

enough that they were selected with

certainty, and a few of these were

selected multiple times. These certainty

PSUs were removed from the county

frame prior to noncertainty PSU

selection.

A PSU was identified as a certainty

if its weighted MOS exceeded 75% of

the initial sampling interval; that is,

PSU h was included in the sample with

certainty if

∑H

h=1Mh Mh > 0.75 90 ,

where H is the number of PSUs on the

entire sampling frame.

Some certainty PSUs were so large

that they warranted more than one study

location; otherwise, weighting factors

would have to be applied to ensure

appropriate representation, and these

weighting factors would reduce the

efficiency of estimates. The number of

study locations allocated to each

certainty PSU was obtained by

comparing the weighted MOS Mh for

the PSU with the initial PSU sampling

interval, (1/90) ΣH

h =1Mh.

A total of 18 study locations in the

full NHANES 2002–2007 90-location

sample were assigned to certainty PSUs.

These locations were in 11 counties; 3

counties contained multiple-study

locations.

Stratification and selection of

noncertainty PSUs

The stratification scheme for the

full NHANES 2002–2007 PSUs was

developed with the primary goal of

efficiency for the 6-year sample and

with the secondary goals of efficiency

for 3-year and annual samples. Recall

that at the time the 2002–2007 was

selected, data were intended to be

released in 3-year cycles. The

stratification scheme has the added

benefit of producing efficient 2-year

samples because it was designed to

ensure that the PSUs comprising the

annual and multiyear samples were

distributed evenly in terms of geography

and certain population characteristics.

For the full 6-year sample, 12 major

strata were defined based on geography

and the MSA status of the PSUs.

Seventy-two minor strata were defined

based on the demographics of the PSUs.

Each major stratum included six minor

strata, and one PSU was selected from

each of these final strata.

The 6-year sample had a

one-PSU-per-minor stratum design; each

annual sample had a one-PSU-permajor-stratum design. That is, all

multiyear samples contained only one

PSU per sampled minor stratum rather

than multiple PSUs from the same

stratum. Annual samples contained only

one PSU from each major stratum.

In forming the major strata, the

variables used were census region

(Northeast, Midwest, South, and West)

and MSA status. This resulted in the

major stratum containing the Southern

non-MSA PSUs being larger than the

other strata. This particular stratum had

a total MOS of approximately 4,990,

while the MOS for the other strata

ranged from 3,300 to 4,200. The Figure

in Appendix II contains a depiction of

the major strata; Table F contains

descriptions of the PSUs within each

major stratum.

The minor strata were constructed

in such a way that they were equal in

size to the extent possible (in terms of

total MOS). The variables used to form

the boundaries of the minor strata were

minority status and the percentage of the

population below the poverty level. The

percentages of black persons and

Mexican-American persons in each PSU

were obtained from the 2000 census.

The percentage of the population below

poverty was based on the 1990 census,

because more current data were

unavailable.

Allocation of PSUs to time period

To have nationally representative

annual samples (a design requirement of

NHANES), study locations had to be

assigned to years in a random fashion.

The certainty PSUs were sorted

according to their MOS, and the

noncertainty PSUs were sorted by order

of selection (for the NHANES

2002–2007 sample, this was within each

major and minor stratum). Within each

major stratum, minor strata were paired.

Each pair was randomly assigned to the

study years 3 years apart. The

assignment of the pairs to the particular

sets of study years and the assignment

of the study years within the pair were

random within the first major stratum,

and all other major strata followed the

same pattern.

The large certainty PSUs were

assigned in a manner that appropriately

reflected their relative size. For

example, because one PSU was large

enough to be selected with certainty in a

1-year sample and contained six study

locations within the 90-location sample

for 2002–2007, one location was

assigned to each year. In 1999, this PSU

was divided into three study locations

along tract boundaries: the northeastern,

southern, and northwestern areas of the

county. These locations have been and

will continue to be fielded in that order.

A few PSUs were selected with

certainty in a 3-year sample but not in a

2- or 1-year sample. These PSUs have

two locations assigned; one of the study

locations was randomly assigned to one

of the first 3 years and the other to one

of the second 3 years in a balanced

fashion (3 years apart). Finally, several

PSUs were selected with certainty in a

6-year sample but not in a 3-, 2-, or

1-year sample. These PSUs were paired;

one PSU in each pair was randomly

assigned such that the two PSUs in each

pair were scheduled 3 years apart.

Targeted number of sampled persons

in each PSU

The initial target number of

examined persons per location was 333

based on the assumption of a total of

5,000 examined persons per year in 15

study locations. Once the sample of

locations was selected, the examination

targets were adjusted. The final target

number of examined persons for

certainty locations was obtained by

adjusting this initial target by the

relative size of the location. For

certainty locations, this was calculated

as the MOS allocated to the survey

location divided by the initial sampling

interval used for selecting noncertainty

PSUs, (1/72) Σ Mh. The relative size heNC

of certainty locations ranged from 0.80

to 1.20.

For all other noncertainty locations,

the initial examination target was
Series 2, No. 155 [ Page 13

adjusted by the relative contribution of

the location’s stratum to the total

noncertainty MOS. Finally, the target

number of identified sample participants

for a given study location was derived

from the desired number of examined

persons by inflating that number to

account for the predicted combined

screener, interview, and examination

response rate for the study location.

For NHANES 2003–2004,

combined screener, interview, and

examination response rates were

predicted using a logistic regression

based on the exam response experience

in NHANES III and 1999–2001. The

same procedure was used for NHANES

1999–2001, using the NHANES III data.

The predicted exam rates were used to

determine the expected number of

screened sample participants needed to

reach each location’s exam target. The

data were used to fit a logistic

regression model at the person-level

with exam participation as the response

and various sample participant

characteristics such as race and Hispanic

origin, age, and gender as well as PSU

characteristics such as population size,

region, poverty level, and education as

independent variables. Probabilities of

exam participation were calculated by

race and Hispanic origin of the sample

participant and selected PSU

characteristics. The number of expected

sample participants screened for each

PSU was calculated by dividing the

appropriate cell probabilities into the

expected number of sample participants

examined for that cell. After summing

the expected number of sample

participants to the location level, the

response rates were calculated as the

total expected number of examined

sample participants divided by the total

expected number of identified sample

participants.

Beginning in 2005, NHANES

response rates (combined screener,

interview, and examination) for each

location have been predicted annually

using a linear regression based on the

actual response rates and location-level

characteristics of prior study locations.

Prediction based on previous experience

has proven more accurate than simply

applying a single response rate across

all study locations.

Each year the model was refit with

the most recent data available at the

time of the prediction. A relatively large

number of geographic, demographic,

and economic variables from the U.S.

Census Bureau were assembled and

brought into a linear regression model

as potential independent variables with

the study location response rate as the

dependent variable. A stepwise

regression was used. The final model for

each year was decided based on a

combination of the regression

correlation coefficient and a statistic that

adjusts for the total number of variables

included in the model. The model was

applied to the values of the selected

variables for the current year’s study

locations to predict their response rates.

After these predicted response rates

were reviewed with senior project staff,

some of the rates were adjusted based

on past experience. Once the response

rate predictions were finalized, they

were applied to the target number of

exams to predict the number of

identified sample participants that would

be required in each study location to

achieve the targets. This became the

initial target number of identified

sample participants.

Selection of segments

The second stage of the design

involved sampling segments within each

PSU (study location). In order to utilize

the most recent available data on new

construction, segments were selected as

a continuous process about 5 to 6

months prior to the start of the field

period for the study location.

The usual practice in area samples

is to list all DUs in sampled segments

and apply a prespecified sampling rate

to the listed DUs. This approach gives

all DUs the desired probabilities of

selection. For example, if the sampling

rate is 50%, then one-half of the DUs

listed in the segments will be included

in the sample. If the number of DUs has

tripled due to new construction (that is,

housing units built since the most recent

decennial census), the same sampling

rate will produce three times as many

interviews and examinations as the

number originally expected. Such

Table F. Descriptions of the major strata formed for selection of the 2002–2007 primary sampling units

Major stratum Description1

1. . . . . . . . . . . . . . . . . . . . . . .

2. . . . . . . . . . . . . . . . . . . . . . .

3. . . . . . . . . . . . . . . . . . . . . . .

4. . . . . . . . . . . . . . . . . . . . . . .

5. . . . . . . . . . . . . . . . . . . . . . .

6. . . . . . . . . . . . . . . . . . . . . . .

7. . . . . . . . . . . . . . . . . . . . . . .

8. . . . . . . . . . . . . . . . . . . . . . .

9. . . . . . . . . . . . . . . . . . . . . . .

10. . . . . . . . . . . . . . . . . . . . . . .

11. . . . . . . . . . . . . . . . . . . . . . .

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

Northeastern PSUs with less than 7.8% of the population in poverty
 

Northeastern PSUs with more than 7.8% of the population in poverty
 

Nonmetropolitan Midwestern and Western PSUs
 

Metropolitan Midwestern PSUs with population less than 9.8% black
 

Metropolitan Midwestern PSUs with population more than 9.8% black
 

Nonmetropolitan Southern PSUs
 

Metropolitan Southern PSUs with population less than 1.4% Mexican American and:
 

- Less than 21% black; or,

- Between 21% and 27.6% black and less than 10.8% of the population in poverty

Metropolitan Southern PSUs with population less than 1.4% Mexican American and:
 

- More than 27.6% black; or,

- Between 21% and 27.6% black and more than 10.8% of the population in poverty

Metropolitan Southern PSUs with population between 1.4% and 3.8% Mexican American
 

Metropolitan Southern PSUs with population more than 3.8% Mexican American
 

Metropolitan Western PSUs with population less than 14% Mexican American
 

Metropolitan Western PSUs with population more than 14% Mexican American
 

1

Threshold values are approximate.
 

NOTES: PSU is primary sampling unit. PSUs selected for 2007 were not used.
 
Page 14 [ Series 2, No. 155

dramatic changes in the segment size

are expected when the data collection

period is several years after the most

recent decennial census for which data

files are available.

If the segment contains much new

construction, the segment MOS may be

inaccurate. As a result, either a

larger-than-expected sample must be

drawn from that segment or a weighting

factor must be applied to all sample

participants selected from that segment.

Because highly variable sample sizes are

not operationally feasible for NHANES,

subsampling within PSUs would be

necessary to attain equal sample sizes

across PSUs; however, this would

require the application of a weighting

factor, which would reduce the

efficiency of the sample.

To update a sampling frame when

the sample is to be selected with respect

to an MOS but a reliable estimate of the

MOS is not available, double sampling

(or two-phase sampling) can be used.

Beginning with the PSUs sampled for

the 2005 NHANES year, after field staff

determined the number of DUs in the

first-phase sample of segments, an

updated MOS that reflected the ratio of

the actual number of DUs to the

expected number of DUs was calculated.

The final sample of segments was

selected by subsampling from the

first-phase segments using the updated

MOS (14).

Stratification within PSUs

The procedures for selecting the

segment sample involve both explicit

and implicit modes of stratification. The

PSU strata described in the

‘‘Stratification and selection of

noncertainty PSUs’’ section and the

minority-density geographical strata

described in the ‘‘Oversampling in

Mexican-American domains’’ section

comprise explicit stratification. To keep

combined blocks within a single-block

group, the stratification is based on

characteristics of the block group in

which segments are located. Within the

geographical strata, there is implicit

stratification created by sorting the area

segments by tract number, block group

within tract, and segment number within

block group, and selecting a systematic

sample with PPS.

Segment MOS

The segment MOS calculation is

similar to that for the PSU MOS. Prior

research on intraclass correlations and

unit costs indicated that an average of

14 examined sample participants per

segment was reasonably close to an

optimum for most statistics in

NHANES. As indicated earlier,

operational requirements make it

necessary to have a fairly constant

number of examined sample participants

per study location, usually about 333.

This implies having 24 segments per

PSU.

