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Suggested citation

Massey JT, Moore TF, Parsons VL, Tadros W. Design and estimation

for the National Health Interview Survey, 1985-94. National Center

for Health Statistics. Vital Health Stat 2(110). 1989.

Library of Congress Cataloging-in-Publication Data

Design and estimation for the National Health Interview Survey.

1985-94.

(Series 2, Data evaluation and methods research ;

no. 110) (DHHS publication ; no. (PHS) 89-1384)

By James T. Massey and others.

Bibliography: p.

Supt. of Docs. no.: HE 20.6209:2/110

1. National Health Interview Survey (U.S.)

2. Health surveys-United States. I. Massey, James T.

II. National Center for Health Statistics (U.S.)

I

III. Series: Vital and health statistics. Series 2,

Data evaluation and methods research ; no. 110.

IV. Series: DHHS publication ; no. (PHS) 89-1384.

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

2. Data Collection. 3. Health Surveys-United States.

4. Research Design. W2 A N148vb no. 110]

RA409.U45 no. 110 362.1'0723 s 88-600448

[RA408.5] [362.1'0723]

ISBN 0-8406-0406-8
U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

Centers for Disease Control

National Center for Health Statistics

Hyattsville, Maryland

August 1989

DHHS Publication No. (PHS) 89-1384

Design and

Estimation for the

National Health

Interview Survey,

1985-94

Series 2:

Data Evaluation and Methods Research

No. 110

A description of the redesign of the National Health Interview Survey, a

national probability sample survey of the civilian noninstitutionalized

population of the United States.
Jacob J. Feldman, Ph.D., Associate Director for Analysis

and Epidemiology

Gail F. Fisher, Ph.D., Associate Director for Planning and

Extramural Programs

Peter L. Hurley, Associate Director for Vital and Health

Statistics Systems

Stephen E. Nieberding, Associate Director for Management

Charles J. Rothwell, Associate Director for Data Processing

and Services

Monroe G. Sirken, Ph.D., Associate Director for Research

and Methodology

Office of Research and Methodology

Monroe G. Sirken, Ph.D., Associate Director

Kenneth W. Harris, Special Assistant for Program

Coordination and Statistical Standards

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

James T. Massey, Ph.D., Chief Survey Design Staff

Andrew A. White, Ph.D., Chief; Statistical Technology

Staff
search and selection of a sample design for the National

Health Interview Survey (NHIS) for the period 1985

through 1994. The NHIS is one of the major surveys of the

National Center for Health Statistics (NCHS), Centers for

Disease Control. Through the NHIS, information concerning the health of the U.S. civilian noninstitutionalized population is collected in household interviews throughout the

United States. The NHIS has been in continuous operation

since 1957, and its sample design has been reevaluated and

modified following each of the last three decennial censuses

of the U.S. population.

The overall responsibility for the development of the

redesign following the 1980 decennial census was carried

out by the Survey Design Staff in the Office of Research

and Methodology (ORM) at NCHS. The Statistical Methods Division of the U.S. Bureau of the Census had the

major responsibility for implementing the design and selecting the sample for the NHIS once the general design

specifications were defined. The Statistical Methods Staff in

ORM had the responsibility for developing the estimation

procedures for the NHIS. The data requirements for the

NHIS for the period 1985-94 were jointly developed by the

Division of Health Interview Statistics, the Office of Analysis and Epidemiology, and the Office of Research and

Methodology at NCHS. The total redesign effort was conducted from 1979 through 1984.

Health Interview Survey was a major undertaking and

many persons contributed to this effort. Some of the persons who contributed to the redesign are cited in the

reports and memoranda referenced at the end of the

report. A number of persons deserve a special thanks for

their contribution. At the National Center for Health Statistics the authors of this report would like to acknowledge

the contributions of Monroe Sirken, Owen Thornberry,

Jack Feldman, Earl Bryant, Robert Fuchsberg, Clinton

Burnbaum, Robert Casady, and Andrew White. At the U.S.

Bureau of the Census, persons deserving special recognition include Charles D. Jones, Gary Shapiro, Rameswar P.

Chakrabarty, Chester Ponikowski, Paul Hsen, and Richard

Hartgen.

This report is organized into four chapters so that

researchers and users of the NHIS data can refer to

different parts of the report for different levels of detail

about the NHIS design. Chapter I presents a general

overview of the NHIS and its sample design. Chapter II

describes the redesign process and the new directions to be

taken by the NHIS during 198.5-94. Major research findings are also included in chapter II. Chapter III provides a

more detailed description of the sample design and how the

sample was selected. Chapter IV presents a description of

the estimators used in the NHIS for analyzing and summarizing the results from the survey.

iii 
NHIS sample design ................................................................................ .

References ........................................................................................ .

Figures:

1. Sample design parameters for 1973-84 and 1985-94 National Health Interview Survey designs ......... . 3

2. Primary sampling units (PSU’s) selected for the National Health Interview Survey, 1985-94. ........... . 4

Chapter II. Redesigning the National Health Interview Survey .............................................. .

Objectives ......................................................................................... .

Model used for redesign ............................................................................. .

Research results for major design features ............................................................. .

Sampling frame.................................................................................. .

Multiple survey linkage ............................................................................ .

Mode of data collection ........................................................................... .

Oversampling small subdomains .................................................................... .

Rotation of the NHIS sample ...................................................................... .

Interaction between design features................................................................. .

Conclusions ....................................................................................... .

References........................................................................................ .

Tables:

A. Number of persons screened for the National Survey of Family Growth (NSFG) subdomains for 3 National

Health Interview Survey (NHIS) design options ................................................. .

B. Percent change in variances for 2 rotational designs relative to 1973-84 National Health Interview Survey

(NHIS) design assuming a 25- and a 50-percent increase in the cluster year-to-year correlation ..... .

Figures:

1. Major design features and design options considered for the redesign of the National Health Interview

Survey .................................................................................... .

2. Two-way interactions for design options considered for the National Health Interview Survey

redesign ................................................................................... .

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10

12

13

14

15

15

16

9

15

7

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Chapter III. The 1985-94 NHIS sample design ........................................................... .

Research for redesign of NHIS sample ................................................................ .

Primary sampling units ............................................................................ .

Secondary sampling units .......................................................................... .

Sensitivity analysis of design parameters............................................................. .

Selection of design parameters..................................................................... .

Stratification variables............................................................................. .

Number of sample PSU’s per stratum ............................................................... .

PSU panels...................................................................................... .

Overlap with other designs......................................................................... .

Selection of NHIS sample........................................................................... .

PSU definitions.. ................................................................................ .

Stratification and selection of PSU’s................................................................. .

Formation of PSU panels.......................................................................... .

Selection of SSU’s ................................................................................ .

Selection of housing units.......................................................................... .

iv

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g

Variance estimation ................................................................................. .

Variance estimation for totals ...................................................................... .

Variances for ratio-adjusted NHIS totals ............................................................. .

Variances for ratios of totals ....................................................................... .

Presentation of variances in NHIS publications ....................................................... .

References ........................................................................................ .

Tables:

A. Adjustment for nonresponse ................................................................. .

B. First-stage factors for use with the full National Health Interview Survey sample, by region, residence, and

whether other than black or black. ............................................................ .

C. 60 age-sex-race cells in the National Health Interview Survey...................................... .

D. Coefficient of variation (CV) for doctor visit totals by selected subdomains. ......................... .

Symbols

- - - Data not available

. . . Category not applicable

Quantity zero

0.0 Quantity more than zero but less than

0.05

Z Quantity more than zero but less than

500 where numbers are rounded to

thousands

* Figure does not meet standard of

reliability or precision (more than

30-percent relative standard error)

# Figure suppressed to comply with

confidentiality requirements

31

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One of the oldest national health surveys, the National

Health Interview Survey (NHIS) has been in continuous

operation since July 1957. As one of the major data collection programs of the National Center for Health Statistics

(NCHS), the NHIS is a principal source of information on

the health of the civilian noninstitutionalized population of

the United States.

The survey covers the civilian noninstitutionalized population of the United States living at the time of the

interview. Because of technical and logistical problems,

several segments of the population are not included in the

sample or in the estimates from the survey. Persons excluded are patients in long-term care facilities, persons on

active duty with the U.S. Armed Forces (although their

dependents are included), and U.S. nationals living in

foreign countries.

The NHIS data are obtained through personal interviews with household members. Interviews are conducted

each week throughout the year in a probability sample of

households. The interviewing is performed by a permanent

staff of interviewers employed by the U.S. Bureau of the

Census. Data collected over the period of a year form the

basis for the development of annual estimates of the health

characteristics of the population and for the analysis of

trends in those characteristics.

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 for adults not at home during the interview,

information is provided by a responsible adult (19 years of

age and over) residing in the household. Between 65 and 70

percent of the adults 17 years of age and over are selfrespondents. Generally, a random subsample of adult

household members is selected to respond for themselves

to additional questions on current health topics that vary

from year to year.

Depending on the household size and the nature and

extent of health conditions of household members, the

length of interview ranges between 20 and 90 minutes. On

average, the interviews require about 50 minutes in the

household.

items constitute approximately 50 percent of the questionnaire and are repeated each year. These items provide

continuous information on basic health variables. Questions on current health topics facilitate a response to changing needs for data and coverage of a wide variety of issues.

This combination yields a unique national health data base.

The questionnaire includes the following types of basic

health and demographic questions:

Demographic characteristics of household members,

including age, sex, race, education, and family income.

Disability days, including restricted-activity and beddisability days, and work- and school-loss days occurring during the 2-week period prior to the week of

interview, as well as bed days during the last 12

months.

Physician visits occurring during the same 2-week period, the interval since the last physician visit, and the

number of visits during the last 12 months.

Acute and chronic conditions responsible for these

days and visits.

Long-term limitation of activity resulting from chronic

disease or impairment and the chronic conditions associated with the disability.

Short-stay hospitalization data, including the number

of hospital episodes during the past year and the

number of days for each stay.

In addition, each of six representative subsamples of

households is asked to respond to questions about one of

six lists of selected chronic conditions. These six lists are a

part of the basic NHIS.

Questions on special health topics change from year to

year in response to current interest and need for data. The

1983 questionnaire contained questions on alcohol, dental

care, physician services, and health insurance. The health

topic questionnaire in 1984 was devoted entirely to issues of

aging; in 1985 it covered health promotion and disease

prevention; and in 1986 it included questions on health

insurance, dental health, vitamin and mineral intake, longest job worked, and functional limitations. The 1987 NHIS
is divided equally between refusals and households where

no eligible respondent could be found at home after repeated calls.

Statistics from the NHIS are published in NCHS reports and in professional journals. A major strength of the

NHIS is its ability to display the basic health characteristics

by many demographic and socioeconomic characteristics.

Most of the descriptive statistics published yearly appear in

NCHS Vital and Health Statistics Series 10 reports. The

survey procedures and a description of the statistics collected are described in two NCHS reports (NCHS, 1975,

1985).

NHIS sample design

From the beginning of the NHIS the U.S. Bureau of

the Census has had a primary responsibility for the sample

design and data collection for the NHIS. Historically, the

NHIS sample design has been linked with the sample

design of the Current Population Survey (CPS), a labor

force survey also conducted by the U.S. Bureau of the

Census. Prior to 1985, the sampling frame for the CPS was

used to select a smaller sample for the NHIS. Although the

primary locations for the NHIS were a subset of the

primary locations for the CPS sample, the surveys were

always conducted in different households within the primary locations. Descriptions of the sample designs have

been published for the NHIS (NCHS, 1958, 1974, 1985)

and the CPS (U.S. Bureau of the Census, 1963, 1978).

The sample design for the NHIS has been revised

following each decennial census of the population. The

revisions following the 1960 and 1970 censuses of the

population were made primarily to update the sampling

frame and to improve the efficiency of the design by using

more current information. The redesign following the 1980

decennial census of the population was a more comprehensive redesign than the previous redesigns. For this redesign,

implemented in 1985, a thorough evaluation was made of

all of the basic design features of the NHIS and a number

of new features were also investigated.

Conceptually, the sampling plan for the NHIS has

remained the same since 1957 and follows a stratified

2

the United States and for selected places of residences

within the United States.

The basic characteristics that define a sample design,

referred to as design parameters, are shown in figure 1 for

the current (1985-94) and the preceding (1973-84) NHIS

designs. The parameters for the 1973-84 design are similar

to the parameters for all of the previous NHIS designs. A

number of factors are considered in the selection of parameters for a sample design. Some of the most important

factors include the analytic objectives and data requirements for the survey; the reliability of the estimates produced by a design for a given set of parameters; the cost of

the survey, including administrative and travel costs; and

the size of interviewer assignments. For a survey such as the

NHIS, which has a number of analytical objectives, it is not

an easy task to determine the best set of design parameters.

The best set of parameters for one survey objective may not

be the best set of parameters for another survey objective.

The final set of parameters selected defines a design that

comes closest to satisfying the total set of survey objectives

and operational requirements.

The first stage of the NHIS sample design consists of

selecting a sample of primary sampling units (PSU’s) from

approximately 1,900 geographically defined PSU’s. A PSU

consists of a county, a small group of contiguous counties,

or a metropolitan statistical area (MSA). The PSU’s collectively cover the 50 States and the District of Columbia. The

1,900 PSU’s are stratified (grouped) using socioeconomic

and demographic variables and then selected with a probability proportional to their population size (pps) within a

stratum. Prior to 1985 the number of PSU’s selected for the

NHIS sample ranged from 356 to 376. In 1985 the number

of PSU’s was reduced to 198. Because the PSU’s are

selected pps, the largest PSU’s in the United States are

selected into the sample with certainty (probability of 1)

and are referred to as self-representing (SR) PSU's. The

PSU’s that are not selected with certainty are referred to as

non-self-representing (NSR) PSU’s. The stratification and

selection of the PSU’s is described in chapter III. The

dramatic reduction in the number of PSU’s for the NHIS

design is primarily a reflection of the increase in interviewer

and travel costs since 1973. In order to maintain the same
Subdomain and minority estimation.. . . . . Poststratification by age-sex-race; PSU

stratification for Hispanics; oversample for

black population within PSU’s

Number of inter-viewers.. . . . . . . . . . . . . . . . . 140

1Metropolitan statistical area as defined by U.S. Office of Management and Budget.

