Series 2

No. 130

Design and Estimation for

the National Health Interview

Survey, 1995–2004

June 2000

Vital and

Health Statistics

From the CENTERS FOR DISEASE CONTROL AND PREVENTION / National Center for Health Statistics

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics
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All material appearing in this report is in the public domain and may be

reproduced or copied without permission; citation as to source, however, is

appreciated.

Suggested citation

Botman SL, Moore TF, Moriarity CL, and Parsons VL. Design and estimation for

the National Health Interview Survey, 1995–2004. National Center for Health

Statistics. Vital Health Stat 2(130). 2000.

Library of Congress Cataloging-in-Publication Data

Design and estimation for the National Health Interview Survey, 1995-2004 /

Steven L. Botman. . . [et al.].

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

methods research ; no. 130) (DHHS publication ; no. (PHS) 2000-1330)

‘‘March 2000.’’

Includes bibliographical references.

ISBN 0-8406-0562-5

1. Health surveys—United States. I. Botman, Steven. II. National Center for

Health Statistics (U.S.) III. Series. IV. Series: DHHS publication ; no. (PHS)

2000-1330

[DNLM: 1. National Health Interview Survey (U.S.) 2. Health

Surveys—United States. 3. Data Collection—United States. 4. Research

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Printed on acid-free paper.
Design and Estimation for

the National Health Interview

Survey, 1995–2004

Series 2:

Data Evaluation and

Methods Research

No. 130

Hyattsville, Maryland

June 2000

DHHS Publication No. (PHS) 2000-1330

Vital and

Health Statistics

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics
National Center for Health Statistics

Edward J. Sondik, Ph.D., Director

Jack R. Anderson, Deputy Director

Jack R. Anderson, Acting Associate Director for

International Statistics

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

Lester R. Curtin, Ph.D., Acting Associate Director for

Research and Methodology

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

Analysis, Epidemiology, and Health Promotion

P. Douglas Williams, Acting Associate Director for Data

Standards, Program Development, and Extramural Programs

Edward L. Hunter, Associate Director for Planning, Budget,

and Legislation

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

Vital and Health Statistics Systems

Douglas L. Zinn, Acting Associate Director for

Management

Charles J. Rothwell, Associate Director for Data

Processing and Services

Office of Research and Methodology

Lester R. Curtin, Ph.D., Acting Associate Director

Lester R. Curtin, Ph.D., Chief, Statistical Methods Staff

Trena Ezzati-Rice, Chief, Survey Design Staff

Jimmie D. Givens, Acting Chief, Statistical Technology Staff

Kenneth W. Harris, Acting Manager, Questionnaire Design

Research Laboratory Staff
Preface

This report presents a detailed

description of the sample design

features for the National Health

Interview Survey (NHIS) for the period

1995–2004. The report is a successor to

an earlier publication, Design and

Estimation for the National Health

Interview Survey, 1985–94. (5)

NHIS is one of the major data

collection programs of the National

Center for Health Statistics (NCHS).

Through NHIS, information concerning

the health of the U.S. civilian

noninstitutionalized population is

collected in household interviews

throughout the United States. NHIS has

been in continuous operation since

1957, and its sample design has been

reevaluated and modified following each

of the last four decennial censuses of

the U.S. population.

The 1995–2004 redesign of NHIS

was a major undertaking that involved a

number of government agencies as well

as several private contractors. The

Survey Design Staff in the Office of

Research and Methodology (ORM),

NCHS, in collaboration with the

Division of Health Interview Statistics

(DHIS), NCHS, had overall

responsibility for the development and

implementation of the 1995–2004 NHIS

redesign. Monroe Sirken, formerly the

ORM Associate Director, played a major

role in the conceptualization and

planning of the research program. The

late James Massey, former Chief of the

Survey Design Staff, and Donald Malec,

formerly of the Survey Design Staff, had

the primary responsibility for directing

the redesign research and coordinating

the research activities conducted by

NCHS, the U.S. Bureau of the Census,

and Westat, Inc. The late Steven

Botman, formerly of the Survey Design

Staff, had the lead responsibility in

helping to implement the 1995–2004

NHIS redesign, and started developing a

manuscript that was the genesis of this

report. Van Parsons of the Statistical

Methods Staff, ORM, contributed to the

development of subdesigns for the

1995–2004 NHIS redesign. The late

Owen Thornberry, formerly the Director,

DHIS, and John Horm, formerly the

acting Chief of the Survey Planning and

Development Branch, DHIS, were the

leading DHIS participants in the

1995–2004 NHIS redesign work.

Under contract with NCHS between

1989 and 1993, Westat, Inc., conducted

a major portion of the research for the

1995–2004 NHIS redesign. The primary

researchers at Westat included David

Judkins, David Marker, and Joseph

Waksberg. As part of Westat’s research,

a sample design referred to as the

‘‘alpha’’ design was developed. It

assumed a 50-percent data collection

budget increase to permit oversampling

of racial and ethnic minorities. The

Westat researchers also developed the

‘‘beta’’ design, a modification of the

alpha design, which assumed no change

in the NHIS data collection budget. The

beta design, which became the design

implemented in 1995, is the design

described in this report. Another NCHS

report, National Health Interview

Survey: Research for the 1995–2004

Redesign, (6) provides a detailed

description of Westat’s research work,

including a description of the alpha

design.

Additional NHIS redesign research

was conducted by the U.S. Bureau of

the Census and coordinated through the

Task Force on Household Survey

Redesign, assembled and directed by the

late Maria Gonzalez of the Office of

Management and Budget (OMB). The

task force was formed to coordinate and

monitor the U.S. Bureau of the Census’

redesigns of those household surveys

conducted by the Bureau for itself and

other Federal government agencies, and

that are simultaneously redesigned after

each decennial census. The task force

played an important role in coordinating

both the technical and funding

requirements for the redesigns of the

surveys.

The U.S. Bureau of the Census has

been the primary data collector for

NHIS since the inception of NHIS. The

U.S. Bureau of the Census also has been

involved with the research and

implementation of the NHIS redesigns.

For the 1995–2004 redesign, the

Demographic Statistical Methods

Division (DSMD) at the U.S. Bureau of

the Census had the primary

responsibility for evaluating alternative

primary sampling unit (PSU) definitions

for NHIS and for implementing the

redesigned sample. Persons at the U.S.

Bureau of the Census deserving special

recognition for their contribution to the

1995–2004 NHIS redesign effort include

Preston Jay Waite, formerly Chief of

DSMD; Thomas Moore, Chief of the

Health Surveys and Supplements

Branch, DSMD; Robert Mangold,

formerly Chief of the Health Surveys

Branch, Demographic Surveys Division;

Patricia Wilson of DSMD; and Lloyd

Hicks, formerly of DSMD.

This report is organized into three

chapters so researchers and other users

of the NHIS data can refer to different

parts of the report for different levels of

detail about the NHIS design. Chapter 1

provides a general overview of NHIS,

chapter 2 provides a detailed description

of the NHIS sample design, and

chapter 3 presents a description of the

estimators used for analyzing NCHS

data.

iii
This report is dedicated to the memory of our late colleagues

Steven L. Botman and James T. Massey,

both formerly in the Survey Design Staff,

Offıce of Research and Methodology,

National Center for Health Statistics.

Both men made significant contributions to the

National Health Interview Survey.

Steven L. Botman James T. Massey
Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

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

Chapter 1. Overview of the National Health Interview Survey ..................................................... 1

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

Sample Design and Basic Subsamples ...................................................................... 2

Data Collection Instruments .............................................................................. 2

Reuse of the Survey Sample .............................................................................. 3

Sample Redesign ....................................................................................... 4

Chapter 2. 1995–2004 NHIS Sample Design ................................................................... 5

Redesign Objectives Achieved ............................................................................ 5

Description of the Sample................................................................................. 5

PSU and Stratum Formation .............................................................................. 5

PSU Stratification ....................................................................................... 6

Special Situations Arising From PSU Definitions ............................................................. 6

PSU Sample Allocation and Selection ...................................................................... 6

Rotating PSU’s ......................................................................................... 6

Panels of PSU’s ........................................................................................ 6

Substrata Within PSU’s .................................................................................. 7

SSU Formation and Sampling ............................................................................. 7

Example of SSU Sampling ............................................................................. 8

SSU Clustering Characteristics .......................................................................... 8

Example of Within-Substratum Sampling Rates ............................................................. 9

Sampling in the Permit Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Sampling of Group Quarters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Sampling of Persons Within Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Interviewer Assignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Balancing the Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Additional Subsampling Situations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Assignment of Screening Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Sampling Rule Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chapter 3. Design and Estimation Structures for the 1995–2004 NHIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Methods for Creating Weights and Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

National Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Base Weight Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Factors Contributing to Annual Weight Fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Household Nonresponse Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Methodology for 1995 and 1996 Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Methodology for 1997 Survey and Beyond . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Estimator Based on the Product of WI and Wnr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Ratio Adjustments for Person-Level Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

First-Stage Ratio Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Second-Stage Ratio Adjustment (Poststratification) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Creation of Other Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

v
Variance Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Simplified Design Structures for Variance Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Variance Estimators for National Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

SR PSU’s: Conceptual NHIS Within PSU Sampling and Estimation Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

NSR PSU’s: Variance Estimator that Accounts for PSU Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Variance Estimators for National Estimators: Combining Across NSR and SR PSU’s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Estimation Variances for Poststratified Totals and Nonlinear Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Public Use Data and Limitations on Design Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Precision Comparisons Between Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Definition of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figures

1. Housing unit distribution by measure .................................................................. 9

2. Housing unit distribution by annual secondary sampling unit ............................................... 9

Tables

1. National Health Interview Survey designs: 1973–84, 1985–94, 1995–2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2. Sampling strata characteristics: National Health Interview Survey, 1995–2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3. Primary sampling units by census region: National Health Interview Survey, 1995–2004 . . . . . . . . . . . . . . . . . . . . . . . . . 12

4. Race and ethnicity density strata and within primary sampling unit parameters: National Health Interview

Survey, 1995–2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

5. Components of conceptual sampling design: National Health Interview Survey, 1995–2004 . . . . . . . . . . . . . . . . . . . . . . 24

6. First-stage ratio adjustment factors: National Health Interview Survey, 1997 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

7. The 88 age-sex-race/ethnicity classes used for poststratification: National Health Interview Survey, 1995–2004 . . . . . . 24

8. Impact of poststratification on variance estimation: National Health Interview Survey, 1995 . . . . . . . . . . . . . . . . . . . . . . 25

9. Sample size and weighted size of persons by domain: National Health Interview Survey, 1995 . . . . . . . . . . . . . . . . . . . . 26

10. Comparison of variance estimates obtained by two methods: National Health Interview Survey, 1995 . . . . . . . . . . . . . . 27

11. Precision comparisons between two survey designs: National Health Interview Survey, 1985–94 and 1995–2004 . . . . . 28

12. Sample size and percent change by domain: National Health Interview Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

vi
Objectives

This report presents an overview, a

detailed description of the sample

design features, and estimation

structures for the 1995–2004 National

Health Interview Survey (NHIS). It is

intended to serve the same role for the

current (1995–2004) National Health

Interview Survey design as the NCHS

publication, Series 2, No. 110, Design

and Estimation for the National Health

Interview Survey, 1985–94, did for the

previous design.

Methods

The 1995–2004 NHIS sample design

uses cost-effective complex-sampling

techniques including stratification,

clustering, and differential sampling

rates to achieve several objectives.

These objectives include improved

reliability of racial, ethnic, and

geographical domains. This report

provides a description of those

methods.

Results

This report presents the operating

characteristics of the 1995–2004 NHIS.

The general sampling structure is

presented along with a discussion of

the weighting and variance estimation

techniques. This report is intended for

the general users of NHIS data

systems. A companion report, Series 2,

No. 126, National Health Interview

Survey: Research for the 1995–2004

Redesign, provides a finer level of

detail on the redesign process.

Keywords: sampling c weighting c

nonresponse adjustment c variance

estimation

Design and Estimation for the

National Health Interview

Survey, 1995–2004

Chapter 1.

Overview of the

National Health

Interview Survey

Lead authors: Steven L. Botman

and Christopher L. Moriarity,

Offıce of Research and

Methodology, National Center for

Health Statistics

Background

The National Health Interview Survey

(NHIS) is the Nation’s primary source of

general health information for the resident

civilian noninstitutionalized population.

NHIS is conducted by the National Center

for Health Statistics (NCHS), a component

of the Centers for Disease Control and

Prevention, U.S. Public Health Service,

Department of Health and Human

Services. In accordance with specifications

established by NCHS, the U.S. Bureau of

the Census, under a contractual

relationship, participates in the planning

for NHIS and the collection of data. NHIS

has continuously collected data since

1957. This continuous data collection has

administrative, operational, and data

quality advantages because fieldwork can

be handled on a continuous basis with an

experienced, stable staff.

NHIS provides estimates on health

indicators, health care utilization and

access, and health-related behaviors for

the U.S. resident civilian

noninstitutionalized population.

Summary reports and reports on special

topics for each year’s data are prepared

by NCHS’ Division of Health Interview

Statistics for publication in Series 10 of

the Vital and Health Statistics

publications series. In these reports,

basic NHIS survey estimates are

published annually for various

population subgroups. These subgroups

include those defined by age, sex, race,

family income, census region

(Northeast, Midwest, South, and West),

place of residence (central city of a

metropolitan area, metropolitan area

but not in a central city, and

nonmetropolitan areas), and other

domains covered on the particular NHIS

statistic. Statistics from NHIS also

appear in other NCHS reports, in

professional journal articles, and in

many other publications.

NCHS releases annual NHIS

microdata files in several forms. Since

1969, public use data files have been

prepared for each year of data

collection. Public use microdata also are

available on compact disk read-only

memory (CD-ROM) for data collection

years starting in 1987. Recently, public

use microdata also have been made

available via the Internet.

The authors appreciate the input of several NCHS staff who reviewed the report at various stages of

development. Deborah D. Ingram, Ph.D., Office of Analysis, Epidemiology, and Health Promotion, served

as peer reviewer and made many useful comments. The authors also thank other NCHS staff who reviewed

the report at various stages of development and made helpful comments and suggestions, particularly Iris

M. Shimizu, Ph.D., and Myron J. Katzoff, Ph.D., Office of Research and Methodology. In addition, the

authors thank Alfred G. Meier of the U.S. Bureau of the Census, who reviewed the report and supplied

much useful material. The report was edited by Klaudia M. Cox and typeset by Zung T. N. Le of the

Publications Branch, Division of Data Services.

Page 1
These microdata files usually

contain information that allows users to

develop direct estimates of sampling

errors that are consistent with the

complex survey design of NHIS. Since

1985, NCHS has based estimates of

NHIS sampling error on the Taylor

series linearization method. This method

can produce estimates that take into

account the sample design, using a

conceptual model of the NHIS sample

design that captures many of its key

features. In particular, for NHIS

variance estimation, NCHS has used the

SUDAAN software (1), which includes

the Taylor series linearization method.

To facilitate the use of software that

permits analysis of NHIS data in which

the sample design is taken into account,

NCHS has coordinated with other

agencies within the U.S. Public Health

Service to support enhancements for the

SUDAAN software.

To maintain respondent

confidentiality in NHIS, NCHS

withholds variables from the National

Health Interview Survey’s public use

data files that could permit explicit or

implicit identification of survey

respondents. One of the major risks for

inadvertent respondent identification is

the inclusion of identifiers on survey

files that place respondents in small

geographic areas (for example, census

block, census block group, county, or

state). Thus, in these data releases,

variables identifying specific geographic

areas smaller than one of the four

census regions usually are withheld to

protect respondent confidentiality.

However, NCHS has released

information that allows identification of

the largest metropolitan areas.

Sample Design and Basic

Subsamples

The National Health Interview

Survey is based on a stratified

multistage sample design. The specific

parameters of the design, however, have

changed over time; a new sample design

is implemented following each decennial

census. For example, the 1973–84

survey design had 386 sample primary

sampling units (PSU’s), the 1985–94

survey design had 198 sample PSU’s,

and the current 1995–2004 survey

design has 358 sample PSU’s. For more

details, refer to table 1 in chapter 2.

The 1995–2004 NHIS has been

designed to produce estimates for the

Nation, for each of the four census

regions, and within census regions by

areas determined by metropolitan and

nonmetropolitan status. Although the

1995–2004 survey samples from all of

the States and the District of Columbia,

it is not designed to produce reliable

State-level estimates for every State.

For the 1995–2004 survey design,

the NHIS sample is partitioned into a

number of subsamples. First, the sample

for the 10-year survey period is

partitioned into subsamples that are

assigned for data collection each year

from 1995 through 2004. Moreover, for

survey administration, data collection,

and processing, the NHIS annual sample

is assigned to four calendar quarters.

Within each quarter, the sample is

assigned to individual weeks. The

portion of the survey sample assigned to

each calendar quarter of the year is

representative of the target population.

The portion of the survey sample

assigned to each week also is

representative of the target population,

although estimates based on weekly

samples tend to be unstable. (Two

reasons for the instability are a small

sample size and the fact that each

sample PSU represented in a quarterly

sample is not necessarily represented in

the weekly sample. This reduces the

precision of variance estimators.) The

assignment of subsamples to specific

data collection periods permits the NHIS

sample to be used to estimate high

frequency measures or to obtain

estimates for large population groups

from a short period of data collection.

Other measures sometimes can be

obtained by accumulating the survey

sample for longer periods of time,

including more than 1 year.

The assignment of the NHIS sample

to weekly and quarterly subsamples also

has a number of operational and

administrative benefits. For example, the

assignment of NHIS to weekly

subsamples results in the field staff having

a continuous workload, which enhances

the quality of the resultant data.

Beginning with the 1985–94 survey

design, the annual NHIS samples have

been partitioned into four panels

(subsamples) each having approximately

the same number of sample households

and conceptually similar statistical

features. Each panel can be considered

as a probability sample of the U.S.

population. (Note that ‘‘panels’’ and

‘‘calendar quarters’’ are not the same;

the set of four panels is a partition of

the annual survey samples that

encompasses the four calendar quarters.)

The panels have several anticipated

uses. They provide a mechanism to

make large cuts in the survey sample

size in case insufficient funds are

available for data collection. Because

the sample is reused as a sampling

frame for other surveys, the panels also

provide a mechanism for NHIS to

provide nonoverlapping samples for

reuse as sampling frames for other

studies. After the 1995–2004 survey

design sample was selected, NCHS

partitioned the new sample into a new

set of four panels.

Data Collection

Instruments

The NHIS data collection

instrument for 1995 and 1996 was

similar to the instrument used before

1995. Prior to 1995, the last major

revision of the instrument occurred in

1982. A major revision of the

instrument occurred in 1997. The data

collection instruments for 1995 and

1996, and 1997 and beyond, are

described below.

NHIS data collection is conducted by

the U.S. Bureau of the Census under an

interagency agreement with NCHS. NHIS

interviewers are employees of the U.S.