Because segments consist of census

blocks or groups of blocks, the segment

MOS is a sum of MOS calculated at the

block level. Further, in study locations

that have experienced significant growth

since the 2000 decennial census, a

two-phase sampling procedure was

followed with the segment MOS

calculated separately for each phase.

For the first phase, let Mhib (1)

denote the MOS of block b in density

stratum i in PSU h, where

Mhib (1) = ∑k*Aik*Chibk* ,

C*

..k* l

A , ik* = ∑ 

l

rik* l C*

..k*.

where

h = PSU;

i = density stratum;

b = block;

k* = the race and Hispanic origin

subdomain (black, Hispanic, and

others income levels combined);

Chibk*= the 2000 population of race and

Hispanic origin k* in block b in

PSU h;

rik*l = the sampling rate of persons in

density stratum i for the (k*

,l)-th

race and Hispanic origin and

sex-age subdomain;

C*

..k*l = the most recent projection of the

year 2004 total population count

for race and Hispanic origin and

sex-age subdomain (k*

,l); and

C*

..k*. = the most recent projection of

the year 2004 total population

count for race and Hispanic

origin subdomain k*

.

The factor Aik* is similar to the Ak used

in the PSU sampling MOS calculation

described in ‘‘Calculation of PSU

MOS;’’ however, because the rik*l vary

across density strata for MexicanAmerican persons, the values for

Mexican-American persons must be

calculated by density stratum. Further,

because income level is not available at

the block level, the value for the

non-low-income other persons was used

for all other persons. Table G contains

the Aik* values used in calculating the

segment MOS for NHANES 2002–

2006.

The MOS for the first-phase

segments j

[1] are the sums of the MOS

Table G. Values of Aik* used in calculating measures of sizes, by race and Hispanic origin and density stratum for segment selection in

National Health and Nutrition Examination Survey, 2002–2006

Numerator of Aik * for race and Hispanic origin k* (denominator is 1,182)

Density stratum (i) Black Mexican American Other

1 ....................... 0.478 0.616 0.14

2 ....................... 0.478 0.694 0.14

3 ....................... 0.478 0.755 0.14

4 ....................... 0.478 0.851 0.14
Series 2, No. 155 [ Page 15

of the block(s) comprising each

segment. These MOS are denoted by

Mhij [1](1).

The MOS used for the second-phase

segment selection is the segment growth

rate. Based on the DU counts obtained

for the first-phase sample segments, the

growth rate is estimated by computing

the ratio of the actual number of DUs

(counted by NHANES staff in the field)

to the expected number of DUs in the

segment (based on the 2000 decennial

census data).

Let U′ 

hij [1] denote the number of

DUs found by NHANES staff in the

field for the first-phase segment j

[1] in

PSU h. The growth (ghij [l]) of the

first-phase segment j

[1] is estimated as

where U[0]

hij [1] is the number of DUs

in segment j

[1] according to the 2000

decennial census.

Thus, the second-phase MOS for

segment j

[1] selected in the first phase

of sampling in PSU h is equal to

U′ 

hij [1]

ghij [1] = [0] , Uhij[1]

M . hij [1](2) = Mhij [1](1)ghij [1]

Number of segments and their

probability of selection

As discussed in the ‘‘Targeted

number of sampled persons in each

PSU’’ section, the person sample sizes

for some survey locations selected with

certainty were adjusted to account for

their size relative to the other selected

locations to minimize the effects of

intraclass correlation. The number of

segments selected in the certainty

locations was also adjusted from 24 to

account for the relative size of the

location. As a result, some survey

locations selected with certainty

contained as few as 19 segments or as

many as 29 segments in the second

phase of segment sampling, denoted

nh(2). To achieve proper within-segment

sampling rates in study locations in

which the segment sample was selected

in two phases, the first-phase sample

must be larger than the ultimate sample.

Beginning in 2005, approximately twice

as many segments were selected than

ultimately needed (50 segments).

For each survey location, the

conditional probability of selection of

first-phase segment j

[1] is

[ n M]

where

Nh(1) = total number of segments in

first-phase segment frame in

the h-th PSU;

nh(1) = total number of first-phase

segments to be selected in the

h-th PSU; and

Mhj[1](1) = first-phase MOS of segment

j

[1] in the h-th PSU.

Given the first-phase segments, the

conditional selection probability of

second-phase segment j

[2] is

where

nh(2) = total number of second-phase

segments to be selected in the

h-th PSU; and

Mhj[2](1) = second-phase MOS of

segment j

[2] in the h-th PSU.

The actual probability of selection of a

segment depends on the MOS of the

segment and the probability of selection

of the location from which the segment

is selected. The overall probability of

selection of a second-phase segment j

[2]

is

Note that in survey locations that do not

require the two-phase procedure, nh(2) =

nh(1) = nh. Also, in survey locations

where no second phase is needed,

Mhj[2](2) = 1. Substituting into the

second-phase probability of selection

above results in

S

so the segment probability

D

of

S

selection

D

within one-phase locations is simply the

first-phase probability of selection.

h(1) hj [1] (1)

P , 1 hj [1](1) = min

∑Nh (1)

j

[1] =1 M , hj [1] (1)

nh(2)Mhj [2](2)

Phj [2] [ , 1 (2) = min

∑n

h (1) , j =1 Mhj [2](2) ]

Phj [2](2)Phj [1](1)Ph =

n n 1 M [2] [1]

hj (2) 21 Mhj (1)

h (2) h (1) Ph n

∑

N

∑ h (1) h (1)

[1] [1] [2] [2]

j =1 Mhj (2) j =1 Mhj (1)2 [1]

nh(1) n P h

hj[2] , 1 (2) = min = min , 1 n =1,

∑ h nh j=1 (1)

Minimum segment MOS

One goal of the sample design is to

create equal probabilities of selection for

each domain within a study location.

This enables the selection of a nearly

self-weighting sample and facilitates the

selection of persons. To create equal

probabilities, the within-segment

sampling rate for a domain in study

locations selected without certainty

should be

. [2]

For locations selected with

certainty, Ph = 1, so the segment

sampling rate should be rhijkl =

rikl/Phj [1](1)Phj [2](2) .

The within-segment sampling rates must

be less than or equal to 1. The most

severe constraint is for domains with the

highest value of rikl. These maximum

sampling rates are referred to as

ˆri

; that is, ˆri = max{r

ikl}, thus

k,l

[3]

Replacing the denominator in expression

[3] with its equivalent as given in

expression [1], the condition given in

expression [3] becomes

rikl rhijkl = PhPhj [1](1)Phj [2](2)

ˆri max{r } = ≤ 1 . k,l hijkl PhPhj [1](1)Phj [2](2)

which is equivalent to

Mhij [1](1) ≥

The first-phase minimum measure

of size is a product of two factors. The

first factor was calculated for the survey

location based on known information.

The second factor was based on the

second-phase measures of size, which

was not known at the time of selection

of the first-phase segments.

For survey locations that do not

require the two-phase process, the

ˆri

≤ 1 Mhij [2](2) Mhij [1](1)

nh (2)nh (1)1 nh (1) Nh (1) ∑j [2] =1 Mhij [2] (2)21∑j [1] =1 Mhij [1] (1) 2Ph

1 ˆr ∑

nh (1) i j [2] =1Mhij [2](2)

nh(2)

Ph

∑

Nh (1)

j [1] =1Mhij [1](1)
 21 2. [4] nh Mhij [2] (1) (2)
Page 16 [ Series 2, No. 155

second factor reduces to 1:

and equation [4] reduces to

,

the minimum MOS for segments.

For survey locations where the full

two-phase process was implemented,

Mhij [2] (2) was not known when the

first-phase segment was selected. In this

case the second factor must be

considered:

This factor would inflate the minimum

MOS to account for expected growth in

the segment due to new construction.

Because the actual values were not

known, an inflation factor constant

across all segments was used. Based on

empirical research, this inflation factor

was set to 1.25.

In implementing the sample

selection, the minimum MOS was made

50% greater than needed to attain the

maximum sampling rates ˆri

. As a result,

the MOS was increased to permit the

selection of a reserve 50% sample.

Within each PSU, the blocks

reported on the block-level census files

in each minority density stratum were

sorted by tract, block group, and block

number. Blocks with MOS below the

minimum were combined with

succeeding blocks until the desired

measure was achieved. To the extent

possible, the combinations were kept to

the same block group. When the

combinations came to the end of a block

group without reaching the minimum,

earlier blocks within the same block

group were added. When necessary,

blocks were combined across block

groups within the same tract to form

segments; however, collapsing of blocks

across tracts was not permitted.

Consequently, the combinations

consisted of blocks in close geographic

[5]

∑ n n h (1) h (1)

j

[2] =1 Mhij [2] (2) ∑j[2] =1 n = h = = 1, nh(1) Mhij [2] (2) nh1 nh

ˆr

≥ i Mhij [1] (1) nh Ph

∑ Nh (1)

j

[1] =1 Mhij [1] (1)

∑nh (1)

j = 1 Mhij [2] (2) ave(Mhij [2] (2)) = .
 nh (1) Mhij [2] (2) Mhij [2] (2)
 

proximity, and, in most cases, they were

adjacent blocks. As a result of the

method of combination, some large

blocks that could have been segments

by themselves were combined with

small blocks.

At the second phase of segment

selection the constraint in expression [4]

is equivalent to

ˆr

The right side of expression [6] is

the minimum MOS for the second-phase

segment selection. Any first-phase

segments, j

[1], with MOS less than the

minimum second-phase MOS were

combined with adjacent segments to

form the second-phase segments, j

[2],

prior to selection.

After second-phase selection, any

j

[2] segments that had been formed as a

combination of first-phase segments to

achieve the second-phase minimum

MOS were disaggregated into their

first-phase components for operational

reasons. The within-PSU probability of

selection was equal for the constituent

segments. After completing the segment

selection, the selected segments were

denoted by j (with the superscript

dropped) to simplify the notation.

Controlling sample size per PSU

Screening and interviewing begins

approximately 3 weeks before the first

examinations in a location. This ensures

that there are enough identified and

interviewed sample participants to fill

available examination sessions. Once the

MEC team arrives at a location (after

conducting exams in a previous location

only days before), examinations for

interviewed sample participants begin.

Examinations continue for

approximately 5 weeks. After the last

examination day, the field staff has

limited time to travel to the next study

location.

This strict time schedule for

examining the sample participants in

each study location necessitates the

advance establishment of a fixed

screening and examination workload in

i Mhij [2](2) ≥ . [6] nh

P P (2)

h hj [1](1)∑ nh (1)

j

[2] =1 Mhij [2] (2)

each location (see the ‘‘Minimum

measures of size’’ section). As with any

survey, it is not possible to predict the

exact number of screened households

that will supply the desired number of

sample participants and examinations.

This is further aggravated by variations

in response rates from location to

location.

A fixed number of sample

participants is expected in the locations

selected without certainty as a result of

the constant sampling rate defined for

each domain-density stratum across all

study locations, rikl. Within the study

location, the sampling rate used in

density stratum i for domain (k,l) is

rikl rikl .
 PhPhj ˆri Ph
 

Phj ˆri =

Therefore, the total number of sample

participants in a noncertainty location is

expected to be

{ }

which can be written as

[ ]

where Chikl is the actual population of

race and Hispanic origin and sex-age

domain (k,l) in location h and density

stratum i, and cNC is the number of

locations selected without certainty.

Note that the first term on the

right-hand side is a constant. To the

extent that the population distribution is

approximately the same as in 2000, Chikl

≈ Chik C..kl / C..k., and the second term is

approximately equal to 1. Therefore, the

number of sample participants is

approximately constant across these

locations.

Because the number of segments

per location is constant at 24 for all but

the certainty PSUs, the variation in

quotas per location is also reflected in

segment sample sizes. In addition, the

changes in the population distribution

since the most recent census are likely

to be greater among segments than

rikl

S

rikl ∑ Ph

D Chikl = ∑ ∑kChik∑l

rikl

C..kl Chikl , i,k,l i,k,l C..k. [ ] cNC C..kl ∑h∑kChik∑l

riklC..k.