2Standard metropolitan statistical area as defined by U.S. Office of Management and Budget.

Poststratification by age-sex-race

120

Figure 1. Sample design parameters for 1973-84 and 1986-94 National Health Interview Survey designs

overall level of precision for NHIS statistics, the increase in

interviewer costs required a sample of fewer PSU’s with

more households per PSU.

The distribution of the NHIS PSU’s across the United

States (based on the 1980 decennial census) is shown in

figure 2. Only 79 of the 198 PSU’s were not included in the

1970-based sample used for the NHIS from 1973 through

1984. The remaining PSU’s were either completely or

partially contained in the 1970-based sample design. Approximately 76 percent of the 1980-based sample was

selected from geographical areas selected for the 1970-

based sample.

Within PSU’s, an iterative program is used to select a

sample of geographically defined area segments. The sample segments are systematically selected so they are distributed throughout each of the PSU’s. The segments are

subdivided into clusters which contain a small number of

housing units. The housing units may be grouped closely

together (compact) or spread over a small geographical

area (noncompact). Historically the cluster size for the

NHIS has ranged from four to nine households. For the

1985-94 design the expected cluster size is eight housing

units for the geographically defined area clusters and four

housing units for building permit clusters of housing units

constructed after the 1980 census (see chapter III for a more

detailed description). Basic demographic and health information is collected on all eligible persons in the occupied

housing units within the sample clusters. The average

number of households interviewed each year within a PSU

is approximately 500 for self-representing PSU’s and 156

for non-self-representing PSU’s.

The total yearly number of interviewed households for

the 1985-94 design is expected to be approximately 49,000.

This represents approximately a 20-percent increase in the

sample size from the 1973-84 design. By traveling to fewer

PSU’s the interviewers can collect information in more

households for approximately the same cost. The design

research conducted by the U.S. Bureau of the Census

indicated that both the data collection costs and the sampling precision for the NHIS should be similar for the

1973-84 and 1985-94 designs.

Several of the design parameters for the NHIS were

modified beginning in 1985. The use of a list sampling

frame constructed from address information collected in

the decennial census was dropped for the new design in

order for NCHS to be able to obtain identifiable household

information for followback surveys. Information from the

1980 census is confidential and could not be provided to

NCHS. An area sampling frame was used to replace the list

sampling frame. The number of non-self-representing

(NSR) PSU’s selected per stratum was changed from one

to two. This change was made to more accurately estimate

the precision of the NHIS statistics. The third change that

was made for the new design was the formation of national

subsamples of PSU’s. Four subsamples of PSU’s, referred

to as panels, were created to provide greater design flexibility to reduce the NHIS sample size from year to year if

necessary, to link other NCHS surveys to some or all of the

NHIS PSU’s, and to be able to easily subsample the NHIS

households for possible subsequent surveys. Each of the

four panels is a representative sample of the United States.

The final and most significant change made in the new

design involved the oversampling of the black population.

In a sample that is selected strictly proportionally to population size, the number of black persons is often not

adequate for producing detailed demographic subdomain

statistics for the black population or for making comparisons with the white population. In the new design, the black

population was oversampled to improve NHIS’s ability to

produce more precise statistics. The oversampling was

3
4
National Center for Health Statistics. 1958. The statistical design

of the Health Household-Interview Survey. By staff of the U.S.

National Health Survey and the Bureau of the Census. Health

Statistics. PHS Pub. No. 584-A2. Public Health Service.

Washington: U.S. Government Printing Office.

g 

U.S. Bureau of the Census. 1978. The Current Population Survey,

design and methodology. Technical Paper No. 40. Washington:

U.S. Government Printing Office.

5
years, the task of defining objectives involves the reassessment of old objectives, both implicit and explicit, defining

new objectives, and merging and establishing priorities for a

single set of objectives. Plewes (1980) states that “possibly

the only task more challenging to the survey statistician

than initially designing a large-scale socioeconomic survey

is that of redesigning one.” Because the redesign involves

an ongoing program, many resources have already been

invested in the existing design, and a shift in objectives often

puts the old objectives in conflict with the new objectives.

To develop a set of objectives for the National Health

Interview Survey (NHIS), the Division of Health Interview

Statistics (DHIS) was asked to specify its goals and data

requirements for the 1985-94 decade. An analytical committee within the National Center for Health Statistics

(NCHS) was also established to provide input from other

offices and divisions within NCHS. Many of the final recommendations from these two groups incorporated recommendations from an earlier Technical Consultant Panel of

the National Committee on Vital and Health Statistics

(NCHS, 1980). A summary of the objectives for the redesign of the NHIS is given below.

l To continue to produce descriptive statistics about the

health and health-related parameters of the civilian

noninstitutionalized population of the United States,

and to monitor change in these variables over time.

The basic variables of interest are extent and nature of

illness, both acute (disability days, work loss, and so

forth) and chronic (limitation of activity); extent and

nature of disability (acute and chronic); incidence and

prevalence of acute and chronic morbidity; utilization

of health-care services; health-care expenditures; utilization of health facilities and health resources; and

other health-related variables.

l To put more emphasis on measures of change in the

NHIS. Research should be conducted to identify alternative sample designs that would provide more precise

measures of change and not seriously reduce the precision of the descriptive statistics that are currently

published by DHIS. The investigation should include

6

l To improve the efficiency and analytical utility of

NCHS’s combined population survey program. Research should be conducted to investigate the coordination and linkage of the NHIS with NCHS’s other

population and establishment surveys.

l To investigate methods that would provide the NHIS

greater flexibility to oversample subgroups of the population, oversample geographic areas of the United

States, conduct follow-back surveys, and respond more

rapidly in the collection and production of statistics on

special health topics.

l To make the NHIS sample design more cost efficient

while maintaining the same overall level of precision.

l To incorporate health and health-related variables in

the implementation of the redesign of the NHIS. Two

specific areas that should be considered are the stratification of the primary sampling units and the differential sampling of the population.

These broad objectives were used as the primary criteria for redesigning the NHIS.

Model used for redesign

Although the NHIS has always been a multipurpose

survey which collected information on a variety of health

topics, the expanded objectives for 1985-94 presented

NCHS with the overwhelming task of selecting a single

sample design that would satisfy all or most of these

objectives. In order to translate these objectives into an

operational design strategy, a model approach was used to

help evaluate different design alternatives.

The model used for the redesign was first developed by

Sirken and Royston (1976) to study the design effects in

retrospective mortality surveys. The model was further

refined and applied to the redesign of NCHS’s populationbased surveys.

Conceptually, the use of a model to define the design

process is straightforward. The first step is to identify the

major design features related to the survey design. A design

feature is defined as a distinctive part of the survey design
After the design options have been defined for each of

the design features, design-option sets are formed. A

design-option set is composed of a single design option

from each of the design features. A design-option set

represents a possible alternative survey design. All possible

design-option sets are then enumerated. The number of

design-option sets is the product of the number of design

options for each feature.

Following enumeration, the design-option sets are systematically evaluated in two phases. During the first phase

the design options within a design-option set are examined

for compatibility with each other. If any two design options

are incompatible, the design-option set is deleted. The

second phase of the evaluation involves the application of a

set of criteria to identify the optimum design-option set.

The optimum design-option set represents the survey design to be implemented. A single criterion might be the

minimum sampling variance or mean squared error for a

fixed cost associated with each of the design-option sets.

The variance or mean squared error might represent an

average value over a number of variables of interest in the

survey. Another criterion might be an evaluation of how

well each of the design-option sets satisfies the major

description of the five design features and their options is

given below, and research on the design features is presented in the next section.

From the inception of the NHIS in 1957 the U.S.

Bureau of the Census has been the data collection agent for

NCHS. Since the early 1970’s a combined list-and-area

frame has been used for selecting the NHIS sample. The

list frame obtained from the decennial census was supplemented by an area frame in areas with incomplete or

insufficient address information and by a building-permit

list frame for housing units constructed since the decennial

census. Under the confidentiality provisions in the authorizing legislation of the U.S. Bureau of the Census, it is not

allowed to release any information collected in the decennial census to other Federal statistical agencies. It cannot,

therefore, release any of the identifiable person or household information to NCHS in areas where the census list

frame is used. Without the identifiable information NCHS

would not be able to analyze its results in geographical

detail, conduct independent follow-back surveys, use the

NHIS sample as a frame for other NCHS surveys, or

analytically link its information to other data sets through

the classification of geographical areas. It is obvious that

Design feature Design options

Sampling frame.. . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Area frame based entirely on geographical areas.

2. List-and-area frame based on 1980 decennial census information (1973-84

design].

Multiple-survey linkage.. . . . . . . . . . . . . . . . . . . . . . . 1. Single integrated survey design for the National Health Interview Survey, the

National Medical Care Utilization and Expenditure Survey. the National Survey of

Family Growth, and possibly for the National Health and Nutrition Examination

survey.

2. Independent designs for each of the Center’s population surveys (1973-84

design strategy).

Mode of data collection.. . . . . . . . . . . . . . . . . . 1. Dual mode of data collection using both face-to-face and telephone interviews.

2. Face-to-face mode of data collection (1973-84 design).

Oversampling small subdomains of the population. 1. Greater probabilities of selecting persons from small domains of special interest

(differential sampling).

2. Same probability of selecting each person in the target population (1973-84

design).

Rotation of NHIS sample.. . . . . . . . . . . . . . . . . . . . . . . . . . 1. Multiple interviews, 1 year apart, at each dwelling unit for a subsample of

dwelling units.

2. Single interview at each dwelling unit (1973-84 cross-sectional design).

Figure 1. Major design features and design options considered for the redesign of the National Health Interview Survey

7
Care Utilization and Expenditure Survey (NMCUES), or

the National Health and Nutrition Examination Survey

(NHANES), NCHS could

l link the surveys analytically and possibly avoid some

duplication of data collection,

use the NHIS information to oversample sociodemographic subdomains of the population for the linked

surveys more efficiently, or

use the NHIS information to oversample persons with

major expenditures in the previous year for the

NMCUES.

A possible disadvantage of using the same design for

all of its population surveys is the inability to have an

optimum design for each survey.

Since 1978, NCHS has invested considerable resources

in the study of conducting health interviews by telephone.

In the redesign the use of both face-to-face and telephone

interviews was considered for the NHIS as a third new

design feature. The main advantages of the telephone

interview are its lower cost and the closer supervision of

interviewers. Before a dual mode of data collection could

be used in the NHIS, however, a number of methodological

questions had to be addressed. The telephone interview

issues are discussed in a paper by Massey, Marquis, and

Tortora (1982). The key issues related to a dual telephone

and face-to-face design for the NHIS include telephone

coverage, telephone response rate, costs of a dual-frame

survey, and adaptability of the NHIS interview to a telephone interview.

The 1973-84 NHIS design gave every person in the

target population an approximately equal chance of being

selected into the NHIS. The oversampling of persons from

small subdomains of the population was considered as a

way to improve the precision of the estimates for the small

subdomains. The most important small domains of special

interest in the NHIS are the black, the Hispanic, the aged,

and the low income populations. The NHIS small sample

sizes for these subdomains have often prohibited detailed

demographic analysis. The key methodological issue related to this fourth design feature was the increase in the

available for followup topics the next year. The disadvantage of using the same housing units from one year to the

next year is the loss in the precision of estimates that

combine the weekly samples over a multiple-year period.

For questions that are repeated in the NHIS from one year

to the next, the weekly samples can be combined over long

periods of time to produce detailed descriptive statistics for

small subdomains of the population.

The approach used to research and select a survey

design with the five design features described above deviated somewhat from the conceptual model. Evaluation of

all possible design-option sets was not attempted; instead,

the new proposed options for the five features were studied

independently and compared with the features of the

1973-84 design. Whenever the independent research investigations yielded conclusive results, a single option was

selected for a design feature. Following this step, the

compatibility between the remaining design options for

different features was evaluated. The final design for the

NHIS for 1985-94 was the set of design options that were

most compatible and that best satisfied the multiple objectives for the NHIS.

Research results for major design

features

The research investigations conducted for the five major design features are described in this section. The research was conducted by NCHS staff, U.S. Bureau of the

Census staff, and private contractors to NCHS.

Sampling frame

The capability to link the NCHS population surveys

and to conduct longitudinal followup surveys of the NHIS

respondents was believed critical to meeting NHIS longrange goals. Establishing these capabilities required the

creation of a sampling frame that was independent of the

decennial census. To address this issue the U.S. Bureau of

the Census was asked to examine the costs of creating and

maintaining an independent area sampling frame versus the

costs associated with its combined list-and-area frame

linked to the decennial census. At the same time NCHS
preparation of segment maps, the listing of households

within area segments, and the selection of sample households would cost approximately $700,000 to $1.1 million

(1980 dollars) more than comparable operations for the list

frame (Jones, 1981; NCHS, 1981). This range of costs is a

function of the cluster size adopted for the survey. Because

NCHS anticipated that a larger cluster size (requiring fewer

segments to list) would be used for an area design, the

lower estimate of an additional $712,000 was assumed for

the transition to an area design. A more complete discussion of cluster size is presented in chapter III. One factor

contributing to the large additional cost for an area sampling frame was the reduction in cost-sharing with other

agencies. Most of the major surveys conducted by the U.S.

