The 2006–2010 National

Survey of Family Growth:

Sample Design and Analysis

of a Continuous Survey

Series 2, Number 150 June 2010
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Lepkowski JM, Mosher WD, Davis KE, Groves RM, Van Hoewyk J. 

The 2006–2010 National Survey of Family Growth: Sample design and analysis 

of a continuous survey. National Center for Health Statistics. Vital Health Stat 

2(150). 2010. 

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Series 2, Number 150

The 2006–2010 National

Survey of Family Growth:

Sample Design and Analysis

of a Continuous Survey

Data Evaluation and Methods Research

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

Hyattsville, Maryland

June 2010

DHHS Publication No. (PHS) 2010–1350
National Center for Health Statistics

Edward J. Sondik, Ph.D., Director

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

Division of Vital Statistics

Charles J. Rothwell, M.S., Director
Contents 

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

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

Executive Summary........................................................................................ 1 

Development of the National Survey of Family Growth .......................................................... 2 

Design Specifications ...................................................................................... 4 

Sample Design............................................................................................ 5 

Overview of Sample Design ............................................................................... 5 

Detailed Sample Design .................................................................................. 6 

Implementing the Sample in a Responsive Design Context ...................................................... 14 

Sample Release Design ................................................................................... 16 

Sampling in Two Phases.................................................................................. 16 

Sample Weighting ......................................................................................... 17 

Overview of Sample Weighting ............................................................................ 17 

Detailed Weighting Procedures............................................................................. 18 

Item Imputation ........................................................................................... 23 

Overview of Item Imputation .............................................................................. 23 

Detailed Item Imputation Procedures ........................................................................ 24 

Variance Estimation........................................................................................ 25 

Overview of Variance Estimation ........................................................................... 25 

Detailed Description of Variance Estimation .................................................................. 25 

Summary of Variance Estimation Principles .................................................................. 26 

Variance Estimation Software .............................................................................. 26 

Sampling Error Computing Units........................................................................... 27 

National Survey of Family Growth Continuous Design: A Guide for Analysts......................................... 27 

Analyzing the Continuous National Survey of Family Growth as Periods of Data Collection........................... 27 

Frequently Asked Questions: Summary in Question—Answer Format ............................................. 29 

References ............................................................................................... 30 

Appendixes

I. Glossary .......................................................................................... 31 

II. Degrees of Freedom in Estimates from the National Survey of Family Growth ................................. 35 

Figures

1. History of the National Survey of Family Growth ........................................................ 3 

2. National Survey of Family Growth 2006–2010: selection of primary sampling units ............................. 7 

3. National Survey of Family Growth 2006–2010: sample allocation summary.................................... 9 

4. Within-household screener and household roster .......................................................... 13 

5. Within-household measures of size and illustration of within-household selection ............................... 14 

Text Tables

A. Sample sizes and design effects by selected characteristics.................................................. 4 

B. Screener propensity model predictors, 2006–2010 National Survey of Family Growth............................ 21 

C. Main interview propensity model predictors, 2006–2010 National Survey of Family Growth ...................... 22 

iii
Acknowledgments 

This report describes the sample

design of the 2006–2010 National

Survey of Family Growth (NSFG),

which uses a new continuous design,

and its implications for statistical

research. Subsequent reports in Series 2

will present detailed results on final

response rates, final weights, results of

imputation, and further guidance for

researchers intending to use the NSFG.

The survey was designed and conducted

by the National Center for Health

Statistics (NCHS) and its survey

contractor, the Institute for Social

Research (ISR), University of Michigan,

of Ann Arbor, Michigan. The sampling

plan was developed by Robert M.

Groves, Steven G. Heeringa, and James

M. Lepkowski of ISR, in consultation

with William Mosher and Karen Davis

of NCHS. The information in this

report is based on survey design

documentation prepared by staff of ISR

and on internal NCHS memoranda.

The 2006–2010 NSFG was jointly

planned and funded by the following

programs and agencies of the U.S.