Bureau of the Census. These interviewers

receive extensive training, and their work

is monitored through a quality assurance

program. Data are collected from each

family in the survey sample using a

face-to-face interview. If a sampled

household contains more than one family,

many aspects of the interview are repeated

for each family in the household.

For 1995 and 1996, the data

collection instrument had three

components: a basic health and

Page 2 [ Series 2, No. 130
demographic ‘‘core’’ questionnaire, a

condition list, and one or more

supplemental questionnaires that

addressed health topics of special public

health interest. The NHIS basic health

and demographic questionnaire consisted

of a fixed set of health and

sociodemographic questions.

Additionally, each household was

randomly assigned one of six condition

lists. The ‘‘core’’ questionnaire and the

condition list responses are used for

developing annual estimates of various

health variables, including acute and

chronic conditions, hospital stays,

medical visits, and limitations of

activities.

The supplemental questionnaires

addressed health issues or topics

identified as being of particular interest

within the U.S. Public Health Service.

From 1985 through 1994, these

supplemental questionnaires on topics

such as cancer control and cancer

epidemiology, AIDS knowledge and

awareness, and health promotion and

disease prevention, were administered to

a randomly selected adult in each family

of the survey sample. For several years

in the 1990’s, the supplemental

questionnaires on health insurance and

access to health care were administered

to the entire survey sample. In 1994 and

1995, NHIS included a supplement on

disability that was administered to the

entire sample. For the last few years

prior to 1997, several supplements, such

as ‘‘Family Resources’’ and ‘‘Childhood

Immunization,’’ were included in the

survey on an annual basis. Household

data collection for the supplemental

questionnaires averaged about 30–90

minutes.

Prior to 1997, the basic strategy for

data collection was for the interviewer

to assemble all available household

members aged 19 years and older at a

sample address. A knowledgeable adult

could report for absent adults. In most

cases, proxy reporting by a

knowledgeable adult was used for

persons under age 19 years, although

persons aged 18 years could report for

themselves and persons aged 17 years

could report for themselves under some

circumstances. As part of the

enumeration of each household in the

survey sample, persons in an individual

household were partitioned into separate

families if multiple families were

present. A separate basic health and

demographic questionnaire was

administered to members of each

individual family in the household.

In 1996, NHIS began testing the

use of computer-assisted personal

interviewing (CAPI) that was based on a

revised data collection instrument.

Beginning in 1997, the survey switched

to a CAPI system and a revised data

collection instrument that was

restructured and shortened in an effort to

reduce respondent burden. Many of the

questions formerly asked of all persons

in the ‘‘core’’ questionnaire are

administered on a sample basis. The

redesigned questionnaire has three parts

or modules: a basic module, a periodic

module, and a topical module.

The basic module functions as the

new ‘‘core’’ questionnaire. Plans are for

it to remain largely unchanged from

year to year. The basic module contains

three components: the family core, the

sample adult core, and the sample child

core. The family core component

collects information on everyone in the

family. Information collected in the

family core component includes

household composition and

sociodemographic characteristics. It also

includes basic indicators of health status

and utilization of health care services.

From each family in the survey, one

sample adult and one sample child (if

any children under age 18 are present)

are randomly selected, and information

on each is collected with the sample

adult core and the sample child core

questionnaires. Because some health

issues are different for children than for

adults, these two questionnaires differ in

some items, but both collect basic

information on health status, health care

services, and behavior.

One important change implemented

in 1997 is that random selection of one

of six condition lists was eliminated.

Instead a checklist of conditions appears

in the sample adult core and sample

child core.

The purpose of a periodic module is

to collect more detailed information on

some of the topics included in the basic

module from the sample persons. It was

used for the first time in 1998.

The role of a topical module is

analogous to the supplemental

questionnaires of the 1982–96 survey;

that is, it is used to respond to public

health data needs as necessary. In 1999,

the first year a topical module was used,

the topical module focused on disease

prevention issues.

The family core component of the

basic module is administered in a

manner similar to the ‘‘core’’

questionnaire prior to 1997. All adult

members of the household 17 years of

age and over who are at home at the

time of the interview are invited to

participate and to respond for

themselves. For children and adults not

at home or unable to respond for

themselves during the interview,

information is provided by a responsible

adult family member (18 years of age or

over) residing in the household. For the

sample adult core component, one adult

per family is randomly selected, and this

individual responds for him/herself to

the questions in this section (i.e., no

proxy response is allowed). Information

for the sample child core component is

obtained from a knowledgeable adult in

the household.

Reuse of the Survey

Sample

During the 1985–94 survey design

period, the sample was reused for

several surveys with smaller sample

sizes. These surveys were for studies

sponsored in part by NCHS’ Division of

Health Interview Statistics or by other

units in NCHS. Such linkages are most

cost-effective when the smaller survey is

based on a subdomain of the population

or if it oversamples domains whose

membership can be determined from the

NHIS interview.

The National Survey of Family

Growth (NSFG), Cycles IV and V

samples, were selected from respondents

in the survey who met the NSFG

eligibility criteria. Cycles IV and V of

NSFG, sponsored by NCHS and other

U.S. Public Health Service agencies,

were surveys of women aged 15–44

years and focused on their reproductive

history and intentions.

Series 2, No. 130 [ Page 3
Phase 2 of the NHIS Disability

Supplement was based on a reuse of

certain respondents in the 1994–95

Phase 1 Disability Supplement.

The 1996 Medical Expenditure

Panel Survey (MEPS), sponsored by the

Agency for Healthcare Research and

Quality, was based on a reuse of a

portion of the 1995 NHIS sample.

Subsequent years of the MEPS are

based on reuse of a portion of the

previous year’s NHIS sample.

Sample Redesign

Since its inception in 1957, the

NHIS sample has been redesigned

following each decennial census of the

population to accommodate changes in

survey requirements and to take into

account the changes in the population

and its distribution (2–5). For the

1995–2004 NHIS sample design

implemented in 1995, the U.S. Bureau

of the Census and Westat, Inc.,

conducted research on issues related to

the sample design using specifications

provided by NCHS (6).

Although the main goals in the

1995–2004 NHIS sample design were

improving the reliability of statistics for

racial, ethnic, economic, and geographic

domains, other issues also were

addressed in the research. The results

often led to conflicting sample

allocations; that is, a sample allocation

that would be optimal for one type of

domain would be far from ideal for

another type of domain. The final sample

allocation was a compromise between

ideal allocations for the various domains.

The primary features in the 1995–

2004 NHIS sample design implemented

in January 1995 are as follows:

1. Use of an all area sampling

frame—The 1995–2004 survey is

based on an area sampling frame

for housing units in place at the

time of the 1990 Census. Each of

the other current demographic

survey samples conducted by the

U.S. Bureau of the Census

(including the Current Population

Survey, the National Crime

Victimization Survey, and the

Survey of Income and Program

Participation) uses a combination of

frames. The combination consists of

an address sampling frame (i.e.,

addresses compiled for the

preceding decennial census) and an

area sampling frame, where the

address information is incomplete.

The use of an all area-frame sample

permits NCHS to release the survey

sample addresses to its contractors

for additional data collection. U.S.

Bureau of the Census confidentiality

constraints do not permit the release

of addresses that were obtained

through listings compiled for the

preceding decennial census. For this

reason, the survey sample has been

based on an all-area sampling frame

starting with the 1985–94 design.

In parts of the country where

local governments issue building

permits, the U.S. Bureau of the

Census supplements the area sample

with a sample of permits for

residential housing units built after

April 1990. In the rest of the

country, the units constructed after

April 1990 are included in the area

frame.

2. State stratification and an increase

in the number of PSU’s— In most

cases, the first-stage sampling strata

do not straddle State boundaries.

The exception is the largest

metropolitan areas. These are

self-representing PSU’s and may

straddle State boundaries. In these

cases, the survey second-stage

samples were drawn independently

within each State component of the

PSU. This State stratification, taken

together with an almost doubling of

the number of PSU’s in the survey

sample, enhances the ability of the

survey to make reliable State-level

estimates for the largest States. The

State stratification and the increase in

the number of PSU’s also will allow

easier integration of a random digit

dialing telephone survey with the

survey as a dual frame survey to

make reliable subnational estimates if

the decision is made in the future to

do this type of integration.

The largest increase in the

number of sample PSU’s occurs in

those representing nonmetropolitan

areas. A contributing factor to this

increase is separate strata for

metropolitan areas versus

nonmetropolitan areas in all States

(except New Jersey, which is

entirely metropolitan). To obtain

this increase, the average number of

sample households assigned to such

primary sampling units was

substantially reduced. For the

survey, two sample PSU’s usually

were selected from each

nonself-representing first-stage

stratum. However, there were 21

nonself-representing strata where

only one sample primary sampling

unit was selected.

3. Oversampling of black and

Hispanic persons—The sample

design implemented in 1995

oversamples black and Hispanic

persons. This is accomplished with

two features. First, the U.S. Bureau

of the Census selected for NHIS a

larger initial sample than would

otherwise be required. A subsample

of the initial sample was selected

for interviewing with housing units

in areas having higher concentrations

of black and Hispanic persons being

retained at higher rates. Second, all

households in the subsample with one

or more black or Hispanic eligible

(i.e., civilian) members are retained in

the survey, and only a subsample of

other households are retained. The

determination of a household’s race

and/or ethnicity status is accomplished

through the administration of a brief

screening interview. (The screening

interview consists of the initial

steps of the regular interview. After

the household roster is determined,

the decision is made to ‘‘screen in’’

or ‘‘screen out.’’)

Approximate oversampling rates

are 2:1 for Hispanic persons and

1.5:1 for black persons. Note that

oversampling of black persons was

done during the 1985–94 survey

design. Oversampling of Hispanic

persons is new to the current design.

Additional detail on the NHIS

1995–2004 sample design is included in

chapter 2. Chapter 3 provides a detailed

description of estimation procedures for

the NHIS 1995–2004 design.

Page 4 [ Series 2, No. 130
Chapter 2.

1995–2004 NHIS

Sample Design

Lead author: Thomas F. Moore,

Demographic Statistical Methods

Division, U.S. Bureau of the

Census

The 1995–2004 National Health

Interview Survey (NHIS) sample design

was implemented in 1995, and this

design will be used through 2004. The

current design will yield more reliable

estimates for health-related

characteristics of Hispanic persons than

those based on the 1985–94 design.

In forming strata for the primary

sampling units (PSU’s), State was used as

a stratification variable. Although the

survey is not designed to produce reliable

estimates for every State, this State

stratification of PSU’s enhances the

survey’s ability to produce data for a few

of the most populous States and enhances

the potential for using NHIS data with

other data to produce State estimates.

Redesign Objectives

Achieved

The following NHIS redesign

objectives were achieved by the

1995–2004 sample design:

(a) Improving the reliability of

estimates for Hispanic persons,

(b) Improving the reliability of

estimates for subnational areas,

including States, and

(c) Continuing to have NHIS serve

as a sampling frame for follow-on

surveys.

Other objectives also were

considered as part of the redesign

research (6).

Description of the Sample

NHIS is based on a stratified,

multistage sampling plan. In the United

States, primary sampling units are

individual counties or contiguous groups

of counties. (‘‘County’’ is used

generically to include county equivalents

such as parishes in Louisiana,

independent cities in Virginia, Maryland,

Missouri, and Nevada, etc.) Outside of

New England, metropolitan areas

(MA’s) are defined at the county level,

and were used as primary sampling

units. In New England, MA’s are

defined at a subcounty level, so New

England County Metropolitan Areas

(NECMA’s), which are defined at the

county level, were used as primary

sampling units instead of MA’s.

Secondary sampling units (SSU’s) are

noncompact clusters of housing units.

Table 1 summarizes the major

features of the designs since 1973. Some

of the terms used in the table are

defined in the glossary and/or are

described later in this chapter. The

figures given for designated housing

units per year, screened households per

year, interviewed households per year,

and interviewed persons per year are all

estimates. ‘‘Designated’’ refers to the

initial designation of units for

interviewing and includes vacant and

other ineligible units and households

where no interviews occur (e.g.,

refusals). ‘‘Screened’’ refers to

households where a successful interview

has taken place, up to the point where

screening can occur for those cases to

be screened (described in more detail

later in this chapter). ‘‘Interviewed’’

refers to households retained after the

screening step (if applicable) and for

which the interview process has been

completed.

Note that table 1 shows information

for the 1995–2004 design for both the

alpha and the beta samples. The alpha

sample is the sample originally selected

for the 1995–2004 survey. The beta

sample is a subsample of the alpha

sample and is the sample actually

fielded for the 1995–2004 survey. The

alpha sample, with an expected annual

interview size of 55,000 households,

was designed and selected under the

assumption of an increase in the NHIS

budget for the 1995–2004 design. When

the budget increase did not occur, the

beta sample, with an expected annual

interview size of 41,000 households,

was selected. All the subsampling to

select the beta sample was done within

primary sampling units (i.e., all the

PSU’s selected for the alpha sample

were retained for the beta sample).

Additional details on how the beta

sample was selected from the alpha

sample are given later in this chapter.

Additional information on the alpha

sample is available in another

publication (6).

PSU and Stratum

Formation

For the 1995–2004 NHIS design,

the United States was partitioned into

1,995 primary sampling units. The 1,995

PSU’s are individual counties or groups

of adjacent counties. Outside of New

England, MA’s were used as primary

sampling units. In New England,

NECMA’s were used as primary

sampling units instead of metropolitan

areas. The PSU’s for the 52 largest

metropolitan areas were assigned to

self-representing (SR) strata, each

containing only one PSU. After the

initial 52 SR strata were formed, the

1,995 PSU’s were then partitioned into a

total of 194 design strata. Because no

individual PSU was allowed to account

for more than 50 percent of a stratum’s

total size (estimated 1995 population),

an additional 43 PSU’s became

self-representing after the initial

stratification process. These 43 PSU’s

were assigned to self-representing strata,

giving a final total of 237 design strata.

All of the remaining 1,900 primary

sampling units were nonself-representing

(NSR), and each was assigned to one of

the 142 NSR strata. In addition to the

selection of the PSU in each of the 95

self-representing strata, two sample

primary sampling units were selected

from 121 of the NSR strata. The

remaining 21 nonself-representing strata

were so small, in terms of total

estimated 1995 population, that only one

sample PSU was selected from each of

these strata.

Metropolitan areas include

consolidated metropolitan statistical

areas (CMSA’s), metropolitan statistical

areas (MSA’s), and primary

metropolitan statistical areas (PMSA’s).

PMSA’s are used as building blocks for

CMSA’s, while MSA’s are

‘‘stand-alone.’’ In areas with PMSA’s

and CMSA’s, CMSA’s were used as

primary sampling units.

Series 2, No. 130 [ Page 5
Table 2 displays information about

several characteristics of the sampling

strata for the 1995–2004 survey and

table 3 shows the distribution of the

self-representing and nonselfrepresenting primary sampling units by

census region.

PSU Stratification

Except for some of the 52 largest

self-representing strata, no stratum

straddles a State boundary. Several of

the largest self-representing strata

straddle State boundaries because those

metropolitan areas straddle State

boundaries. Even for these SR strata,

because of the way the second-stage

sample is drawn, one could consider

State as a stratification variable for each

stratum. Within a State, the approximate

number of strata and sample PSU’s was

fixed in the design. PSU’s in a State

were partitioned by metropolitan and/or

nonmetropolitan status.

If additional strata were required,

the U.S. Bureau of the Census stratified

the PSU’s by the poverty rate. The

stratification computer program was

written for the redesign of several

Census sample surveys and was based

on a clustering algorithm developed by

Friedman and Rubin (7). The procedure

began with a random stratification. It

systematically created a large number of

stratifications by moving PSU’s from

one stratum to another or by exchanging

pairs of PSU’s in different strata. The

resulting between-PSU variance for each

stratification variable was calculated.

The criterion for comparing possible

stratifications was the sum of

between-PSU variances for the

stratification variables; the smaller the

sum, the better the stratification. A

constraint that limited the variability of

the stratum size to plus or minus

25 percent of the average stratum size in

a State also was used. Because the

survey used only poverty to stratify

within State and metropolitan and/or

nonmetropolitan status, the stratification

generally grouped PSU’s with the

lowest poverty rates in one stratum, the

next lowest in the next stratum, and so

forth. The size constraint caused some

exceptions to this pattern. There is

variability in stratum definitions from

State to State due to variations in

poverty and the size constraint.

Special Situations Arising

From PSU Definitions

To coordinate sampling with the

Current Population Survey and other

ongoing census sample surveys, the U.S.

Bureau of the Census partitions some

PSU’s, especially the self-representing

PSU’s for the largest metropolitan areas

that have components in several States,

into finer units (Basic Primary Sampling

Unit Components) prior to selection of

secondary sampling units. Also, one

design PSU has components in two

different census regions. For these

situations, partitioning the PSU’s into

Basic Sampling Unit Components prior

to second-stage sampling results in

introducing State-level stratification. It

occurs because these components always

respect State boundaries.

For in-house variance estimation,

NCHS uses the basic conceptual model

with 237 strata and 95 self-representing

PSU’s, with some collapsing as

necessary. Additional information on

variance estimation is in chapter 3.

PSU Sample Allocation and

Selection

Within a nonself-representing

stratum, two PSU’s usually were

selected for the survey sample. In 21

nonself-representing strata, however,

only one PSU was selected for the

sample. A total of 358 PSU’s was

selected for the sample. One

nonself-representing PSU is so small

that it will be ‘‘rotated’’ out of the

sample in 2000 and another PSU will be

rotated in to take its place.

Primary sampling units were

selected without replacement with

probability proportional to the estimated

1995 PSU population size using

Durbin’s procedure (8).

Rotating PSU’s

During the partition of the United

States into PSU’s, any that were too

small (less than 3,000 housing units, as

enumerated in the 1990 Census) to

provide the minimum sample needed for

the survey was defined as a potential

rotating PSU. It was placed in a rotation

cluster for sample selection. The rotation

cluster included any other PSU in the

same stratum that was too small to

provide the minimum sample size. If the

combined size of the rotation cluster

was still too small, the next smallest

PSU was added to the cluster.

The rotation cluster was treated as a

single primary sampling unit for

sampling. If it was selected, the PSU’s

in the rotation cluster were randomly

ordered, and a random start was chosen

to determine in which primary sampling

unit the sample would start. A sequence

of samples was designated based on the

random start and the size of the PSU

and continued on to the next primary

sampling unit.

One such rotation cluster was

selected for NHIS. The initial PSU

includes the sample for 1995 through

1999. In 2000, it will be rotated out of

the sample and another PSU in the

rotation cluster will be rotated in to take

its place for the remainder of the design.

Because of their small size, no

oversampling is done in rotating primary

sampling units.

Panels of PSU’s

NCHS partitioned the survey

sample into four panels of primary

sampling units. Each panel is a

subsample of the full set of 358 sample

PSU’s with the following properties:

+ Each panel has the same sampling

properties.