Bureau of the Census for other agencies use the same

sampling frame, and many of the costs associated with the

construction of the sampling frame are shared.

The second major cost increase for an area design

involves the updating and maintenance of the sampling

frame for the decade 1985-94. The initial cost estimates

from the U.S. Bureau of the Census for the maintenance of

the sampling frame were very large because of the field

work required to update an area frame on a continuing

basis. Also, as the area segments began to vary in size over

the decade because of growth and reduction in size, the

precision of NCHS statistics would be adversely affected.

After further investigation both problems were solved by

using a new-construction frame of building permits to

update the sampling frame. The new-construction frame

was originally used by the U.S. Bureau of the Census with

the transition to a list frame. The older area sampling

and NMCUES instead of independent designs for the three

surveys. The feasibility study also included the ability of the

NHIS sample to meet the sample size requirements for the

other two surveys.

In evaluating the adequacy of the NHIS sample as a

sampling frame for the NSFG and the NMCUES, the

comparability of the designs had to be taken into account.

The NHIS yearly sample for 1973-84 consisted of approximately 40,000 interviewed households selected in 376 different locations (primary sampling units or PSU’s). The

NMCUES sample size requirement is 10,000 households in

approximately 100 PSU's. For the NSFG approximately

14,000 sample persons are selected in 100 PSU’s. The

target population for the NSFG, however, is restricted to

women of childbearing ages (15-44 years) with a heavy

oversampling of the black female population within this age

group. The 1980 NSFG had to screen 55,000 households to

produce the required sample of 14,000 sample persons.

Even with a requirement to oversample households

with large health expenditures, it was determined that the

NHIS yearly sample could produce a more than adequate

sample for the NMCUES. Before the NSFG sample size

requirements were investigated, two other NHIS design

options were assumed. Both were considered likely options

for the new NHIS design. The first option assumed that the

number of PSU’s in the NHIS was 200 instead of 376, and

the second option assumed that the black population would

be oversampled in the NHIS. The number of persons

screened for the NSFG subdomains using each of the three

NHIS design options (Moore, 1981; NCHS, 1981) is shown

in table A. The 1973-84 NHIS would screen only one-half

Table A. Number of persons screened for the National Survey of Family Growth (NSFG) subdomains for 3 National Health Interview

Survey (NHIS) design options

Survey design

Black women, ever married, Black women, single,

15–44 years of age 15–44 years of age

Women other than

black, ever married,

15-44 years of age

Women other than

black, single,

15-44 years of age

Cycle III, NSFG. . . . . . . . . . . . . .

100 of 376 PSU’s1 from NHIS . . . . . . . . . .

100 of 200 PSU’s1 from NHIS . . . . . . . . .

100 of 200 PSU’s from NHIS plus 

oversampling of black population . . . . . . .

3,600 2,601 5.401 2,401

1,286 948 5,854 2,490

1,968 I,450 8,757 3,726

2,600 I,960 7,421 3,168

1PSU's = primary sampling units.

9
linked with respect to their designs, a cost analysis was

undertaken. By using the NHIS as a sampling frame for the

NMCUES and oversampling persons with chronic activity

limitations, the current level of precision for NMCUES

could be achieved with a sample that was smaller by 8 to 13

percent. This translates into a cost savings between 4 and 7

percent. For a single NMCUES costing approximately $15

million, a savings between $600,000 and $1.5 million could

be achieved. If the NMCUES were repeated twice during

the lo-year design period a savings between $1.2 and $2.1

million could be achieved. For the NSFG the major benefit

of using the NHIS as a sampling frame is the ability to

screen for women of childbearing age by race. Using the

design assumptions in table A it appears that the screening

for the NSFG could be reduced by 60 to 90 percent. A

conservative estimate of the screening costs in the NSFG is

one-fourth of the total cost of the survey, or $850,000. Thus

the total estimated savings that would result from using the

NHIS as a screener for the NSFG is between $500,000 and

$750,000. If the NSFG were conducted twice in a decade,

the savings would be between $1 million and $1.5 million.

If the NMCUES and NSFG are each conducted twice

during the lo-year NHIS design period, a savings between

$2.2 million and $3.7 million could be realized by using the

NHIS as a sampling frame. These savings would more than

offset the additional cost of $1.4 million required for an

area sampling frame. These savings do not include the

savings that would result from not having to design the

NMCUES and the NSFG separately. Much of the initial

field work for these surveys could be significantly reduced.

After reviewing the results of these analyses, NCHS

made a decision to switch to an area frame design and

made a commitment to the concept of linking its population

surveys. Even without the potential cost savings of integrating its population surveys, the need for an area frame

independent of the decennial census was crucial for meeting many of the NHIS long-range objectives.

One final note with regard to the U.S. Bureau of the

Census confidentiality requirements: Even though the use

of an area frame would release NCHS from the U.S.

Bureau of the Census confidentiality provisions, NCHS

10

surveys and the establishment surveys. NCHS population

surveys include the National Health Interview Survey

(NHIS), the National Health and Nutrition Examination

Survey (NHANES), the National Survey of Family Growth

(NSFG), and the National Medical Care Utilization and

Expenditure Survey (NMCUES). NCHS establishment surveys include the National Hospital Discharge Survey

(NHDS), the National Nursing Home Survey (NNHS), and

the National Ambulatory Medical Care Survey (NAMCS).

The ISD research would also investigate follow-back linkages for these surveys such as the potential for telephone

follow-back surveys. The goal was to increase the analytical

potential of the total NCHS survey program as well as to

reduce its cost.

The first priority of the ISD research was to investigate

the linkages between the population surveys, especially

those surveys on which the redesign of the NHIS would

have the most impact. To date, research on the survey

linkages between the NHIS and the NSFG and between the

NHIS and the NMCUES has been completed. Research is

currently underway to evaluate the potential of linking the

NHIS and the NHANES and on the use of the NHIS PSU’s

for the NHDS. Planning has begun for a National Health

Care Survey (NHCS) that would link all of the establishment surveys together into a single survey. The use of the

NHIS PSU’s for the NHCS will also be studied. And finally,

a research study has been conducted to evaluate the feasibility of conducting telephone follow-back surveys from the

NHIS.

All of these surveys will not be linked in the same way.

The linkages that can be considered between any two

surveys depend on the sampling unit definitions at each

stage of sampling for the two surveys. For linkages between

the population surveys and the establishment surveys, the

use of a common set of geographical PSU’s is currently

being considered. For linkages between population surveys,

the use of the same or a subset of the same PSU’s, housing

units, households (related family members), and individuals is being evaluated. The discussions in this report will be

limited to the research conducted on the linkages between

the NHIS, the NSFG, and the NMCUES.
gg 

mation to evaluate a number of alternative options and

designs. Based on the recommendations from this first

phase of research studies, pilot studies were conducted

during a second phase to develop and validate the linkage

and data collection procedures in the field. Reports from

these studies have been or soon will be published in NCHS

Series 2 methodological reports (NCHS, 1985, 1987a,

1987b, 1987c). Brief summaries of the major findings from

these studies are given below.

The first research study (NCHS, 1985)—the effects of

integrating the NHIS and the NSFG—was conducted for

NCHS by the Westat Corporation. Seven different linked

designs along with the design for the third cycle of the

NSFG were compared using the following criteria to evaluate the alternatives:

l Sample size necessary to achieve fixed and identical

levels of precision for all alternatives.

l Alternative field and interviewing methods available

for the sampling procedures.

l Cost of implementing the designs.

l Anticipated response rates.

l Time schedules.

l Potential administrative or operating problems.

The linked sample designs varied by the number of

PSU’s, sampling unit definition, method of interview assignments, and degree of subsampling NHIS households without eligible women. The major findings from the study were

as follows:

All of the alternative linked designs required significantly smaller sample sizes than the Cycle III NSFG

model to achieve the sample precision. The range was

from 10,400 to 11,500 persons compared with 14,000

persons (no allowance for extra nonresponse). This

translates into direct cost savings of more than

30 percent for some of the linked designs.

l The NHIS could provide an adequate sample for the

NSFG.

l Response rates should remain approximately the same

for the linked design versus an independent design.

pp

versus personal visits, and (3) length of time between the

NHIS interview and the NSFG interview-various lengths

from 1 to 15 months. The dependent variables for the study

were response rate, level of effort, and cost. The field trial

was conducted in 10 of the NHIS PSU’s with a sample size

of 1,315 NHIS households. The major findings from the

field trial were as follows:

l Although the difference between the overall response

rate for the housing unit sample and the selected

person sample (83.5 percent versus 82.1 percent) was

not statistically significant, it appears that somewhat

higher response rates would be achieved with a housing

unit definition.

l There is no single best linkage strategy. The tradeoff

between increased response rates and reductions in

costs makes the best sampling unit definition a subjective decision.

l The mode of initial contact did not appear to affect

response rates for either type of sample unit. The mode

of initial contact did, however, have an important effect

on the overall level of effort and associated costs. The

ratio of personal visits per completed interview was

significantly lower for cases assigned to a telephone

mode of initial contact than for cases initially contacted

in person.

l There were no clear findings with respect to the effects

of elapsed time on response rates. It appears that the

major impact of elapsed time would be seen in the level

of tracking necessary for the selected person sample.

These findings do not lead to the determination of the

optimum design for the linked NSFG. They do, however,

provide valuable information for selecting a final design.

The second research study (NCHS, 1987b)—the

effects of integrating the NHIS and the NMCUES—was

conducted for NCHS by the Research Triangle Institute

(RTI). The following four designs were compared:

l An unlinked NMCUES design.

l A NMCUES design linked to the NHIS dwelling units.

11
few months would normally be required to accumulate a

NHIS sample large enough for the NMCUES.

The field trial for the linkage between the NHIS and

the National Medical Expenditure Survey (NMES) was

conducted by the Westat Corporation (NCHS, 1987c).

NMES represents the next cycle of the national health

expenditure surveys following NMCUES. The field trial

evaluated alternative strategies for defining the sampling

unit-household and housing units-and the mode of initial household contact-telephone and in-person. The criteria used in the evaluation were response rates, level of

effort, and costs. The sample size for the field trial was 600

households in eight NHIS PSU’s. The findings of the field

trials were as follows:

l The overall response rates for the household sample

and the housing unit samples were not significantly

different (86.0 percent versus 88.4 percent). The direction of the difference, however, was in the expected

direction and was similar to results from the NSFG

field trial.

l The response rate associated with the in-person mode

of initial contact for the NMES did appear to be higher

than the response rate for initial telephone contacts

(90.1 percent versus 84.4 percent). This result was not

statistically significant. The telephone mode of initial

contact for the NMES did significantly reduce the

overall level of effort and costs associated with a completed interview.

l None of the experimental treatments in the field trial

appeared to interact with any of the demographic

subdomains of interest for the NMES.

l The response rates for telephone and in-person interviews in the second round of interviews of the field trial

were similar. The quality of the data collected on the

telephone, however, as measured by the rates of item

nonresponse for selected questionnaire items, was

somewhat poorer than that for the in-person views.

These results, like the NSFG linkage field results, do

not lend themselves to a single best design strategy. They

do provide information for selecting a design.

12

both telephone and face-to-face interviews could be used

for the initial NHIS household interview.

Since 1978 NCHS has been actively involved in telephone survey research in an effort to improve the efficiencies of its surveys. Much of the research has involved the

NHIS and has included the comparability of telephone and

face-to-face interviews and some of the implications of

using a dual-frame, telephone and face-to-face, design for

the NHIS (Casady, Snowden, and Sirken, 1981; NCHS,

1987d; Sirken and Casady, 1982). The research results from

the various studies all indicated potential advantages of

conducting some of the NHIS interviews by telephone.

In 1982 a joint NCHS and U.S. Bureau of the Census

Telephone Survey Task Force was established to propose

recommendations for the research and development of a

dual-frame NHIS. The task force was charged with identifying the key methodological issues that should be researched for a dual-frame design and for preparing a plan

for addressing these issues and implementing a dual-frame

design by 1986. In March 1983 the task force made the

following recommendations for the development of the

dual-frame NHIS (Joint NCHS-Bureau of the Census

Telephone Survey Task Force, 1983).

l Explore improvements in total dual-frame survey design and estimation methods through theory development, modeling, and simulation.

l Develop a random digit dialing (RDD) sample design

for the NHIS using existing methods.

l Adapt the NHIS questionnaire and field procedures

for a telephone interview and conduct an RDD feasibility study of the telephone questionnaire.

l Develop a computer-assisted telephone interview

(CATI) for the NHIS.

l Estimate the primary sources of nonsampling errors

for telephone and face-to-face interviewing procedures.

l Develop new, rapid coding and data-processing procedures for the NHIS data-collection activities.

l Conduct a dual-frame feasibility study.

In order to accomplish these objectives, it was recommended that both an operational telephone system and a
established a telephone interviewing system for conducting

the NHIS-RDD feasibility study. The data collection for

this study was from January through May of 1984. The

following nine specific objectives were defined for the

feasibility study.

l Test the feasibility of conducting the entire core component of the NHIS questionnaire by telephone.

l Estimate the response rate for the telephone component of a dual-frame NHIS.

l Estimate the costs for conducting the telephone component as part of a dual-frame design.

l Evaluate alternative questionnaire structures in terms

of length and effect on estimates.

l Identify operational problems associated with administering the NHIS by telephone.

l Develop and evaluate procedures for identifying and

handling special places, such as group quarters and

hotels, over the telephone.

l Conduct preliminary development and testing of estimation procedures, including nonresponse and poststratification adjustments.

l Test procedures for the assignment, management, and

completion of samples for producing valid estimates.

l Evaluate the operational feasibility and effect on response rates of using a most knowledgeable respondent

rule.