Department of Health and Human

Services:

+	 The Eunice Kennedy Shriver

National Institute for Child Health

and Human Development (NICHD)

+	 The Office of Population Affairs

(OPA)

+	 The CDC’s National Center for

Health Statistics (NCHS/CDC)

+	 The CDC’s Division of HIV/AIDS

Prevention (DHAP/CDC)

+	 The CDC’s Division of Sexually

Transmitted Disease Prevention

(DSTDP/CDC)

+	 The CDC’s Division of

Reproductive Health (DRH/CDC)

+	 The Children’s Bureau of the

Administration for Children and

Families (ACF)

+	 The Office of the Assistant Secretary

for Planning and Evaluation

(OASPE).

NCHS gratefully acknowledges the

contributions of these programs and

agencies, and all others who assisted in

designing and carrying out the NSFG.

The authors of this report gratefully

acknowledge the comments of Lester R.

Curtin, Ph.D., of NCHS for his peer

review of the manuscript. This report

was prepared under the general direction

of Charles J. Rothwell, Director of the

Division of Vital Statistics (DVS), and

Stephanie J. Ventura, Chief, of the

Reproductive Statistics Branch (RSB).

The report was edited by Gail V.

Johnson, CDC/NCHM/Division of

Creative Services, Writer-Editor

Services Branch. Typesetting was done

by Zung T. Le and graphics were

produced by Odell Eldridge

(contractor), NCHS/OD, Office of

Information Services, Information

Design and Publishing Branch.

iv
The 2006–2010 National Survey

of Family Growth: Sample

Design and Analysis of a

Continuous Survey

by James M. Lepkowski, Ph.D., Institute for Social Research, University

of Michigan; William D. Mosher, Ph.D., and Karen E. Davis, M.A.,

National Center for Health Statistics; Robert M. Groves, Ph.D., and John

Van Hoewyk, Ph.D., Institute for Social Research, University of Michigan

Objective

The National Survey of Family

Growth (NSFG) collects data on

pregnancy, childbearing, men’s and

women’s health, and parenting from a

national sample of women and men

15–44 years of age in the United

States. This report describes the

sample design for the NSFG’s new

continuous design and the effects of

that design on weighting and variance

estimation procedures. A working

knowledge of this information is

important for researchers who wish to

use the data.

Two data files are being released—

the first covering 2.5 years (30 months)

of data collection and the second after

all data have been collected. This

report is being released with the first

data file. A later report in this Series will

include specific results of the weighting,

imputation, and variance estimation.

Methods

The NSFG’s new design is based on

an independent, national probability

sample of women and men 15–44

years of age. Fieldwork was carried out

by the University of Michigan’s Institute

for Social Research (ISR) under a

contract with the National Center for

Health Statistics (NCHS). In-person,

face-to-face interviews were conducted

by professional female interviewers

using laptop computers.

Results

Analysis of NSFG data requires the

use of sampling weights and estimation

of sampling errors that account for the

complex sample design and estimation

features of the survey. Sampling

weights are provided on the data files.

The rate of missing data in the survey

is generally low. However, missing data

were imputed for about 600 key

variables (called ‘‘recodes’’) that are

used for most analyses of the survey.

Imputation was accomplished using a

multiple regression procedure with

software called IVEware, available from

the University of Michigan website.

Keywords: survey methodology c

imputation c variance estimation c

continuous interviewing

Executive Summary

The National Survey of Family

Growth (NSFG) obtains detailed

information on factors affecting

childbearing, marriage, and parenthood

from a national probability sample of

women and men 15–44 years of age.

This Series 2 report describes the

procedures used to select the sample,

develop the sampling weights, impute

missing data, and estimate sampling

errors. This information should be useful

for those who intend to do statistical

research with NSFG data and for survey

methodologists who want to compare

their procedures to those used in the

NSFG.

This report is a significant departure

from previous NSFG Series 2 reports in

two ways. First, previous NSFG Series

2 reports have been released 3–4 years

after data collection ended, which meant

that many data analysts had to do their

initial analyses before the Series 2

report was available. This report, in

contrast, is being released to coincide

with the first release of data from the

continuous NSFG, so that NSFG data

users can use the information when they

are ready to do their initial analyses.