+ Each panel produces unbiased

estimates for the U.S. population.

+ Each nonself-representing PSU is

assigned to only one panel.

In addition to the properties listed

above, the panels are approximately

equal in the number of sample housing

units. The assignment of PSU’s to

panels was intended to keep sampling

variability small for a single-panel

estimator.

The panels enable NCHS to restrict

the portion of the NHIS sample that is

reused as the sampling frame for other

surveys to a subset of the full NHIS

design. The panels also allow large

Page 6 [ Series 2, No. 130
sample reductions to be made, if

necessary, by dropping one or more

panels. This is more efficient than

dropping weeks of interviewing or

randomly chosen sample units.

To produce unbiased estimates for

the U.S. population, the larger

self-representing PSU’s had to be in all

panels. The U.S. Bureau of the Census

divided the set of secondary sampling

units in the 13 largest self-representing

PSU’s into four parts and assigned each

part to a panel. The set of SSU’s in the

20 medium SR PSU’s was divided into

two parts and assigned either to panels 1

and 3 or to panels 2 and 4. The small

self-representing primary sampling units

and the nonself-representing primary

sampling units were assigned to single

panels defined by NCHS.

The U.S. Bureau of the Census

designated the self-representing PSU’s

as ‘‘large,’’ ‘‘medium,’’ or ‘‘small.’’ In

general, PSU’s with populations greater

than 3,000,000 were designated as

‘‘large,’’ PSU’s with populations

between 1,600,000 and 3,000,000 were

classified as ‘‘medium,’’ and PSU’s with

populations less than 1,600,000 were

labeled as ‘‘small.’’ Some exceptions

were made to reduce the variability in

the sample sizes of the panels.

Substrata Within PSU’s

Within each PSU, the survey uses

an area frame. In some PSU’s, a permit

frame also is used. For the area sample,

NHIS partitions the 1990 Census blocks

(and in some cases ‘‘combined blocks’’)

in the primary sampling unit into 20

substrata based on the 1990 Census

concentration of black and Hispanic

persons. The substrata definitions are

consistent across PSU’s. Hence, in some

primary sampling units, some of the 20

substrata may be empty. In all PSU’s,

addresses corresponding to dwelling

units built prior to April 1, 1990, are

subject to sampling from these 20

substrata. In areas where governmental

units issue and maintain building

permits, dwelling units built since April

1, 1990, are subject to sampling only

from the list of building permits. If a

dwelling unit built since April 1, 1990,

is encountered in the area frame, the

interview is terminated and the unit is

considered to be out of scope. When the

permit frame is used in a PSU, it is

considered to be the 21st substratum in

that primary sampling unit. However, in

areas that do not issue building permits

and in rotating PSU’s, all dwelling units

regardless of when they were built are

subjected to sampling in the 20 substrata

in the area sample. Table 4 provides

more information about the 21 substrata.

Units in higher density black or

Hispanic areas are sampled at higher rates

to improve the reliability of estimates for

black and Hispanic persons. This is the

first component of the oversampling

strategy. The second component is

discussed in the ‘‘Assignment of

Screening Code’’ section.

It should be noted that density

substratum 20 was so small that no survey

sample cases were selected from it.

SSU Formation and

Sampling

Using the U.S. Bureau of the

Census’ usual procedure in area

segmentation, clusters of housing units

were formed within the substrata to

create secondary sampling units

(SSU’s). The sampling was done in

conjunction with other census surveys,

and operational techniques of implicit

stratification and systematic sampling

were used. An in-depth discussion of the

methods used has been published (9). A

simplified structural framework is given

below. The ultimate sampling unit

consists of a cluster containing an

expected four housing units or

equivalents, based on 1990 Census

information. These clusters may be

empty or may include ineligible units

(e.g., vacant housing units) at the time

of interview. For cost and statistical

efficiency, the expected number of

housing units to be covered annually by

screening and sampling within an SSU

was planned to be 8 or 12 for the area

frame substrata and 4 for the permit

frame substratum (see table 4, ‘‘Beta

SSU size’’ column). For the area frame

substrata, cluster sizes of expected size 8

or 12 were created by joining together 2

or 3 adjacent ultimate sampling units of

expected size 4. However, the actual

number of units may vary from the

expected number, especially in PSU’s

where the survey does not use a permit

frame. Moreover, about 20 percent of the

addresses in a national area sample

typically do not include any persons

eligible for the survey.

Within a substratum, each

secondary sampling unit is part of a

‘‘super-SSU’’ consisting of 12

‘‘annual-SSU’s’’ (the terms SSU or

annual-SSU refer to the annual sampling

unit). The 12 annual-SSU’s in a

super-SSU are intended to serve the

survey for the entire 10-year design

period and to provide 2 years of

‘‘reserve sample’’ as a contingency (e.g.,

if there was a delay in the

implementation of the next NHIS

design). Thus, any housing unit within a

super-SSU can be in sample at most

once in the 10-year design period.

The sampling process used to form

the super-SSU’s within the primary

sampling unit substrata is outlined

below. The following simplifications are

made for ease of presentation:

Selection of a 10-year sample

(without the reserve sample) is

discussed.

The subsampling is described as

‘‘within-PSU,’’ rather than ‘‘within

Basic Primary Sampling Unit

Components,’’ which were discussed in

the ‘‘Special Situations Arising From

PSU Definitions’’ section.

The generic term ‘‘block’’ is used to

denote both single 1990 Census blocks

and ‘‘combined blocks.’’ Combined

blocks are formed by joining 1990

Census blocks with a small number of

housing units, as enumerated in the

1990 Census, with adjacent blocks.

The steps in the sampling process

are:

1. All blocks within a PSU’s density

substrata are sorted in geographic

order using the following 1990

Census variables: district office

number, address register area

number, and block number. Each

block is assigned an associated

measure of size defined as the

number of housing units enumerated

in the 1990 Census.

2. Each block contains an integral

number of ‘‘measures’’ where each

measure contains about four housing

Series 2, No. 130 [ Page 7
units. The total number of measures

varies from block to block, and a

partition of the block into real

measures does not occur unless the

block is actually sampled.

3. Using systematic sampling

procedures, a sequence of ‘‘hit’’

measures is selected. Each ‘‘hit’’

measure defines the first measure in

a super secondary sampling unit.

The super-SSU will contain the hit

measure plus the next consecutive

19 or 29 measures (to yield a total

of 8–12 expected housing units per

year). The number of measures in

the super-SSU varies by type of

density substratum; it is the

substratum’s entry in table 4, ‘‘Beta

SSU size’’ column, multiplied by

{10/4} (the {10/4} comes from

{10-year sample/measure of size

equal to 4}). At this stage of

sampling, every measure has the

same probability of being ‘‘hit.’’

One could characterize the

sampling process as a systematic

sample of sets of measures, or

super-SSU’s, from a hypothetical

universe listing. Two possible

conceptualizations of systematic

sampling for either measures or

super-SSU’s can be made.

A sampling interval SImea on a

population Mmea of measures yields a

sample of (Mmea /SImea) super-SSU’s

of size k measures each.

One may also think of a

corresponding population of

super-SSU’s of size MSSU =

(Mmea /k). Here, a sampling interval

of SISSU = SImea /k on MSSU yields

the same sample size.

A representation of systematic

sampling as a single sampling

interval for either measures or

super-SSU’s will satisfy the

following self-weighting criterion:

For density substratum type (j),

within PSU(i) sampled with

probability πi

, πi / SISSU(j,i) =

constant (j) (i.e., a constant that

depends only on j), where

SISSU(j,i) is the sampling interval

for density substratum type (j)

within PSU(i).

By the definition of a sampling

interval, the conditional probability

that a super-SSU is selected from

substratum type (j), given that

PSU(i) was selected, is either

1/SISSU(j,i) or k/SImea(j,i), where k

is the number of consecutive

measures.

Thus, the unconditional

probability that a super-SSU from

substratum type (j) is in sample is

πi c [1/SISSU(j,i)], which simplifies

to constant (j).

4. The annual secondary sampling units

within each selected super-SSU are

constructed by combining every two

or three consecutive measures,

depending upon classification of

density substratum.

Example of SSU Sampling

The following example illustrates

the formation of super-SSU’s as

outlined in steps 1–4.

(Step 1) Let blocks A–H represent

eight blocks in a substratum of a PSU

that are in consecutive order as a result

of sorting on selected characteristics

(figure 1). The column ‘‘Housing unit

count’’ shows the 1990 Census housing

unit count for each block. In this figure,

a number is assigned to each housing

unit in the block to illustrate how

housing units would be assigned to

different measures within the block.

(Step 2) The column ‘‘Measure

count’’ in figure 1 shows the number of

measures each block contains. For

example, block B consists of 19 housing

units that are partitioned into 5

measures.

(Step 3) Suppose that measure 2 in

block B is a sample ‘‘hit’’ measure from

the population of measures. If this

substratum requires an expected 80

housing units for the 10-year survey

design period (i.e., the expected

annual-SSU size is eight housing units),

then the next 20 measures will be used

to form the super-SSU. Starting with

measure 2 in block B, the super-SSU

will include measures from blocks B–G.

(Step 4) A well-defined U.S.

Bureau of the Census listing process

partitions the selected blocks B–G into

the specified number of measures. The

housing units within these selected

blocks are listed by some adjacent or

neighbor order. (Note: in this example,

it is not necessary for the housing

units in blocks A or H to be listed.)

The assignment of the actual measure

labels will look somewhat like those

presented in figure 1 (e.g., the first six

adjacently listed housing units in

block B are assigned to measures

1,2,3,4,5,1, respectively). To form the

annual-SSU’s, the 20 measures labeled

(B,2) to (G,1) are consecutively

paired. Thus,

SSU (year 1) = measures (B,2),(B,3)

SSU (year 2) = measures (B,4),(B,5)

SSU (year 3) = measures (C,1),(C,2)

SSU (year 4) = measures (C,3),(C,4)

SSU (year 5) = measures (D,1),(D,2)

SSU (year 6) = measures (E,1),(E,2)

SSU (year 7) = measures (E,3),(F,1)

SSU (year 8) = measures (F,2),(F,3)

SSU (year 9) = measures (F,4),(F,5)

SSU (year 10) = measures (F,6),(G,1).

Figure 2 shows the year each

housing unit in the super-SSU will be in

a sample.

SSU Clustering Characteristics

There are several clustering aspects

of the sample housing units within

secondary sampling units that should be

noted.

1. An examination of the sample housing

units in an annual-SSU in figure 2

shows that in any given year, some

housing units may be neighbors.

However, the sample housing units

will tend to be spread through the

geography of a given block.

2. From one year to the next, there is a

geographical clustering of sample

housing units (i.e., sample housing

units of a given year usually are

neighbors of sample housing units of

the next year). However, the

geographical clustering weakens

over time.

3. The annual-SSU’s may cross blocks

in any given year (e.g., years 7 and

10 in the example). The blocks in an

SSU may not be adjacent.

4. The super-SSU’s are not

well-defined clustered population

units like PSU’s, but are artifacts of

a block sort and random systematic

sampling.

Page 8 [ Series 2, No. 130
The within-substratum SSU

sampling parameters needed to form

selection probabilities and weights are

provided in table 4. This table provides

the alpha-design parameters along with

the subsampling rates to achieve the

beta-design that was implemented for

the 1995–2004 design period.

Example of Within-Substratum

Sampling Rates

Consider SSU sampling in density

substratum 12 of a self-representing

primary sampling unit from table 4. This

substratum consists of all 1990 Census

blocks in the PSU that contained

30–60 percent black persons and

5–10 percent Hispanic persons according

to the 1990 Census. The reference group

will be the permit substratum 21, which

is not oversampled.

For simplicity, suppose that each

substratum’s universe can be defined as a

set of population alpha annual-SSU’s of a

size defined by the ‘‘Alpha SSU size’’

column of table 4 (e.g., substratum 12

contains population secondary sampling

units of size 12 housing units (3

measures) and substratum 21 contains

SSU’s of size 4 housing units

(1 measure)).

Prior to any oversampling, a

baseline sampling interval (referred to

previously as SISSU) for the alpha-design

was defined as 1,748 in all substrata in a

self-representing PSU.

1. In substratum 21, 1 in every 1,748

SSU’s is sampled.

2. In substratum 12, 1 in every

1,748/2 = 874 SSU’s are sampled (the

oversampling rate for the alpha-design

in substratum 12 is 2; refer to the

‘‘Original SSU unit oversampling

rate’’ column of table 4).

Then, to reduce the alpha-design

SSU’s to the NHIS beta-design, the

following steps are taken:

1. In substratum 21, the ‘‘SSU unit

reduction’’ column of table 4

indicates that a reduction of 30 out

of 101 SSU’s occurred. Hence,

71/101 of the SSU’s are retained.

Thus, the base weight is

(101/71) c 1,748 = 2,486.5915

2. In substratum 12, the ‘‘SSU unit

reduction’’ column of table 4

indicates that a reduction of 25 out

of 101 SSU’s occurred. Hence,

76/101 of the SSU’s are retained.

Also, referring to the ‘‘Alpha SSU

size’’ and ‘‘Beta SSU size’’ columns

of table 4, the expected SSU size

was reduced from 12 to 8. Thus, the

base weight is (101/76) c (12/8) c 874

= 1,742.25

These base weights are the inverses

of the probability of annual-SSU

selection.

Continuing with the step of

oversampling black and Hispanic

households after the secondary sampling

unit has been selected (refer to the

‘‘Nonminority household subsample

rate’’ of table 4):

1. In substratum 21, there is no

oversampling. All housing units in

the SSU are targeted for interview.

Thus all target housing units

receive the SSU base weight,

2486.5915.

2. In substratum 12, a proportion

(0.7032) of all households are

targeted for interview. The remaining

are screened and interviewed only if

Figure 1. Housing unit distribution by measure

Block

Housing

unit

count

Measure

count Housing unit measure identification

A ........ 8 2 12121212

B . . . . . . . . 19 5 1234512345123451234

C . . . . . . . . 14 4 12341234123412

D ........ 9 2 121212121

E . . . . . . . . 12 3 123123123123

F . . . . . . . . 22 6 1234561234561234561234

G . . . . . . . . 17 4 12341234123412341

H ........ 9 2 121212121

NOTES: Blocks ordered by a sorted list are a subset of eight blocks shown for illustration. Housing units ordered by adjacent units within block.

Figure 2. Housing unit distribution by annual secondary sampling unit

Block

Housing

unit

count

Measure

count Year1 housing unit is in sample

A .......... 8 2 x x x x x x x x

B . . . . . . . . . . 19 5 x 1 1 2 2 x 1 1 2 2 x 1 1 2 2 x 1 1 2

C . . . . . . . . . . 14 4 3 3 4 4 3 3 4 4 3 3 4 4 3 3

D .......... 9 2 5 5 5 5 5 5 5 5 5

E . . . . . . . . . . 12 3 6 6 7 6 6 7 6 6 7 6 6 7

F . . . . . . . . . . 22 6 7 8 8 9 9 10 7 8 8 9 9 10 7 8 8 9 9 10 7889

G . . . . . . . . . . 17 4 10 x x x 10 x x x 10 x x x 10 x x x 10

H .......... 9 2 x x x x x x x x x

11 is first year, 2 is second year, 10 is 10th year, etc. An x represents a housing unit not in the sample.

NOTES: Blocks ordered by a sorted list are a subset of eight blocks shown for illustration. Housing units ordered by adjacent units within block.

Series 2, No. 130 [ Page 9
one or more eligible (i.e., civilian)

black or Hispanic residents are

found. Thus, households containing

one or more eligible black or

Hispanic residents are in sample

with certainty; their multiplicative

base weight is

SSU weight c 1 = 1,742.25

Nonminority households are

subsampled and their base weights

are

(1,742.25 / 0.7032) = 2,477.60

An example for density substratum

type (j), within nonself-reporting PSU(i)

sampled with probability πi

, would be

similar. The one difference would be

that the original sampling interval

would be adjusted from 1,748 to satisfy

the self-weighting requirement

πi/SISSU(j,i) = constant (j) (in this

example, constant (j) is the reciprocal of

the value in table 4, the ‘‘Alpha SSU

base weight’’ column) , where SISSU(j,i)

is the sampling interval for density

substratum type (j) within PSU(i).

Sampling in the Permit Frame

The proportion of the survey sample

selected from the permit frame is about

5 percent of the total sample at the

beginning of the 10-year design cycle

and increases by about 1 percent per

year. Hypothetical measures are selected

during within-PSU sampling in

anticipation of the construction of new

housing units. Identifying the addresses

for these permit measures involves a

listing operation conducted at building

permit offices, clustering of addresses to

form measures, and associating these

addresses with the hypothetical

measures in the sample.

The U.S. Bureau of the Census

conducts the Building Permit Survey,

which collects information each month

from all building permit offices in the

United States about the number of

housing units authorized to be built. The

survey results are converted to measures

with an expected size of four housing

units. These measures are continuously

accumulated and linked with the frame

of hypothetical measures used to select

the survey sample. This linking

identifies which building permit offices

contain measures that are in sample.

U.S. Bureau of the Census field

representatives then visit these building

permit offices to obtain lists of addresses

of units that were authorized to be built.

These lists are used to identify the

sample units. To the extent possible, the

U.S. Bureau of the Census attempts to

form groups of sample units that have

some geographic clustering.

Sampling of Group Quarters

Noninstitutional, nonmilitary group

quarters contain persons eligible for

inclusion in NHIS. The sampling of

group quarters is done exclusively from

the area frame. If a group quarters unit

is encountered during the address listing

operation that occurs prior to NHIS

interviewing, the NHIS address lister

uses reference materials to determine

whether the group quarters is

noninstitutional and nonmilitary. If so, it

is included in the address list. The

permit frame never is used to select

group quarters; any group quarters unit

encountered among permit frame sample

cases is considered to be out of scope.

Interviewing within noninstitutional,

nonmilitary group quarters is similar to

household interviewing.

Sampling of Persons Within

Households

In 1995 and 1996, persons within

households were selected for NHIS

questionnaire supplements using

probability sampling methods. For

example, in 1995 the ‘‘Year 2000

Objectives’’ supplement was

administered to one sample adult

randomly chosen from each family

residing in half of the sample

households. Beginning in 1997, one

sample adult and one sample child

(if children are present) are randomly

selected from each family residing in a

sample household for administration of

a large portion of the survey interview.

Interviewer Assignments

The NHIS sample areas have been

divided into 170 assignment areas of

one or more counties. The areas were

defined by the U.S. Bureau of the

Census’ Field Division for the survey.

Generally there is a single interviewer

for each assignment area, with several

interviewers assigned to the larger

assignment areas. The sample for each

quarter is divided into approximately

930 weekly interviewer assignments.

The number of weekly interviewer

assignments in each assignment area is

part of the input to the balancing

procedure discussed below.

The survey samples secondary

sampling units in each assignment area

and quarter, and they are grouped into

weekly interviewer assignments.