The major findings from the study were as follows:

l For the NHIS, RDD response rates of 85 percent (for

an average interview length of about 50 minutes) are

feasible. However, interviewer and field staff experience are critical factors in achieving such rates. Over

the duration of the study, response rates increased by

12 percentage points, and item nonresponse levels were

substantially reduced.

l The length of the NHIS interview and nature of the

questions did not appear to cause any operational

problems.

l The person-by-person questionnaire version resulted in

higher reporting of health events than did the family

ppgpy g

the number of unresolved cases.

There was a problem with identifying special places

(using the NHIS definitions) in the RDD survey.

Roughly 30 percent were classified as nonresidential

units, and 20 percent were classified as residential units

other than special places.

Cost estimates from the RDD feasibility study cannot

be used to estimate the telephone costs for a continually operating dual-frame telephone and face-to-face

survey. Many of the costs associated with the feasibility

study were development costs for a telephone survey

system and costs associated with a one-time survey.

A detailed description of the feasibility study and an

analysis of the results are presented in a joint report by the

U.S. Bureau of the Census and NCHS (1985) and in

several papers presented at American Statistical Association meetings (Chapman and Roman, 1985; Hogue and

Chapman, 1984; Tompkins and Massey, 1986).

On the whole the results of the NHIS-RDD feasibility

study were positive, although there were some indications

that a dual-mode NHIS would not be as cost effective as

originally thought. The administrative costs of operating

two modes of data collection simultaneously significantly

reduce the cost savings produced by telephone interviews.

Following the completion of the feasibility study, a decision

was made by NCHS not to change the NHIS to a dualmode survey. This decision was based primarily on the

incompatibility of the RDD telephone component with

other design features being considered for the NHIS (see

later discussions in this chapter).

Oversampling small subdomains

One of the objectives that the NHIS has always emphasized has been publication of detailed descriptive health

statistics for the U.S. population. This is the rationale for

having a cross-sectional sample design that can be accumulated from week to week and from year to year. There are,

however, a number of important small subdomains of the

U.S. population for which detailed statistics cannot be

13
Statistical Methods Division (SMD) within the U.S. Bureau

of the Census considered four different approaches for

oversampling the four special subdomains to improve the

precision of the related statistics. The four methods were

snowball sampling, screening of additional households,

oversampling at the first stage of sampling (PSU’s), and

oversampling at the second stage of sampling (clusters of

housing units). The first two methods were discarded very

early because of the complexities and costs associated with

the procedures. Except for the investigation of Hispanic

statistics, oversampling at the second stage of sampling was

the primary method considered for improving the precision

of estimates for the special subdomains. This method is

described in a paper by Waksberg (1973) and involves the

stratification of the secondary sampling units (SSU’s)

within the PSU’s. For a given subdomain two strata of

SSU’s are formed within each PSU. One stratum contains a

very high proportion of the subdomain of interest; the

second stratum does not.

Different sampling rates are used within the two strata

to oversample and improve the precision of the subdomain

of interest. Information obtained in the decennial census

would be used for the stratification. The effectiveness of

this approach depends on how clustered the subdomain of

interest is within the PSU’s, that is, on how well the

stratification can separate the subdomain of interest from

other persons within the PSU. Using this method of stratification the following results were obtained by SMD.

l Oversampling at the second stage of sample selection

within PSU’s was not an effective way to improve the

precision of the statistics for the aged or for persons

with low income. These subdomains, especially the

aged, are not highly clustered within PSU’s, and only

very small gains in precision could be achieved. Significantly improving the precision of statistics for these

subdomains would require other methods that would

be costly.

l The reliability of estimates for black persons could be

improved considerably (8-25 percent) by optimally

oversampling black persons in PSU’s with large black

pgppy pp

Thus, it appears that the stratification method of oversampling subdomains would be most effective for black persons

for the duration of the NHIS redesign.

The latter result led SMD to investigate the possibility

of adding additional PSU’s in the five southwestern States

containing the largest Hispanic populations. The total number of PSU’s was constrained to be constant. The results of

this analysis indicated that significant improvements for the

precision of Hispanic estimates could be made in the

Southwest, but that only modest improvements (about

5 percent) could be made at a national level. These improvements were partially offset by a decline in the precision for other subdomains in the Southwest and in the total

U.S. population.

Rotation of the NHIS sample

One of the important new objectives for the redesign of

the NHIS was to improve the survey’s ability to evaluate the

dynamics of change. The dynamics of change refer to the

distribution of changes that occur among individuals. For

example, how often do individuals go back and forth from a

well state of health to a sick state of health over a given

period of time ? In order to obtain better measures of

change a new design feature was proposed. The new proposed feature was the incorporation of a rotational sample

into the design. This would involve selecting a subsample of

the NHIS housing units interviewed during one calendar

year and reinterviewing them in subsequent years as part of

the subsequent year’s sample.

The previous NHIS sample designs retained the same

PSU’s from year to year but selected new households in

adjacent clusters to be interviewed each year. The rotational designs would take advantage of the correlations

between PSU’s and clusters from one year to the next to

improve NHIS’s ability to measure change, whereas the

previous designs only take advantage of the PSU year-toyear correlations.

SMD investigated a number of alternative rotational

patterns that involved reinterviewing sample persons 1 or 2

14
(1982a). The two rotational designs shown in table B would

reselect one-fourth and one-half of the NHIS sample housing units from one year to the next. It can be seen that

under the right set of circumstances improvements of as

much as 30 percent can be made in estimating year-to-year

change using the rotational designs. This is accompanied by

a loss in precision for the 2-year means. For most health

statistics an increase in the cluster year-to-year correlations

of as much as or more than 0.25 for the rotational designs is

unlikely.

Two other issues related to the rotational designs were

raised by NCHS and SMD. The first issue involved the

sampling unit to use for the rotational designs. In the SMD

analysis it was assumed that housing units would be selected and surveyed from one year to the next. This would

not be a satisfactory sampling unit definition for analyzing

the individual dynamics of change. In order to evaluate the

dynamics of change the same sample of persons must be

interviewed from one year to the next. Although this would

be feasible, a considerable amount of tracking and cost

would be required to follow sample persons from one year

to the next. No further analysis was conducted, but NCHS

has analyzed this issue for the NMCUES.

The second issue raised was the possibility of conducting the NHIS reinterviews in subsequent years by telephone

for households with telephones. This would significantly

reduce the data-collection costs for the NHIS over the

decade. This issue was to be reevaluated after the

Table B. Percent change in variances for 2 rotational designs

relative to 1973–84 National Health Interview Survey (NHIS)

design assuming a 25- and a 50-percent increase in the cluster

year-to-year correlation

Estimate

Year-to-year change . . . . .

2-year mean. . . . . . .

1-year mean. . . . . . .

Percent of sample rotated from year-to-year

One-fourth One-half

 = 0.25 = 0.5 = 0.25 = 0.5

- 7 -19 -13 -31

+4 +6 +8 +14

-1 - 4 -1 - 5

NOTE: The year-lo-year correlation between clusters for the 1973–84 design is assumed to be

negligible.

new options are also given in figure 2.

The design options that are least compatible are the

linked population survey option with the RDD telephone

option and the linked survey option with the sample rotation option. For the first interaction, the NSFG and the

initial interview in the NMCUES both require that the

interview be done in-person. Following up RDD telephone

interviews with a face-to-face interview for the linked surveys would be extremely costly and inefficient due to the

lack of geographical clustering of housing units in RDD

surveys. The optimum designs for the NSFG and the

NMCUES are highly clustered designs at both the first and

second stages of sample selection. An RDD sample is not

feasible for either of these surveys. The rotation design

feature is slightly more compatible with the linked survey

feature, although the compatibility is still rated as poor.

This is because an overlapping sample for the NHIS from

year to year will reduce the total number of eligible households for the NSFG and the NMCUES. Research results

show that the NHIS sample is already strained to meet the

sample design requirements for the NSFG.

The compatibility between all of the other new options

is acceptable, and, in fact, the oversampling of black persons

will greatly enhance the linkage between the NHIS, the

NSFG, and the NMCUES. The compatibility of the threeand four-way interactions between the new design options

requires that all of the two-way interactions be compatible.

Conclusions

Based on the research results presented in the last

section and the long-range goals of NCHS, the decision was

to make the NHIS sample the cornerstone of the NCHS

Integrated Survey Design (ISD) program. This meant

switching to an area frame for the NHIS and beginning to

establish the survey linkages between the NHIS, the NSFG,

and the NMCUES. This also opened up the possibility of

conducting follow-back or longitudinal panel surveys for

the NHIS supplemental topics.

After the area frame and linkage options had been

selected, it was recommended that black persons be oversampled in the NHIS for the 1985-94 design period

15
NHIS.

The rotation of the NHIS sample was not adopted for

the NHIS because of its incompatibility with the linking of

the NCHS population surveys. It was also reasoned that

follow-back interviews could be conducted for the NHIS on

an ad hoc basis and that a continuous rotating sample was

not required. The suggestion of conducting follow-back

interviews by telephone had considerable appeal and will be

used by the NHIS in the future.

The use of a dual-frame telephone and face-to-face

survey was not adopted for the 1985 redesign because of its

incompatibility with an ISD. It was recommended that the

Division of Health Interview Statistics investigate other

uses of RDD telephone surveys. The recommendation was

accepted.

Although the conceptual model used to investigate the

design features and design options was considerably modified in practice, the model proved extremely valuable in the

planning and organizing of the research for the redesign of

the NHIS. Because of the many complexities involved in

designing or redesigning a major national survey, the task

of coordinating all of the research activities for the NHIS

redesign was enormous. Using a model for the redesign

effort made the task more manageable.

Once all of the major design features had been selected

for the NHIS, the specific parameters for the NHIS design

had to be determined. This process is described in

chapter III.

References

Casady, R. J., C. B. Snowden, and M. G. Sirken. 1981. A study of

dual frame estimators for the National Health Interview Survey.

Proceedings of the Survey Research Methods Section, American

Statistical Association, pp. 444-447.

Chakrabarty, R. P. 1985. Redesign of the National Health Interview Survey. Proceedings of the Survey Research Methods Section,

American Statistical Association, pp. 252–257.

Chapman, D. W., and A. M. Roman. 1985. An investigation of

substitution for an RDD survey. Proceedings of the Survey Research Methods Section, American Statistical Association,

pp. 269-274.

16

M. Shapiro. July 30. U.S. Bureau of the Census. Washington, D.C.

Hsen, P. L. 1982a. HIS Redesign: Alternative Approach of

Variance Estimation for the Rotation Patterns. Internal memorandum from Rameswar P. Chakrabarty to Gary M. Shapiro.

Feb. 3. U.S. Bureau of the Census. Washington, D.C.

Hsen, P. L. 1982b. HIS Redesign: Comparison of Variance

Estimation for the Rotation Patterns Assuming Different Percentage of Between PSU Variance Component. Internal memorandum from Paul L. Hsen to Rameswar P. Chakrabarty. Aug. 19.

U.S. Bureau of the Census. Washington, D.C.

Joint NCHS-Bureau of the Census Telephone Survey Task Force.

1983. Research and Development Recommendations Leading to

a Dual Frame National Health Interview Survey. Unpublished

report.

Jones, C. D. 1981. HIS Redesign-Level One Research: Cost

Comparisons for Sample Design Alternatives. Unpublished letter

to Monroe Sirken, NCHS. May 26.

Massey, J. T., K. H. Marquis, and R. D. Tortora. 1982. Methodological issues related to telephone surveys by Federal agencies.

Proceedings of the Social Statistics Section, American Statistical

Association, pp. 63-72.

Massey, J. T., and M. G. Sirken. 1982. A model for designing

NCHS’s population-based surveys. Proceedings of the Social Statistics Section, American Statistical Association, pp. 285-290.

Mazur, C. 1983. HIS Redesign: Differential Sampling to Achieve

a Reduction in Black Subgroup Variances. Internal memorandum

from Rameswar P. Chakrabarty to Gary M. Shapiro. June 15. U.S.

Bureau of the Census. Washington, D.C.

Moore, T. F. 1981. HIS Redesign: HIS as Frame for FGS and

MCUES. Internal memorandum from Rameswar P. Chakrabarty

to Gary M. Shapiro. Jan. 14. U.S. Bureau of the Census.

Washington, D.C.

National Center for Health Statistics. 1980. National Health

Interview Survey, Report of the National Committee on Vital and

Health Statistics. DHHS Pub. No. (PHS) 81-1160. Public Health

Service. Hyattsville, Md.

National Center for Health Statistics. 1981. Discussion of the

Major Design Features for the Redesign of the NHIS. Unpublished memorandum. Mar. 20. Office of Research and Methodology. Hyattsville, Md.
Pub. No. (PHS) 87-1377. Public Health Service. Washington: U.S.

Government Printing Office.

National Center for Health Statistics, B. G. Cox, R. E. Folsom,

and T. G. Virag. 1987b. Design alternatives for integrating the

National Medical Expenditure Survey with the National Health

Interview Survey. Vital and Health Statistics. Series 2, No. 101.

DHHS Pub. No. (PHS) 87-1375. Public Health Service.

Washington: U.S. Government Printing Office.

National Center for Health Statistics, N. A. Mathiowetz and E. P.

Ward. 1987c. Linking the National Medical Expenditure Survey

with the National Health Interview Survey. Vital and Health

Statistics. Series 2, No. 102. DHHS Pub. No. (PHS) 87-1376.

Public Health Service. Washington: U.S. Government Printing

Office.

National Center for Health Statistics, O. T. Thornberry, Jr., et al.

1987d. An experimental comparison of Telephone and Personal

Health Interview Surveys. Vital and Health Statistics. Series 2, No.

106. DHHS Pub. No. (PHS) 87-1380. Public Health Service.

Washington: U.S. Government Printing Office.