∑i,k,lriklChikl C..kl = [ 1 ] [ C..kl] ∑h,i,k,lChikrikl , cNC C..k. ∑i,k,lrikl ChikC..k.
Series 2, No. 155 [ Page 17

among locations. The average segment

size is thus expected to vary more than

the average location size, but even this

variation is generally within a moderate

range. The approximate equality that

exists in sample participant sample sizes

per location and segment does not occur

in the screening sample. The amount of

screening in a location is partially based

on what proportion of the location

population lives in high-density strata.

The amount of screening per segment

will vary considerably among the

density strata. Consequently, it is

necessary to use a procedure that can

produce samples that are either

somewhat larger or somewhat smaller

than those arising from the application

of the self-weighting sampling rates. See

the ‘‘Selection of sample participants’’

section for more information on this

procedure.

Selection of DUs and persons

The third stage of sample selection

consisted of DUs including certain types

of group quarters. All DUs in the

sample segments were listed, and a

subsample of DUs was designated for

screening to identify potential sample

persons for interviews and examinations.

The subsampling rates are designed to

produce a national approximately equal

probability sample of DUs in most of

the United States, and higher rates for

the geographical strata with high

minority concentrations. Within each

geographical stratum, there was an

approximately equal probability sample

of DUs across all PSUs. While the

discussion in this section is phrased in

terms of the 2002–2006 locations, the

same procedures also apply to the

1999–2001 locations.

Within-segment sampling rates

Within segments, DUs were

selected with equal probability at a rate

equal to the maximum within-segment

sampling rate required to attain the

subdomain sampling rates. That is, the

sampling rate used to select DUs within

segment j in PSU h is

max{r

ikl} i,k,l

, PhPhj

where i is the density stratum of

segment j.

Sample participants were selected

within DUs using the ratio of the

subdomain sampling rate to the

maximum subdomain sampling rate.

Thus, the overall selection probability

for a person in race and Hispanic origin

and sex-age-income subdomain (k, l) in

density stratum i is

Pr[select PSU h] c Pr[select segment

hj|select PSU h] c Pr[select a given DU

in segment hj|select segment hj]

Pr[domain (ikl) flagged for selection in

the given DU|the given DU in segment

hj selected]

max{r

ikl} i,k,l rikl = P . h Phj Ph Phj max{r

ikl} = rikl

i,k,l

It is easily shown that these probabilities

yield approximately equal sample sizes

for each PSU.

Selection of sample participants

Once the DU sample was released

to the field, each DU was screened to

determine whether it is occupied,

vacant, or for seasonal use only. Only

occupied DUs, or households, were

eligible. Once the sampled households

were identified, a sample of persons to

be interviewed and examined from each

individual household was selected. All

eligible members within a household

were listed and a subsample of

individuals was selected based on sex,

age, race and Hispanic origin, income,

and pregnancy status. Sample

participants were selected at rates

established to ensure that the target

sample sizes by subdomain were

achieved, and the average number of

sample participants per household was

maximized.

Considerable subsampling was

needed to reduce the screening sample

of households to the desired number of

sample participants. If independent

random or systematic selections had

been made for the subdomains, in most

cases, only one person in a household

would have been selected and the

average sample size per household

would have been quite low, not much

above one.

Experience with recent cycles of

NHANES and with HHANES indicated

that response rates improve when

sample sizes within households are

larger. Therefore, a method of

subsampling was used to maximize the

number of sample participants per

household (conversely, this method

minimizes the number of households

containing sample participants). The

effect of within-household clustering is

not a large concern for NHANES

because most analyses are done within

subdomains and there is generally little

within-household clustering at the

subdomain level.

The method begins with the

designated screening sample from which

persons are to be subsampled. The

persons are classified into Q subdomains

with sampling rates r1, r2, ... rQ. The

subdomains are ordered by subsampling

rate so that rq ≤ rq+1. Note that the

screening rates are set so that rQ = 1;

that is, the screening rate is equal to the

maximum subsampling rate.

The set of households designated

for screening is partitioned into L

unequally sized random subsets, such

that the sizes of the subsets are

proportionate to r1, r(2)–r(1), r(3)–r(2)1,...,

r(q+1) – r(q), rQ – r(Q-1). It is clear that the

sum of these proportions is equal to

rQ = 1, so that each screened household

is assigned to exactly one of the sets.

The subsampling is then carried out as

follows:

+	 In the first random subset of

households (corresponding to

100*r1% of all screened

households), all persons in the

household are designated as sample

participants.

+	 In the second random subset of

households (corresponding to

100 * (r(2) – r(1))%, of all screened

households), all persons in the

household are sample participants

except those in the subdomain (1);

therefore, those persons in the first

subdomain were selected only in the

first random subset, with probability

r1;
Page 18 [ Series 2, No. 155

+	 In the third random subset of

households, all persons in the

household are sample participants

except those in the subdomains (1)

and (2). Thus, those in the second

subdomain were selected only in the

first two random subsets, with

probability r1 + (r(2) – r(1)) = r(2).

+	 This procedure is continued in this

manner through the Q-th random

subset, for which only persons in

subdomain Q are sample

participants.

This sampling procedure was

implemented using a set of sampling

flags that designate for each DU, the

domains eligible for sampling. The

interviewers were not required to carry

out any subsampling operation. They

were instead instructed by the system

(based on the set of domain flags

provided for each household) on which

persons to include as sample

participants. Note that because the

sampling domain flags were prepared in

advance of the screening, they were

based on the expected distribution of the

screened sample by race and Hispanic

origin, sex, age, and income rather than

the distribution actually achieved. Thus,

this procedure was expected to produce

small deviations in the sample from the

desired number in each domain. Such

deviations are inevitable when

subsampling rates must be established

before the screening is completed.

Instead of unrestricted

randomization, a pseudorandom

procedure was used that guaranteed that

all sampled DUs within each sequence

of 100 consecutive DUs were assigned

different random numbers (because the

random number assigned determined the

set of domains to be selected). To start,

a random number between 0.00 and

0.99 was assigned to the first DU, with

a separate initial random number used in

each study location. The number 0.41

was then used like a skip interval and

was added successively to obtain the

random number for the next case. The

random number was then used in the

manner described above to determine

the sampling domain flags assigned to

each case.

Initially, a screening sample was

drawn for each study location using

sampling rates 50% larger than those

required to attain the target sample sizes

in each domain. Each study location’s

screening sample was then divided into

release groups. Each group was a

systematic subsample of the screening

sample, with the screening sample

sequenced by segment number and a

temporary geographically based

sequence number prior to subsampling.

Thus, each release group contained

cases from all segments, except as

limited by release group and segment

size. Table H gives the expected

distribution of the sample of DUs across

release groups.

In most study locations the 50%

release group (that is, group A) was

released to the interviewers first. The

yield from this group was monitored

and used to project estimates of the total

yield of sample participants expected

from this group. Based on these figures,

additional groups (or portions of groups)

were released as needed. The sample

was monitored on a daily basis to

determine whether additional release

groups were required. The cases in

group Z had sampling flags indicating

that all persons in the household should

be sampled; it was designed to be used

as a last resort, only when the sample

yield in a study location was low after

all other groups had been released. This

release group was never utilized in

NHANES 1999–2006.

Special samples

Supplemental sample of pregnant

women

To improve the precision of the

estimates for pregnant women, a

supplemental sample of pregnant women

was selected. Only women aged 15–39

were eligible for this supplemental

sample. The original plan was to include

in the sample all pregnant women (that

is, all women reported by the screener

respondent to be pregnant); however,

taking all non-Hispanic white or other

pregnant women in the high-minority

density strata would have reduced the

efficiency of the sample because of the

impact on the differential probabilities

of selection.

Thus, the decision was made to

select pregnant women according to the

maximum sampling rates in each density

strata. Because the sampling rates do not

vary by density strata for black persons

and others, the same rate was used to

sample all pregnant women in these

groups. Mexican-American women were

sampled at different rates depending on

their density stratum. Therefore, the

subsampling approach described above

yielded a self-weighting sample of

others and black pregnant women. The

sampling rates for the pregnant women

are shown in Table J.

Examination session subsamples

NHANES has two examination

session subsamples: the morning

subsample and the afternoon or evening

subsample. Sample participants selected

for the morning sessions were instructed

Table H. Release group distribution for National Health and Nutrition Examination Survey,

2002–2006

Release group	 Percent for 150% sample1

A....................... 50 
 

B....................... 10 
 

C....................... 8 
 

D....................... 8 
 

E....................... 6 
 

F....................... 6 
 

G....................... 3 
 

H....................... 3 
 

I. . . . . . . . . . . . . . . . . . . . . . . . 2 
 

J ....................... 2 
 

K....................... 1 
 

Z....................... 1 
 

1

The amount the National Health and Nutrition Examination Survey needs (100% sample) plus a 50% reserve.

NOTE: The same distribution was used in 1999–2001.
Series 2, No. 155 [ Page 19

to fast overnight; those selected for the

afternoon or evening sessions were also

instructed to fast, but for a shorter

period of time. Data that are sensitive to

fasting times should be analyzed

separately for these two groups.

Because it is generally more

convenient for household members to

come to the MEC at the same

time—which is believed to favorably

affect response rates—the examination

session subsample assignment was made

at the household level. The assignment

was based on the household identifier

(ID). If the household ID was an even

number, the household was assigned to

the morning subsample; if the household

ID was an odd number, the household

was assigned to the afternoon or

evening subsample. The examination

session subsample was assigned

immediately after DUs were selected.

Although the examination session

subsamples were designed to be

approximately half-samples, some

deviations resulted. Additionally, sample

participants did not always report to the

assigned examination session. For

example, some sample participants

assigned to be examined in a morning

session may have been unable to report

to the MEC at that time; in such cases,

the sample participants were permitted

to schedule afternoon or evening

examinations.

Examination and laboratory

subsamples

The examination component of

NHANES consisted of medical, dental,

and physiological measurements, as well

as numerous laboratory tests to assess

various aspects of health. For some of

these components, subsampling was

required to reduce respondent burden

and facilitate the scheduling and

completion of examinations.

Sample participants were assigned

to examination and laboratory

subsamples by first using an algorithm

to randomly divide the sample

participants into 12 groups;

combinations of these groups were

predetermined to create the various

subsamples. In some cases, sample

participants were assigned to multiple

subsamples. After subsample

assignment, weighting factors were

attached to each sample participant

record as appropriate to reflect this stage

of subsampling. Table III provides the

specifications for the components

requiring subsampling.

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

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

sample observation. Weighting takes

into account several features of the

survey: the differential probabilities of

selection for the individual domains,

nonresponse to survey instruments, and

differences between the final sample and

the total population.

NHANES-sampled participants were

weighted in order to accomplish the

following objectives:

1. To compensate for differential

probabilities of selection among

subgroups (race and Hispanic

origin and sex-age-income and

pregnancy subdomains, and

persons living in different

geographic strata sampled at

different rates).

2. To reduce biases arising from the

fact that nonrespondents may be

different from respondents.

3. To fix weighted sample data to

match an independent estimate

from the U.S. Census Bureau, of

the target population totals.

4. To 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, etc.).

5. To 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 above). The

second step adjusted for nonresponse

(objective 2). In the third step, the

sample weights were poststratified to

U.S. Census Bureau estimates of the

U.S. population to simultaneously

accomplish objectives three, four, and

five. These steps were performed for

respondents to each stage of the survey:

the screener, personal interview, and

MEC examination.

Because the sample design for the

1999–2001 PSU selection differed

significantly from that for the

2002–2006 sample, weighting the

1999–2000 and 2001–2002 samples also

differed somewhat from the latter years.