Second, in continuous interviewing,

specific results for each public-use data

file will be released as soon as they are

available on the NSFG website and in

subsequent Series 2 reports. This

procedure should deliver more

information into the hands of data users

in a timely way than in past NSFG

cycles. This report describes the sample

design and related topics for the first 4

years of the continuous NSFG.

The NSFG is designed and

administered by the National Center for

Health Statistics (NCHS), an agency of

the U.S. Department of Health and

Human Services, in response to Section

306 of the Public Health Service Act,

which directs NCHS to ‘‘collect

statistics on... family formation,

growth, and dissolution,’’ as well as

‘‘determinants of health’’ and

‘‘utilization of health care.’’

Accordingly, the purpose of the survey

is to produce reliable national statistics

on:

+	 Factors affecting pregnancy—

including sexual activity,

contraceptive use, and infertility

+	 The medical care associated with

contraception, infertility, and

childbirth

+	 Factors affecting marriage, divorce,

cohabitation, and adoption

+	 What women and men do to raise

their children

+	 Men’s and women’s attitudes about

sex, childbearing, and marriage

The 2006–2010 NSFG was

conducted by the University of

Michigan’s Institute for Social Research

(ISR) under a contract with NCHS. The

2006–2010 NSFG was the first time the

NSFG was fielded using a continuous

design. A ‘‘continuous design’’ means

that NSFG interviewing will be done

Page 1
Page 2 [ Series 2, No. 150

every year as long as funding and other

circumstances permit. The fieldwork

plan for the first continuous

interviewing sample called for

interviewing to be done in 4 years.

Interviewing for the 2006–2010 survey

began on or about July 1, 2006.

Interviews were conducted with a

national probability sample of women

and men 15–44 years of age living in

households in the United States. The

interviews were administered in person

by trained female interviewers using

laptop or notebook computers, a

procedure called computer-assisted

personal interviewing (CAPI). The

interviews were designed to average

approximately 80 minutes for women

and 60 minutes for men.

The 2006–2010 NSFG was based

on a sampling plan that was intended to

provide larger and more cost-effective

samples than ever before in the NSFG’s

history. The 2006–2010 national sample

was drawn from 110 major areas, or

primary sampling units (PSUs), divided

into four national subsamples. Each of

the four subsamples was worked for 1

year, so the entire 110–PSU design

could be completed in a 4-year period.

The entire 4-year data file is expected to

yield at least 5,000 interviews per year,

or 20,000 interviews in 110 PSUs for

the full 4-year time period (mid-2006 to

2010)—the largest sample in the

NSFG’s history.

Black, Hispanic, teenage, and

female respondents were sampled at

higher rates than others. Sampling

weights were used to compensate for the

different sampling rates of these various

groups, and for different nonresponse

rates. Sampling errors were estimated

using software that takes the weights

and the stratified cluster sample design

features into account. Such software is

now widely available in such packages

as SAS, Stata, SPSS, SUDAAN, and

others.

In addition, for key variables,

referred to in this and other NSFG

reports as ‘‘recodes,’’ item-missing

values have been replaced in the data

file by predicted or imputed values. The

imputed values in the approximately 600

‘‘recode’’ variables were identified with

a companion variable (or ‘‘imputation

flag’’) that indicates whether the value

for a particular case was imputed or

reported.

The rest of this report describes

how the sample was designed and

selected, how sampling weights are

computed and adjusted to compensate

for the different sampling rates and

other factors, and how missing data are

imputed for selected recodes. This report

concludes with a section on ‘‘NSFG

Continuous Design: A Guide for

Analysts,’’ which includes some

recommendations for those wishing to

do research with the NSFG. The section

includes 10 Frequently Asked Questions

(FAQ’s) to clarify these practical issues

for analysts further.