Olsen, C. 1981. HIS Redesign: Improvement of Small Domain

Estimates. Internal memorandum from Christina Olsen to Rameswar P. Chakrabarty. Jan. 14. U.S. Bureau of the Census.

Washington, D.C.

Olsen, C. 1982. HIS Redesign: Differential Sampling to Achieve a

Reduction in Demographic Subgroup Variances. Internal memorandum from Rameswar P. Chakrabarty to Gary M. Shapiro.

May 3. U.S. Bureau of the Census. Washington, D.C.

py g

paper, Proceedings of 44th Session of the International Statistical

Institute. Madrid.

Sirken, M. G., and P. N. Royston. 1976. Design effects in retrospective mortality surveys. Proceedings of the Social Statistics

Section, American Statistical Association, pp. 773-777.

Tadros, W. 1982a. HIS Redesign: Differential Sampling to

Achieve a Reduction in Demographic Subgroup Variances, II.

Internal memorandum from Rameswar P. Chakrabarty to Gary

M. Shapiro. Sept. 22. U.S. Bureau of the Census. Washington,

D.C.

Tadros, W. 1982b. HIS Redesign: Estimating National Variance

Changes if Black or Spanish Persons Are Oversampled. Internal

memorandum from Rameswar P. Chakrabarty to Gary M. Shapiro. Oct. 8. U.S. Bureau of the Census. Washington, D.C.

Tompkins, L., and J. T. Massey. 1986. Using a most knowledgeable respondent rule in a household telephone survey. Proceedings

of the Survey Research Methods Section, American Statistical

Association, pp. 281-286.

U.S. Bureau of the Census and National Center for Health

Statistics. 1985. The Results of the 1984 NHIS/RDD Feasibility

Study: Final Report. Unpublished report.

Waksberg, J. 1973. The effect of stratification with differential

sampling rates on attributes of subsets of the population. Proceedings of the Social Statistics Section, American Statistical Association, pp. 429-434.

17
period from early 1979 to mid-1983 when the final choice

of the new design was made. This research resulted in the

changes to the design parameters displayed in figure 1 of

chapter I.

The research program included work in both the operational aspects of the NHIS and its sample design. This

section centers on the sample design research; a description

of the research activities used to determine design parameters and the procedure used to select the NHIS sample

follows.

Research for redesign of NHIS

sample

In the past, the NHIS surveys conducted by the U.S.

Bureau of the Census (hereafter referred to as Census

Bureau) were closely linked to the Current Population

Survey (CPS) in order to minimize costs. Consequently, the

NHIS had design features similar to the CPS (U.S. Bureau

of the Census, 1978). Traditionally, the Census Bureau

redesigns its major demographic surveys following the decennial Census of Population, and early in 1979 a largescale research project was begun for the NHIS. The NHIS

did not use CPS interviewers and, accordingly, did not need

to be restricted to CPS primary sampling units (PSU’s).

Also, the redesigned survey would use an area sampling

frame within PSU’s rather than the multiframe design of

the CPS. For these reasons, the NHIS was designed independently of the CPS.

The primary objective for the NHIS redesign was to

select a design that maintained the precision of the NHIS at

the level of the 1973-84 design at the lowest possible cost

and was compatible with the analytical objectives of the

survey. The NHIS redesign research included the following:

l Reevaluating the PSU definitions.

l Determining the optimum number of PSU’s for the

sample.

l Determining the optimum size of the secondary sampling unit (SSU).

l Determining the total number of sample households.

Primary sampling units

Several options existed regarding how PSU’s should be

defined, including whether to continue to use multicounty

PSU’s, or to use single county or subcounty level PSU’s.

The obvious benefit of smaller PSU’s would be a decrease

in travel within sample PSU’s. It was also possible that the

between-PSU variance would be reduced. The use of single

county or subcounty PSU’s was investigated by the Census

Bureau, using labor force data from the 1980 census. The

findings from the research indicated that cost savings would

probably be small and that variances might actually increase (Kostanich, 1982). Multicounty PSU’s were judged

to be more efficient and were retained for the redesign.

In the 1973-84 NHIS design, the sample was spread

over 376 sample PSU’s and over 52 weeks of the year.

Interviewer assignment areas, consisting of several PSU’s,

were formed, and one interviewer interviewed all the PSU’s

in the area. As a result, travel to and from PSU’s was

extensive and consumed about 35 percent of the direct field

costs for travel and interviewing. Travel to and from PSU’s

could be reduced by decreasing the number of PSU’s or by

hiring more interviewers. However, more interviewers

would mean less work annually for each interviewer. Because adequate work loads must be preserved for NHIS

interviewers, who work on no other Census Bureau surveys,

the preferred way to reduce the number of PSU’s with no

resident interviewer was to reduce the total number of

sample PSU’s (Tadros, Moore, and Chakrabarty, 1982).

To determine the optimum number of PSU’s for the

NHIS it is necessary to estimate the components of variance (precision of NHIS statistics) and the components of

cost. To estimate the precision of NHIS statistics under

alternative numbers of PSU’s, the following formula (see

Moore, 1988, for derivation) was used to estimate the

variance of potential designs for 1985–94 relative to that of

the 1973-84 design:

(1)

18
obtained from a variety of sources. Data presented in

table A are from the National Center for Health Statistics

(NCHS). These data showed that in the 1973-84 design the

between-PSU variance proportion, B, for the major subgroups in the population was in the 0 to 20 percent range

for many important health characteristics (NCHS, 1982). A

value of B = 0.10 was used in the research to represent

most important health items.

viewers, each working about 26 weeks a year.

The intraclass correlation is a measure of homogeneity

within clusters. Results of a study (NCHS, 1973) on

 showed that for compact clusters of six housing units,

 ranged from 0.01 to 0.17 for the majority of health

characteristics, with the important items (for example,

number of chronic conditions, number of bed days, number

of restricted activity days) ranging from 0.02 to 0.06. A

value of 0.04 was used for the intraclass correlation. Preliminary work showed that the optimal number of sample

PSU’s was not very sensitive to the value of 

If clusters of six housing units were used, the Census

Bureau’s field staff planned to form interviewer assignments of three clusters to yield expected assignment sizes of

18 housing units. For clusters of eight housing units, assignments would include two or three clusters, averaging 2.15

clusters per assignment. Thus, assignments would include

16 or 24 housing units. For a given sample size, the number

of interviewers and weekly assignments can be estimated

with this information.

The average number of sample housing units per cluster (secondary sampling units within PSU’s) in the 1973-84

design was four. Most of the potential new designs were

An assumption was made that interviewers would be

hired in PSU’s based on the sample size of the PSU’s.

PSU’s with about 26 weeks or more of work would all have

at least one interviewer, and the remaining interviewers

needed for that sample size would be hired from some of

the smaller PSU’s. This approach permitted estimation of

the number of PSU’s with no resident interviewer and the

number of weekly assignments that would require travel.

Data-collection costs were estimated as a function of the

total number of weekly assignments and the number of

assignments requiring travel to the PSU.

Table A. Estimated percent of between-PSU variance for the 1973–94 National Health Interview Survey design by selected health

variables and demographic domains

Sex Race Age

Health variable Total Male Female Black

Other than

black

Under 15

years

15-44

years

45-64

years

65 years

or over Average

Total restricted activity days . . . . . .

Total bed days . . . . . . . . . . . .

Total physician visits . . . . . . . . .

Mean number of physician visits. . . . . . .

Proportion seeing physician . . . . . . .

Total dental visits . . . . . . . . . . .

Mean number of dental visits. . . . . . . .

Proportion seeing dentist . . . . . . . . .

Mean number of chronic conditions . . . .

Proportion with chronic condition . . . . .

Average. . . . . . . . . . . . . . . . . . . . . . . . . . ..

‘Estimated between-PSU variance is negative.

NOTE: National estimates are projected from Balanced Repeated Replication method calculations for the South Region. Program was developed by Bean and estimates were developed by Casady,

of the National Center for Health Statistics.

19
They have been compact, that is, adjacent units for the most

part, and noncompact, that is, spread over a larger geographical area such as a block. In the 1973-84 design,

clusters in the address frame were compact with an expected size of four, and clusters in the area and newconstruction frames were a noncompact four. Increasing

the cluster size reduces travel costs but increases variances

if the intraclass correlation is positive.

The change to clusters of size four came early in the

1970’s after studies of unit costs and intraclass correlations

for NHIS showed that, for constant costs, this would result

in lower variances for health statistics than the larger

cluster sizes considered. Actually, this was optimal only

when taking into account the savings that would result from

using the same cluster size in both CPS and NHIS. Considering only NHIS, the optimum cluster size would have been

larger (Shapiro, 1971).

For the post-1980 census redesign, the Census Bureau

updated the earlier cost study (Rogers, 1981). Costs were

updated in three different ways. Regression techniques

were used to estimate, first, total interviewer travel mileage

for various cluster sizes, and second, unit costs for different

cluster sizes. In the third method, a cost function was

defined in terms of interviewing costs, costs of travel between clusters, and costs of travel between home and

cluster.

The final step was to compare costs and within-PSU

variances of items within a range of intraclass correlations

and for different cluster sizes. All comparisons led to the

conclusion that a larger cluster size would be more efficient

for the NHIS. Table B summarizes the results (Huang,

1982).

Because data for some of the components of method

3’s cost function were available only for clusters of size

four, data for the other cluster sizes had to be estimated

from existing data and reasonable assumptions. Because

method 3’s estimates were of lower quality than the other

methods, the choice was eventually limited to the results of

methods 1 and 2. Using clusters of size seven would have

made coordination of the within-PSU sampling procedure

with other Census Bureau surveys more complex, so it was

2 0

were evaluated through a sensitivity analysis presented in

the next section.

As in the 1973-84 design, clusters of size four were

recommended for new-construction units sampled from

building permits. There were two reasons for this. As a

rule, new-construction units are not close together. A

new-construction cluster may be scattered over a relatively

large area, so there is little to gain from using a larger

cluster size. Interviewer work loads were a second consideration. NHIS interviewers would be better able to handle

incremental work-load increases of four housing units as

opposed to some larger number.

Sensitivity analysis of design parameters

Upon completion of the research on the number of

PSU’s and cluster size, the Census Bureau prepared 21

design alternatives to study the sensitivity and impact of

changes in the design parameters on cost and variance, and

to verify that the results from the models used in the

research agreed with cost and variance estimates obtained

from fully specified designs. The 21 designs are shown in

table C.

The first six columns give the design number and the

parameters of that design. Both the multiframe and area

frame designs assumed that there would be a separate

new-construction frame. The variance ratios shown in the

next two columns were computed with values for the

number of NSR PSU’s and households and the cluster size

substituted into formula (1). For the upper bound an

intraclass correlation of 0.04 was used for all designs. This

gave a conservative estimate of the change in variance. The

lower bound accounted for changes in variances resulting

from improvements in sampling methods. This was a more

speculative estimate based on research for the NHIS and

other Census Bureau surveys. The between-PSU portion

was reduced by 20 percent to account for improvements in

the stratification procedure. The intraclass correlation for

the area designs was reduced to 0.035 because of the

noncompact segments, and the within-PSU portion was

reduced by an additional 1 percent to account for improved

within-PSU sampling methods.
13. . . . . . . . . 200 are a 8 61,400 137 1.00 0.94 4,653 3,476 609

14. . . . . . . . . 200 area 8 61,400 137 1.00 0.96 4,635 3,476 609

15. . . . . . . . 200 area 8 53,100 131 1.13 1.07 4,180 3.078 609

16. . . . . . . . . . 200 area 8 52,500 130 1.14 1.08 4,137 3,041 609

17. . . . . . . . 200 are a 8 51,500 129 1.16 1.10 4,100 3,014 609

18. . . . . . . . . 200 area 8 50,000 128 1.19 1.13 4,021 2,948 609

19. . . . . . . . . 222 area 6 56,700 112 1.00 0.95 4,585 3,439 609

20. . . . . . . . . 160 are a 6 59,800 120 1.00 0.94 4,559 3,377 609

21 . . . . . . . . 242 area 6 50,000 109 1.10 1.05 4,167 3.084 609

5 5

3 7

4 9

4 8

4 7

4 6

31

3 9

2 8

513 3 4

513 34

444 2 9

439 2 9

430 2 8

418 27

506 3 6

534 3 8

446 3 2

1 Design used by the National Health Interview Survey from 1973 through 1984.

The remaining columns in table C show the costs for

each design. The phase-in costs are the costs of recruiting,

training, and initial observation of the additional interviewers who must be hired before the implementation of the

new design. The implementation costs include research, the

selection of up to 10 years of sample, which is a small

portion of the costs, and all the preparatory work by field

staff above that normally required to select the first year’s

sample, as well as the costs associated with the newconstruction sample. The total phase-in and implementation costs have been spread over the lo-year life of the

sample to facilitate comparisons between designs.

The last column in table C was included to give an

estimate of the increase in travel due to using noncompact

clusters of housing units. It was separated from yearly

operating costs because of the very rough approximations

used.

Designs 1 through 4, all using the PSU’s of the 1973-84

NHIS, show the effect of moving to an area sample and

then to clusters of six and eight housing units. Phase-in

costs are low because only a few more new interviewers

would be required. Implementation costs are also relatively

low because use of the existing PSU’s results in some

savings.

Designs 5 and 6 are optimum designs under the constraint that the variance ratio upper bound equal 1.0. They

are no-travel designs for cluster sizes six and eight. Every

PSU would have a resident interviewer and there would be

no need for travel to PSU’s.

NCHS felt that to produce and analyze data for rural

areas, a minimum of 200 PSU’s was needed. Designs 7

through 12 are all 200-PSU designs with clusters of size six.