For that reason, this section is divided

into two parts: the first section describes

those weighting procedures unique to

the 1999–2002 samples; otherwise, the

Table J. Sampling rates used to sample pregnant women in National Health and Nutrition Examination Survey, 2002–2006

Numerator of sampling rate1 Density Density Density Density

Race and Hispanic origin stratum 1 stratum 2 stratum 3 stratum 4

Black .............................. 1.00 1.00 1.00 1.00

Mexican American ...................... 1.00 1.90 3.00 3.75

Other .............................. 1.00 1.00 1.00 1.00

1

The denominator of the sampling rates is 1,182.

NOTE: Similar rates were used in 1999–2001.
Page 20 [ Series 2, No. 155

second section describes the methods

used for the 2003–2006 samples, which

generally apply to the earlier years.

The national inflation weights

described in ‘‘Weighting the sample data

for 2003–2006’’ were the starting point

for the screener weight calculation.

Those weights were then adjusted for

nonresponse (see ‘‘Nonresponse

adjustment and trimming of weights for

2003–2006’’) to the screener and then

post-stratified (see ‘‘Post-stratification

for 2003–2006’’). The resulting weights

were then the starting point (or base

weights) for the calculation of the

interview weights, which were then

adjusted for nonresponse to the

interview, and again post-stratified.

Finally, those post-stratified interview

weights were the base weights used in

the calculation of the MEC examination

weights. Those weights were adjusted

for nonresponse to the MEC

examination and then post-stratified. See

the ‘‘Computing final weights for

2003–2006’’ section for the calculation

of the final interview and MEC

examination weights.

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 the variability

in the weights, subject to operational

and analytic constraints. Additionally,

measures such as weight trimming were

implemented to reduce the variability in

the weights for NHANES. The impact

of weight variability is minimal when

estimates are for the demographic

subdomains used in the design, but

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.

Weighting the Sample Data

for 1999–2002

Weighting procedures for the first

two 2-year cycles, 1999–2000 and

2001–2002 (as well as special weights

for 1999–2002), were very similar to

those described in ‘‘Weighting the

sample data for 2003–2006,’’ with a few

notable differences. First, an additional

adjustment to the base weights was

included to account for the number of

newly constructed DUs completed

between DU sample selection and data

collection. Second, because the

NHANES 1999–2001 PSUs were

selected as part of a larger sample, the

adjustment described in the ‘‘Weighting

the sample data for 2003–2006’’ section

required some modifications. Third,

special 4-year weights (for 1999–2002)

were required for these years to

accommodate multiyear analyses using

these data. These deviations from the

general weighting methodology are

described in turn below.

Adjustment for new

construction

Building permit data were used to

select segments that contained housing

units built between 1990 and the

6-month period prior to data collection

for all study locations in 1999 and for

the first four locations in NHANES

2000 (16 study locations in all). Census

data were used to select segments that

contained housing units built before

1990; building permit data were used to

select segments that contained housing

units built after 1990. Because segments

were selected about 6 months prior to

the data collection in each study

location, a new construction factor was

calculated to account for housing units

built after new construction segments

were selected and prior to the beginning

of data collection.

For those 16 study locations, a new

construction factor, denoted by fi(nc) was

calculated as

∑(TOTMOSnc1 + TOTMOSnc2) fi(nc) = , ∑TOTMOSnc1

where TOTMOSnc1 is the total MOS for

new construction segments in the

original frame and TOTMOSnc2 is the

total MOS for new construction

segments in the supplemental frame

(that is, the frame of permits issued after

the selection of new construction

segments). For all other study locations

in the 1999–2002 sample, the new

construction factor was set equal to 1.

This factor was included in the base

weight calculation described in the

‘‘Calculating basic national inflation

weights’’ section.

Adjustment for subsampling of

1999–2001 PSUs

Originally, the 1999–2001 sample

was part of a larger 6-year design

developed with the target of 20 study

locations per year or 120 overall.

Simultaneously, a subsample with 14

study locations per year was also

developed in anticipation of a smaller

NHANES. As the design evolved, a

sample with 15 study locations each

year was adopted. In 1999, 15 study

locations were scheduled over the

course of the calendar year, but in order

to conduct some pilot testing (and due

to budgetary considerations) the first

three scheduled study locations were

used as field test sites, so that sample

was reduced to 12 locations. All other

years contained 15 locations.

Sample weights begin with each

sampled person’s probability of

selection. For persons selected in the

1999–2001 sample, because this

probability represents the probability of

the person being selected over a 6-year

sample, the chance of the person being

selected in only one of the 6 years is

that probability divided by six. In

addition, the reduction of three study

locations in 1999, or 87 instead of 90

over the projected 6-year interval, was

reflected by further adjustment. As a

result, weights were adjusted by a factor

of 7.25 (= 6 × 15/12 × 87/90) in 1999

and 5.8 (= 6 × 87/90) in 2000 and 2001.

This factor is referred to as the annual

weighting factor (AWFi

) in the

‘‘Weighting the Sample Data for

2003–2006’’ section.

Calculation of 4-year weights

Sample weights for NHANES

1999–2000 were based on population

estimates developed by the U.S. Census

Bureau before the 2000 decennial

census counts became available. The

2-year sample weights for NHANES

2001–2002, and all other subsequent

2-year cycles, are based on population

estimates that incorporate the year 2000

census counts. Because different

population bases were used, the 2-year
Series 2, No. 155 [ Page 21

weights for 1999–2000 and 2001–2002

are not directly comparable. So that

analysts could combine the 1999–2000

with 2001–2002 survey years in

analyses, 4-year sample weights were

created to account for the two different

reference populations. Because sample

weights for the subsequent years after

2002 were based on population

estimates after the 2000 census, special

multiyear sample weights were not

needed for these samples. For more

information about combining multiple

2-year data sets for analyses, see the

‘‘Combining 2-year weights to analyze

other multiyear samples’’ section.

Weighting the Sample Data

for 2003–2006

Calculating basic national

inflation weights

The first-stage (or base) weight for

each sample participant, denoted by

wi(BASE), was calculated as the reciprocal

of the sample participant’s probability of

selection. These sampling rates are

provided in Table II and their derivation

is described in ‘‘Variance estimation for

publicly released data.’’

The base weight for a sample

participant is simply the reciprocal of

the sampling rate for the sampling

domain of the sample participant

(denoted ri here, where the subscript i

indicates the sample participant). For

NHANES 2003–2006, the base weight

was adjusted further to account for the

proportion of DUs released, the

proportion of deselected DUs, and the

number of years in the sample being

weighted. The final base weight was

calculated as

1

w = . i(Base) ri

fi(release) fi(deselect) fi(year)

The following section briefly

describes each component of this

calculation.

Adjustments for the number of

release groups fielded

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. See the

‘‘Selection of DUs and persons’’ section

for a description of this procedure. The

sample participant base weight was

adjusted according to the proportion of

the total sample released to the field.

The release factor, denoted by fi(release) ,

was calculated as

1
 = Di

f . i(release)

Di represents the proportion of

sampled DUs released for screening in

the location sample participant i was

selected. If response rates are as

predicted and the MOS used during

sampling were current, the subsample

factor would be approximately 1.5. That

is, approximately two-thirds of the

sampled cases were expected to be

released.

In rare instances, less than one-half

of the sample selected was released,

yielding a factor greater than 2.0. This

situation resulted when the

demographics of the segments were not

as expected. Factors greater than 2.0 are

generally much higher than those for

other locations and, as a result, are

likely to dominate the weights for the

sample. When such a large factor

resulted from the sample release in a

study location, its value was trimmed to

2.0 to bring the weights down to a more

reasonable level.

Deselection of released DUs

The sample yield monitoring and

evaluation methods used in NHANES

III and subsequently in NHANES

1999–2006, occasionally suggested that

the expected number of sample

participants from released DUs would

exceed the target sample size for the

study location. In these instances, DUs

were deselected or randomly removed

from the set of DUs released, but not

yet screened, in order to keep the

sample size near the target. To account

for the deselection, an adjustment factor

was applied to the base weight of

sample participants identified in the

remaining units. In 1999–2006, the

expected number of sample participants

exceeded the manageable sample size in

five study locations. The deselection

factors for those locations were 1.58,

1.82, 2.05, 2.07, and 3.08. The factor,

denoted by fi(deselect) , was calculated as

1

f = . i(deselect) (1–Di

)

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 one.

Adjustment for the number of years

in the sample

Because the original selected

sample was intended to be fielded over

6 years, the base weights calculated

from the original sampling rates also

correspond to a 6-year sample. In

weighting subsets of those 6 years, the

following factor must be applied:

AWFi f = . i(year) Number of years in sample

AWFi represents the factor which,

when applied to the weights, takes the

6-year weights and converts them to

annual weights. 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 2003–2004

sample is two.

Nonresponse adjustment and

trimming of weights for

2003–2006

Nonresponse adjustment

If every selected household had

agreed to complete the screener and

every selected person had agreed to

complete the interview and the medical

examination, weighted estimates using

the base weights described in the

‘‘Weighting the Sample Data’’ section

would be approximately unbiased

estimates of characteristics for the

civilian noninstitutionalized U.S.

population. But in reality, some of the

sample participants who were screened

refused to be interviewed (interview

nonresponse) and some of the

interviewed sample participants refused

the medical examination (examination

nonresponse). Thus, nonresponse bias

may result. Bias in the survey estimates

occurs when the characteristics of

nonrespondents are very different from
Page 22 [ Series 2, No. 155

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 unit nonresponse in

NHANES for each stage of

nonresponse. 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 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 sampling 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:

+	 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,

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 was used to identify variables

most highly related to response

propensity. See Table IV for the

variables used to form the nonresponse

adjustment cells.

The nonresponse adjustment factors,

fi(NR), were calculated as

= ∑i=1

nas wi(Base) , fi(NR) ∑i=1

nar wi(Base)

where wi(Base) is the base weight for the

i-th sample participant; nas is the total

sample size in the a-th nonresponse

adjustment cell; and nar is the number of

respondents in the a-th nonresponse

adjustment 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) .

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, as was the case in

NHANES. Even a few unexpectedly

large sampling weights can seriously

inflate the variance of survey estimates.

Thus, weight trimming procedures may

be used to reduce the impact of any

such large sample participant weights on

the estimates produced from the sample.

Because trimming introduces bias in

the estimates, the hope was that the

resulting reduction in variances would

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 and Hispanic origin and

sex-age-income and pregnancy sampling

domains. Because Mexican-American

persons, pregnant women, and others

with low income (beginning in 2000)

were oversampled in NHANES, the

weights in their domains may be

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 and reason for selection (income

or pregnancy) 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, this is an important

characteristic to have in a trimming

procedure.

The trimming factors, fi(TR) , were

calculated as

where nb is the sample size of the b-th

race and Hispanic origin and sex-ageincome and pregnancy 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

∑n

b

i=1ti

fi(TR) =

∑n , b

i=1wi(Base) fi(NR)

wi(TR) = wi(NR) fi(TR) .

Post-stratification

The final step in the weighting

procedure was post-stratification to

known population totals to compensate

for undercoverage or overcoverage of

certain demographic groups and for any

residual differential nonresponse among

these groups. Post-stratification of

sample weights to independent

population estimates is used for several

purposes. In most household surveys,

certain demographic groups in the U.S.

population (for example, young black

males) experience fairly high rates of
Series 2, No. 155 [ Page 23

undercoverage in survey efforts.

Post-stratification 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.

Post-stratification can also help to

reduce the variability of sample

estimates and achieve consistency with

accepted U.S. figures for various

subpopulations.

Post-stratification involves applying

a ratio adjustment to the survey weights.

Broad classes—called post-stratification

cells or post-strata—are constructed

using auxiliary data, and a single ratio

adjustment factor is applied to all units

in a given post-stratification cell. The

numerator of the ratio is a ‘‘control

total’’ obtained from a secondary source

(these are available from http://

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

denominator is a weighted total obtained

using the survey weights. Therefore, at

the post-stratum level, estimates

obtained using the post-stratified survey

weights will correspond to the control

totals used. Because post-stratification is

a ratio adjustment, this process will

improve the efficiency of estimates

provided the variables used in

constructing post-stratification 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 post-stratum

level, and the overall gains in efficiency

due to post-stratification tend to be

small.