Interviewers have an expected 10–12

completed interviews per week. To

minimize travel in a weekly interviewer

assignment, secondary sampling units

were grouped in the same county

whenever possible. SSU’s from adjacent

counties were used to complete weekly

assignments that could not be formed

within a single county.

Balancing the Sample

For operational reasons, the specific

weeks that each interviewer receives an

assignment should be as evenly spaced

as possible throughout all 52 weeks of

the year, not simply within the 13 weeks

of each quarter. Because some

assignment areas have more than 13

weekly interviewer assignments and

more than one interviewer, the weekly

interviewer assignments in each

assignment area were distributed evenly

among the weeks. The census regional

office staffs then distribute the weekly

interviewer assignments among the

interviewers in multi-interviewer

assignment areas.

For estimation purposes, the

weekly interviewer assignments are

distributed among the 13 weeks in a

quarter. This minimizes the variation

among the weeks in the number of

measures and number of expected

completed interviews in the following

categories:

+ Each census regional office

+ Each census region (Northeast,

Midwest, South, West) and the total

United States

+ Each census region and the total

United States by type of PSU

(SR and NSR)

Page 10 [ Series 2, No. 130
+ Each census region and the total

United States by the geographic

categories C, B, U, R (C is the

central cities of a metropolitan area;

B is the urbanized area not in

category C; U is the urban places

not in an urbanized area and not in

category C; R is all other areas), and

by new construction

The U.S. Bureau of the Census

developed software to balance the

sample within these operational and

estimation constraints.

Additional Subsampling

Situations

During the address listing

operation that occurs prior to

interviewing, sometimes a larger than

expected number of housing units is

identified in a secondary sampling

unit. If the address lister finds more

than twice the number of expected

units in an SSU, the lister subsamples

the units. This causes an adjustment in

the probability of sample selection for

all units in that SSU.

Sometimes the NHIS interviewer

encounters extra units at a sample

address that were not identified during

the listing operation. Often when three

or more additional units are identified,

the interviewer subsamples one of the

units. This causes an adjustment in the

probability of sample selection for the

particular unit that is selected. This

type of subsampling is rare and is

carried out to maintain reasonable

interviewer workloads.

Assignment of Screening

Code

The second component of the

oversampling strategy involves the

elimination of some sample

households that do not contain any

black or Hispanic persons. First,

screening codes are assigned to sample

addresses. Then screening interviews

eliminate some households.

Prior to data collection, but after

the address listing operation and SSU

level subsampling (if necessary), some

of the addresses from each SSU are

randomly assigned a screening code of

I (interview) by the U.S. Bureau of

the Census. Others are assigned a

screening code of S (screen). The

proportion of addresses assigned to

either I or S depends on the sampling

substratum from which the SSU was

drawn (see the ‘‘Nonminority

household subsample rate’’ column of

table 4). The S cases are spread out as

much as possible within the SSU. As

indicated in the ‘‘Nonminority household

subsample rate’’ column of table 4, all

sample cases drawn from the permit frame

are assigned an I code.

For 1995 and 1996, the

assignment of screening codes was

carried out by using a systematic

sampling procedure using integerlength sampling intervals. For

example, if 75 percent of the addresses

in a particular substratum were to be

assigned a screening code of I, all of

the addresses in half of the SSU’s in

this substratum would be selected, and

the addresses in the other half of the

SSU’s would be selected at a rate of 1

of every 2. All the selected units

would be assigned a code of I and the

remainder a code of S. This systematic

sampling procedure was necessary

because the subsampling was a

systematic sampling clerical procedure

carried out in the Census regional

offices, and the U.S. Bureau of the

Census headquarters staff wanted the

regional office staff to use

integer-length sampling intervals.

Starting in 1997, the assignment

of screening codes was automated.

Integer-length sampling intervals were

no longer needed, and a single

sampling interval is used in each

substratum. In the example above,

using a uniform systematic selection

rate of 3 out of every 4, selected units

would be assigned a code of I and the

remainder would be assigned the S

code.

Sampling Rule

Implementation

The interviewers conduct the usual

NHIS basic health and demographic

interview for every address that

contains a household and has a

screening code of I.

For every household at addresses

with a code of S, the interviewers

conduct the interview by collecting the

household roster and the race and

ethnicity for each household member.

If the S household contains one or

more eligible (i.e., civilian) black or

Hispanic persons, then the household

is retained in the sample and the

interviewer completes the remainder of

the interview. If the S household

contains neither an eligible black

person nor an eligible Hispanic

person, then the household is not

retained in the sample, and the

interviewer does not complete the

remainder of the interview.

Because I and S codes are

assigned prior to interviewing, there

can be discrepancies between expected

subsampling rates and actual

subsampling rates. For example, all

the I codes in a secondary sampling

unit could be assigned to addresses

that contain households, while all of

the S codes in the SSU are assigned to

addresses that are vacant housing

units. In this case, no subsampling

would occur in the secondary

sampling unit. Another example is

when all households in an SSU that do

not include any eligible black or

Hispanic persons are assigned I codes.

In this case, no subsampling would

occur. Presumably, subsample

fluctuations at the secondary sampling

unit level balance out over the entire

sample.

During 1995 and 1996, if the

interviewer had made two

unsuccessful attempts to conduct

screening at an occupied sample unit

with a code of S, the interviewer could

contact two neighbors to ask if the

sample household contained any black

or Hispanic persons. If both neighbors

agreed the household did not contain

any black or Hispanic persons, the

household did not need to be

interviewed. Otherwise, the

interviewer was supposed to continue

trying to contact the sample household

to determine whether a complete

interview was required.

Beginning in 1997, information

from neighbors no longer was used for

screening purposes.

Series 2, No. 130 [ Page 11
Table 1. National Health Interview Survey designs: 1973–84, 1985–94, 1995–2004

Design period 1973–84 1985–94

1995–2004

Alpha Beta

Sampling frame ......................... Address, area,

permit, group

quarters

Area, permit Area, permit Area, permit

PSU definitions . . . . . . . . . . . . . . . . . . . . . . . . . One or more

counties

One or more

counties

One or more

counties

One or more

counties

1970 SMSA’s 1983 MSA’s 1990 MSA’s 1990 MSA’s

SR sample PSU’s . . . . . . . . . . . . . . . . . . . . . . . 156 52 95 95

NSR sample PSU’s . . . . . . . . . . . . . . . . . . . . . . 220 146 263 263

Total sample PSU’s . . . . . . . . . . . . . . . . . . . . . . 376 198 358 358

Sample PSU’s per NSR stratum .............. 1 2 Normally 2,

sometimes 1

Normally 2,

sometimes 1

First level of stratification ................... 4 census

regions

4 census

regions

50 States and the

District of Columbia

50 States and the

District of Columbia

Designated housing units per year ............. 51,000 61,400 124,000 70,000

Screened households per year . . . . . . . . . . . . . . . Not applicable Not applicable 99,000 57,000

Interviewed households per year .............. 40,000 49,000 55,000 41,000

Interviewed persons per year ................ 108,000 132,000 159,000 107,000

SSU size (except permit frame) — expected number

of housing units . . . . . . . . . . . . . . . . . . . . . . . . 4 8 8, 12, 16 8, 12

Permit frame SSU size — expected number of

housing units .......................... 4 4 4 4

Number of panels . . . . . . . . . . . . . . . . . . . . . . . Not defined 4 Not defined 4

Minority sampling techniques ................ None Oversample for

black persons

Oversample and

screen for black and

Hispanic persons

Oversample and

screen for black and

Hispanic persons

NOTE: PSU is primary sampling unit, SSU is secondary sampling unit, SR is self-representing, NSR is nonself-representing, SMSA is Standard Metropolitan Statistical Area as defined by the U.S.

Office of Management and Budget in 1959, MSA is Metropolitan Statistical Area as defined by the U.S. Office of Management and Budget in 1983, and MA is Metropolitan Area as defined by the U.S.

Office of Management and Budget in 1990.

Table 2. Sampling strata characteristics: National Health Interview Survey, 1995–2004

Stratum type

Number

of

strata

Universe

coverage

(population)

Sample

PSU’s

Size of strata

(population)

SR . . . . . . . . . . . . . . . . . . . . . 95 64% 95 90,000–18,000,000

NSR, 2 PSU’s . . . . . . . . . . . . . . 121 34% 242 380,000–1,000,000

NSR, 1 PSU . . . . . . . . . . . . . . . 21 2% 21 90,000–360,000

Total . . . . . . . . . . . . . . . . . . . . 237 100% 358 . . .

. . . Category not applicable.

NOTE: PSU is primary sampling unit; SR is self-representing; NSR is nonself-representing.

Table 3. Primary sampling units by census region: National Health Interview Survey, 1995–2004

Type of primary

sampling unit

Census region

Northeast Midwest South West Total

Self-representing . . . . . . . . . . . . . . . . . . . . . . . . 22 19 34 20 95

Large . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 4 2 13

Medium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 7 4 5 20

Small . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 10 26 13 62

Nonself-representing . . . . . . . . . . . . . . . . . . . . . . 29 76 119 39 263

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 95 153 59 358

NOTES: The designation of self-representing primary sampling units as ‘‘large,’’ ‘‘medium,’’ or ‘‘small’’ was done by the U.S. Bureau of the Census. In general, primary sampling units with populations

greater than 3,000,000 were designated as ‘‘large,’’ PSU’s with populations of 1,600,000–3,000,000 were designated as ‘‘medium,’’ and PSU’s with populations less than 1,600,000 were designated as

‘‘small.’’

Page 12 [ Series 2, No. 130
Table 4. Race and ethnicity density strata and within primary sampling unit sampling parameters: National Health Interview Survey,

1995–2004

Substratum

label

1990 Census density Percent

of U.S.

population

in 1990

Original

SSU unit

oversampling

rate3

Revised

SSU unit

oversampling

rate4

SSU

unit

reduction5

Alpha

SSU

size6

Beta

SSU

size7

Alpha

SSU

base

weight8

Beta

SSU

base

weight9

Nonminority household1

Percent

black2

Percent

Hispanic2

Subsample

rate10

Base

weight11

1 . . . . . . . . . . . . <10 <5 55.3 1.60 1.00 17 16 12 1,092.5000 1,751.4683 0.7032 2,490.7114

2 . . . . . . . . . . . . <10 5–10 7.7 2.00 1.50 0 16 12 874.0000 1,165.3333 0.4688 2,485.7793

3 . . . . . . . . . . . . <10 10–30 8.0 3.20 1.50 38 16 12 546.2500 1,167.6455 0.4688 2,490.7114

4 . . . . . . . . . . . . <10 30–60 3.1 4.00 1.60 61 12 12 437.0000 1,103.4250 0.4395 2,510.6371

5 . . . . . . . . . . . . <10 60+ 3.5 4.00 2.30 43 8 8 437.0000 760.9828 0.3057 2,489.3123

6 . . . . . . . . . . . . 10–30 <5 4.6 1.60 1.00 38 12 12 1,092.5000 1,751.4683 0.7032 2,490.7114

7 . . . . . . . . . . . . 10–30 5–10 1.4 2.00 1.50 25 12 12 874.0000 1,161.5000 0.4688 2,477.6024

8 . . . . . . . . . . . . 10–30 10–30 1.9 3.50 1.50 58 12 12 499.4286 1,173.0764 0.4688 2,502.2961

9 . . . . . . . . . . . . 10–30 30–60 0.9 4.00 1.50 63 8 8 437.0000 1,161.5000 0.4688 2,477.6024

10 . . . . . . . . . . . . 10–30 60+ 0.7 4.00 2.00 51 8 8 437.0000 882.7400 0.3516 2,510.6371

11 . . . . . . . . . . . . . 30–60 <5 2.3 1.70 1.00 12 12 8 1,028.2353 1,750.3106 0.7032 2,489.0652

12 . . . . . . . . . . . . 30–60 5–10 0.5 2.00 1.00 25 12 8 874.0000 1,742.2500 0.7032 2,477.6024

13 . . . . . . . . . . . . 30–60 10–30 0.8 3.50 1.00 58 12 8 499.4286 1,759.6146 0.7032 2,502.2961

14 . . . . . . . . . . . . 30–60 30–60 0.7 4.00 1.50 63 8 8 437.0000 1,161.5000 0.4688 2,477.6024

15 . . . . . . . . . . . . 30–60 60+ 0.2 4.00 2.00 51 8 8 437.0000 882.7400 0.3516 2,510.6371

16 . . . . . . . . . . . . 60+ <5 6.6 1.95 1.05 49 8 8 896.4103 1,741.1045 0.7032 2,475.9735

17 . . . . . . . . . . . . 60+ 5–10 0.6 2.00 1.00 51 8 8 874.0000 1,765.4800 0.7032 2,510.6371

18 . . . . . . . . . . . . 60+ 10–30 0.9 3.50 1.20 66 8 8 499.4286 1,441.2082 0.5860 2,459.3996

19 . . . . . . . . . . . . 60+ 30–60 0.2 4.00 1.50 63 8 8 437.0000 1,161.5000 0.4688 2,477.6024

20 . . . . . . . . . . . . 60+ 60+ 120.0 4.00 1.50 63 8 8 437.0000 1,161.5000 0.4688 2,477.6024

21 . . . . . . . . . . . . permit permit . . . 1.00 1.00 30 4 4 1,748.0000 2,486.5915 1.0000 2,486.5915

. . . Category not applicable.

1Nonminority includes everyone except black and Hispanic persons.

2For intervals of form ‘‘x-y,’’ the lower endpoint is included and the upper endpoint is not included.

3Alpha-design is the oversampling rate for secondary sampling units.

4Beta-design is the oversampling rate for secondary sampling units.

5The number of alpha-design secondary sampling units that are removed per 101 alpha-design secondary sampling units to obtain the beta-design secondary sampling units.

6Expected number of housing units in each alpha-design secondary sampling unit.

7Expected number of housing units in each beta-design secondary sampling unit.

8Annualized base weight of alpha-design secondary sampling units. A given entry in this column is equal to 1,748 divided by the corresponding entry in the ‘‘Original SSU unit oversampling rate’’

column. For example, the first entry in this column, 1092.5000, is equal to 1,748/1.6.

9Annualized base weight of beta-design secondary sampling units. A given entry in this column is equal to the corresponding entry in the ‘‘Alpha SSU base weight’’ column, multiplied by a factor to

compensate for SSU unit reductions (see the ‘‘SSU unit reduction’’ column), and multiplied by a factor to compensate for change in the SSU size (see the ‘‘alpha SSU size’’ and ‘‘beta SSU size’’

columns). For example, the first entry in this column, 1751.4683, is equal to 1092.5000 c [101/(101–17)] c (16/12).

10Expected fraction of nonminority households sampled in NHIS secondary sampling units.

11Base weight of nonminority household in NHIS SSU. A given entry in this column is equal to the ratio of the corresponding entries in the ‘‘Beta SSU base weight’’ column and the ‘‘nonminority

household subsample rate’’ column. For example, the first entry in this column, 2490.7114, is equal to (1751.4683/0.7032).

12Quantity more than zero but less than 0.05.

NOTE: SSU is secondary sampling unit.

Series 2, No. 130 [ Page 13
Chapter 3.

Design and Estimation

Structures for the

1995–2004 NHIS

Lead author: Van L. Parsons,

Ph.D., Offıce of Research and

Metholodogy, National Center for

Health Statistics

Introduction

The National Health Interview

Survey (NHIS) is designed to make

inferences about the civilian

noninstitutionalized population of the

United States. While a general

description of the NHIS sample design

is presented in chapter 2 of this report,

this chapter focuses on the design

structures needed in making statistical

inference. The NHIS program at the

National Center for Health Statistics

(NCHS) focuses its attention on making

design-based inferences about the health

of persons and households in the target

population. This is accomplished by

inflating the responses of each surveyed

person or household in NHIS, referred

to as elementary units, by a national

weight factor. This is called a national

weight because it permits an

(approximately) unbiased design-based

estimator of any U.S. target population

total. With this weight an unbiased

estimator, Xˆ, for any given true

population characteristic total, X, can be

expressed as a weighted sum over all

elementary units:

Xˆ = ∑u

Wf(u) x(u) (1)

where

u represents an elementary unit of

NHIS

x(u) is the characteristic or response

for unit u

Wf(u) is the final national weight for

unit u

This estimator of total is used to

generate the NHIS estimates of totals,

percents, and rates that appear in NCHS

official publications. A national weight is

provided on the NHIS Public-Use

databases that allows users to directly

create estimators of the form in equation

(1). In the sections that follow, the

technical aspects of the procedures used

to create national weights and the

procedures used to estimate the

variances of NHIS estimators are

discussed.

Methods for Creating

Weights and Estimators

Complex estimation techniques are

required for the survey because it is

based on a highly stratified multistage

probability sample. The true sampling

distributions of any survey

implementing complex clustering

structures, implicit stratification, and

systematic sampling tend to be

mathematically intractable. Therefore,

the survey design will be conceptualized

in a somewhat simplified framework.

This conceptual design will provide a

tractable structural model that captures

the most important design features. The

primary sampling unit (PSU) and

within-PSU sampling steps discussed in

chapter 2 can be expressed as a

hierarchical sampling design with levels

and probabilities shown in table 5.

It should be noted that the number

of super-secondary sampling units

(SSU’s) in the survey is a random

variable, but with very little variability;

hence the super-SSU sample sizes will

be treated as fixed.

The number of eligible households

in an SSU also is a random variable. A

housing unit (HU) may be classified as

an eligible household or an ineligible

unit. Nationally, about 20 percent of all

addresses yield an ineligible

classification (e.g., vacant). New

construction and/or destruction of

housing units within a block in the

5 years or more after the 1990 Census

results in a degradation of the original

frame composition information. It is

possible, although a rare event, that

extreme conditions can occur within a

secondary sampling unit at the time of

sampling. Either no eligible

households exist or too many new

HU’s exist (use of a permit frame

and/or field subsampling, as discussed

in chapter 2, dampen the influence of

the latter condition). The annual

number of persons in the sample also

is a random variable. As a

consequence, the annual surveys

exhibit different sample counts of

households and persons.

The three broad estimation criteria

that NCHS applies when deciding on

estimation strategies to use for survey

data are:

1. The estimation methods must be

design-based for finite populations.

That is, the randomness of the data

is a result of sampling finite

universes having no imposed

distributional assumptions. This is in

contrast to a model-based approach

where the data typically have

imposed distributional assumptions.

The design-based methods may be

thought of as nonparametric and

robust.

2. The design-based methods should be

practical and should permit

(approximately) unbiased estimators

of population totals.

3. The design-based methods should

permit practical variance estimation

strategies to assess the stability of

the estimator.

To satisfy these criteria, NCHS, as

well as many other sponsors of large

government surveys, has been using

standard accepted design-based methods

discussed in such classic references as

Cochran (10), Kish (11), and Hansen,

Hurwitz and Madow (12).