Design 8 is the same as 7 except that the PSU’s are a subset

of the 376 PSU’s of the 1973-84 NHIS. There is a savings

in phase-in costs because fewer interviewers would be

replaced, but the potential variance reductions (lower

bound) are less. Designs 9 through 12 would save roughly

$400,000 to $600,000; they do not maintain the reliability of

the 1973-84 design. Designs 13 through 18 are analogous

to 7 through 12 but have clusters of eight housing units and

larger samples.

The last three designs were attempts to increase efficiency by considering how the annual interviewing work

load is assigned. For example, in designs 7 through 12,

interviewers in NSR PSU’s would work in their home

PSU’s and one or two additional ones. Interviewers working

in only one other PSU have less work than those working in

two others and, therefore, may not be efficiently employed.

If the sample size in design 7 could be reduced and the

number of interviewers reduced by a proportionately

greater amount so that each interviewer in NSR PSU’s

worked in exactly two PSU’s requiring travel, and the

number of PSU’s were increased so that the variance ratio

remained 1.0, then perhaps the total costs would also

decrease. This strategy failed to work in design 19. Increased travel costs more than offset the savings in phase-in

costs. The opposite approach, increasing the sample size

and reducing the number of PSU’s, worked better. Interviewers in NSR PSU’s would work in their home PSU’s and

one other. The total costs for design 20 were less than those

of design 7. Design 21 used the approach of design 19 but

allowed a lo-percent variance increase. It compared

favorably with design 10.

21
strata that are homogeneous with respect to characteristics

of interest. This is expected to improve the precision of

those and related characteristics. The 1973-84 NHIS

shared the design of the CPS, including the PSU stratification, which was based primarily on demographic and labor

force variables.

For the 1985–94 design, NHIS PSU’s were stratified

independently of other surveys. The best stratifiers would

have been health variables, but health statistics were available only for sample PSU’s and could not be used as

stratifiers. Instead, variables that were highly correlated

with health variables were sought for stratifiers. Relationships between health variables and the 1970 Census of

Population and 1972 Economic Census variables that were

available for all PSU’s were examined. A set of 80 key

health variables from the 1978 NHIS was identified by

NCHS; 29 variables were selected from the 1970 Census of

Population and 3 variables from the 1972 Economic Census. Data from the 1980 census were not available in time

to be used during the research phase but were used in the

actual stratification process.

Potential stratifiers had to satisfy four conditions:

1. Stratifying variables must be available for all PSU’s.

2. They should be relatively highly correlated with health

variables.

3. The correlations should be stable over a period of

many years.

4. The stratifiers should be sufficiently variable between

PSU’S.

The correlation analysis (Hsen, 1983b; Ponikowski,

1982, 1983c) produced a set of census variables that were

most correlated with the NCHS key health variables. Because of the 6- to 8-year difference between the two data

sets, any high correlations that were observed were considered to be sufficiently stable over time. Variables had to

show sufficient fluctuation over the population PSU’s to be

considered useful. When potential stratifiers were highly

correlated with each other, only one of them was kept.

Percent Hispanic was added as a stratifier because of

22

Persons with Medicaid. . . -0.07 0.49 0.24 -0.29 0.20 0.14

Persons under 65 years of

age with no private

insurance . . . . . . . . . 0.23 0.45 0.25 -0.14 0.14 -6.24

‘Stratification variables are the following: 1 — Hispanic; 2 — persons below poverty level; 3 —

households with income less than $15,000 ($7,000 used for research); 4 — persons in urban

areas; 5 — unemployed persons; 6 — persons employed in manufacturing.

special interest in improving estimates for this

subpopulation.

Table D shows results of the correlation analysis for

selected health variables and the final stratification variables.

Number of sample PSU’s per stratum

In the 1973-84 design, one PSU was selected with

probability proportionate to size from each NSR stratum.

The between-PSU variance was estimated by collapsing

“similar” strata so there would be two observations per

superstratum. This biased the variance estimates by adding

a between-stratum component. A two-PSU-per-stratum

design would allow unbiased estimates of between-PSU

variance.

The drawback to a two-PSU-per-stratum design was

that only half as many strata would be formed as under a

one-PSU-per-stratum design. There might be an increase

in between-PSU variance because of a poorer stratification.

For the 1985-94 design two PSU’s were selected per NSR

stratum based on research that indicated that any increase

in the true between-PSU variance from a two-PSU-perstratum design would probably be small (Moore, 1984;

Ponikowski, 1983b).

PSU panels

One of the major requests from NCHS was that the

Census Bureau study the feasibility of forming panels of

PSU’s for the 1985-94 NHIS (Sirken, Bryant, and

Feldman, 1982). Panels of PSU’s are subsets of sample

PSU’s that can be used alone or in combination to provide

a national sample. Forming panels would facilitate linking
benefits from the panel construction. Four panels would

provide added flexibility. They could be constructed by

combining pairs of NSR strata and assigning each PSU to

a different panel. Then subsamples or reductions of approximately 25, 50, or 75 percent could be accommodated.

Therefore, a decision was made to construct four panels in

the 1985-94 design.

Overlap with other designs

Two possibilities for maximizing the overlap of NHIS

PSU’s with those of another design were investigated. One

possibility was to maximize the overlap with the 1973-84

NHIS. This would give the greatest chance of retaining

experienced interviewers. However, examination of this

possibility showed that the number of interviewers retained

would not have been much greater for maximum overlap

than for independent selection.

The second possibility was to maximize the overlap

with other Census Bureau surveys in order to share some of

the sampling costs. However, because NHIS and the other

surveys used different sampling techniques (area frame

versus census address lists), NHIS would not be able to

share very much of these costs. Furthermore, NHIS interviewers work exclusively on NHIS, so interviewing staffs

could not be shared.

The advantages of maximizing overlap were small at

best. One major disadvantage was that conditional probabilities for PSU selection would have to be computed in

order to make unbiased between-PSU variance estimates.

These probabilities would have been quite difficult, if not

impossible, to compute. A decision to maximize overlap

would have been inconsistent with the decision to choose

two PSU’s per stratum. Therefore, NHIS PSU’s were

chosen independently of other survey designs.

Selection of NHIS sample

PSU definitions

Primary sampling units in NHIS are generally single

counties or, more frequently, groups of contiguous

exception of a few MSA’s that cross regional boundaries.

Approximately 1,900 PSU’s were defined from the more

than 3,000 counties and equivalents; after stratification, the

sample PSU’s were selected from these.

Stratification and selection of PSU’s

Stratification was done within the four geographical

regions by MSA or non-MSA status, using 1980 Census of

Population data (Hsen, 1981a, 1983a, 1983b; Huang, 1982;

Ponikowski, 1982, 1983a, 1983c). The computer program

used for stratification was based on a clustering algorithm

developed by Friedman and Rubin (1967). The procedure

began with a random stratification within a specified number of strata. Pairwise exchanges were made between

PSU’s in different strata, and the resulting between-PSU

variance 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. For additional information on the

stratification program, see Kostanich et al. (1981).

The sample design adopted for the NHIS requires that

PSU’s with the largest populations be designated as selfrepresenting; each of the SR PSU’s is treated as a separate

stratum and is included in the sample with certainty. It also

requires combination of the remaining PSU’s into strata,

referred to as NSR strata, with the selection of two PSU’s

from each stratum. The 52 largest MSA’s were designated

as self-representing; the rest of the Nation was divided into

73 strata. A summary of SR and NSR strata by region is

given in table E.

Two PSU’s per NSR stratum were selected. The selection methodology allowed sampling without replacement

with probability proportionate to the projected 1985 population within the stratum (Ponikowski, 1983b).

Formation of PSU panels

Four national subdesigns, or panels, were constructed

from the 1985-94 design. The panels were made approximately equal in terms of the number of sample housing

units. NSR strata were paired within regions by combining

23
Population

Total ..................... . 108,631,714 14,595,388 30,751,544 47,380,179 15,904,603

Average ................... . 1,466,106 1,459,539 1,537,577 1,480,630 1,455,873

MSA population .............. . 52,779,849 8,716,181 13,958,993 22,206,138 7,898,537

Population outside MSA ......... . 55,351,865 5,879,207 16,792,551 25,174,041 8,006,066

NOTE: PSU is primary sampling unit; SR is self-representing; NSR is nonself-representing; MSA is Metropolitan Statistical Area as defined by U.S. Office of Management and Budget.

strata that were similar with respect to the original stratification variables. Then the four PSU’s in each combined

stratum were randomly assigned to panels one through

four.

To divide the SR area into panels, the PSU’s were

classified as small, medium, or large. Generally, small SR

PSU’s had a total population of less than 1.4 million

persons; medium, between 1.4 and 2.4 million; and large,

more than 2.4 million. Some exceptions were made so that

the number of SR PSU’s to be stratified within a region for

panel assignment would be divisible by four.

In the 14 medium-size SR PSU’s the weekly interviewer assignments were randomly divided into halves. The

halves were treated as separate PSU’s for panel assignment.

Small SR PSU’s and the medium SR PSU halves were

stratified within regions into groups of four using the same

stratification variables used for NSR PSU’s and a total

population variable. The small SR PSU’s were assigned to

panels within the new strata, and each half-sample in a

medium-size PSU was assigned to a different panel (Hsen,

1982b; Parsons, 1984a).

The weekly interviewer assignments in the 12 largest

PSU’s were randomly divided into four equal parts, each

part being assigned to a different panel. Table F shows

there were 12 large SR PSU’s contained in all 4 panels; 14

medium SR PSU’s, each in 2 panels; and 26 small SR and

146 NSR PSU’s in 1 panel each.

Selection of SSU’s

In the 1973-84 design, housing units in the Census

Bureau’s population surveys were selected in two steps.

First, census enumeration districts (ED’s), geographic

areas defined for use in the 1970 Census of Population

containing on average 350 housing units, were sorted

24

according to geographical variables. ED’s were sorted

within CBUR categories (C, the central city of an MSA; B,

the urbanized area not in category C; U, urban place, not

an urbanized area and not in category C; and R, all other

ED’s) by identification number, a geographical code that

tended to place contiguous ED’s together in the sort. ED’s

were then sampled systematically with a random start and

probability proportionate to size. The within-PSU sampling

interval identified sample clusters within the ED.

The redesign provided an opportunity to evaluate new

methods that would take into account specific needs of

individual surveys and still avoid duplication of sample

housing units in different Census Bureau surveys.

Census tracts, blocks, block groups, block faces, and

housing units were considered as alternatives to ED’s for

the sort unit. Blocks are usually well-defined rectangular

pieces of land bounded by streets, roads, streams, railroad

tracks, or other features. Data limitations at the block level

prevented evaluation of blocks as a sort unit. This also

applied to block faces, the housing units on one side of a

block, and to individual housing units. Use of block faces or

housing units would also have increased the cost and

complexity of sampling operations.

Table F. Distribution of self-representing (SR) and nonselfrepresenting (NSR) primary sampling units (PSU’s) by region

and type

Total SR PSU’S

number NSR

Region of PSU’s Large Medium Small PSU’S

All regions 198 12 14 26 146

Northeast 30 3 1 6 20

Midwest 51 3 4 4 40

South 84 4 4 12 64

Wes t 33 2 5 4 22
of the PSU was greater than 125,000 persons, or by proportion of owner-occupied housing if the PSU was smaller.

Within a cluster, ED’s were sorted by ED number, which is

based on geography.

The variables used to cluster ED’s were as follows:

1. CBUR.

2. Median value of owner-occupied housing.

3. Number of children under 6 years of age.

4. Total population aged 65 years and over.

5. Number of owner-occupied housing units.

6. Number of mobile homes or trailers.

7. Number of units lacking some or all plumbing.

8. Number of owner-occupied units of value less than

$40,000.

9. Number of renter-occupied units with rent less than

$200 per month.

10. Number of one-room housing units.

11. Total black population.

12. Total Hispanic population.

The last two variables were used only if the population was

greater than 5 percent of the PSU population.

Research had shown this procedure to be superior to

the sorting procedure used in the 1973-84 design. As most

of the within-PSU variance was due to the variance within

ED’s, and because the stratification of ED’s reduced only

the variance between ED’s (Kobilarcik, Statt, and Moore,

1982; Moore, 1982), the reductions in total within-PSU

variances due to the new sort are small, but nontrivial.

Selection of housing units

Within a sample PSU, sample ED’s are subdivided into

blocks or other small land areas with well-defined boundaries. One such block or “chunk” is designated for the

sample with probability proportionate to its estimated number of housing units. A Census Bureau interviewer visits

the area well in advance of the scheduled interview date

and creates a list of all the housing units in the sample

block. Several subsamples are systematically drawn so as to

form noncompact clusters of four housing units each. Clusters of four are formed in order to be compatible with other

Oversampling the black population

The sample size of a subdomain can be increased by

differential sampling within PSU’s. This means that part of

a PSU would be sampled at a higher rate (oversampled)

than the rest of the PSU. For NHIS, PSU’s with at least 5-

but with less than 50-percent black population were divided

into two unequal strata in such a way that the smaller

stratum contained the higher concentration of the black

population of the PSU. The sampling rate was increased in

the smaller stratum and decreased in the larger stratum so

that the total sample was the same as it would have been

without differential sampling.

This was accomplished iteratively. In the first trial

stratification, the smaller stratum included only one ED,

the one with the largest percent black population. In successive iterations, the ED with the next largest percent was

added to the smaller stratum. The process continued until

either the smaller stratum would become the larger stratum

if another ED were added, or until it contained all of the

black population in the PSU.

For each pair of strata the following quantities were

calculated. The index = 1 represents the stratum with the

high density black population; = 2 represents the

complement.

 = the population of stratum i (i = 1, 2)

 = the proportion of stratum i that is the black

population

 = the population variance for the total number

of black persons in stratum i

 = the population variance for a characteristic

total of interest in stratum i (total heads of

families was used in the research)

Without differential sampling, the sample size would

be where r is the sampling rate within the

PSU. To keep the same sample size with differential sampling, the following must hold:

where is the rate for stratum 

(2)

25
and that the design effect for the

teristic is

total population charac-

(4)

where and are population variances without

regard to strata.