A major effect of post-stratification

is that it implicitly imputes for unit

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 post-stratification cells. This

is obviously an oversimplification as the

missed persons are likely to be different;

however, in the absence of any detailed

information on the characteristics of the

missed persons, post-stratification

appears to be the only reasonable

technique available for reducing the bias

due to undercoverage and nonresponse.

All control totals were obtained

using undercount-adjusted weights from

the March Supplement to the CPS from

the year closest to the midpoint of the

sample years being weighted. These

CPS weights have undergone

post-stratification to the U.S. 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. The post-stratification,

therefore, brings the weighted totals up

to the level of the presumed total

noninstitutionalized civilian population

in the United States.

The post-stratification factors, fi(PS) ,

were calculated as

Nc fi(PS) = , ∑nc

i=1wi(TR)

where Nc is the control total for

post-stratification cell c containing the

i-th sample participant, and nc is the

sample size of the post-stratification cell

c containing the i-th sample participant.

Thus, the post-stratified weights, wi(PS),

were calculated as

wi(PS) = wi(NR) fi(PS) .

Computing final weights for

2003–2006

The final weight for each sample

participant 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) .

More specifically, the final screening

weight was calculated as

wi(S) = wi(Base) fi(NR,S) fi(TR,S) fi(PS,S) ,

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) ,

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) .

Only the interview and MEC

examination weights were released to

the public.

Any sample participant who did not

respond to the sample participant

interview was assigned an interview

weight of ‘‘0.’’ These sample

participants were considered ineligible

for the examination and assigned a

MEC examination weight of ‘‘0.’’ These

records were not released to the public.

Sample participants who did complete

the interview and were eligible for the

examination but did not respond, were

assigned MEC examination weights of

‘‘0’’ 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 MEC

examination weights should be used for

analyses of data from the MEC

exclusively, or in conjunction with the

household interview data. This includes

data from the MEC interview, MEC

examination, or laboratory data on the

full MEC sample.

Subsample weights

As discussed in the ‘‘Special

samples’’ section, some NHANES

respondents were also asked to

participate in survey components that

were statistically defined (or random)

subsamples of the NHANES MECexamined sample. Data collected from

these participants include a variety of

lab, nutrition or dietary, environmental,

audiometry, and mental health

components. Each of these subsamples

was selected in a manner such that each

subsample is a nationally representative

sample.

For example, some but not all

participants were selected to give a

fasting blood sample on the morning of

their MEC exam. The subsamples

selected for these components were

chosen at random with a specified

sampling fraction (for example, one-half

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
Page 24 [ Series 2, No. 155

relevant to data collection (such as day

of the week for the dietary recall data).

When data collected via one of

these subsamples were released, special

survey weights were constructed for that

subsample and included in the data file.

These weights differ from the full

examination weight, and the subsamplespecific weights must be used for

statistical estimation of measures

collected only in that 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, there would have to be

random overlap between the subsamples

and appropriate weights would need to

be recalculated. For example, no sample

weights are provided for the overlap in

the fasting subsample with an

environmental subsample. See the

respective survey protocol or

documentation for more specific

information on each subsample.

Combining 2-year weights to

analyze other multiyear

samples

Only 2-year weights were calculated

for NHANES 2003–2004 and

2005–2006. To combine these cycles for

4-year estimates, the 4-year weights are

calculated as one-half times the 2-year

weights.

The 4-year sample weights for

NHANES 1999–2002 may be combined

with the 2-year weights for the later

survey cycles (NHANES 2003–2006) to

create 6- or 8-year weights: for 6-year

weights for 1999–2004, the 6-year

weight is equal to two-thirds times the

4-year 1999–2002 weight for survey

years 1999–2000 and 2001–2002, and

one-third times the 2-year weight for

survey years 2003–2004. For 8-year

weights from 1999–2006, the 8-year

weight is equal to one-half times the

4-year 1999–2002 weights and

one-fourth times the 2-year weights for

survey years 2003–2004 and 2005–2006.

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.

While 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.

To combine 2001–2002 with

2003–2004 and 2005–2006 to produce

6-year estimates, only 2-year weights

are used and the 6-year weights are

calculated as one-third times the 2-year

weights. Future years of data can

continue to be added using the same

methods as above.

Variance Estimation

Sampling errors should be

calculated for all survey estimates to aid

in determining statistical reliability of

those estimates. For complex sample

surveys, exact mathematical formulas

for variance estimates are usually not

available. Variance approximation

procedures are required to provide

reasonable, approximately unbiased, and

design-consistent estimates of variance.

The 2-year NHANES samples are

limited in analytic capabilities. Even

though each 2-year sample is nationally

representative, it was selected from only

30 PSUs and the sample sizes for some

specific race and Hispanic origin-sexage-income-pregnancy 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 (that is, 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. The first section

(‘‘Variance Estimation for NHANES

1999–2002’’) summarizes the variance

estimation procedures unique to the

1999–2002 samples, while the second

section (‘‘Variance Estimation for

NHANES 2003–2006’’) describes the

creation of variables necessary for

variance estimation on the public- and

restricted-use data files for the

2003–2006 samples. Two variance

approximation procedures which 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

of the replication approach is to select

subsamples repeatedly from the whole

sample, calculate the statistic of interest

for each of these subsamples (or

‘‘replicates’’), and then 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 the derivation of replicates from a

full sample. The jackknife procedure

retains most of the sample in each

replicate, whereas the BRR approach

retains only a portion of the sample in

each replicate.

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 only provided 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.

For the linearization approach,

nonlinear estimates are approximated by

linear ones 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

the use of the Taylor Series

Linearization methods for variance

estimation in all NHANES surveys.

SUDAAN, Stata, and the SAS survey

procedures can be used to obtain

variance estimated by this method.
Series 2, No. 155 [ Page 25

Variance Estimation for

NHANES 1999–2002

Variance estimation for publicly

released data

In considering the most appropriate

variance estimation technique for the

NHANES 1999–2001 PSUs, an issue of

concern was the fact that it was not

feasible to account for the effect of

subsampling counties from the NHIS

PSUs on the variances of the estimates.

The NHANES 1999–2001 sample of

PSUs was essentially selected using

systematic sampling from a sorted list

with no explicit stratification. As a

result, the delete-one jackknife method

was used to create replicate weights

because this method is generally used

when explicit stratification has not been

used to select the sample, even though

systematic sampling may have been

used. Further, because the delete-one

jackknife method provides a completely

unstratified variance estimate, it

introduces some amount of betweenstratum variation that does not exist.

This additional variation was desirable

as a means to compensate for the fact

that variance estimates could not

explicitly account for the subsampling

from the NHIS PSUs.

With the delete-one jackknife

method, each replicate was created by

deleting all cases from each study

location at a time, thereby making it

possible to identify all records from a

particular study location. The risk of

location identification, coupled with the

fact that the data files contain some

geographic data and other characteristics

of the area, led to concerns about

disclosure risks in the release of the

NHANES 1999–2000 data files. As a

result, NCHS initiated research to

examine the disclosure risks of

NHANES before the release of these

data. Various methods for splitting each

PSU into two dissimilar pseudo-PSUs

for the purpose of replication were

considered, and the most optimal

method, named the cluster-split method,

was used to create replicates for the

1999–2000 sample (3).

Beginning in 2002, NHANES

became a stratified design with

two PSUs per stratum for the 2-year

samples. Given this and the great

number of replicates needed for

continual 2-year releases, NCHS decided

that in future data releases, only PSU

and strata indicators will be released for

variance estimation. As a result, a new

method of variance estimation had to be

developed for use with the publicly

released data.

Continued research resulted in the

creation of the masked variance units

described in the next section, and these

units were created for the 1999–2002

sample. While the replicate weights for

the 1999–2000 sample still exist on the

public-use file, it is recommended that

the masked variance units be used with

the Taylor Series Linearization method

in the analysis of any data in the

NHANES 1999–2002 sample.

Variance Estimation for

NHANES 2003–2006

Variance estimation for publicly

released data

PSUs are selected from strata

defined by geography and proportions of

minority populations as described in the

‘‘Stratification and selection of PSUs for

2002–2006’’ section. 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.

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 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.

They 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 variance estimates. MVUs

have been created for all 2-year survey

cycles from NHANES 1999–2000

through 2005–2006. They can also be

used for analyzing any combined 4-, 6-,

or 8-year data set.

Many surveys swap data values

between cases for disclosure limitation.

Rather than swapping individual values,

however, the NHANES procedure used

in NHANES 2005–2006 (16) 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. 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 NHANES 2003–2006, estimates were

first calculated for every segment in all

study locations for comparative

purposes. These estimates provide

general descriptions of the segments

such as percentage of Hispanic sample

participants, prevalence of home

ownership, and obesity prevalence that

should be similar for swapped segments.

Study locations that were the most at

risk for data disclosure (locations with

smaller populations or in rural areas)

were then 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 of swapping the pair of

segments. The distance measure to

determine the effect of swapping the

pair segments on variance was

calculated as
- - v(xl

|Sr) – v(xl|S) D = ∑q

, l=1|

­ v(xl

|S) |

Page 26 [ Series 2, No. 155

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, andv(xl

|S) is

the variance of the estimate before

swapping. Note that for the 1999–2002

data, the simpler distance measure,

|(xl|Sr) – xl D = Σ |S)|, was used. This l

q

=1 - ­

measure solely considers the differences

in the selected estimates, rather than

incorporating the variances of those

estimates.

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 only

swapped once. Consideration was also

given to pairs of segments from at-risk

study locations; these pairs were

minimized where possible.

Further research by Park (15)

indicated that variance estimates very

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 (that is, the number of

study locations determined to be at risk

and the number of segments swapped

per location) is limited.

Variance estimation in the RDC

For the current sample design,

NHANES data are released to the public

in 2-year data cycles. In addition to

public-use data files, special data sets

are available only through the NCHS’

RDC. These special data sets are for (a)

data items collected for an odd number

of calendar years (1, 3, or 5 years); (b)

data sets with geographically linked data

to some other contextual data files

(often supplied by the data user); and

(c) data items 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 MEC weight. This

single-year weight can be combined

with the MEC weight provided on the

2-year public-use file to create a 3-year

MEC weight. All single-year sample

weights were calculated in the same

manner as the public-use 2-year weights

described in the ‘‘Weighting the Sample

Data’’ section of this report. If a 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 given the

restrictions of the RDC such as the

prohibition of publication of PSU-level

estimates. Further, any subnational

estimate that is generated out of an

RDC analysis must be reviewed and

approved by NCHS staff to protect the

confidentiality of sample respondents.

More information on the RDC and

lists of special NHANES data files are

available on the NCHS website

(http://www.cdc.gov/nchs/). Information

on proposals for use of stored specimens

is also available on the NCHS website.

References

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J, Feldman J. The Department of

Health and Human Services Survey

Integration Plan: Current progress.

Proceedings of the Government

Statistics Section, American Statistical

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2.Botman SL, Moore TF, Moriarity CL,

Parsons VL. Design and estimation for

the National Health Interview Survey,

1995–2004. National Center for Health

Statistics. Vital Health Stat 2(130).

2000.

3.Dohrmann S, Curtin LR, Mohadjer L,

Montaquila J, Le T. National Health

and Nutrition Examination Survey:

Limiting the risk of data disclosure

using replication techniques in variance

estimation. Proceedings of the Survey

Research Methods Section, American

Statistical Association 807–12. 2002.

4.Engel A, Murphy RS, Maurer K,

Collins E. Plan and operation of the

HANES I Augmentation Survey of

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5.Hunter EL, Arnett R. Survey

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Plan and initial program of the Health

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7.National Center for Health Statistics.

Plan, operation, and response results of

a program of children’s examinations.

National Center for Health Statistics.

Vital Health Stat 1(5). 1967.

8.National Center for Health Statistics.