The first data release includes over

13,000 interviews conducted from about

July 1, 2006, through December 2008. It

is not expected that all readers will need

to read every section of this report. As a

result, the report is designed to describe

the 2006–2010 NSFG sample design at

three levels of detail:

+	 First, this Executive Summary and

the ‘‘Guide for Analysts’’ are for

readers seeking a general,

nontechnical understanding of the

survey procedures.

+	 Second, summaries for each major

section of the text are included to

provide somewhat more information

on each design feature.

+	 Third, in the rest of the report, the

full technical details are provided

for the interested reader. Thus, those

who read the entire report will find

some topics described first in less

detail in the section summaries and

then in full detail.

Development of the

National Survey of

Family Growth

The NSFG was established at the

National Center for Health Statistics

(NCHS) in 1971. Cycle 1 was

conducted in 1973 (1). Before then,

smaller national surveys of married

women were conducted by private

organizations in 1955 and 1960 (2,3). In

1965 and 1970, they were conducted by

university researchers with federal

funding (4,5).

As shown in Figure 1, the NSFG

has been conducted seven times since

1973 by NCHS. A ‘‘cycle’’ consisted of

planning, pretest, fieldwork, data

processing, file preparation, and

documentation for a single survey, but

the year given is the year the interviews,

or most of the interviews, were done.

Cycle 1 interviewing was conducted in

1973, when 9,797 women 15–44 years

of age were interviewed, making Cycle

1 the largest sample at that time for a

U.S. national fertility survey. Cycle 1 in

1973 and Cycle 2 in 1976 were

restricted to women who were currently

or formerly married and focused

primarily on pregnancy history,

contraceptive use, birth intentions,

marriage histories, and a variety of

social and economic characteristics (1,6).

Cycles 1 and 2 interviews were

conducted in either English or Spanish,

at the respondent’s choice. This practice

has continued through all NSFG

surveys.

NSFG Cycle 3 in 1982 expanded

the sampling frame to include all

women aged 15–44 regardless of marital

status, making it possible to study the

contraceptive use, sexual activity, and

use of family planning services of

unmarried women and teenagers as well

as the married population (7). Cycle 3

sampled teenage females (15–19 years

of age) at a higher rate to gain

information relevant to growing concern

over teenage pregnancy.

NSFG Cycle 4, fielded in 1988,

responded to important fertility-related

questions of the day, including detailed

questions on cohabitation, adoption, and

sexually transmitted diseases.

Specifically, new questions covering

respondents’ knowledge of chlamydia,

genital herpes, and AIDS-related

knowledge and behavior were

introduced in Cycle 4 (8).

Several changes were made in

NSFG Cycle 5, conducted in 1995, in

response to recommendations that the

NSFG should increase the number and

depth of measures used to predict

fertility-related variables. These changes

included the first oversample of

Hispanic women, and converting the

survey from paper-and-pencil
Cycle Year

Survey

contractor

Scope or

population

covered

1 1973 NORC Ever-married

women 15–44

Number

of

interviews

9,797

Source

of

sample

Independent

101 PSUs

Oversamples

Black women

Average

length in

minutes

60

Incentive

payment

No

2 1976 Westat Ever-married

women 15–44

8,611 Independent

79 PSUs

Black women 60 No

3 1982 Westat All women

15–44

7,969 Independent

79 PSUs

Black women

teens

60 No

4 1988 Westat All women

15–44

8,450 NHIS

156 PSUs

Black women 70 No

5 1995 RTI All women

15–44

10,847 NHIS

198 PSUs

Black women

Hispanic women

100 $20

6 2002 University of

Michigan ISR

Men 15–44

Women 15–44

12,571 Independent

121 PSUs

Blacks

Hispanics

teens

Men=60

Women=85

$40

Continuous 2006–

2010

University of

Michigan ISR

Men 15–44

Women 15–44

NOTE: PSU is primary sampling unit. NHIS is National Health Interview Survey.