For a given stratification, all variables are determined

except k. For a predetermined value of DE(z), k, and then

DE(y), can be calculated.

Research showed that most of the potential reduction

in DE(y) could be obtained with a value of 1.05 for DE(z).

Allowing increases to 1.10, 1.15, or more achieved only

small additional reductions in DE(y). For each trial stratification, DE(z) was set equal to 1.05. Then, the stratification

giving the lowest value for DE(y) was used for sample

selection.

In all, oversampling was used in 102 PSU’s. According

to preliminary estimates, the proportion of black persons in

the redesigned sample is 16 percent, whereas without oversampling it would have been 11 percent (Chen, 1981;

Mazur, 1983, 1984; Olsen, 1982; Tadros, 1982a, 1982b;

Waksberg, 1973).

Randomization of assignments

In general, efficient work loads in a calendar quarter

take the following into account. First, they give about 7 to 8

weeks of work for an interviewer. Second, they should not

exceed 24 housing units per week. In the 1973-84 design,

where clusters were an expected four housing units, the

typical interviewer assignment comprised four clusters in a

week. For the 1985-94 design, the clusters in a PSU

scheduled for interview in a calendar quarter were combined into weekly assignments of two or sometimes three

clusters each. Old-construction clusters were combined

clerically so that, ordinarily, a week’s interviews in a PSU

would be in the same county or in two adjacent counties.

The scheduling of interviewer assignments for specific

weeks is done so that each regional office will have approx26

weekly assignments as they occur. Sample balance is maintained to the extent possible, but restrictions on how these

clusters are scheduled may lead to a slightly poorer balance

than what existed at the beginning of the design. The main

difficulty is that a single new-construction cluster in a PSU

must be added to a week that already contains an assignment. If two new-construction clusters are to be added,

they may be placed in a week in which the interviewer has

no work in that or any other PSU.

References

Chen, I. 1981. HIS Redesign: Sampling Schemes to Improve

Small Domain Estimates. Internal memorandum from Inez Chen

to Rameswar P. Chakrabarty. May 28. U.S. Bureau of the Census.

Washington, D.C.

Friedman, H. P., and J. Rubin. 1967. On some invariant criteria

for grouping data. J. Am. Stat. Assoc. 62: 1159-1178.

Hsen, P. 1981a. HIS Redesign: Research on Order of PSU and

Strata Formations. Internal memorandum from Robert T.

O’Reagan to Gary M. Shapiro. June 29. U.S. Bureau of the

Census. Washington, D.C.

Hsen, P. 1981b. HIS Redesign: Definition of PSU’s—Number of

SR PSU’s Required. Internal memorandum from Rameswar P.

Chakrabarty to Gary M. Shapiro. Nov. 4. U.S. Bureau of the

Census. Washington, DC.

Hsen, P. 1982a. HIS Redesign: Definition of PSU’s—Potential

Problem Counties if these Counties Are Defined as PSUs. Internal memorandum for documentation. Oct. 25. U.S. Bureau of the

Census. Washington, D.C.

Hsen, P. 1982b. HIS Redesign: Methods for Forming Panels of

PSU’s. Internal memorandum from Rameswar P. Chakrabarty to

Gary M. Shapiro. Nov. 24. U.S. Bureau of the Census.

Washington, DC.

Hsen, P. 1983a. HIS Redesign: Stability of County Level Data for

Minority Stratifiers Within Four Urban Categories-South Region. Internal memorandum from Rameswar P. Chakrabarty to

Gary M. Shapiro. Mar. 25. U.S. Bureau of the Census.

Washington, D.C.

Hsen, P. 1983b. HIS Redesign: Followup of Correlation Analysis

Between the Census Variables and the Health Variables. Internal

memorandum for documentation. July 1. U.S. Bureau of the

Census. Washington, D.C.
from Rameswar P. Chakrabarty to Gary M. Shapiro. June 15. U.S.

Bureau of the Census. Washington, DC.

Mazur, C. 1984. Documentation of Oversampling Within the

Redesigned 1980 HIS PSU. Internal memorandum for documentation. April 25. U.S. Bureau of the Census. Washington, DC.

Moore, T. F. 1982. Modification to Recommended Procedure for

ED Sort. Internal memorandum to Larry Cahoon. Oct. 27. U.S.

Bureau of the Census. Washington, D.C.

Moore, T. F. 1984. HIS Redesign: Documentation of Decision To

Select Two PSU’s per Stratum. Internal memorandum from Gary

M. Shapiro to Charles D. Jones. May 18. U.S. Bureau of the

Census. Washington, D.C.

Moore, T. F. 1988. HIS Redesign: Derivation of Variance Formula for Comparing Alternate Designs. Draft based on internal

memoranda. U.S. Bureau of the Census. Washington, DC.

National Center for Health Statistics, J. Waksberg, R. H. Hanson,

and C. A. Jacobs. 1973. Reliability of estimates with alternative

cluster sizes in the Health Interview Survey. Vital and Health

Statistics. Series 2, No. 52. DHEW Pub. No. (HSM) 73-1326.

Health Services and Mental Health Administration. Washington:

U.S. Government Printing Office.

National Center for Health Statistics. 1982. Components of Variance From NHIS. Memorandum from Robert Casady to Rameswar P. Chakrabarty at U.S. Bureau of the Census. Apr. 1.

Hyattsville, Md.

Olsen, C. 1982. HIS Redesign: Differential Sampling to Achieve a

Reduction in Demographic Subgroup Variances. Internal memorandum from Rameswar P. Chakrabarty to Gary M. Shapiro.

May 3. U.S. Bureau of the Census. Washington, D.C.

Parsons, V. 1984a. HIS Redesign: A Method for Forming Panels

of PSU’s Internal memorandum from Rameswar P. Chakrabarty

to Gary M. Shapiro. Feb. 1. U.S. Bureau of the Census.

Washington, D.C.

Parsons, V. 1984b. HIS Redesign: Final Scheduling and Balance

of the Weekly HIS Assignments. Internal memorandum from

Rameswar P. Chakrabarty to Gary M. Shapiro. July 2. U.S.

Bureau of the Census. Washington, D.C.

Rogers, P. 1981. HIS Redesign: Description of Cost Data From

Field Division. Internal memorandum for documentation.

June 30. U.S. Bureau of the Census. Washington, D.C.

Shapiro, G. 1971. Optimal Segment Size for Health Interview

Survey. Internal memorandum from Joseph Waksberg to Monroe

Sirken. March 2. U.S. Bureau of the Census. Washington, D.C.

Sirken, M., E. Bryant, and J. Feldman. 1982. Recommendations

for the NHIS Redesign. Unpublished memorandum to Robert A.

Israel. Dec. 2. National Center for Health Statistics. Hyattsville,

Md.

Tadros, W. 1982a. HIS Redesign: Differential Sampling to

Achieve a Reduction in Demographic Subgroup Variances, II.

Internal memorandum from Rameswar P. Chakrabarty to Gary

M. Shapiro. Sept. 22. U.S. Bureau of the Census. Washington,

D C.

Tadros, W. 1982b. HIS Redesign: Estimating National Variance

Changes if Black or Spanish Persons Are Oversampled. Internal

memorandum from Rameswar P. Chakrabarty to Gary M.

Shapiro. Oct. 8. U.S. Bureau of the Census. Washington, D.C.

Tadros, W., T. F. Moore, and R. P. Chakrabarty. 1982. Determining the optimum number of primary sampling units to be selected

for the Health Interview Survey. Proceedings of the Survey Research Methods Section, American Statistical Association,

pp. 217-222.

Tadros, W. 1984. HIS Redesign: Combining Sample Measures

Into Weekly Assignments. Internal memorandum from Rameswar

P. Chakrabarty to Gary M. Shapiro. June 4. U.S. Bureau of the

Census. Washington, D.C.

U.S. Bureau of the Census. 1978. The Current Population Survey,

design and methodology. Technical Paper No. 40. Washington:

U.S. Government Printing Office.

Waksberg, J. 1973. The effect of stratification with differential

sampling rates on attributes of subsets of the population. Proceedings of the Social Statistics Section, American Statistical Association, pp. 429-434.

27
pg

(NCHS) focuses its attention upon making 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 the NHIS, referred to as sample elementary units, to a national level. In

either case, for each unit in the sample, a weight is assigned

that permits estimation of population totals. An estimator,

 for any given population characteristic total, can be

expressed as a weighted sum over all sample elementary

units, more precisely,

(1)

where u represents a sample elementary unit of the NHIS,

 is the characteristic or response for unit and

 is the NHIS national weight for unit This estimator is used to generate the NHIS estimates of totals,

percents, and rates. In the sections that follow, the technical

aspects of the procedures used in creating national weights,

and the procedures used in estimating the variances of

NHIS estimators, are discussed.

Conceptual design model for the

NHIS

Complex estimation techniques are required for the

NHIS because the survey is based upon a highly stratified

multistage probability sample. The development of these

techniques is facilitated by presenting the NHIS design,

discussed in detail in the previous chapter, in a somewhat

idealized framework. The conceptual NHIS design, presented below, will provide a structural model for the important design features.

The NHIS is a complex multistage probability sample

with the following design structure: The primary sampling

units (PSU’s) of the universe are partitioned into 125

strata: 52 self-representing (SR) strata and 73 non-selfrepresenting (NSR) strata. From this PSU universe, a

sample of 198 PSU’s is taken. All 52 PSU’s from the SR

28

selection probabilities will be denoted by the probability

that sample PSU i, i = 1,2, is chosen, and the joint

probability that sample PSU’s 1 and 2 are chosen.

For an SR stratum (or SR PSU) s, the certainty of

selection will be expressed = 1.

Within each PSU, a sampling universe for second-stage

sampling is created. As discussed in the section “Selection

of NHIS sample” of chapter III, the eligible housing units

of each sampled PSU are partitioned into as many as three

substrata:

Substratum 1: All housing units from the building permit frame. For some PSU’s this substratum is empty.

Substratum 2: All housing units in the area frame to be

oversampled. It is anticipated that a large proportion of the

population in this substratum will be black persons. For

some PSU’s this substratum will be empty.

Substratum 3: All housing units in the area frame not in

substratum 2. As substrata 1 and 2 may both be empty,

some PSU’s have all their housing units in this stratum.

The housing units of each substratum are grouped into

clusters to serve as secondary sampling units (SSU’s). The

expected cluster size is four housing units in substratum 1

and eight housing units in substrata 2 and 3.

A mean sampling interval (SI) for the U.S. target

population of SSU’s is determined. For a given sample PSU

i within stratum s, the mean sampling interval is 

but different sampling rates are used within each of the

three substrata. The sampling interval for substratum can

be expressed as where (oversample), and 1 (undersample). The values of and 

are chosen, in part, to satisfy the equation:

where and are the number of SSU’s in substrata 2

and 3, respectively.

Using the appropriate sampling interval, the sample

SSU’s are selected by a systematic sample using a random

start. Furthermore, the sampling of SSU’s within each

substratum is independent of the sampling of SSU’s within

any other substratum of any PSU.
define the national NHIS weight, for each sample

elementary unit. This national weight is the product of up

to four weighting factors:

1. Inverse of the probability of selection

2. Household nonresponse adjustment

3. First-stage ratio adjustment

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

the national household weight.

The first weight to be defined is the inverse of the

probability of selection. The probability that a given person

or household elementary unit is selected in the sample is

determined by the product of the conditional probabilities

of selection at each stage of sample selection: PSU within

stratum, SSU within substratum of sampled PSU, household within SSU, and person within household (for supplemental surveys). The reciprocal of this product, say is

the first component weight of Infrequently, this weight

will be modified. In the rare case in which less than 25

percent of the households of an SSU are subsampled, the

conditional probability of selection will be taken as 0.25.

This truncation of a weighting factor usually occurs in fewer

than 10 SSU’s a year, and thus the biases introduced by

such a modification should be small.

In an ideal hypothetical sampling situation having no

nonsampling error components-for example, frame problems, nonresponse, or interviewer effects-equation (l),

with substituted for 

(2)

will provide an unbiased estimator for the population total,

X. Such an estimator is referred to as a Horvitz-Thompson

estimator.

Historically, usually less than 5 percent of all eligible

NHIS sampled households do not respond. This household

nonresponse will most likely bias the estimator of equation

households in an SSU respond, this nonresponse adjustment is truncated to 2.0. Hence, the household nonresponse adjustment facto is defined

 = minimum (HE/HR , 2.0).

An illustration of this adjustment is given in table A.

The estimator produced by substituting the product of

WI and Wn, for Wf in equation (1),

(3)

should produce approximately unbiased estimators for the

population total, X. The weight is used to define the

final national weight, W,, for households and is used to

produce all NHIS estimates based upon household characteristics. For example, an estimate of the number of households below the poverty threshold that have a member with

limited activity status due to a health problem would be

computed using this weight. When person characteristics

are to be estimated, further weighting adjustments lead to

improved national estimates.

The third and fourth weighting factors to be defined

are ratio adjustments. Statistical sampling theory (Cochran,

1977) has demonstrated that in many situations the estimators obtained by a ratio estimation procedure are often

more reliable than the basic inflation weight estimators

expressed by equation (2). More precisely, if are

basic inflation weight estimators of two population characteristic totals, X and Y, respectively, and the “true” total Y is

known, then the ratio estimator will often

Table A. Adjustment for nonresponse

Households Eligible

SSU eligible households SSU

number 1

for infer-view not responding adjustment factor 1

1........ . 6 1.0000

2........ . 6 1 1.2000

3........ . 8 1.0000

4........ . 4 3 2.0000

1SSU stands for secondary sampling unit.