Plan and operation of a health

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12–17 years of age. National Center for

Health Statistics. Vital Health Stat 1(8).

1969.

9.Miller HW. Plan and operation of the

Health and Nutrition Examination

Survey, United States, 1971–1973.

National Center for Health Statistics.

Vital Health Stat 1(10a). 1973.

10.National Center for Health Statistics.

Plan and operation of the Health and

Nutrition Examination Survey, United

States, 1971–1973. National Center for

Health Statistics. Vital Health Stat

1(10b). 1977.

11.McDowell A, Engel A, Massey JT,

Maurer K. Plan and operation of the

second National Health and Nutrition

Examination Survey, 1976–80. National

Center for Health Statistics. Vital

Health Stat 1(15). 1981.

12.Maurer KR. Plan and operation of the

Hispanic Health and Nutrition

Examination Survey, 1982–84. National

Center for Health Statistics. Vital

Health Stat 1(19). 1985.

13.Ezzati TM, Massey JT, Waksberg J, et

al. Sample design: Third National
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[ Page 27

Health and Nutrition Examination

Survey. National Center for Health

Statistics. Vital Health Stat 2(113).

1992.

14.Montaquila JM, Bell B, Mohadjer L,

Rizzo L.A methodology for sampling

households late ina decade.

Proceedings of the Survey Research

Methods Section, American Statistical

Association 311–15. 1999.

15.Park I. Primary sampling unit (PSU)

masking and variance estimation in

complex surveys. Survey Methodology

34(2):183–94. 2008.

16.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 1761–7. 2006.
Page 28 [ Series 2, No. 155

Appendix I. Glossary

Centers for Disease Control and

Prevention (CDC)—CDC is one of the

major operating components of the U.S.

Department of Health and Human

Services.

Computer-assisted personal

interviewing (CAPI)—An interviewing

technique in which the interviewer uses

a laptop computer. The laptop displays

the question text for the interviewer to

read and provides any other necessary

instructions to the interviewer.

Interviewers record the respondent’s

answers using the keyboard. Software

directs the interviewer to the next

appropriate question based on the

answers entered. For the National Health

and Nutrition Examination Survey

(NHANES), the screening interview and

all subsequent interviews conducted in

the household utilize CAPI. At

screening, the CAPI system is also the

mechanism with which eligible

household members are selected for the

survey.

U.S. 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 who are least able

to help themselves. CDC, including the

National Center of Health Statistics

(NCHS), 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

with the intention of meeting goals for

those specific demographic groups. For

NHANES, sampling domains are

defined by race and Hispanic origin,

income, age, and gender. See ‘‘Sampling

domain.’’

Domain flags—See ‘‘Sampling

domain flags.’’

Double sampling—A general term

for a method used in a number of

statistical applications, such as

stratification and regression or ratio

estimation. An application of double

sampling is to update a sampling frame

when the sample is to be selected with

respect to a measure of size (MOS) but

a reliable estimate of that measure is not

available. For NHANES, double

sampling or two-phase sampling, was

used in second-stage units (SSUs or

segments) late in the decade 2000–2010

when the population counts from the

U.S. Census Bureau, used in the

calculation of the MOS, were old and

feared to be no longer representative of

the study location. In the NHANES

study locations for which an accurate

MOS is not available, a larger-thanneeded sample of segments was selected

in the first phase. After field staff

determined the number of dwelling units

(DUs) in the first-phase sample of

segments, an updated MOS that

reflected the ratio of the actual number

of DUs to the expected number of DUs

was calculated. The final sample of

segments was selected by subsampling

from the first-phase segments using the

updated MOS.

Dwelling unit (DU)—Also ‘‘housing

unit.’’ A house, an apartment, a mobile

home or trailer, a group of rooms, or a

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 any other

individuals in the building and which

have direct access from outside the

building or through a common hall. In

this report, the term is used generally to

mean those DUs eligible (that is,

excluding institutional group quarters),

or that could be eligible (that is, vacant

at the time of sampling, but could be

occupied once screening begins) for the

survey.

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. Those

living in group quarters are usually 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: institutionalized group

quarters and noninstitutionalized group

quarters.

Institutionalized group quarters—

Group quarters providing formally

authorized and supervised care or

custody in institutional settings, such as

correctional facilities, nursing facilities

or skilled nursing facilities, in-patient

hospice facilities, mental (psychiatric

hospitals) facilities, group homes for

juveniles, and residential treatment

centers for juveniles. Institutionalized

group quarters are not included in the

NHANES sample. (For group quarters

included in NHANES, see

‘‘Noninstitutionalized group quarters.’’)

Household—The group of persons

living in an occupied dwelling unit.

Low income—Beginning in 2000,

NHANES split the sampling domains

for others based on their income status:

low income and non-low income.

Low-income persons were defined as

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 SSUs aggregated into

groups for the purpose of 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

variances that would have been

estimated using the ‘‘true’’ design

variance estimates. MVUs have been

created for all 2-year survey cycles from
Series 2, No. 155 [ Page 29

NHANES 1999–2000 through

2005–2006. They can also be used for

analyzing any combined 4-, 6-, or

8-year data set.

Maximum sampling rate—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.

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 raceethnicity-income groups of interest.

National Center for Health

Statistics (NCHS)—NCHS is the

principal health statistics agency in the

United States. It 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, the National Health Interview

Survey, NHANES, the National Health

Care Surveys, and the National Survey

of Family Growth, among others.

CDC’s NCHS is part of the U.S.

Department of Health and Human

Services (HHS).

National Health Interview Survey

(NHIS)—A continuing household survey

with the purpose of securing accurate

and current statistical information on the

amount, distribution, and effects of

illness and disability in the United

States. NHIS is one of NCHS’ major

data collection programs. The study

locations selected for NHANES

1999–2001 were linked to NHIS at the

county level, as they were selected from

a frame that included all counties in two

panels of the NHIS PSUs.

Noninstitutionalized group quarters—

Group quarters that do not provide

formally authorized and supervised care or

custody in institutionalized settings. These

include such places as college or

university housing, group homes intended

for adults, residential treatment facilities

for adults, workers’ group living quarters

and Job Corps centers, and religious group

quarters.

Noninstitutionalized group quarters are

included in the NHANES sample.

Noninstitutionalized civilian

population—Includes all people living

in households, excluding

institutionalized group quarters and

those persons on active duty with the

military. This is the target population for

NHANES.

Office of Management and Budget

(OMB)—OMB reviews survey designs,

materials, and questionnaires proposed

for use by government agencies. The

review is conducted by the OMB’s

Office of Information and Regulatory

Affairs.

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 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’’).

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

assure that the identities of the

respondents are protected. This file is

disseminated by NCHS to encourage

widespread use of the survey data.

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.

PPS sampling is used in NHANES in

the selection of PSU and SSUs.

Race and Hispanic origin—The

terms race and Hispanic origin are used

in this report as they were used in

sample selection, and usually referred to

a single term: race-ethnicity. The racial

and ethnic groups this term refers to are

Mexican-American persons, nonMexican-American black persons, and a

third group consisting of all other

persons.

Release group—A systematic

subsample of a study location’s

screening sample, with the screening

sample sequenced by segment number

and a temporary geographically based

sequence number. Each release group

contained cases from all segments,

except as limited by release group and

segment size. In most study locations

the 50% release group (that is, group A)

was released to the interviewers first.

The yield from this group was

monitored and used to project estimates

of the total yield of sampled persons

(SPs) expected from this group. Based

on these figures, additional groups (or

portions of groups) were released as

needed. The sample was monitored on a

daily basis to determine whether

additional release groups were required.

Replicates—Subsamples selected

repeatedly from a sample used in some

variance estimation approaches. With

these approaches, the statistic of interest

is calculated for each of the subsamples,

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 1999–2006 multiyear

samples; the delete-one jackknife

method was used in the creation of

replicate weights for the 1999–2000

sample.

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 interviews are considered

interview respondents. Persons agreeing

to complete the in-home interviews and

a mobile examination center (MEC)

examination are considered MEC

respondents.

Response rate—The number of

survey respondents divided by the

number of persons selected into the

sample. The response rates referred to in

this document specifically 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 containing respondent records

from a survey which contain some
Page 30 [ Series 2, No. 155

information that may, if released to the

public, risk disclosure of individual

survey respondents. These data are

available only through NCHS’ Research

Data Center. These special data sets are

(a) for data items that were collected for

an odd number of calendar years (1, 3,

and 5 years); (b) for data sets with

geographically linked data to some other

contextual data files (often supplied by

the data user); (c) for data items that are

determined to be too sensitive or too

detailed to be released to the public due

to confidentiality restrictions; and (d) for

surplus sera projects where past

biological samples have been stored and

subsequently used based on a formal

proposal submitted as a special study;

these could be on the full sample or a

special subsample.

Sampling domain—See ‘‘Domain.’’

There were 53 sampling domains in

NHANES 1999, and 76 sampling

domains in NHANES 2000–2006.

Table B of this report contains the

specific sampling domains for these

years.

Sampling domain flags—Strings of

‘‘0’’ and ‘‘1’’ attached to each sampled

DU in the CAPI system. There was one

string for each race-ethnicity-income

group, with each digit of the string

representing one of the specific

age-gender sampling domains. If the

digit corresponding to an age-gender

domain in a race and Hispanic origin

string contained a ‘‘1,’’ then all persons

in that DU with the matching

demographic characteristics were

included in the sample. A ‘‘0’’ and ‘‘1’’

in each string was set based on the

sampling rates.

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 and

Hispanic origin and sex-age-income

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.

Sampling variance—The sampling

variance is a measure of the variation of

a statistic, such as a proportion or a

mean, which is due to having taken a

sample instead of collecting data from

every person in the full population. It

measures the variation of the estimated

proportion or mean over repeated

samples. The sampling variance is zero

when the full population is observed, as

in a census. For NHANES, the sampling

variance estimate is a function of the

sampling design and the population

parameter being estimated (that is, a

proportion or a mean). Many common

statistical software packages compute

‘‘population’’ variances by default; these

may underestimate the sampling

variance. Estimating the sampling

variance requires special software, such

as those discussed in this report.

Sampling weight—For a respondent

in NHANES, this is the estimated

number of persons in the target

population that he or she represents. For

example, if a man in the sample

represents 12,000 men in his raceethnicity-income-age category, then his

‘‘sampling weight’’ is 12,000. The

NHANES sampling weights were

adjusted for different sampling rates (of

the race-ethnicity-income-age-gender

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 these

adjustments, it is sometimes called the

‘‘final’’ sampling weight.

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 or screening

interview consisted of a household

roster, collecting the income level of the

household, and the race and Hispanic

origin, age, and gender of all members.

In NHANES, only persons aged 18 and

over can answer the screener.

Screening—The process of

conducting, or attempting to conduct,

the screening interview in the DUs

contained in the groups released.

Occupied DUs (households) are

‘‘screened’’ through the screening

interview. Other units can also be

‘‘screened’’; the process for these units

is simply verification that they are

vacant, or simply not DUs. See

‘‘Screener.’’

Screening sample—The sample of

DUs selected for a study location.

Secondary sampling unit (SSU)—

The second-stage selection unit in a

multistage area probability sample. For

NHANES, these are typically referred to

as ‘‘segments.’’

Segment—A group of housing units

located near one another, all of which

were considered for selection into the

sample. For NHANES, segments consist

of a census block or groups of blocks.

For NHANES, the selection of segments

comprises 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 of course be selected with differing

probabilities, but such differences in

selection probabilities at various stages

cancel out. NHANES 1999–2006 is a

self-weighting sample of persons within

each sampling domain.

Simple random sample—A sample

in which all members of the population

are selected directly and have an equal

chance to be selected for the sample.

The NHANES sample is not a simple

random sample. The NHANES sample

was stratified, selected in stages, and

employed unequal chances of selection

for the respondents, by race and

Hispanic orign, income, age, and gender.

Such designs are referred to as

‘‘complex’’ and require special software

to estimate the variance of statistics

computed from a sample with a

complex design.