National Weights

The NHIS estimator of a

characteristic total as presented in

equation (1) uses methodology based on

the features of the complex multistage

probability sample to define a national

weight, Wf , for each elementary unit.

This national weight is the product of

up to four of the following weighting

factors:

1. Inverse of the probability of

selection

2. Household nonresponse adjustment

3. First-stage ratio adjustment

Page 14 [ Series 2, No. 130
4. Second-stage ratio adjustment

(poststratification)

When the elementary unit is an

individual, all four weighting factors

define the person’s final weight. Because

the NHIS ratio adjustments are based on

person characteristics, only the first two

weighting factors are used to define a

national household weight.

NCHS creates weights for each

calendar quarter of the NHIS sample,

using information provided by the U.S.

Bureau of the Census. These weights

permit national estimates to be made for

each quarter. Quarterly weights are

divided by 4 to create annual weights,

which are used when making national

estimates from a calendar year of the

survey sample.

Base Weight Estimator

The overall probability that a unit is

in sample is the product of the

conditional selection probabilities

presented in table 5. This basic inflation

weight is defined as:

WI(u) = 1/Probability (unit u is in sample).

Roughly speaking, based on

probabilistic sampling, unit u represents

WI(u) population units. This weight

depends in part on the minority status of

the household and substratum class to

which the unit u belongs. WI is the first

component weight of Wf in equation (1).

Table 4 in chapter 2 presents the target

household inflation weights in the ‘‘Beta

SSU base weight’’ and ‘‘Nonminority

household base weight’’ columns.

Infrequently, this base sampling

weight, WI , will be modified. If, during

the HU selection process of step 4 in

table 5, a secondary sampling unit is

determined to contain too many housing

units for interview, then a subsample

will be selected. If the subsample

consists of less than 1/4 of all HU’s in

the secondary sampling unit, then the

conditional probability of selection will

be truncated by the U.S. Bureau of the

Census at 1/4. This is a rare occurrence,

and the biases introduced by such a

modification should be small.

In an ideal hypothetical sampling

situation having no nonsampling error

components (e.g., frame problems,

nonresponse, and interviewer effects),

equation (1) with WI substituted for Wf

becomes

Xˆ 0 = ∑u

WI(u) x(u) (2)

which will provide an unbiased

estimator for the true population total,

X. Such an estimator is referred to as a

base weight estimator or as a

Horvitz-Thompson estimator.

Factors Contributing to

Annual Weight

Fluctuations

Complex sampling implemented by

specified guidelines is difficult to

achieve in the real world. Some types of

special situations that may occur during

the survey and will have an influence on

weighting procedures for a given year

are:

1. Initial start-up problems during the

first year of the survey may result in

minor deviations from the sampling

plan.

2. NHIS budgetary changes may result

in additional subsampling to reduce

the sample. For example, in 1985

and 1986 the NHIS samples were

reduced by 25 and 50 percent,

respectively, but 1987–94 were full

budget years. Similar contingency

plans exist for the 1995–2004 design

(see the section ‘‘Panels of PSUs’’ in

chapter 2).

3. Phase-in of new field operations may

modify the sample. In 1996, the

transition from paper and pencil to

computer-assisted interview resulted

in only 5/8 of the full survey sample

being targeted for eventual public

release.

4. In recent years, the survey has

used one or more weeks at the

beginning of the year (during

quarter 1) for interviewer training.

During this period, no data are

collected for release. In this

situation, all sampled units for

quarter 1 have their basic inflation

weight increased by an appropriate

factor to inflate to 13 weeks. For

example, if 1 week was used for

training, the factor is 13/12; if 2

weeks were used for training, the

factor is 13/11.

5. Unexpected one-time events may

alter the design. For example,

interviewing for the 1995 survey

was not completed due to a

government shutdown in December

1995. As a result, the within-PSU

sampling came up 3 weeks ‘‘short’’

for several PSU’s that year. For

quarter 4 of 1995, the basic inflation

weight was increased by a factor of

13/10.

In this report, only the anticipated

weighting adjustments are considered

for a full sample of the survey.

Household Nonresponse

Adjustment

During 1995–97, the first 3 years of

the current survey design, the household

nonresponse rates for the core survey

were about 6.2, 6.2, and 8.2 percent,

respectively. Although small, this

household nonresponse will most likely

bias an estimator of the form shown in

equation (2), and consequently, a

weighting adjustment for household

nonresponse is justified. This need

results in the creation of the second

weight factor, the household

nonresponse adjustment.

The standard household

nonresponse adjustment is done by

inflating the sampling weights for all

responding households within an SSU to

compensate for the nonresponding

households within that same secondary

sampling unit. A special situation does

occur. Typically, 5–15 SSU’s in a

quarter have 100 percent nonresponse.

In such a situation, no nonresponse

adjustment is made. It is assumed that

the poststratification ratio adjustment

will compensate for the nonresponse.

In the 1985–94 NHIS design, when

all eligible households (i.e., households

with one or more civilian members) in a

secondary sampling unit were sampled

with certainty (i.e., no screening

occurred), NCHS used the simple

nonresponse adjustment

 all eligible households in SSU

Σ all responding households in SSU

Series 2, No. 130 [ Page 15
(Note that this unweighted sum usually

is equivalent to the sum obtained using

the base weights because the base

weight is constant within an SSU. The

only exception is the rare occurrence

when the interviewer discovers three or

more additional units at a sample

address. Additional information is

available in ‘‘Additional Subsampling

Situations’’ in chapter 2.

In the 1995–2004 NHIS design,

households that have no black or

Hispanic persons are subsampled at the

SSU level in the area frame, as

described in chapter 2. The subsampling

occurs after all housing units in the SSU

have been randomly divided by the U.S.

Bureau of the Census into two groups,

coded I and S, prior to interviewing. All

sampled households that have no black

or Hispanic persons must come from the

I group. Those in the S group are

screened out. All black and Hispanic

households in either group are

interviewed. This within-SSU

subsampling creates two issues that did

not have to be addressed in previous

survey designs:

1. The race/ethnicity of some

households cannot be determined

because the interviewer never

succeeds in making contact with the

household. If the true status is black

or Hispanic, then these are

nonresponding households. If the

true status is ‘‘other,’’ then the

household may be nonsampled or

sampled, but nonresponding.

2. Even with no nonresponse, the base

weight estimated total number of

households within the SSU, based

on the interviewed sample, may not

be equal to the true total number of

households within the secondary

sampling unit. (The expected value,

however, is unbiased.) A simple

hypothetical example is: Suppose the

I/S sampling rule requires that within

every substratum of type a, a

subsample of 1 of every 2 household

units within an SSU be taken. All

households that have no black or

Hispanic persons in this I subsample

are targeted for interview, but all

black and Hispanic households in

the entire SSU are targeted. Thus,

every sampled black and Hispanic

household will receive a component

weight of 1, while every sampled

‘‘other’’ household will have a

weight of 2. The sum of the ‘‘other’’

household weights will be an even

number that may not equal the true

value.

Methodology for 1995 and

1996 Surveys

In 1995 and 1996, NCHS used the

following method to adjust for

nonresponse:

Each household in a given SSU was

classified as belonging to 1 of 3 groups:

M = black or Hispanic household

O = ‘‘other race’’ household

U = unknown race and ethnicity status

household

The class O households were

subsampled, but all class M households

were selected for interview.

Let WH = conditional inflation

weight restricted to step 5 of table 5

(and any other special inflation factors

as discussed in the ‘‘Base Weight

Estimator’’ section above).

The following weighted sums (with

respect to WH) were computed across

the SSU:

WH (M) = weighted sum of sample

class M households

WH (O) = weighted sum of sample

class O households

WH (Mres) = weighted sum of responding

sample class M households

WH (Ores) = weighted sum of responding

sample class O households

The nonresponse adjustment for

identified class M or O households in

the SSU was defined as

NROM = [WH (M) + WH (O)]

[WH (Mres) + WH (Ores)]

This does not include the unknown

class U. To compensate, let

N(M,O,U) = unweighted sum of all

households (M,O,U) in the SSU, and

N(M,O) = unweighted sum of M and O

households in the SSU.

Note, here the unweighted sums

N(M,O,U) and N(M,O) are over the

entire SSU, while the weighted sums,

WH( ), are computed only on the

sampled component.

The nonresponse adjustment factor

for the SSU was defined as

NR = [N(M,O,U) / N(M,O)] c NROM

If all households were successfully

screened with respect to race/

ethnicity, i.e., if N(M,O,U) = N(M,O),

then

NR = NROM

The final household nonresponse

adjustment factor for the SSU, Wnr, was

defined as

Wnr = minimum (NR,2.0)

That is, the final factor was truncated to

2 to control the variability in the

weights due to this factor. Typically,

fewer than 0.5 percent of SSU’s used

this truncated factor.

Methodology for 1997 Survey

and Beyond

Beginning in 1997, NCHS has used

a different method to adjust for

nonresponse. This method accounts for

the difference in eligibility for inclusion

in the survey, based on use of the I and

S screening codes, as described in

chapter 2.

All of the households in a given

SSU that belong to one of the following

four groups are eligible:

MI = black or Hispanic household,

I screening code

MS = black or Hispanic household,

S screening code

OI = ‘‘other race’’ household,

I screening code

UI = unknown status household,

I screening code

None of the households in the SSU

in the following group are eligible:

OS = ‘‘other race’’ household,

S screening code

Some, none, or all of the

households in the SSU in the following

group are eligible:

US = unknown status household,

S screening code

If the number of households in the

US group is not zero, the proportion of

eligible households in the SSU is

estimated using information from

Page 16 [ Series 2, No. 130
households with known race and/or

ethnicity. The eligible proportion is

estimated by summing the number of

MI

, MS, OI, and OS households in the

SSU and then computing:

MINPROP = (MI + MS)

(MI + MS + OI + OS)

assuming the denominator is not zero;

otherwise, MINPROP is set equal to 0.

Once MINPROP is defined, the

complementary proportion OTHPROP is

defined as [1−MINPROP] if the

denominator of MINPROP is not zero;

OTHPROP is set equal to 0 if the

denominator of MINPROP is zero.

Let WH = conditional inflation

weight restricted to step 5 of table 5

(and any other special inflation factors

as discussed in the ‘‘Base Weight

Estimator’’ section).

The following weighted sums (with

respect to WH) are computed across the

SSU:

WH (MI

) = weighted sum of sample

class MI households

WH (MS) = weighted sum of sample

class MS households

WH (OI

) = weighted sum of sample

class OI households

WH (MI, res) = weighted sum of

responding sample class

MI households

WH (MS, res) = weighted sum of

responding sample class

MS households

WH (OI, res) = weighted sum of

responding sample class OI

households

Then, the nonresponse factor for the

SSU is computed as:

NR = [WH (MI

) + WH (MS) + WH (OI)

+ f1(UI

)+f2 (US)] / [WH (MI, res)

+ WH (MS, res) + WH (OI, res)]

where f

1(UI

) denotes a partition of the

households in UI using MINPROP and

OTHPROP, with appropriate WH factors

applied to each piece, and f

2 (Us)

denotes estimation of the black and/or

Hispanic households in US using

MINPROP, and the appropriate WH

factor applied to that piece.

More specifically,

f1 (UI

) = WH(OTH) (OTHPROP c UI)

+ WH(MIN) (MINPROP c UI

)

f2(US) = WH (MIN) (MINPROP c US)

where

WH(OTH) (OTHPROP c UI

) =

weighted sum of the ‘‘other race’’

estimated proportion of UI

households, where the weight

WH(OTH) is the weight applied to

‘‘other race’’ households in the SSU.

WH(MIN) (MINPROP × UI

) = weighted

sum of the black and Hispanic

estimated proportion of UI

households, where the weight WH(MIN)

is the weight applied to black and

Hispanic households in the SSU.

WH(MIN) (MINPROP × US) =

weighted sum of the black and

Hispanic estimated proportion of US

households, where the weight

WH(MIN) is the weight applied to

black and Hispanic households in

the SSU.

In essence, the nonresponse factor

NR consists of a numerator that is an

estimate of the total number of eligible

households in a given SSU, and a

denominator that is the total number of

interviewed households. Weight factors

that account for subsampling within the

SSU are included as appropriate.

The final household nonresponse

adjustment factor for the SSU, Wnr, is

defined as

Wnr = minimum (NR, 2.0)

That is, the final factor is truncated

to 2 to control the variability in the

weights due to this factor. Typically,

fewer than 0.5 percent of SSU’s use this

truncated factor.

Estimator Based on the

Product of WI and Wnr

The estimator produced by

substituting the product of WI and Wnr

for Wf in equation (1)

Xˆ′ = ∑u

WI(u) c Wnr(u) c x(u) (3)

should produce approximately unbiased

estimators for the population total X, as

long as the true nonresponding

population does not significantly differ

from the responding population.

Beginning in 1997, the weight

WI c Wnr is used to define a final national

weight for households, and it is used to

produce estimates of household

characteristics. (In 1995 and 1996, the

weight WI was used to define a final

national weight for households.)

Ratio Adjustments for

Person-Level Weights

The third and fourth weighting

factors to be defined are ratio

adjustments. Statistical sampling theory

has demonstrated that in many

situations, the estimators obtained using

a ratio estimation procedure often have

smaller mean squared error (MSE) than

the base weight estimators expressed by

equation (2). More precisely, if X′ and

Y′ are base weight estimators of two

population characteristic totals, X and Y,

respectively, and the ‘‘true’’ total Y is

known, then the ratio estimator

X′′ = (X′/Y′) Y for X will have smaller

MSE than the estimator X′ when there

is a high positive correlation between X′

and Y′ and the sample size is large.

The ratio adjustment also is used to

help correct survey bias due to

systematic undercoverage. It should be

noted that an observed survey estimator

of the form shown in equation (3) may

be larger or smaller than the true value

just by chance alone. There are some

populations that the U.S. Bureau of the

Census has identified as ‘‘difficult to

sample.’’ For example, historically, there

has been survey undercoverage of the

young black male population in the

NHIS, and the estimator of equation (3)

may be negatively biased in estimating

young black male population

characteristic totals. Such a bias due to

undercoverage is often reduced by the

use of the ratio adjustments.

Note that the ratio adjustments are

applied at the person level, which can

introduce variation in the person-level

weights within a given sampled

household. The previous two components

of the weights, the inverse of the

probability of selection and the household

nonresponse adjustment, are equal for all

persons in a given sampled household.

Series 2, No. 130 [ Page 17
First-Stage Ratio Adjustment

The first-stage ratio adjustment is

used in an attempt to reduce the

between-PSU variance component of

sampling variation among the

nonself-representing (NSR) PSU’s.

For each of six residence and/or

race-ethnicity classes within the

nonself-reporting strata of each of the

four census regions, the first-stage ratio

adjustments are defined using the

following methodology and are also

presented in table 6.

The first-stage ratio adjustment

factors are created by the U.S. Bureau

of the Census. The 24 residence and/or

race-ethnicity classes in the United

States defined within the NSR PSU’s

will be indexed by the letter c,

c = 1,2,...,24.

Let

Zc= the projected 1995 population

total for class c over all NSR PSU’s

in the population.

Considering only the first stage of

sample selection as presented in the

conceptual design (refer to step 1 in

table 5), the sample of NSR PSU’s can

produce an unbiased estimator of Zc ,

say, Zˆ

c ,

Zˆ c = ∑

NSR PSU’s(si)

Zsic / πsi

where Zsic = projected 1995 population

total for class c in sample NSR PSU i

of stratum s, and πsi = the selection

probability of PSU i of stratum s (refer

to step 1 in table 5).

Note, Zsic is based on U.S. Bureau

of the Census projections and not on the

fielded sample.

The first-stage ratio adjustment factor

associated with class c is defined as

Fc = Zc / Zˆ c

with adjustment (i.e., collapsing with

another class and/or truncation)

occurring if the factor falls outside the

interval [0.8125, 1.3].

The U.S. Bureau of the Census

suggested that the lower and upper

bounds should satisfy the symmetric

equations L = 1/(2−(1/U)) and

U = 1/(2−(1/L)). With the upper bound

specified at U = 1.3, the lower bound is

determined to be L = 0.8125.

A universal first stage ratio

adjustment, Wr 1, can be defined for each

sample person by defining a new class

index, c = 0, to denote all persons not

receiving the Fc ratio adjustment:

Wr1 = Wr1(c) = Fc

if c = 1,2,...,24 for NSR PSUs

Wr1 = Wr1(c) = 1

if c = 0 for SR PSU’s

A first-stage ratio-adjusted national

estimator, Xˆ′′, of a population total, X, is

defined by equation (1) when the weight

Wf is replaced with the product of the

first three component weights:

WI c Wnrc Wr1 ,

Xˆ ′′ = ∑u

WI(u) c Wnr(u) c Wr1 (y) c x(u)

(4)

As shown in table 2, about 64 percent

of the U.S. population resides in the

self-representing strata, which do not

receive the first-stage ratio adjustment.

The research of Parsons and

Casady (13) on the previous 1985–94

NHIS design has shown that the

inclusion of the first-stage ratio

adjustment factor had very little

impact on NHIS estimates.

Table 6 shows the first-stage ratio

adjustment factors for 1997. These are

typical values for the entire 1995–2004

NHIS design. However, they were

different in 1996 because of the reduced

sample, and there will be a small change

starting in 2000 when one nonselfrepresenting PSU rotates out and

another nonself-representing PSU rotates

in (refer to ‘‘Rotating PSU’s’’ in

chapter 2).

Second-Stage Ratio Adjustment

(Poststratification)

The main advantages of the

ratio-estimation process are exploited

by the introduction of a second ratio

factor, the poststratification adjustment

weight. This weight assures that the

NHIS estimates for 88 age-sex-race

and/or ethnicity classes of the civilian

noninstitutionalized population of the

United States (shown in table 7) agree

with independently determined

population controls prepared by the

U.S. Bureau of the Census.

Furthermore, these independent

controls also include an adjustment for

net undercoverage in the 1990 Census,

and are the same controls used for the

Current Population Survey (CPS).

Thus, the national population estimates

for any combination of the age-sexrace and/or ethnicity groups from the

two surveys are the same, which

greatly enhances comparability of the

two surveys.

Each month, the U.S. Bureau of the

Census produces national estimates for

the 88 age-sex-race and/or ethnicity

classes. While the survey is conducted

weekly, the poststratification adjustment

is only computed for quarterly estimates.