5,000

per year

Independent

110 PSUs in

4 years

Blacks

Hispanics

teens

Men=60

Women=80

$40

Series 2, No. 150 [ Page 3

Figure 1. History of the National Survey of Family Growth

interviewing to computer-assisted

personal interviewing (CAPI) in an

effort to improve the consistency and

quality of the data. In addition, several

event histories were introduced to

increase the analytic usefulness of the

birth and pregnancy, contraception, and

marital histories the NSFG had always

collected. Those histories included an

education history, a work history, and a

cohabitation history. A life history

calendar was added to help organize all

the event history information. A file of

contextual data was also created,

allowing researchers to examine the

ways in which characteristics of the

place of residence—census tract, local,

or state—influence behaviors.

Information was also collected on

respondents’ sexual partners, the

wantedness of pregnancies, the

consistency of contraceptive use, the

circumstances under which first

intercourse occurred, and the use of

family planning services. On the

technical side, CAPI on laptop

computers replaced the paper and pencil

questionnaire, improving both the

quality and timeliness of the data.

Another innovation was the use of audio

computer-assisted self interviewing

(ACASI), in which respondents used

laptops to hear and read the most

sensitive questions and enter answers by

themselves (9,10).

In Cycle 6, conducted in 2002, the

sample was expanded to include males

15–44 years of age, with a new

questionnaire specially designed for

them. The male questionnaire and other

changes were informed by a set of

reports from survey experts as well as

by a large pilot study conducted in

2001. The extensive planning and

research for Cycle 6 have been

described elsewhere (11) (see

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

sr_01/sr01_042.pdf).

The experience of the 2002 NSFG

was used to inform the design of the

continuous NSFG, where a ‘‘continuous

design’’ simply means that NSFG

interviewing will be done every year

indefinitely, as long as funding and

other circumstances permit. The

fieldwork plan for the first continuous

interviewing sample was to complete

interviewing in 4 years. After that,

another sample would be drawn for the

next few years of interviewing. The

continuous interviewing design was an

innovation in the NSFG series motivated

by the need to produce larger sample

sizes with a constant budget, in the

face of increasing uncertainty in the

performance of large, complex

face-to-face surveys in the United

States. These uncertainties arise

because of unknown eligibility rates in

samples of U.S. addresses, unknown

contact and cooperation rates, and

various inefficiencies in the staffing

organization of large one-time field

effort designs. The 2002 NSFG

utilized over 250 interviewers over a

12-month period. This expansion

raised several challenges: quality

control with large sets of interviewers

working for only a few months is

difficult; the survey costs are

increased by the inevitable learning

curves that new interviewers

experience in the first months; attrition

among interviewers is more likely

with small workloads; and interviewer

workloads can be inefficient in size,

which reduces average interviewer

productivity.

To produce more predictable results

and to better control costs, NSFG

continuous interviewing used about 40

interviewers working consistently over

the year with workloads designed to
Page 4 [ Series 2, No. 150

maximize their productivity. The sample

design of continuous interviewing

(described in the following text)

attempted to deal with each of these

challenges to control costs and increase

quality.

The NSFG provides data needed by

several federal programs in addition to

NCHS, including:

+	 Programs of the Office of

Population Affairs (OPA). This

office is concerned with teenage

sexual activity and pregnancy, and

the use of Title X Family Planning

Services. OPA is the lead agency in

charge of the Healthy People 2010

and 2020 objectives on Family

Planning, most of which are based

on NSFG data.

+	 The Eunice Kennedy Shriver

National Institute for Child Health

and Human Development (NICHD)

uses the data to inform and shape

its extramural (grant) research

programs related to fertility and

reproductive health in the United

States and to provide a data

resource for private and

university-based researchers.

+	 The Centers for Disease Control

and Prevention’s (CDC) Division

of HIV/AIDS Prevention

(DHAP/CDC) and the Division of

Sexually Transmitted Disease

Prevention (DSTDP/CDC) need

reliable information on the sexual

and drug-use-related behaviors that

increase the risk of HIV and STD

transmission.

+	 Other programs also use and

support the NSFG, including the

Office of the Assistant Secretary

for Planning and Evaluation

(OASPE)—for a wide range of

data including data on men and

fatherhood; the Children’s Bureau

of the Office of Planning

Research, and Evaluation of the

Administration for Children and

Families (ACF)—for data on

adoption and related issues;

and the CDC’s Division of

Reproductive Health (DRH/CDC)—

for data on teenage pregnancy and

unintended pregnancy, among other

issues.