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 PSUs selected with certainty

were divided into multiple study

locations and fielded at different times.

Survey location—See ‘‘Study

location.’’

Strata and stratification—The

partitioning of a population of PSUs

into mutually exclusive categories
Series 2, No. 155

[ Page 31

(strata). Typically, stratification is used

to increase the precision of survey

estimates for subpopulations important

to the survey’s objectives. For the

selection of PSUs fielded in 2002–2006,

PSUs were stratified based on region,

MSA status, and various population

demographics. In NHANES 1999–2006,

strata were designated for within-PSU

selection based on the density of

Mexican-American persons living in that

area.

Target population—The population

to be described by estimates from the

survey. In NHANES 1999–2006, the

target population was the resident

civilian noninstitutionalized population

of the United States, which excluded 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 District of Columbia.

Two-phase segment selection—See

‘‘Double sampling.’’

Variance unit—A collection of

PSUs aggregated into groups and

excluded when forminga replicate for

variance estimation. For NHANES,

usually an entire PSU corresponds toa

variance unit.

Variance stratum—The cluster of

variance units used when forminga

replicate for variance estimation. For

NHANES, usually PSU sampling strata

correspond to the variance strata.

Weight—See ‘‘Sampling weight.’’
es and Figure

ening requirements for National Health and Nutrition Examination Survey, 2002–2007

Projected number

Projected of households

number of screened Projected Number of

households to attain target no. number of additional

Target screened to of exams over examined examined

Projected number have one 6 years in self- persons persons

population of exams examined weighting in ‘‘basic’’ to attain

ncome-sex-age sampling domain in 2004 for 1 year person area sample area sample target

Male Under age 1 year 587,103 48 213 60,639 285 0

and 1–2 years 1,137,668 85 113 57,434 510 0

female 3–5 years 1,696,528 85 76 38,699 510 0

Male 6–11 years 1,791,468 85 68 34,933 510 0

Male 12–15 years 1,352,908 92 97 53,486 552 0

Male 16–19 years 1,223,145 92 110 60,639 552 0

Male 20–39 years 4,611,191 85 32 16,066 510 0

Male 40–59 years 4133420 85 38 19,504 510 0

Male 60 years and over 1,694,838 85 87 44,310 510 0

Female 6–11 years 1,733,088 85 72 36,782 510 0

Female 12–15 years 1,316,008 85 95 48,439 510 0

Female 16–19 years 1,248,516 85 102 52,273 510 0

Female 20–39 years 5,623,481 85 25 12,998 510 0

Female 40–59 years 5,042,331 85 30 15,313 510 0

Female 60 years and over 2,478,671 85 68 34,558 510 0

Male Under age 1 year 576,027 92 209 60,639 290 262

and 1–2 years 1,127,767 90 114 61,383 533 7

female 3–5 years 1,604,386 85 80 40,897 510 0

Male 6–11 years 1,512,695 85 77 39,392 510 0

Male 12–15 years 935,649 92 125 68,932 486 66

Male 16–19 years 868,279 92 142 78,549 426 126

Male 20–39 years 3,736,086 85 38 19,307 510 0

Male 40–59 years 2,292,162 85 60 30,663 510 0

Male 60 years and over 728,735 95 202 115,176 300 270

Female 6–11 years 1,411,518 85 85 43,154 510 0

Female 12–15 years 847,952 92 139 76,896 435 117

Female 16–19 years 808,686 92 149 82,442 406 146

Female 20–39 years 3,654,437 85 35 17,859 510 0

Female 40–59 years 2,219,359 85 55 28,070 510 0

Female 60 years and over 880,495 92 165 91,067 368 184

Page 32[ Series 2, No. 155
ening requirements for National Health and Nutrition Examination Survey, 2002–2007—Con.

Projected number

Projected of households

number of screened Projected Number of

households to attain target no. number of additional

Target screened to of exams over examined examined

Projected number have one 6 years in self- persons persons

population of exams examined weighting in ‘‘basic’’ to attain

ncome-sex-age sampling domain in 2004 for 1 year person area sample area sample target

Male Under age 1 year 528,844 45 223 60,143 270 0

and 1–2 years 1,045,330 54 105 33,911 324 0

female 3–5 years 1,591,622 54 79 25,548 324 0

Male 6–11 years 1,691,439 27 67 10,837 162 0

Male 12–15 years 1,217,776 27 93 15,052 162 0

Male 16–19 years 1,137,953 27 105 17,003 162 0

Male 20–29 years 1,995,232 27 64 10,390 162 0

Male 30–39 years 1,550,762 27 78 12,617 162 0

Male 40–49 years 1,552,642 27 81 13,041 162 0

Male 50–59 years 1,240,377 27 104 16,915 162 0

Male 60–69 years 1,018,725 27 115 18,580 162 0

Male 70–79 years 653,253 27 242 39,202 162 0

Male 80 years and over 345,484 16 622 59,738 96 0

Female 6–11 years 1,640,043 27 69 11,177 162 0

Female 12–15 years 1,185,777 27 95 15,459 162 0

Female 16–19 years 1,289,219 27 137 22,143 162 0

Female 20–29 years 2,780,447 27 42 6,808 162 0

Female 30–39 years 2,131,932 27 56 9,076 162 0

Female 40–49 years 1,901,695 27 65 10,525 162 0

Female 50–59 years 1,699,254 27 7 11,261 162 0

Female 60–69 years 1,459,540 27 93 15,103 162 0

Female 70–79 years 1,423,241 27 103 16,761 162 0

Female 80 years and over 1,115,698 27 140 22,620 162 0

Male Under age 1 year 2,174,859 70 58 24,508 420 0

and 1–2 years 4,298,893 70 34 14,386 420 0

female 3–5 years 6,545,504 70 22 9,148 420 0

Male 6–11 years 7,000,436 70 21 8,834 420 0

Male 12–15 years 5,040,067 71 27 11,501 426 0

Male 16–19 years 4,986,659 76 28 12,602 456 0

Male 20–29 years 11,670,740 79 13 5,980 474 0

Male 30–39 years 13,051,517 81 14 6,562 486 0

Male 40–49 years 16,293,817 82 10 5,072 492 0

Male 50–59 years 13,165,863 79 13 6,242 474 0

Male 60–69 years 8,020,536 80 20 9,460 480 0

Male 70–79 years 5,323,446 79 33 15,437 474 0

Male 80 years and over 2,627,703 70 67 28,166 420 0

Series 2, No. 155[ Page 33
ening requirements for National Health and Nutrition Examination Survey, 2002–2007—Con.

Projected number

Projected of households

number of screened Projected Number of

households to attain target no. number of additional

Target screened to of exams over examined examined

Projected number have one 6 years in self- persons persons

population of exams examined weighting in ‘‘basic’’ to attain

ncome-sex-age sampling domain in 2004 for 1 year person area sample area sample target

Female 6–11 years 6,668,178 70 22 9,275 420 0

Female 12–15 years 4,821,197 68 28 11,515 408 0

Female 16–19 years 4,621,916 70 28 11,629 420 0

Female 20–29 years 11,094,039 75 12 5,590 450 0

Female 30–39 years 13,128,858 79 12 5,624 474 0

Female 40–49 years 16,348,739 79 10 4,583 474 0

Female 50–59 years 13,472,653 75 12 5,524 450 0

Female 60–69 years 8,613,202 72 20 8,558 432 0

Female 70–79 years 6,151,219 67 30 12,112 402 0

Female 80 years and over 3,834,159 68 55 22,428 408 0

Female . . . 280,025,082 4,966 6,275 2,169,145 28,615 1,178

Page 34 [ Series 2, No. 155
ase weights for National Health and Nutrition Examination Survey, 2002–2006

Numerator of sampling rate1 Base weights by domain

sex-age sampling domain

Density

stratum 1

(less than 10%)

Density

stratum 2

(10%–29%)

Density

stratum 3

(30%–59%)

Density

stratum 4

(60% or more)

Density

stratum 1

(less than 10%)

Density

stratum 2

(10%–29%)

Density

stratum 3

(30%–59%)

Density

stratum 4

(60% or more)

Male

and

emale

Male

Male

Male

Male

Male

Male

Female

Female

Female

Female

Female

FemaleMale

and

emale

Male

Male

MaleMale

Male

Male

Female

FemaleFemaleFemale

Female

FemaleMale

and

emale

Male

Male

Male

Male

Male

Male

Male

Male

Male

Male

Female

Under age 1 year

1–2 years

3–5 years

6–11 years

12–15 years

16–19 years

20–39 years

40–59 years

60 years and over

6–11 years

12–15 years

16–19 years

20–39 years

40–59 years

60 years and over

Under age 1 year

1–2 years

3–5 years

6–11 years

12–15 years

16–19 years20–39 years

40–59 years

60 years and over

6–11 years

12–15 years16–19 years20–39 years

40–59 years

60 years and over

Under age 1 year

1–2 years

3–5 years

6–11 years

12–15 years

16–19 years

20–29 years

30–39 years

40–49 years

50–59 years

60–69 years

70–79 years

80 years and over

6–11 years

1.00

0.95

0.64

0.58

0.88

1.00

0.26

0.32

0.73

0.61

0.80

0.86

0.21

0.25

0.57

1.00

1.00

0.67

0.65

1.00

1.000.32

0.51

1.00

0.71

1.001.000.29

0.46

1.00

0.99

0.56

0.42

0.18

0.25

0.28

0.17

0.21

0.22

0.28

0.31

0.65

0.99

0.18

1.00

0.95

0.64

0.58

0.88

1.00

0.26

0.32

0.73

0.61

0.80

0.86

0.21

0.25

0.57

1.50

1.00

0.67

0.65

1.00

1.250.32

0.51

1.50

0.71

1.251.350.29

0.46

1.50

0.99

0.56

0.42

0.18

0.25

0.28

0.17

0.21

0.22

0.28

0.31

0.65

0.99

0.18

1.00

0.95

0.64

0.58

0.88

1.00

0.26

0.32

0.73

0.61

0.80

0.86

0.21

0.25

0.57

2.20

1.00

0.67

0.65

1.00

1.500.32

0.51

2.20

0.71

1.251.600.29

0.46

1.60

0.99

0.56

0.42

0.18

0.25

0.28

0.17

0.21

0.22

0.28

0.31

0.65

0.99

0.18

1.00

0.95

0.64

0.58

0.88

1.00

0.26

0.32

0.73

0.61

0.80

0.86

0.21

0.25

0.57

3.00

1.05

0.67

0.65

1.50

1.500.32

0.51

3.00

0.71

1.601.600.29

0.46

2.00

0.99

0.56

0.42

0.18

0.25

0.28

0.17

0.21

0.22

0.28

0.31

0.65

0.99

0.18

1,181.78

1,247.72

1,851.77

2,051.41

1,339.84

1,181.78

4,460.50

3,674.15

1,617.30

1,948.31

1,479.43

1,370.92

5,513.22

4,679.81

2,073.66

1,181.78

1,181.78

1,752.24

1,819.19

1,181.78

1,181.78

3,711.67

2,337.11

1,181.78

1,660.61

1,181.781,181.784,012.72

2,552.99

1,181.78

1,191.53

2,113.24

2,804.99

6,612.62

4,760.85

4,214.64

6,897.10

5,679.75

5,494.95

4,236.68

3,856.90

1,828.03

1,199.60

6,411.69

1,181.78

1,247.72

1,851.77

2,051.41

1,339.84

1,181.78

4,460.50

3,674.15

1,617.30

1,948.31

1,479.43

1,370.92

5,513.22

4,679.81

2,073.66

787.85

1,181.78

1,752.24

1,819.19

1,181.78

945.43

3,711.67

2,337.11

787.85

1,660.61

945.43875.39

4,012.72

2,552.99

787.85

1,191.53

2,113.24

2,804.99

6,612.62

4,760.85

4,214.64

6,897.10

5,679.75

5,494.95

4,236.68

3,856.90

1,828.03

1,199.60

6,411.69

1,181.78

1,247.72

1,851.77

2,051.41

1,339.84

1,181.78

4,460.50

3,674.15

1,617.30

1,948.31

1,479.43

1,370.92

5,513.22

4,679.81

2,073.66

537.17

1,181.78

1,752.24

1,819.19

1,181.78

787.85

3,711.67

2,337.11

537.17

1,660.61

945.43738.61

4,012.72

2,552.99

738.61

1,191.53

2,113.24

2,804.99

6,612.62

4,760.85

4,214.64

6,897.10

5,679.75

5,494.95

4,236.68

3,856.90

1,828.03

1,199.60

6,411.69

1,181.78

1,247.72

1,851.77

2,051.41

1,339.84

1,181.78

4,460.50

3,674.15

1,617.30

1,948.31

1,479.43

1,370.92

5,513.22

4,679.81

2,073.66

393.93

1,125.51

1,752.24

1,819.19

787.85787.85

3,711.67

2,337.11

393.93

1,660.61

738.61738.61

4,012.72

2,552.99

590.89

1,191.53

2,113.24

2,804.99

6,612.62

4,760.85

4,214.64

6,897.10

5,679.75

5,494.95

4,236.68

3,856.90

1,828.03

1,199.60

6,411.69

Series 2, No. 155[ Page 35
ase weights for National Health and Nutrition Examination Survey, 2002–2006—Con.