The quarters and the dates of the

population estimates used as the controls

are:

NHIS Quarter Population Estimates

January-March

April-June

July-September

October-December

February 1

May 1

August 1

November 1

For each quarter, 88 age-sex-race

and/or ethnicity adjustment weights are

computed, for a total of 352 adjustment

weights annually. If a represents one of

the 88 age-sex-race and/or ethnic

classes, Y(a) represents the U.S. Bureau

of the Census population estimate for

class a, and Yˆ ′′(a) represents the NHIS

first-stage ratio-adjusted national total

for class a, that is:

Yˆ′′(a) = ∑u

WI(u) c Wnr(u) c Wr1 (u) c Ia(u),

where

Ia(u) = 1 if person u is in class a

= 0 otherwise

then the second-stage ratio adjustment

for class a, Wr2 , is defined as:

Wr2(a) = Y(a) / Yˆ ′′(a)

In implementing the above

second-stage ratio adjustment, NCHS

generally requires each class a to

contain at least 30 sample persons. If a

class contains too few sample persons,

that class will be pooled with an

adjacent age class. The two-stage ratio

adjusted national estimator, Xˆ, of a

population total, X, is defined by

formula (1) with the weight Wf defined

by the product of the four component

Page 18 [ Series 2, No. 130
weights: WI c Wnr c Wr 1 c Wr 2, i.e.,

Xˆ = ∑u

WI(u) c Wnr(u) c Wr1(u) c Wr2(u) c x(u)

(5)

In the 1973–84 and 1985–94 NHIS

designs, both the first-stage and

poststratification ratio adjustments were

structured similarly to the structures

presented in tables 6 and 7, but the

racial classes were black and nonblack.

The introduction of oversampling

Hispanic households and the use of

Hispanic class ratio adjustments in 1995

have improved the precision of Hispanic

domain statistics.

Creation of Other Weights

The preceding discussion has

outlined the procedure for creating

household-level and person-level

weights for the survey. Other weights

also were created for 1995 and 1996

NHIS supplements, and beginning in

1997, for sample adult, sample child,

and family-level files. The basic strategy

for creating these other weights is

similar to the preceding discussion.

Some form of the household-level

weight or the person-level weight

always is the starting point for

creating the other weights. As

appropriate, additional levels of

sampling are accounted for. In some

instances, an additional nonresponse

adjustment is done (e.g., someone may

have responded to the main survey,

but refused to respond to a

supplement). Usually, some form of

poststratification is done; for example,

the sample adult weight uses a smaller

set of poststrata than the 88 poststrata

listed in table 7.

Variance Estimation

Most of the estimates produced by

NCHS from the survey are totals and

ratios of totals, such as means, percents,

and rates. All such totals are produced

using the final national weight as

described in the preceding sections.

These estimators are subject to both

sampling and nonsampling errors. The

nonsampling errors such as response

errors, defective sample frames,

nonresponse, and undercoverage are

difficult quantities to measure, but every

effort is made to minimize such errors at

each step of the NHIS operation. The

sampling error, however, can be

measured by the variance of the

estimator.

While equation (1) provides a

functional form that permits simple

computation of point estimates, the

variances of such estimators are more

difficult to compute. The functional

form of a variance estimator depends

on the nature of the survey design and

methods used to adjust the weights.

Some complexities in the survey

design that require some special

techniques are:

1. The higher levels of sampling (e.g.,

selection of SSU’s within a PSU)

are the result of a very complicated

process involving the partitioning of

block groups, estimating measures of

size, and applying systematic

sampling techniques. Even given the

census information about the PSU, it

would be extremely difficult to

define a ‘‘user friendly’’ sampling

mechanism that captured all the true

stochastic structure of the system

and could be implemented with a

standard variance estimation

procedure.

2. Although there are 21 density

substrata definitions, most PSU’s have

only a few such substrata within the

PSU that are nonempty. Many density

strata within a primary sampling unit

have just one sampled SSU.

3. Some nonself-representing strata

have only one sampled PSU.

4. Some self-representing strata are

small. They are part of large

multi-State metropolitan areas, but

sampled as distinct areas.

5. To protect the confidentiality of

survey respondents, NCHS cannot

release design information that

could be used to identify smaller

geographical areas in which the

survey was conducted. Small

sample areas with rare

socioeconomic-demographic

characteristics must not be

explicitly or implicitly identifiable

by design information.

6. With weighting adjustments

applied to the base sampling

weight, estimators of total become

nonlinear in nature. This

complicates the variance estimation

procedure.

7. In practice, data analysts who study

NHIS data use large-sample theory

when making inferences about

populations. Variance estimation

procedures suitable for large

subpopulations may be unstable for

smaller subpopulations. NCHS

targets stable, all-purpose variance

estimation structures that should be

easy to implement with existing

computer software.

Simplified Design

Structures for Variance

Estimation

Wolter (14) and Rust (15) wrote

excellent reviews of design-based

variance estimation for complex

surveys. Of the available methods, the

two most commonly used are the

Taylor series linearization method and

the Balanced Repeated Replication

(BRR) method. Software for analysis

of complex surveys include PC CARP,

STATA, SUDAAN, VPLX, and

WESVAR. A comparison of these

software packages is beyond the scope

of this report, but an Internet world

wide web page, Summary of Survey

Analysis Software, currently located at

http://www.fas.harvard.edu/~stats/

survey-soft/survey-soft.html,

provides references and discussion.

Currently, NHIS public use files

contain design information suitable for

the Taylor series linearization method

(also suitable for some replication

software).

Variance Estimators for

National Estimators

First, a variance estimation

structure is developed for

self-representing PSU’s. Then, a

structure is developed that accounts

for the PSU sampling for

nonself-representing PSU’s. The two

structures are then combined to give a

Series 2, No. 130 [ Page 19
variance estimator for national

estimators. These structures are

described in more detail below.

SR PSU’s: Conceptual NHIS

Within PSU Sampling and

Estimation Structures

1. The super-SSU will be considered

as a well-defined population cluster

where within-super-SSU sampling

inflation weights, steps 3 to 6 of

table 5, produce an unbiased

estimator of super-SSU total.

2. The super-SSU sampling, step 2 of

table 5, can be treated as a

traditional ‘‘with replacement

sampling’’ from an infinite

population of super-SSU’s within a

substratum. All population

super-SSU’s within a substratum

have the same selection probability.

Sampling is independent over

substrata.

Under assumptions 1 and 2 above,

one can develop a variance estimator for

the estimated total at the substratum

level. The following indices shall be

used to denote the levels of nesting

within the survey design:

s stratum

i PSU

j substratum

k sampled annual-SSU

u sampled elementary unit within

annual-SSU k.

For substratum j nested within PSU

i, nested within stratum s, let

Nsij = the number of population

super-SSUs in substratum j

the number of super-SSUs

sampled in substratum j

the probability in step 2 of

table 5 times Nsij

nsij =

{

Wjlsi = Nsij / nsij = super-SSU selection

weight within substratum j

Wuk|sij = within super-SSU

conditional selection weight for unit u in

annual-SSU k computed using the

inverses of probabilities of selection as

specified in steps 3–6 of table 5.

An unbiased estimator of the total

of a characteristic X for substratum j

may be expressed as:

Xˆ sij = ∑

k=1

nsij

( ∑

uεSSUk

Wj | si c Wuk | sij c xu)

= 

k = 1

nsij

Xˆ sijk

and an unbiased estimator of its

variance is:

Var(Xˆ sij) = nsij Sˆ

sij

2

where

Sˆ sij

2 = ∑

k = 1

nsij

(Xˆ sijk − X ̄ sij.)

2 / (nsij−1)

X ̄

sij. = ∑

k = 1

nsij

Xˆ sijk / nsij

These functional forms can be

extended over all the substrata within

the primary sampling unit to obtain an

unbiased estimator of the PSU total

and its corresponding variance

estimator:

Xˆ si = ∑

j=1

21

∑

k = 1

nsij

Xˆ sijk and

Var(Xˆ si) = ∑

j = 1

21

nsij Sˆ

sij

2

(6)

Treating the sampling as ‘‘with

replacement’’ when it actually is

‘‘without replacement’’ results in a

positive, but not dominating, variance

estimator bias. Furthermore, because

many substrata may be empty or have

few sampled SSU’s, the substrata may

be collapsed within PSU by substrata

characteristics to form fewer substrata.

Using these new substrata will typically

result in a more stable variance

estimator, but greater positive variance

estimation bias.

Typically, the variance estimator

with a reduced set of substrata will be



jεCsi

nCsi(j) Sˆ Csi(j)

2

(7)

where Csi is some collapsing of the

original 21 substrata of PSU i in stratum

s, with the n and S2 terms defined as

in equation (6), but on the new collapsed

substrata. For example, one possible

collapsing would be Csi = {Csi(1), Csi(2)},

where

Csi(1) = {1,2,3,4,5,6,7,8,9,10,11,12,13,

16,17,18,21}

Csi(2) = {14,15,19,20}

NSR PSU’s: Variance Estimator

that Accounts for PSU

Sampling

The variance estimators presented

above in equations (6) and (7) appear

reasonable for totals restricted to

self-representing strata. In the

nonself-representing strata, the variance

estimator should also reflect the

first-stage selection of PSU’s. First,

consider a hypothetical NHIS having

100 percent response and using only the

weights determined from steps 1–6 in

table 5. The basic inflation estimator of

equation (2) is:

Xˆ 0 = ∑u

WI(u) c x(u)

which can also be expressed as

Xˆ 0 = ∑

s:stratum

∑

i:PSU

Xˆ si..

πsi

= ∑

s:stratum

∑

i:PSU

Xˆ wsi..

Here, Xˆ

wsi.. is the estimator of PSU total

in equation (6), inflated by 1/πsi. The

variance estimator of Xˆ wsi ..

corresponding to equation (6) will be

denoted as



j = 1

21

nsij Sˆ wsij

2

where

Sˆ wsij

2 = ∑

k = 1

nsij

(Xˆ wsijk−X ̄ wsij.)

2 / (nsij−1)

using the SSU totals based on the entire

inflation weight, WI

.

Page 20 [ Series 2, No. 130
Variance Estimators for

National Estimators:

Combining Across NSR and

SR PSU’s

An estimator for the variance of Xˆ

0

is

Vaˆr(Xˆ 0) = ∑

sεNSR2

[(πs1πs2−πs12)

πs12

(Xˆ ws1..−Xˆ ws2..)

2

+ ∑

i = 1

2

πsi ∑

jεCsi

nCsi(j) Sˆ

wCsi(j)

2 ]

+ ∑

CstreNSR1

∑

ieCstr

m(Cstr(i))

Sˆ wCstr( i )

2 + ∑

seSR

∑

jeCsi

nCsi(j)

Sˆ wCsi(i)

2

(8)

Here, the set NSR2 is a set of NSR

strata with two sampled PSU’s. The

variance estimators for these strata are

the so-called Yates-Grundy-Sen 2-stage

forms with the Csi(j) representing the

collapsed PSU substrata as discussed in

equation (7). The set NSR1 is a union of

collapsed original NSR strata (denoted

by Cstr) defined in a manner analogous

to the example that was given in the

discussion that follows equation (7).

Here, m(Cstr(i)

) is the number of PSU’s

in a given set, and the PSU’s are treated

as being sampled with replacement from

this collapsed stratum. Only the

first-stage unit will be used for variance

computation. The S2 form is the

variation of PSU totals, Xˆ

wsi.. , within a

Cstr(i) collapsed set.

The set SR is a set of SR strata,

with possibly collapsed original strata

and/or substrata, defined in a manner

analogous to the example given in the

discussion following equation (7).

As mentioned earlier, about

94 percent of NHIS households

respond. Therefore, it is assumed that

the nonresponse-adjusted weight can

be treated as an inflation weight

within the SSU sampling level with

little bias. Furthermore, for national

NHIS estimators, previous research by

Parsons and Casady (13) has shown

that the first-stage adjustment seems to

have little impact on the magnitude of

the estimated variances. With this in

mind, equation (8) is extended to

cover the nonresponse and first-stage

ratio weighting adjustments. For the

nonresponse-adjusted estimator Xˆ ′ of

equation (3) or the first-stage

ratio-adjusted national estimator, Xˆ ′′,

of equation (4) an approximate

variance estimator is given by

equation (8), but with the WI weight

replaced by the Wnr and Wr1 weights,

respectively.

Estimating Variances for

Poststratified Totals and

Nonlinear Statistics

The final national weight estimator

X of equations (1) and (5) incorporates a

poststratification adjustment. This is the

form of the estimator that is presented

in official NCHS publications and is the

form that most analysts study. This

estimator is nonlinear because of the

poststratification adjustment. A

commonly used method for estimating

the variance of a nonlinear statistic is to

‘‘linearize’’ the statistic using Taylor

series methods and then to use

equation (8) applied to the linearized

form. Estimators of totals using

poststratification weights can be

linearized. Basically, equation (8) is

used to estimate the variance of the

linearized total, but a prepoststratification inflation weight is used in

the computation of equation (8).

Several of the variance estimation

methods just discussed will be examined

using NHIS data in tables 8–10. A set of

NHIS variables used in these tables are:

+ Binary variables:

ACT = 1 if person has an activity

limitation

LDR = 1 if person has not seen a

doctor in 2 or more years

HLT_FP = 1 if person reports fair or

poor health

BIN10 is a randomly generated

variable with a Bernoulli distribution

and a probability of success 0.10 for

each person

+ Event count variables over past

year:

HDI = number of hospital discharge

incidents

HED = number of hospital episode

days

+ Event count variables over past two

weeks:

AIC = number of acute incidence

conditions

BED = number of bed days

EPI = number of injury episodes

RAD = number of restricted activity

days

TDV = number of doctor visits

The tables that use these ‘‘two-week

recall’’ variables present all tabulations

in an annualized time frame. Further

discussion of these variables will be

presented as needed and will focus on

specific tables.

In practice, implementation of

computer software packages based on

linearization often requires the final

weight, Wf , which may include a

poststratification adjustment, to be

treated as an inflation weight. For

example, in the SUDAAN version 7.5

software, regression statistics can be

linearized, but not with a simultaneous

linearization of the poststratification

weights. Thus, SUDAAN regression

computations for variance assume the

final poststratification weight is an

inflation weight. For estimated totals,

this practice tends to lead to somewhat

inflated variance estimators. For ratios

of totals (e.g., means or percents) the

impact varies. Table 8 presents some

comparisons of variance estimates from

the 1995 survey obtained by treating

final weights as inflation weights versus

a linearization of the final weight.

Table 9 supplies auxiliary information to

the domains shown in table 8. The

presentation in table 8 is not intended to

be a comprehensive study of this issue.

However, one can see that the impact of

the linearization of the poststratification

weights on the estimated standard errors

of totals can be substantial, while the

corresponding impact on the standard

errors of means is somewhat marginal,

in general. For many health variables,

empirical evidence suggests that the

inflation in the estimated standard errors

of means may be of little practical

importance. The treatment of the final

weight, Wf, as an inflation weight, may

be reasonable if software limitations

warrant such a simplification. It should

Series 2, No. 130 [ Page 21
be noted that the population domains

that are aggregates of several component

poststratification classes should be

expected to have a greater variance

reduction than those population domains

covered by few poststratification classes.

In general, economic-type variables may

exhibit a greater impact than health-type

variables. For regression-type analysis,

the inclusion of age-sex-race and/or

ethnic predictors tends to reduce the

impact of treating the final weight as an

inflation weight.

Public Use Data and

Limitations on Design

Structures

NCHS forbids the disclosure of

information that may compromise the

confidentiality promised to survey

respondents. These concerns about

confidentiality require the omission or

concealing of some design information

from public-use databases. The

following types of information have

been subject to omission or concealment

on public-use databases. Policies on

design information release often change,

and NHIS data users should check

database documentation for the available

design information.

1. Most of the distinct probabilities of

selection of table 5 are not released,

although some products of sequential

weights are released. In particular,

the πsi and πsij probabilities have not

been released.

2. Original strata, density substrata, and

PSU’s may have been collapsed with

others to avoid implicit or explicit

geographical disclosure or collapsed

to create convenient forms for

variance estimation. For example,

the PSU counts in table 2 will not

agree with tabulations from publicuse databases.

Obviously, without knowledge of

the πsi and πsij, equation (8) must be

replaced with a reasonable substitution.

Here, all NSR2-type strata may be

treated as the NSR1-type strata (i.e., the

strata having two sampled PSU’s are

treated as being sampled with

replacement). No second-stage

component would be included in the

functional form for these NSR1-type

strata because overestimating the

variance on the average is expected. In

the SR strata, the second-stage variation

would still be used. Thus, with this

limited information, an approximation

for equation (8) will be

Vaˆr(Xˆ 0) = ∑

sεNSR2

(Xˆ ws1..−Xˆ ws2..)

2

+ ∑

CstrεNSR1

∑

iεCstr

m(Cstr(i)) Sˆ

wCstr(i)

2

+ 

sεSR

∑

jεCsi

nCsi(j) Sˆ

wCsi(j)

2

(9)

where,

NSR2 is a set of original NSR

strata, each with two sampled primary

sampling units,

NSR1 is the set of collapsed strata,

Cstr, defined in a manner analogous to

the discussion that follows equation (7),

and

SR is a set of SR strata, possibly

collapsed original strata.

The above form can be expressed in

a condensed form:

Vaˆr(Xˆ 0) = ∑c

mc Sˆ

wc

2

(10)

where c represents a (collapsed) stratum,

either NSR or SR, and Sˆ2

wc is the sample

variance of the mc weighted PSU totals

within a nonself-representing stratum or

the mc weighted SSU totals within a

self-representing stratum. This functional

form is easily implemented. Sample

SUDAAN code for equations (9) or (10)

and additional explanations of NHIS

public-use variance estimation are

documented (16) and are provided with

released data. Table 10 provides some

comparisons for these two design

structures. SUDAAN software, using the

design structures of equation (8) both

with and without a linearization of the

final poststratification weight and using

equation (9) on public-use design

structures, are implemented in this table.

For both means and totals, there is a

tendency for standard errors using

equation (8) to be slightly smaller than

those produced by equation (9). It also

appears that the poststratification

implementation has a greater impact than

the choice of equation (8) or (9),

especially for the standard errors of totals.

Beginning with the 1997 survey,

some geographical disclosure concerns

resulted in a further coarsening of the

released public-use design information.

The techniques of stratum collapse,

stratum partitioning and SSU mixing

were used to coarsen the selfrepresenting design structures with little

anticipated bias, but at the expense of

loss of degrees of freedom. (These

techniques are discussed briefly in

Eltinge (17) and Parsons and Eltinge

(18)). The result was a public-use design

structure with an imposed 2-PSU’s per

stratum design with over 300 nominal

degrees of freedom. The variance

estimator takes the generic form of the

first term of equation (9) and is

implemented by many software

packages.

Precision Comparisons

Between Surveys

As discussed in another

publication (6), the funding for the

originally proposed alpha-design survey

was reduced, and the beta version was

implemented for the 1995–2004 NHIS.

In planning the general cost and

precision, objectives were:

1. The 1995–2004 design would have

comparable funding to the previous

1985–94 design.

2. The precision of estimators for black

domains would be comparable to the

previous design.

3. The precision of estimators for

Hispanic domains would be

improved over the previous design

and comparable to those for black

domains.

4. The precision for white and total

domains may be allowed to drop to

compensate for meeting objectives 1,

2, and 3.