29
gp, 

sample size is large.

The ratio adjustment is also used to help correct survey

bias due to undercoverage. Historically, there has been

survey undercoverage of persons in the NHIS, and the

estimator of equation (3) will, in general, provide underestimates for many target population characteristic totals.

This bias due to undercoverage is often reduced by the use

of the ratio adjustments.

The first-stage ratio adjustment is used in an attempt to

reduce the between-PSU variance component of sampling

variation among the NSR PSU’s. For each of four raceresidence classes within the NSR strata of each of the four

geographical regions, the first-stage ratio adjustments are

defined using the following methodology (table B).

The 16 race-residency-region classes defined within the

NSR PSU’s will be indexed by the letter c, c = 1,2,. . . ,16.

Let

Zc = the projected 1985 census 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, the sample of NHIS

NSR PSU’s leads to an unbiased estimate of Zc, say 

where is the projected 1985 census total for class c in

sample NSR PSU i of stratum s.

The first-stage ratio adjustment factor associated with

class c is defined as

with the value being truncated if it falls outside the interval

[0.7,1.3]. Now, the only sampled individuals who receive the

first-stage adjustment as a weighting factor are those

individuals who reside in housing units of substrata 2 or 3 in

the NSR PSU’s. Those individuals who reside in housing

units of substratum 1, the building permit frame, of the

NSR PSU’s are not adjusted, as those housing units are not

included in the projected 1985 census NSR class totals. A

universal first-stage ratio adjustment, Wr1, can be defined

for each sample person by defining a new class index, c =

0, to denote all persons not receiving the ratio adjustment:

A first-stage ratio-adjusted national estimator, a

population total, X, is defined by equation (1) when the

weight Wf is replaced with the product of the first three

component weights: 

(4)

About 55 percent of the NHIS sample is collected from

residences that do not receive the first-stage ratio adjustment. Preliminary research has shown that the inclusion of

the first-stage ratio adjustment factor will have very little

impact upon NHIS estimates (Parsons and Casady, 1987).

The main advantages of the ratio-estimation process

are exploited by the introduction of the second ratio factor,

the poststratification adjustment weight. This weight assures that the NHIS estimates for 60 age-sex-race classes of

the civilian noninstitutionalized population of the United

States (shown in table C) agree with independently determined controls prepared by the U.S. Bureau of the Census.

Furthermore, the independent controls are the same ones

used for the Current Population Survey (CPS). Thus, the

national population estimates for any combination of the

age-sex-race 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 age-sex-race classes. Although

the NHIS is conducted weekly, the poststratification adjustment is computed only for NHIS quarterly estimates. The

30
 = 1 if person u is in class a

= 0 otherwise

then the second-stage ratio adjustment weight for class a,

 is defined as

The two-stage ratio-adjusted national estimator, of a

population total, X, is defined by equation (1) with the

weight defined by the product of the four component

weights: 

Variance estimation

Most of the estimates produced from the NHIS are

totals and ratios of totals, such as means, percents, and

rates. All such totals are produced using the final national

inflation weight as described in the preceding sections.

These estimates 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.

Equation (5), which generates the NHIS estimates,

permits very simple computation of the estimates; however,

the variances of such estimators are more difficult to

compute. The functional form of a variance estimator

depends upon the nature of the survey design and the firstand second-order estimation procedures. For the NHIS

both the multistage, unequal probability design and the

ratio adjustments complicate variance estimation methodology. Wolter (1985) and Rust (1985) present excellent

reviews of variance estimation for complex surveys. Of the

available methods, NCHS has chosen the

Taylor-linearization methods for use with the current design. Prior to the 1985 NHIS, the Balanced Repeated

p

first step is to define a variance estimator for an NHIS

estimator of a total where the sample weight is the inverse

of the probability of selection. Such a total is that expressed

by X0 in equation (2). This defines a linear estimator, and

its theoretical variance and an estimator of this variance can

both be expressed in a mathematically tractable form. The

theory for linear estimators will then lead to variance

approximations for ratio-adjusted totals.

Consider the general form of the four estimators, 

 given by equations (2) through (5):

(6)

where is a generic total based upon the generic weight

In order to present variance formulas, some notation

and definitions are needed.

The following indexes will be used to denote the nesting within the NHIS design:

31
For the estimator equation (6) with the

variance equation (7) will provide a slight overestimate (in

terms of mathematical expectation) of the theoretical variance of X0 whenever there is a 100-percent response rate.

For the estimator equation (6) with 

the variance equation (7) will provide a slight overestimate

(in terms of mathematical expectation) of the theoretical

variance if the household nonresponse adjustment can

be treated as the inverse of a probability of selection. This

artificial stage can be considered as the selection of responding households by means of a probability sample

within the SSU. Furthermore, it must be assumed that

person response within responding households is 100

percent.

The estimator equation (6) with 

 provides NHIS estimates almost identical to

those produced by X ́. The introduction of the first-stage

ratio adjustment appears to have little impact, and the

variance equation (7) should be adequate.

The estimator equation (6) with 

 provides NHIS person estimates. The

use of the variance equation (7) will usually overinflate the

sampling variance. For example, because estimates of poststratification age-race-sex classes are ratio-adjusted, the

variances of such estimators are zero. The use of equation

(7) may lead to nonnegligible variances.

The NHIS estimator X of equation (5) is not a linear

estimator; therefore, a mathematically tractable expression

for its variance does not exist. Woodruff (1971), however,

has suggested a procedure in which a nonlinear estimator

of total is “linearized” by a Taylor series approximation;

and then equation (7) is applied to this linear form to

produce a variance estimator for the original nonlinear

total.

Variances for ratio-adjusted NHIS totals

Of the two ratio adjustments, only the second stage has

any significant impact on the NHIS variance estimates

(NCHS, 1977; Parsons and Casady, 1987). Consequently,

pppy

poststratification cell.

3. Each person unit, u, has a second-stage ratio adjustment factor, corresponding to its class,

a. For this unit define the transformation:

4. The estimator of variance for is defined by

equation (7), but now the Y and terms are

computed using the weight specified in step 1

and replacing the elementary unit’s response, x(u),

with the transformed value as specified in

step 3.

Further discussion of this transformation has been

documented (Casady et al., 1987; Parsons and Casady,

1987).

Variances for ratios of totals

The linearization procedures easily extend to the ratios

of two NHIS totals. Suppose that and are two NHIS

ratio-adjusted-totals, and the ratio of the totals is expressed

as R = The variance estimator can be defined by

an extension of the algorithm above.

1. Steps 1, 2, and 3, above, are followed to produce

linear transformations for the numerator and denominator totals, and respectively.

2. The transformation for the ratio is then defined:

3. Step 4, above, is followed but uses as just

computed.

Equation (7) and the linearization transformations are

easily programmed.

Presentation of variances in NHIS publications

Many of the NHIS publications contain a very large

number of tabular estimates. Individual estimates of

32
, gpy ,

each class having the same a and b parameters.

If a large number of estimates of totals can be grouped

into classes satisfying the second assumption, then regression techniques can be used to estimate the parameters a

and b for the class.

NCHS uses this procedure in its Current Estimates

reports. First, the published totals are grouped by the main

variable of interest, for example, doctor visits, bed days, and

acute conditions. All subdomain totals are listed with the

main variable. Next, a representative sample of totals is

selected from the group. The methodology discussed in the

previous sections is then used to compute variances for

these sample totals. Finally, iterative regression techniques

are used to fit the model of the first assumption to these

computed values. The ultimate result is the estimation of

the a and b parameters for the group from which the

representative sample was taken.

As an illustration, in the 1985 Current Estimates report

(NCHS, 1986) it is reported that for the health variable

“total number of doctor visits” the a parameter is 0.000082

and the b parameter is 172,892.1. These parameters were

estimated by using a representative sample of 64 doctor

Hansen, M. H., W. M. Hurwitz, and W. G. Madow. 1953. Sample

Survey Methods and Theory, Vol. I, Methods and Application; Vol.

II, Theory. New York: John Wiley and Sons.

National Center for Health Statistics, G. Jones. 1977. HES Variance and Cross Tabulation Program, Version 2. Unpublished

documentation. Mar. 10. Hyattsville, Md.

National Center for Health Statistics, A. J. Moss and V. L.

Parsons. 1986. Current estimates from the National Health Interview Survey: United States, 1985. Vital and Health Statistics. Series

10, No. 160. DHHS Pub. No. (PHS) 86-1588. Public Health

Service. Washington: U.S. Government Printing Office.

Parsons, V. L., and R. J. Casady. 1987. Variance estimation and

redesigned Health Interview Survey. Proceedings of the Section on

Survey Research Methods, American Statistical Association, pp.

406-411.

Rust, K. 1985. Variance estimation for complex estimators in

sample surveys. J. 0ffic. Stat. 1: 381-397.

Wolter, K. M. 1985. Introduction to Variance Estimation. New

York: Springer-Verlag.

Woodruff, R. S. 1971. A simple method for approximating the

variance of a complicated estimate. J. Am. Stat. Assoc. 66:411–414.

Table D. Coefficient of variation (CV) for doctor visit totals by selected subdomains

Subdomain

All ages

Currently employed 

While, family income $9,000–$19,000

Living in MSA, 355–64 years of age

Male, 65-74 years of age

West Region, 18-24 years of age

Family income $9,000–$13.000, 25–34 years of age

Unemployed, 18-24 years of age

Black, family income under $9,000, under 5 years of age

1 Computed using linearization.

2 Computed using formula

3 MSA is metropolitan statistical area as defined by U.S. Office of Management and Budget.

Estimate Estimated Smoothed

of total cv 

1

cv 

2

1,230.868 1.32 1.49

481,816 2.03 2.10

209,880 3.77 3.01

118.396 4.16 3.93

53,246 4.80 5.77

27,892 8.80 7.93

16,256 11.04 10.35

7,175 13.34 15.55

4,740 14.60 19.12

33
SERIES 3.

SERIES 4.

SERIES 5.

SERIES 6.

SERIES 10.

SERIES 11.

SERIES 12.

SERIES 13.

SERIES 14.

statistical methodology including experimental tests of

new survey methods. studies of vital statistics collection

methods, new analytical techniques, objective evaluations

of reliability of collected data, and contributions to

statistical theory. Studies also include comparison of

U.S. methodology with those of other countries.

Analytical and Epidemiological Studies-Reports presenting analytical or Interpretive studies based on vital

and health statistics, carrying the analysis further than

the expository types of reports in the other series.

Documents and Committee Reports—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.

Comparative International Vital and Health Statistics

Reports-Analytical and descriptive reports comparing

U.S. vital and health statistics with those of other countries.

Cognition and Survey Measurement-Reports from the

National Laboratory for Collaborative Research in Cognition and Survey Measurement using methods of cognitive

science to design, evaluate, and test survey Instruments.

Data From the National Health Interview Survey—Statistics on illness, accidental injuries, disability. use of hospital, medical, dental. and other services, and other

health-related topics, all based on data collected in the

continuing national household interview survey.

Data From the National Health Examination Survey and

the National Health and Nutrition Examination Survey—

Data from direct examination, testing, and measurement

of national samples of the civilian noninstitutionalized

population provide the basis for (1) estimates of the

medically defined prevalence of specific diseases in the

United States and the distributions of the population

with respect to physical, physiological, and psychological characteristics and (2) analysis of relationships

among the various measurements without reference to

an explicit finite universe of persons.

Data From the Institutionalized Population Surveys—Discontinued in 1975. Reports from these surveys are Included in Series 13.

Data on Health Resources Utilization—Statistics on the

utilization of health manpower and facilities providing

long-term care, ambulatory care, hospital care, and family

planning services.

Data on Health Resources: Manpower and Facilities—

Statistics on the numbers, geographic distribution, and

characteristics of health resources Including physicians,

dentists, nurses, other health occupations, hospitals,

nursing homes, and outpatient facilities.

SERIES 20.

SERIES 21.

SERIES 22.

SERIES 23.

SERIES 24.

from the National Center for Health Statistics' health and

demographic surveys. Many of these releases are followed

by detailed reports in the Vital and Health Statistics

Series.

Data on Mortality-Various statistics on mortality other

than as included in regular annual or monthly reports.

Special analyses by cause of death. age, and other demographic variables; geographic and time series analyses;

and statistics on characteristics of deaths not available

from the vital records based on sample surveys of those

records.

Data on Natality, Marriage, and Divorce—Various statistics on natality, marriage, and divorce other than as

included in regular annual or monthly reports. Special

analyses by demographic variables; geographic and time

series analyses; studies of fertility; and statistics on

characteristics of births not available from the vital

records based on sample surveys of those records.

Data From the National Mortality and Natality Surveys—

Discontinued in 1975. Reports from these sample surveys

based on vital records are Included in Series 20 and 21,

respectively.

Data From the National Survey of Family Growth—

Statistics on fertility, family formation and dissolution,

family planning, and related maternal and infant health

topics derived from a periodic survey of a nationwide

probability sample of women 15-44 years of age.

Compilations of Data on Natality, Mortality, Marriage,

Divorce, and Induced Terminations of Pregnancy-Advance reports of births. deaths, marriages, and divorces

are based on final data from the National Vital Statistics

System and are published annually as supplements to the

Monthly VitaI Statistics Report (MVSR). These reports are

followed by the publication of detailed data in Vital Statistics of the United States annual volumes. Other reports

including induced terminations of pregnancy issued periodically as supplements to the MVSR provide selected findings based on data from the National VitaI Statistics

System and may be followed by detailed reports in VitaI

and Health Statistics Series.

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

reports published in these series, contact:

Scientlfic and Technical Information Branch

National Center for Health Statistics

Centers for Disease Control

Public Health Service

Hyattsville, Md. 20782

301-436-850 0
DHHS Publication No. (PHS) 89-1364. Series 2. No. 110