Numerator of sampling rate1 Base weights by domain

sex-age sampling domain

Density

stratum 1

(less than 10%)

Density

stratum 2

(10%–29%)

Density

stratum 3

(30%–59%)

Density

stratum 4

(60% or more)

Density

stratum 1

(less than 10%)

Density

stratum 2

(10%–29%)

Density

stratum 3

(30%–59%)

Density

stratum 4

(60% or more)

Female

Female

Female

Female

Female

Female

Female

Female

Female

Male

and

emale

Male

Male

Male

Male

Male

Male

Male

Male

Male

Male

Female

Female

Female

Female

Female

Female

Female

Female

Female

Female

12–15 years

16–19 years

20–29 years

30–39 years

40–49 years

50–59 years

60–69 years

70–79 years

80 years and over

Under age 1 year

1–2 years

3–5 years

6–11 years

12–15 years

16–19 years

20–29 years

30–39 years

40–49 years

50–59 years

60–69 years

70–79 years

80 years and over

6–11 years

12–15 years

16–19 years

20–29 years

30–39 years

40–49 years

50–59 years

60–69 years

70–79 years

80 years and over

0.25

0.37

0.11

0.15

0.17

0.19

0.25

0.28

0.37

0.40

0.24

0.15

0.15

0.19

0.21

0.10

0.11

0.08

0.10

0.16

0.25

0.46

0.15

0.19

0.19

0.09

0.09

0.08

0.09

0.14

0.20

0.37

0.25

0.37

0.11

0.15

0.17

0.19

0.25

0.28

0.37

0.40

0.24

0.15

0.15

0.19

0.21

0.10

0.11

0.08

0.10

0.16

0.25

0.46

0.15

0.19

0.19

0.09

0.09

0.08

0.09

0.14

0.20

0.37

0.25

0.37

0.11

0.15

0.17

0.19

0.25

0.28

0.37

0.40

0.24

0.15

0.15

0.19

0.21

0.10

0.11

0.08

0.10

0.16

0.25

0.46

0.15

0.19

0.19

0.09

0.09

0.08

0.09

0.14

0.20

0.37

0.25

0.37

0.11

0.15

0.17

0.19

0.25

0.28

0.37

0.40

0.24

0.15

0.15

0.19

0.21

0.10

0.11

0.08

0.10

0.16

0.25

0.46

0.15

0.19

0.19

0.09

0.09

0.08

0.09

0.14

0.20

0.37

4,635.75

3,236.31

10,526.80

7,896.05

6,808.54

6,363.46

4,745.01

4,275.58

3,168.03

2,923.98

4,981.26

7,833.83

8,111.62

6,231.07

5,686.54

11,982.62

10,921.02

14,130.14

11,480.78

7,574.95

4,642.10

2,544.28

7,726.62

6,223.44

6,162.56

12,819.78

12,741.09

15,635.92

12,973.67

8,373.95

5,916.60

3,195.13

4,635.75

3,236.31

10,526.80

7,896.05

6,808.54

6,363.46

4,745.01

4,275.58

3,168.03

2,923.98

4,981.26

7,833.83

8,111.62

6,231.07

5,686.54

11,982.62

10,921.02

14,130.14

11,480.78

7,574.95

4,642.10

2,544.28

7,726.62

6,223.44

6,162.56

12,819.78

12,741.09

15,635.92

12,973.67

8,373.95

5,916.60

3,195.13

4,635.75

3,236.31

10,526.80

7,896.05

6,808.54

6,363.46

4,745.01

4,275.58

3,168.03

2,923.98

4,981.26

7,833.83

8,111.62

6,231.07

5,686.54

11,982.62

10,921.02

14,130.14

11,480.78

7,574.95

4,642.10

2,544.28

7,726.62

6,223.44

6,162.56

12,819.78

12,741.09

15,635.92

12,973.67

8,373.95

5,916.60

3,195.13

4,635.75

3,236.31

10,526.80

7,896.05

6,808.54

6,363.46

4,745.01

4,275.58

3,168.03

2,923.98

4,981.26

7,833.83

8,111.62

6,231.07

5,686.54

11,982.62

10,921.02

14,130.14

11,480.78

7,574.95

4,642.10

2,544.28

7,726.62

6,223.44

6,162.56

12,819.78

12,741.09

15,635.92

12,973.67

8,373.95

5,916.60

3,195.13

tes may be calculated by dividing the numerator by 1,182.

Page 36[ Series 2, No. 155
xamination, and laboratory subsamples in National Health and Nutrition Examination Survey, 2003–2006

Sample fraction

st Sample collected Ages included (of the age group) Random groups included1

2004) .......... Interview 20–39 years 1/2 1, 3, 5, 7, 9, 11

.............. Examination 20–69 years 1/2 0, 2, 4, 6, 8, 10

ticides.......... Blood 12 years and over 1/3 4, 6, 8, 9

s, furans, PCBs .... Blood 12 years and over 1/3 1, 2, 5, 11

.............. Blood 12 years and over 1/3 0, 3, 7, 10

.............. Tap water, blood 20–59 years (2003–2004) 1/2 4, 5, 6, 7, 8, 9

Tap water 12 years and over (2005–2006)

.............. Tap water 12 years and over 1/2 0, 1, 2, 3, 10, 11

hosphate pesticide Urine 6 years and over 1/3 4, 6, 8, 9

..............

.............. Urine 6 years and over 1/3 1, 2, 5, 11

Series 2, No. 155[ Page 37
ation of nonresponse adjustment cells for weighting all samples from 1999 to 2006

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

1999–2000 2001–2002 2003–2004 2005–2006 1999–2002

1: 0–5, 6–19, 20–59, 1: Under age 1, 1–5, 6–11, 1: Under age 1, 1: Under age 1, 1: Under age 1, 1–5,

60 and over 12–19, 20–59, 1–5, 6–11, 12–19, 1–5, 6–11, 6–11, 12–19,

60 and over 20–59, 60 and over 12–19, 20–59, 60 20–59, 60 and over

and over

2: Mexican American, 2: Mexican American, 6: Mexican American, 6: Mexican American, 2: Mexican American,

black, other black, other black, white, other black, other black, other

3: Male, female 3: Male, female 4: 20–59 only 4: 20–59 only 3: Male, female

. . . 4: 1–2, 3–4, 5–6, 2: 1–2, 3–4, 5–6, 2: 1–2, 3–4, 5–6, 4: 1–4, 5–6, 7

7 or more 7 or more 7 or more or more

. . . 5: 15–19 only 5: 15–19 only 5: 15–19 only 5: 15–19 only

. . . . . . 3: Urban, suburban, rural 3: Urban, suburban, rural . . .

1: 0–5, 6–19, 1: Under age 1, 1–5, 1: Under age 1, 1: Under age 1, 1: Under age 1,

20–59, 60 and over 6–11, 12–19, 1–5, 6–11, 12–19, 1–5, 6–11, 12–19, 1–5, 6–11, 12–19,

20–59, 60 and over 20–59, 60 and over 20–59, 60 and over 20–59, 60 and over

5: Mexican American, 4: Mexican American, 5: Mexican American, 5: Mexican American, 4: Mexican American,

black, other black, other black, other black, other black, other6: 20 and over only 6: 20 and over only 6: Under age 1, 6: Under age 1, 6: 20 and over only

20 and over only 20 and over only

3: 1–2, 3–4, 3: 1–2, 3–4, 4: 1–2, 3–4, 5–6, 4: 1–2, 3–6, 7 or more 3: 1–2, 3–4,

5–6, 7 or more 5–6, 7 or more 7 or more 5–6, 7 or more

. . . . . . . . . . . . . . .

2: Less than high school, 2: Less than high school, 3: Less than high school, 3: Less than high school, 2: Less than high school,

high school, high school, high school, high school, more more than high school

more than high school more than high school more than high school than high school

4: Excellent, 5: Excellent or unknown, 7: Excellent or very good, 7: 60 and over only; 5: Under age 6;

very good, good, very good, good, good or fair, Excellent or very good, Excellent or unknown,

fair, poor, unknown fair, poor poor or unknown good or fair, very good, good,

poor or unknown fair, poor

6 and over;Excellent, very good,good,fair, poor,unknown

. . . 7: 60 and over only; 8: 20 and over only; 8: 20 and over only; 7: 60 and over only;

yes, no, unknown yes or unknown, no yes, no or unknown yes, no, unknown

. . . . . . 2: Urban, suburban, rural 2: Urban, suburban, rural . . .

Page 38[ Series 2, No. 155
MM

Major stratum Description1 

1 Northeastern PSUs with less than 7.8% of the population in poverty 

2 Northeastern PSUs with more than 7.8% of the population in poverty 

3 Nonmetropolitan Midwestern and Western PSUs 

4 Metropolitan Midwestern PSUs with population less than 9.8% black 

5 Metropolitan Midwestern PSUs with population more than 9.8% black 

6 Nonmetropolitan Southern PSUs 

7 Metropolitan Southern PSUs with population less than 1.4% Mexican American and: 

- Less than 21% black; or, 

- Between 21% and 27.6% black and less than 10.8% of the population in poverty 

8 Metropolitan Southern PSUs with population less than 1.4% Mexican American and: 

- More than 27.6% black; or, 

- Between 21% and 27.6% black and more than 10.8% of the population in poverty 

9 Metropolitan Southern PSUs with population between 1.4% and 3.8% Mexican American 

10 Metropolitan Southern PSUs with population more than 3.8% Mexican American 

11 Metropolitan Western PSUs with population less than 14% Mexican American 

12 Metropolitan Western PSUs with population more than 14% Mexican American 

PSUs selected with certainty 

1

Threshold values are approximate.
 

NOTE: PSU is primary sampling unit. PSUs selected for 2007 were not used. 


SOURCE: CDC/NCHS, 1990 and 2000 censuses (data for major strata); variables used include census region 


(Northeast, Midwest, South, and West) and metropolitan statistical area status.
 

Figure. Major strata formed for selection of the 2002–2007 PSUs

Series 2, No. 155 [ Page 39
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:

Information Dissemination Staff

National Center for Health Statistics

Centers for Disease Control and Prevention

3311 Toledo Road, Room 5412

Hyattsville, MD 20782

1–800–232–4636

E-mail: cdcinfo@cdc.gov

Internet: http://www.cdc.gov/nchs
U.S. DEPARTMENT OF

HEALTH & HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

3311 Toledo Road

Hyattsville, MD 20782

OFFICIAL BUSINESS

PENALTY FOR PRIVATE USE, $300

MEDIA MAIL
 

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CS228636

DHHS Publication No. (PHS) 2012–1355, Series 2, No. 155