In table 11, some precision

measures were estimated for the 1995

and 1994 NHIS samples for some select

variables discussed earlier in this

chapter. Table 12 presents some

auxiliary information for the domains

shown in table 11. For table 11,

SUDAAN software, along with a design

Page 22 [ Series 2, No. 130
corresponding to equation (8), was used.

The final weight was treated as an

inflation weight. It should be kept in

mind that the coefficients of variation

(CV’s) presented in table 11 are random

variables subject to sampling variability,

and one should consider trends and/or

patterns as opposed to looking at single

sample realizations. Furthermore, the

variables TDV and AIC are subject to

outlier response, which will increase

estimated CV instability for any given

year. BIN10, a randomly generated

binary response for each sampled person

having probability of success 0.10, has

been included as a standard. This

variable is independent of the design.

One may attribute its CV to a ‘‘pure’’

clustering and sampling process having

no interaction with the variable itself.

One caveat about this table is that

1995 was the start-up data collection

year of the current NHIS design. Several

sampling changes were made before the

sampling parameters were actually

finalized. Furthermore, 3 weeks of

intended interviews were lost due to the

government shutdown in December

1995. To make table 11 values look

somewhat more representative of a

‘‘full’’ NHIS, the 1995 sample sizes

were inflated by 51/48 and CV’s were

deflated by (48/51)1/2. Some general

observations are:

1. Objectives 2 and 3 appear to have

been met. For black persons, some

of the 1995 CV’s are smaller than

the 1994 CV’s, some are larger;

there is no distinct pattern. Sample

sizes for black domains tend to be

smaller in 1995 than in the previous

design. The 1995 sample tends to

have at least an 80 percent increase

in Hispanic domain sample sizes.

2. The Hispanic domain CV’s for 1995

show great improvement over 1994.

A comparison of corresponding

black and Hispanic domains by

CV’s does not show a consistent

order relation pattern.

3. For Objective 4, there seems to be

no great loss of precision. Again,

there is no consistent order relation

pattern when comparing 1994 to

1995.

Based on this limited study, there is

no reason to believe that general

objectives 1–4 were not achieved.

References

1. Shah BV, Barnwell BG, Bieler GS.

SUDAAN User’s Manual, Release 7.5.

Research Triangle Park, NC: Research

Triangle Institute. 1997.

2. National Center for Health Statistics.

The statistical design of the Health

Household-Interview Survey. Health

Statistics. PHS Pub. No. 584-A2.

Public Health Service. Washington:

U.S. Government Printing Office. 1958.

3. National Center for Health Statistics.

Health Interview Survey procedure,

1957–1974. National Center for Health

Statistics. Vital Health Stat 1(11). 1975.

4. Kovar MG, Poe GS. The National

Health Interview Survey design,

1973–1984, and procedures, 1975–83.

Vital Health Stat 1(18). 1985.

5. Massey JT, Moore TF, Parsons VL,

Tadros W. Design and estimation for

the National Health Interview Survey,

1985–94. Vital Health Stat 2(110).

1989.

6. Judkins D, Marker D, Waksberg J, et

al. National Health Interview Survey:

Research for the 1995–2004 Redesign.

Vital Health Stat 2(126). 1999.

7. Friedman HP, Rubin J. On some

invariant criteria for grouping data. J.

Am. Stat. Assoc. 62:1159–1178. 1967.

8. Durbin J. Design of multi-stage surveys

for the estimation of sampling errors.

Appl. Stat. 16:152–164. 1967.

9. U.S. Bureau of the Census. The current

population survey, design and

methodology. Technical Paper 40.

Washington: U.S. Government Printing

Office. 1978.

10. Cochran WG. Sampling techniques

(3rd ed). New York: Wiley. 1977.

11. Kish L. Survey sampling. New York:

Wiley. 1965.

12. Hansen MH, Hurwitz WM, Madow

WG. Sample survey methods and

theory, Vol. I, Methods and

Applications; Vol. II, Theory. New

York: Wiley. 1953.

13. Parsons VL, Casady RJ. Variance

estimation and the redesigned Health

Interview Survey. In: Proceedings of

the section on survey research methods.

American Stat Assn. 406–411. 1987.

14. Wolter KM. Introduction to variance

estimation. New York: Springer-Verlag.

1985.

15. Rust K. Variance estimation for

complex estimators in sample surveys.

J. Offic. Stat. 1:381–397. 1985.

16. National Center for Health Statistics.

Variance estimation for person data

using the NHIS public use person data

tape, 1995–2004 National Center for

Health Statistics. NHIS Public-Use

Data document. Available at the NCHS

NHIS Internet WWW site

http://www.cdc.gov/nchs/nhis.htm.

1998.

17. Eltinge JL. Evaluation and reduction of

cluster-level identification risk for

public-use survey microdata files. In:

Proceedings of the section on survey

research methods. American Stat Assn,

1999. To be published.

18. Parsons VL, Eltinge JL. Stratum

partition, collapse and mixing in

construction of balanced repeated

replication variance estimators. In:

Proceedings of the section on survey

research methods. American Stat Assn,

1999. To be published.

Series 2, No. 130 [ Page 23
Table 5. Components of conceptual sampling design: National Health Interview Survey, 1995–2004

Sample

step

Sample

unit

Within level

unit

Conditional probability

of selection

1. PSU1 Stratum πsi, for PSU i from stratum s

πsij, joint selection probability for PSU’s

i and j from stratum s

2. Super-SSU1 PSU’s substratum Pr(super-SSU | substratum)

3. Annual-SSU1 Super-SSU 1/10

4. HU2 SSU Pr(HU | SSU)

5. Household2 HU 1 if black/Hispanic household,

Pr (‘‘other’’) if nonblack/non-Hispanic household

6. Person(s) Household Pr (person | household)

1See chapter 2 for details.

2HU is the residential dwelling selected, without regard to its occupants (if any). The household is the collection of occupants selected by characteristics within the HU (see chapter 2).

NOTE: PSU is primary sampling unit, SSU is secondary sampling unit, and Pr is probability.

Table 6: First-stage ratio adjustment factors: National Health Interview Survey, 1997

Residence,

race, and ethnicity

Census region

Northeast Midwest South West

Metropolitan area

Hispanic .................. 0.941306 1.120916 0.906019 0.851367

Non-Hispanic black ........... 0.894335 1.033701 1.038407 0.879097

Non-Hispanic other ........... 1.001781 0.995499 0.985160 1.043640

Nonmetropolitan area

Hispanic .................. 0.853849 1.194508 0.812500 0.956770

Non-Hispanic black ........... 0.812500 0.814337 1.063537 1.121297

Non-Hispanic other ........... 0.995420 0.997996 1.009349 1.010929

Table 7. The 88 age-sex-race/ethnicity classes used for poststratification: National Health Interview Survey, 1995–2004

Age

Hispanic Non-Hispanic black Non-Hispanic other

Male Female Male Female Male Female

Under 1 year ........................... X X X X X X

1–4 years ............................. X X X X X X

5–9 years ............................. X X X X X X

10–14 years ........................... X X X X X X

15–17 years ........................... X X X X X X

18–19 years ........................... X X X X X X

20–24 years ........................... X X X X X X

25–29 years ........................... X X X X X X

30–34 years ........................... X X X X X X

35–44 years ........................... X X X X X X

45–49 years ........................... X X X X X X

50–54 years ........................... X X X X X X

55–64 years ........................... X X X X X X

65–74 years1 ........................... X X X X X X

75 years and older ....................... X X X X X X

1Age categories 65–74 years and over were collapsed into one category, 65 years and over, for Hispanic persons.

Page 24 [ Series 2, No. 130
Table 8. Impact of poststratification on variance estimation: National Health Interview Survey, 1995

[Treatment of the final poststratification weight as a base weight versus a linearization of the final poststratification weight]

Domain and

variable

Estimated totals Estimated means

Number in

thousands1 CVpost2

Ratio

CVbase/

CVpost3 Mean4 CVpost5

Ratio

CVbase/

CVpost6

All persons

ACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38,521 1.09 1.26 0.15 1.09 1.08

AIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456,872 1.65 1.15 1.74 1.65 1.00

BED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,593,023 2.09 1.06 6.08 2.09 1.01

EPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61,304 3.70 1.01 0.23 3.70 0.99

HDI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27,505 1.82 1.10 0.11 1.82 1.03

HED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134,271 2.80 1.01 0.51 2.80 1.01

LDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36,133 1.23 1.26 0.14 1.23 1.00

RAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,097,080 1.55 1.11 15.64 1.55 1.02

TDV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,547,136 1.27 1.17 5.91 1.27 1.02

All females

ACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,237 1.22 1.25 0.15 1.22 1.12

AIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246,914 2.14 1.10 1.84 2.14 1.01

BED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915,213 2.58 1.06 6.81 2.58 1.01

EPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28,212 5.12 1.01 0.21 5.12 1.00

HDI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,070 2.25 1.07 0.12 2.25 1.01

HED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71,556 3.15 1.03 0.53 3.15 1.02

LDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13,154 1.86 1.14 0.10 1.86 1.01

RAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,349,449 1.86 1.09 17.49 1.86 1.01

TDV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 921,550 1.63 1.13 6.86 1.63 1.01

Currently employed persons

ACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11,729 1.80 1.10 0.09 1.76 1.00

AIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180,163 2.44 1.07 1.44 2.39 1.00

BED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397,326 3.47 1.03 3.18 3.45 1.00

EPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27,538 5.48 1.01 0.22 5.46 1.00

HDI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,951 3.12 1.04 0.07 3.10 1.00

HED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31,347 3.78 1.01 0.25 3.75 1.00

LDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22,505 1.29 1.25 0.18 1.25 1.00

RAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,324,768 2.41 1.06 10.61 2.38 1.00

TDV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602,286 2.18 1.09 4.82 2.15 1.00

Family income over $35,000

ACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,518 3.02 1.03 0.11 2.66 1.01

AIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75,804 4.33 1.03 1.78 3.99 1.00

BED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173,114 5.79 1.01 4.06 5.58 1.00

EPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,528 9.92 1.01 0.22 9.85 1.00

HDI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,311 5.43 1.03 0.08 5.20 1.01

HED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13,004 6.94 1.00 0.31 6.78 1.01

LDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,396 3.27 1.05 0.13 2.86 1.00

RAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483,943 4.29 1.02 11.35 4.02 1.00

TDV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237,663 4.52 1.03 5.58 4.28 1.00

College graduate, ages 35–44 years

ACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956 5.73 1.02 0.08 5.59 1.00

AIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18,612 7.06 1.02 1.60 6.81 1.00

BED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41,005 10.57 1.00 3.52 10.43 1.00

EPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,340 18.32 1.00 0.20 18.29 1.00

HDI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809 9.79 1.02 0.07 9.62 1.00

HED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,372 11.80 1.01 0.20 11.72 1.01

LDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,698 4.52 1.03 0.15 4.14 1.00

RAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123,529 7.64 1.01 10.61 7.36 1.00

TDV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61,898 5.56 1.03 5.32 5.33 1.00

Black, ages 65–74 years

ACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737 4.90 1.51 0.44 4.82 1.00

AIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,367 20.67 1.01 0.81 20.68 1.00

BED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24,154 14.48 1.05 14.34 14.50 0.99

EPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 47.88 1.03 0.13 47.89 1.03

HDI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476 11.96 1.18 0.28 11.99 1.04

Series 2, No. 130 [ Page 25
Table 8. Impact of poststratification on variance estimation: National Health Interview Survey, 1995—Con.

[Treatment of the final poststratification weight as a base weight versus a linearization of the final poststratification weight]

Domain and

variable

Estimated totals Estimated means

Number in

thousands1 CVpost2

Ratio

CVbase/

CVpost3 Mean4 CVpost5

Ratio

CVbase/

CVpost6

Black, ages 65–74 years—Continued

HED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,573 13.40 1.08 1.53 13.43 1.03

LDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 11.92 1.03 0.09 11.89 0.99

RAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57,212 10.93 1.08 33.98 10.95 1.00

TDV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,664 8.92 1.20 9.90 8.93 1.02

1Estimated total is based upon poststratification weight.

2Estimated CV of total using linearized final weight, shown as a percent.

3A given entry in this column is the ratio of the CV of total using the final weight as an inflation weight, divided by the corresponding entry in the ‘‘Estimated totals CVpost’’ column.

4Estimated mean based upon poststratification weight.

5Estimated CV of mean using linearized final weight, shown as a percent.

6A given entry in this column is the ratio of the CV of mean using the final weight as an inflation weight, divided by the corresponding entry in the ‘‘Estimated means CVpost’’ column.

NOTE: CV is coefficient of variation; ACT is persons with activity limitation; AIC is number of acute incidence conditions, based on 2-week recall of event; BED is number of bed days, based on 2-week

recall of event; EPI is number of injury episodes, based on 2-week recall of event; HDI is number of hospital discharge incidents; HED is number of hospital episode days; LDR is persons who have

not seen a doctor in 2 or more years; RAD is restricted activity days, based on 2-week recall of event; and TDV is total doctor visits, based on 2-week recall of event.

Table 9. Sample size and weighted size of persons by domain: National Health Interview Survey, 1995

Domain Sample size Weighted size1

All persons ................................... 102,467 261,890,000

All females ................................... 53,658 134,319,000

Currently employed .............................. 46,730 124,900,000

Family income over $35,000 ........................ 15,707 42,622,000

College graduate, ages 35–44 years ................... 4,162 11,644,000

Black, ages 65–74 years . . . . . . . . . . . . . . . . . . . . . . . . . . 772 1,684,000

1Numbers rounded to the nearest thousand.

Page 26 [ Series 2, No. 130
Table 10. Comparison of variance estimates obtained by two methods: National Health Interview Survey, 1995

[Yates-Grundy-Sen variance estimator with poststratification versus public-use, collapsed strata, single-stage, with-replacement design]

Variable

All

persons

Sex Age

Male Female

0–17

years

18–44

years

45–64

years

65 years

and over

LDR1

Sample size ........................... 102,467 48,809 53,658 29,711 40,801 20,000 11,955

Weighted size .......................... 261,889,549 127,570,237 134,319,312 70,670,755 108,040,689 51,713,265 31,464,840

Mean ................................ 0.13784 0.17991 0.09789 0.08845 0.18484 0.14484 0.07587

Standard error (full-p) ...................... 0.00170 0.00221 0.00182 0.00258 0.00248 0.00298 0.00268

Standard error (full) ....................... 0.00170 0.00222 0.00184 0.00265 0.00249 0.00299 0.00269

Standard error (public-p) .................... 0.00174 0.00228 0.00183 0.00265 0.00253 0.00301 0.00269

Standard error (public) ..................... 0.00174 0.00231 0.00184 0.00271 0.00254 0.00303 0.00269

Total ................................ 36,098,739 22,950,542 13,148,198 6,250,515 19,970,576 7,490,307 2,387,341

Standard error (full-p) ...................... 44,4710 282,405 244,714 182,641 268,084 154,026 84,414

Standard error (full) ....................... 560,398 357,742 280,044 210,839 329,031 182,239 88,117

Standard error (public-p) .................... 455,090 291,298 245,310 187,526 273,660 155,834 84,523

Standard error (public) ..................... 578,577 369,213 283,769 212,932 340,961 187,949 89,183

TDV2

Sample size ........................... 102,467 48,809 53,658 29,711 40,801 20,000 11,955

Weighted size .......................... 261,889,549 127,570,237 134,319,312 70,670,755 108,040,689 51,713,265 31,464,840

Mean ................................ 5.90468 4.89733 6.86141 4.29787 4.87876 7.08472 11.09687

Standard error (full-p) ...................... 0.07511 0.08320 0.11217 0.09066 0.11858 0.16180 0.27613

Standard error (full) ....................... 0.07693 0.08483 0.11368 0.09241 0.11917 0.16290 0.27573

Standard error (public-p) .................... 0.07587 0.08303 0.11331 0.09007 0.11749 0.16023 0.28469

Standard error (public) ..................... 0.07704 0.08503 0.11403 0.09116 0.11805 0.16109 0.28387

Total ................................ 1,546,373,944 624,753,629 921,620,316 303,733,537 527,105,033 366,374,024 349,161,350

Standard error (full-p) ...................... 19,671,625 10,613,283 15,067,049 6,406,983 12,811,560 8,367,336 8,688,269

Standard error (full) ....................... 23,120,033 11,875,947 16,950,069 7,296,215 13,916,041 9,418,063 10,273,218

Standard error (public-p) .................... 19,870,304 10,592,542 15,219,637 6,365,507 12,693,608 8,285,799 8,957,873

Standard error (public) ..................... 24,081,902 12,019,642 17,611,040 7,264,252 13,955,259 9,457,535 10,601,351

HLT_FP3

Sample size ........................... 101,277 48,266 53,011 29,183 40,423 19,834 11,837

Weighted size .......................... 258,974,266 126,232,939 132,741,328 69,441,900 107,059,972 51,315,313 31,157,082

Mean ................................ 0.10127 0.09125 0.11080 0.02555 0.06624 0.16633 0.28322

Standard error (full-p) ...................... 0.00137 0.00159 0.00165 0.00116 0.00153 0.00342 0.00487

Standard error (full) ....................... 0.00148 0.00166 0.00179 0.00117 0.00153 0.00348 0.00496

Standard error (public-p) .................... 0.00140 0.00164 0.00167 0.00116 0.00155 0.00343 0.00493

Standard error (public) ..................... 0.00152 0.00174 0.00182 0.00118 0.00157 0.00351 0.00501

Total ................................ 26,225,462 11,518,295 14,707,167 1,774,227 7,091,475 8,535,450 8,824,311

Standard error (full-p) ...................... 356,321 201,393 218,719 80,234 163,364 176,090 152,043

Standard error (full) ....................... 419,927 227,337 255,322 83,095 173,960 199,162 207,802

Standard error (public-p) .................... 362,920 207,377 222,266 80,641 166,380 176,367 154,171

Standard error (public) ..................... 428,842 234,288 260,217 83,466 178,137 201,110 209,683

1LDR is last doctor visit over 2 years ago.

2TDV is number of doctor visits in past year.

3HLT_FP is health classified fair or poor.

NOTE: The following NHIS design structures and SUDAAN were used for variance estimation:

(full): use equation (8) of this report, with final weight treated as inflation weight

(full-p): use equation (8) of this report, along with linearization for poststratification

(public): use equation (9) of this report, with final weight treated as inflation weight

(public-p): use equation (9) of this report, along with linearization for poststratification.

Series 2, No. 130 [ Page 27
Table 11. Precision comparisons between two survey designs: National Health Interview Survey, 1985–94 and 1995–2004

Domain and variable1

Mean

1994

Mean

19952

Percent of

CV 19953

Percent of

CV change

Deft

19944

Deft

1995

All persons

LDR . . . . . . . . . . . . . . . . . . . . 0.14 0.14 1.23 3 1.58 1.58

ACT . . . . . . . . . . . . . . . . . . . . 0.15 0.15 1.18 9 1.51 1.57

HLT_FP .................. 0.10 0.10 1.46 0 1.62 1.56

TDV . . . . . . . . . . . . . . . . . . . . 6.09 5.91 1.30 3 1.25 1.27

AIC . . . . . . . . . . . . . . . . . . . . . 1.71 1.74 1.66 −3 1.38 1.32

BIN10 ................... 0.10 0.10 0.98 0 1.09 1.05

Age 65 years and over:

LDR . . . . . . . . . . . . . . . . . . . 0.08 0.08 3.53 10 1.09 1.11

ACT . . . . . . . . . . . . . . . . . . . 0.38 0.37 1.44 2 1.30 1.21

HLT_FP ................. 0.28 0.28 1.75 −8 1.38 1.20

TDV . . . . . . . . . . . . . . . . . . . 11.30 11.10 2.48 −9 1.16 1.09

AIC . . . . . . . . . . . . . . . . . . . 1.10 1.03 5.36 2 1.19 1.09

BIN10 .................. 0.10 0.10 2.81 1 1.10 1.03

Black

LDR . . . . . . . . . . . . . . . . . . . . 0.13 0.13 3.21 −3 1.57 1.44

ACT . . . . . . . . . . . . . . . . . . . . 0.16 0.16 2.75 −14 1.76 1.41

HLT_FP .................. 0.15 0.14 2.97 −15 1.79 1.40

TDV . . . . . . . . . . . . . . . . . . . . 5.41 5.18 3.34 1 1.13 1.14

AIC . . . . . . . . . . . . . . . . . . . . . 1.54 1.39 4.74 −2 1.39 1.26

BIN10 ................... 0.10 0.11 2.64 0 1.09 1.07

Age 18–44 years:

LDR0 .................. 0.17 0.17 3.70 −6 1.36 1.25

ACT . . . . . . . . . . . . . . . . . . . 0.12 0.12 4.59 0 1.28 1.25

HLT_FP . . . . . . . . . . . . . . . . . 0.11 0.12 4.41 −7 1.30 1.17

TDV . . . . . . . . . . . . . . . . . . . 4.96 4.52 5.23 −13 1.15 1.04

AIC . . . . . . . . . . . . . . . . . . . 1.54 1.33 7.32 19 1.08 1.18

BIN10 .................. 0.10 0.11 4.43 6 1.06 1.13

Age 65 years and over:

LDR . . . . . . . . . . . . . . . . . . . 0.07 0.08 10.37 14 0.99 1.04

ACT . . . . . . . . . . . . . . . . . . . 0.48 0.46 3.78 11 1.26 1.18

HLT_FP ................. 0.43 0.42 4.42 2 1.46 1.27

TDV . . . . . . . . . . . . . . . . . . . 11.97 10.42 7.08 −9 1.10 0.99

AIC . . . . . . . . . . . . . . . . . . . 0.94 0.80 16.17 −29 1.54 0.95

BIN10 .................. 0.09 0.10 8.41 −24 1.37 0.98

Hispanic

LDR . . . . . . . . . . . . . . . . . . . . 0.18 0.19 2.16 −31 1.56 1.53

ACT . . . . . . . . . . . . . . . . . . . . 0.11 0.11 3.08 −20 1.43 1.57

HLT_FP . . . . . . . . . . . . . . . . . . 0.11 0.11 3.09 −21 1.41 1.57

TDV . . . . . . . . . . . . . . . . . . . . 4.83 4.43 3.49 −3 1.07 1.35

AIC . . . . . . . . . . . . . . . . . . . . . 1.68 1.62 4.11 −15 1.23 1.41

BIN10 . . . . . . . . . . . . . . . . . . . 0.11 0.10 2.25 −22 1.04 1.08

Age 18–44 years:

LDR . . . . . . . . . . . . . . . . . . . 0.25 0.26 2.28 −30 1.29 1.28

ACT . . . . . . . . . . . . . . . . . . . 0.09 0.07 4.59 −19 1.19 1.21

HLT_FP ................. 0.09 0.09 4.57 −13 1.17 1.36

TDV . . . . . . . . . . . . . . . . . . . 4.29 3.38 5.46 −7 1.01 1.17

AIC . . . . . . . . . . . . . . . . . . . 1.31 1.25 5.83 −19 1.09 1.17

BIN10 . . . . . . . . . . . . . . . . . . 0.11 0.10 3.52 −16 1.02 1.09

Age 65 years and over:

LDR . . . . . . . . . . . . . . . . . . . 0.09 0.10 10.05 −26 1.05 1.07

ACT . . . . . . . . . . . . . . . . . . . 0.42 0.40 4.55 −14 1.08 1.22

HLT_FP ................. 0.35 0.35 4.97 −31 1.27 1.19

TDV . . . . . . . . . . . . . . . . . . . 9.78 11.05 8.58 −9 1.12 1.04

AIC . . . . . . . . . . . . . . . . . . . 1.40 0.99 18.27 −9 1.10 1.09

BIN10 .................. 0.08 0.10 10.34 −25 1.01 1.16

Page 28 [ Series 2, No. 130
Table 11. Precision comparisons between two survey designs: National Health Interview Survey, 1985–94 and 1995–2004—Con.

Domain and variable1

Mean

1994

Mean

19952

Percent of

CV 19953

Percent of

CV change

Deft

19944

Deft

1995

All females

Age 18–44 years:

LDR . . . . . . . . . . . . . . . . . . . 0.10 0.11 2.41 6 1.15 1.22

ACT . . . . . . . . . . . . . . . . . . . 0.10 0.10 2.38 5 1.16 1.16

HLT_FP ................. 0.08 0.08 2.65 −3 1.18 1.11

TDV . . . . . . . . . . . . . . . . . . . 6.39 6.38 3.17 45 1.10 1.37

AIC . . . . . . . . . . . . . . . . . . . 1.83 1.79 3.17 10 1.10 1.19

BIN10 .................. 0.10 0.10 2.15 2 1.07 1.06

Age 65 years and over:

LDR . . . . . . . . . . . . . . . . . . 0.07 0.07 4.51 3 1.07 1.03

ACT . . . . . . . . . . . . . . . . . . . 0.39 0.38 1.64 −2 1.21 1.06

HLT_FP ................. 0.28 0.28 2.12 1 1.16 1.10

TDV . . . . . . . . . . . . . . . . . . . 11.77 11.61 3.25 −13 1.24 1.09

AIC . . . . . . . . . . . . . . . . . . . 1.29 1.12 6.80 10 1.14 1.08

BIN10 .................. 0.10 0.10 3.64 1 1.07 1.02

White female

Age 18–44 years:

LDR . . . . . . . . . . . . . . . . . . 0.10 0.10 2.81 11 1.13 1.25

ACT . . . . . . . . . . . . . . . . . . . 0.10 0.10 2.64 6 1.13 1.16

HLT_FP ................. 0.07 0.07 3.14 −3 1.15 1.11

TDV . . . . . . . . . . . . . . . . . . . 6.67 6.67 3.64 51 1.11 1.41

AIC . . . . . . . . . . . . . . . . . . . 1.87 1.86 3.42 9 1.08 1.18

BIN10 .................. 0.10 0.10 2.38 4 1.05 1.06

Age 65 years and over:

LDR . . . . . . . . . . . . . . . . . . 0.07 0.07 4.81 2 1.07 1.02

ACT . . . . . . . . . . . . . . . . . . . 0.38 0.37 1.77 −3 1.19 1.05

HLT_FP ................. 0.26 0.26 2.30 0 1.13 1.07

TDV . . . . . . . . . . . . . . . . . . . 11.62 11.67 3.50 −14 1.24 1.09

AIC . . . . . . . . . . . . . . . . . . . 1.31 1.11 7.35 15 1.10 1.08

BIN10 .................. 0.10 0.10 3.88 4 1.03 1.03

Black female

LDR . . . . . . . . . . . . . . . . . . . . 0.09 0.09 5.03 10 1.33 1.37

ACT . . . . . . . . . . . . . . . . . . . . 0.16 0.16 3.21 −10 1.48 1.22

HLT_FP .................. 0.16 0.16 3.20 −15 1.54 1.20

TDV . . . . . . . . . . . . . . . . . . . . 6.21 5.86 3.97 6 1.07 1.11

AIC . . . . . . . . . . . . . . . . . . . . . 1.64 1.53 5.56 −4 1.28 1.14

BIN10 ................... 0.10 0.10 3.48 −2 1.10 1.01

Age 18–44 years:

LDR . . . . . . . . . . . . . . . . . . . 0.09 0.11 6.20 3 1.12 1.19

ACT . . . . . . . . . . . . . . . . . . . 0.11 0.11 5.46 −1 1.13 1.07

HLT_FP ................. 0.13 0.13 5.06 −2 1.18 1.08

TDV . . . . . . . . . . . . . . . . . . . 5.50 5.33 5.60 1 1.06 1.02

AIC . . . . . . . . . . . . . . . . . . . 1.78 1.58 8.06 1 1.13 1.06

BIN10 .................. 0.09 0.11 5.54 −2 1.07 1.07

Age 65 years and over:

LDR . . . . . . . . . . . . . . . . . . . 0.06 0.06 15.55 31 0.97 1.07

ACT . . . . . . . . . . . . . . . . . . . 0.48 0.48 4.15 10 1.13 1.04

HLT_FP ................. 0.42 0.43 5.21 2 1.34 1.20

TDV . . . . . . . . . . . . . . . . . . . 14.26 11.26 8.76 −13 1.15 0.99

AIC . . . . . . . . . . . . . . . . . . . 1.09 1.02 18.45 −41 1.76 0.96

BIN10 .................. 0.09 0.08 11.61 −7 1.26 0.92

Hispanic female

Age 18–44 years:

LDR . . . . . . . . . . . . . . . . . . . 0.15 0.16 3.74 −30 1.10 1.09

ACT . . . . . . . . . . . . . . . . . . . 0.09 0.08 5.75 −20 1.13 1.15

HLT_FP . . . . . . . . . . . . . . . . . 0.11 0.11 4.98 −20 1.11 1.19

TDV . . . . . . . . . . . . . . . . . . . 5.68 4.25 5.64 −13 1.06 1.16

AIC . . . . . . . . . . . . . . . . . . . 1.47 1.36 7.58 −21 1.10 1.14

BIN10 . . . . . . . . . . . . . . . . . . 0.11 0.10 4.60 −20 1.00 1.06

Series 2, No. 130 [ Page 29
Table 11. Precision comparisons between two survey designs: National Health Interview Survey, 1985–94 and 1995–2004—Con.

Domain and variable1

Mean

1994

Mean

19952

Percent of

CV 19953

Percent of

CV change

Deft

19944

Deft

1995

Age 65 years and over:

LDR . . . . . . . . . . . . . . . . . . . 0.08 0.09 13.33 −31 1.07 1.04

ACT . . . . . . . . . . . . . . . . . . . 0.43 0.40 5.46 −12 1.00 1.12

HLT_FP ................. 0.35 0.35 6.09 −27 1.13 1.11

TDV . . . . . . . . . . . . . . . . . . . 9.87 11.55 10.69 −18 1.18 1.00

AIC . . . . . . . . . . . . . . . . . . . 1.48 1.17 22.02 −4 1.01 1.08

BIN10 .................. 0.09 0.10 12.83 −24 0.99 1.08

1LDR is proportion of persons with no doctor visit in 2 years or more, ACT is proportion of persons with an activity limitation status, HLT_FP is proportion of persons with fair or poor health, TDV is

mean number of doctor visits per person, AIC is mean number of acute incidence conditions per person, and BIN10 is proportion of persons with a 10 percent characteristic, independent of design.

2Values are adjusted to account for 1995 reduced sample.

3 (CV(1995) - CV(1994))/CV(1994) * 100.

4Deft is sqrt (Variance (Mean | NHIS) / Variance (Mean | SRS)), SRS based on observed domain sample size.

Table 12. Sample size and percent change by domain: National Health Interview Survey

Domain

Adjusted sample size

in thousands

for 1995

Percent change

from 1994

to 1995

All persons ........................................ 108.9 −6

Age 65 years and over ............................... 12.7 −13

Black ............................................ 14.7 −11

Age 18–44 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 −10

Age 65 years and over . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 −23

Hispanic .......................................... 22.1 +89

Age 18–44 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 +86

Age 65 years and over . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 +86

Female .......................................... (1

) (1)

Age 18–44 years ................................... 22.8 −5

Age 65 years and over . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 −1

White .......................................... (1

) (1)

Age 18–44 years .................................. 18.5 −4

Age 65 years and over . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 −12

Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 −13

Age 18–44 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 −1

Age 65 years and over . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.7 −29

Hispanic ........................................ (1

) (1)

Age 18–44 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 +85

Age 65 years and over . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.7 +84

1Data not available.

Page 30 [ Series 2, No. 130
Appendix

Glossary

Acronyms

CAPI computer-assisted personal

interviewing

CD-ROM Compact Disk-Read Only

Memory

CPS Current Population Survey

CMSA consolidated metropolitan

statistical areas

CV coefficient of variation

DHIS Division of Health Interview

Statistics

DSMD Demographic Statistical

Methods Division

HU Housing unit

MA metropolitan area

MEPS Medical Expenditure Panel

Survey

MSA metropolitan statistical area

MSE mean square error

NCHS National Center for Health

Statistics

NECMA New England County

Metropolitan Area

NHIS National Health Interview

Survey

NSFG National Survey of Family

Growth

NSR nonself-representing

OMB Office of Management and

Budget

ORM Office of Research and

Methodology

PMSA primary metropolitan

statistical area

PSU primary sampling unit

SI sampling interval

SR self-representing

SSU secondary sampling unit

WWW world wide web

Definition of Terms

Address frame—is a portion of the

1973–84 NHIS sample frame consisting

of addresses compiled during the 1970

decennial census.

Alpha sample—is the sample

originally selected for the 1995–2004

NHIS. This sample is described in more

detail in another publication (6).

Area frame—is a portion of the

1985–94 and 1995–2004 NHIS sample

frames consisting of geographic areas

where address listing operations are

conducted to obtain a list of addresses

from which NHIS sample cases are

selected.

Beta sample—is the sample actually

used for the 1995–2004 NHIS. It is a

subsample of the alpha sample.

Condition list—is a portion of the

NHIS data collection instrument used in

1995–96 consisting of a list of medical

conditions (e.g., arthritis, deafness,

gallstones, diabetes, heart disease,

asthma). There were six different

condition lists; one of the six lists was

chosen randomly for each interviewed

household.

Group quarters frame—is a portion

of the 1973–84 NHIS sample frame

consisting of a list of group quarters

compiled during the 1970 decennial

census. ‘‘Group quarters’’ are defined by

the U.S. Bureau of the Census as a type

of residential quarters where the

residents share common facilities or

receive authorized care or custody.

Household—is an occupied

residential housing unit.

Nonself-representing primary

sampling unit—is a primary sampling

unit selected from a sampling stratum

containing other primary sampling units

( i.e., a primary sampling unit selected

with probability less than 1).

Oversample—is a sampling

procedure designed to give a

demographic or geographic population a

larger proportion of representation in the

sample than the population’s proportion

of representation in the overall

population.

Permit frame—is a portion of the

1985–94 and 1995–2004 NHIS sample

frames consisting of residential building

permits.

Screen—is an interviewing

procedure whereby households who do

not meet specified criteria (e.g.,

households that do not contain any

civilian black or Hispanic members) are

not administered a full-length interview.

In NHIS, the screening procedure

consists of the initial portion of the

NHIS interview, up to and including the

point where the household composition

is determined.

Self-representing primary sampling

unit—is a primary sampling unit that is

the only member of its sampling stratum

(i.e., a primary sampling unit selected

with certainty).

Series 2, No. 130 [ Page 31
Vital and Health Statistics

series descriptions

SERIES 1. Programs and Collection Procedures—These reports

describe the data collection programs of the National Center

for Health Statistics. They include 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—These reports are

studies of new statistical methods and include analytical

techniques, objective evaluations of reliability of collected data,

and contributions to statistical theory. These studies also

include experimental tests of new survey methods and

comparisons of U.S. methodology with those of other

countries.

SERIES 3. Analytical and Epidemiological Studies—These reports

present analytical or interpretive studies based on vital and

health statistics. These reports carry the analyses further than

the expository types of reports in the other series.

SERIES 4. Documents and Committee Reports—These are final

reports of major committees concerned with vital and health

statistics and documents such as recommended model vital

registration laws and revised birth and death certificates.

SERIES 5. International Vital and Health Statistics Reports—These

reports are analytical or descriptive reports that compare U.S.

vital and health statistics with those of other countries or

present other international data of relevance to the health

statistics system of the United States.

SERIES 6. Cognition and Survey Measurement—These reports are

from the National Laboratory for Collaborative Research in

Cognition and Survey Measurement. They use methods of

cognitive science to design, evaluate, and test survey

instruments.

SERIES 10. Data From the National Health Interview Survey—These

reports contain 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. They are based on data collected in a

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—

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 12. Data From the Institutionalized Population Surveys—

Discontinued in 1975. Reports from these surveys are

included in Series 13.

SERIES 13. Data From the National Health Care Survey—These reports

contain statistics on health resources and the public’s use of

health care resources including ambulatory, hospital, and longterm care services based on data collected directly from

health care providers and provider records.

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

Discontinued in 1990. Reports on the numbers, geographic

distribution, and characteristics of health resources are now

included in Series 13.

SERIES 15. Data From Special Surveys—These reports contain 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.

SERIES 16. Compilations of Advance Data From Vital and Health

Statistics—Advance Data Reports provide early release of

information from the National Center for Health Statistics’

health and demographic surveys. They are compiled in the

order in which they are published. Some of these releases

may be followed by detailed reports in Series 10–13.

SERIES 20. Data on Mortality—These reports contain 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—These reports

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

Discontinued in 1975. Reports from these sample surveys,

based on vital records, are now published in Series 20 or 21.

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

reports contain statistics on factors that affect birth rates,

including contraception, infertility, cohabitation, marriage,

divorce, and remarriage; adoption; use of medical care for

family planning and infertility; and related maternal and infant

health topics. These statistics are based on national surveys

of women of childbearing age.

SERIES 24. Compilations of Data on Natality, Mortality, Marriage,

Divorce, and Induced Terminations of Pregnancy—

These include advance reports of births, deaths, marriages,

and divorces based on final data from the National Vital

Statistics System that were published as supplements to the

Monthly Vital Statistics Report (MVSR). These reports provide

highlights and summaries of detailed data subsequently

published in Vital Statistics of the United States. Other

supplements to the MVSR published here provide selected

findings based on final data from the National Vital Statistics

System and may be followed by detailed reports in Series 20

or 21.

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

these series, contact:

Data Dissemination Branch

National Center for Health Statistics

Centers for Disease Control and Prevention

6525 Belcrest Road, Room 1064

Hyattsville, MD 20782-2003

(301) 458–4636

E-mail: nchsquery@cdc.gov

Internet: www.cdc.gov/nchs/
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HEALTH & HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, Maryland 20782-2003

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