Design Specifications

The NSFG’s 2006–2010 sample

design was based on the following

objectives:

+	 The target population for the

continuous NSFG was to be the

household population of men and

women aged 15–44 in households in

the 50 states and the District of

Columbia.

+	 Screening interviews were to be

conducted in each sampled

household, to determine if anyone

15–44 years of age lived there and

if so, to select one person from the

household for the NSFG interview.

+	 The selection was to be random but

to sample certain subgroups at

higher rates. Those groups included

teenagers (15–19 years of age),

Hispanic men and women, and

non-Hispanic black men and

women.

+	 Data collection was to be conducted

only by in-person, face-to-face

interviewing, with the respondent’s

privacy and confidentiality ensured.

+	 Questionnaires and interviews were

to be available in English and

Spanish.

+	 Interviews were expected to last an

average of 80 minutes for women

and 60 minutes for men.

+	 Given the sensitivity of the

interview content, all interviewers

were to be female. That is, men and

women were to be interviewed by a

female interviewer.

+	 Data collection was to be completed

using CAPI. One section of the

questionnaire was to be administered

using ACASI, in which the

respondent would listen to a

pre-recorded audio reading of the

questions with a headset connected

to the laptop. The questions were

also to be displayed on the computer

screen. Survey instruments were to

be programmed on laptop computers

meeting specified requirements.

+	 The available funds appeared to

allow a sample of approximately

4,400–5,000 interviews per year,

yielding 17,000–20,000 interviews

over a 4-year data collection period.

Of these, about 45 percent were to

be males and 55 percent females;

about 20 percent teenagers 15–19

years of age; about 20 percent

Hispanic; and about 20 percent black

or African American.

+	 The following sample sizes and

design effects were expected

(Table A).

+	 The sample was to be designed so

that any number of individual years

could be combined to form a

nationally representative sample for

analysis. Practical considerations,

such as sample size and design

effects, however, made it likely that

more than 1 year of data would be

needed for most analytical purposes.

+	 Signed informed consent was

required for every selected eligible

respondent 15–44 years of age.

Minors 15–17 years of age were

required to have the signed consent

of a parent before being asked for

their own signed assent.

+	 Interviewers were to ask questions

about fertility, contraceptive use,

sources and types of family planning

services, and maternal and child

health using structured questions

similar to those used in the 2002

Table A. Sample sizes and design effects by selected characteristics

Characteristic Sample N Design effect

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17,600–20,000 3.9

15–19 ................................ 3,500–4,000 1.5

Race

Hispanic ............................... 3,500–4,000 1.6

Black or African American . . . . . . . . . . . . . . . . . . . . 3,500–4,000 1.6

Sex

Male . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,900–9,000 2.2

Female ............................... 9,700–11,000 2.8
Series 2, No. 150 [ Page 5

(Cycle 6) NSFG, and read by an

interviewer (in the CAPI part of the

interview) or heard on a headset (in

the ACASI part of the interview).

+	 The contractor, in cooperation with

NCHS, was to design and

implement procedures for measuring

and controlling the quality of data

collection and data preparation

procedures, including verification of

a sample of interviews.

Sample Design

Overview of Sample Design

The first step in the sampling

procedure was to select a national

sample of 110 primary sampling units

(PSUs). PSUs are counties or groups of

adjacent counties. This national sample

was divided into four parts, each of

which was a nationally representative

sample. The fieldwork plan envisioned

working the sample over a 4-year

period, so one of these four national

quarter samples was used each year. As

this report was written, an average of

about 5,500 men and women were being

interviewed each year.

The PSUs used in each annual

quarter sample consisted of the eight

largest metropolitan areas in the United

States (which become eight individual

PSUs in the sample and remain in the

sample each year) and 25 smaller

metropolitan and nonmetropolitan PSUs

that change each year. (In the first year,

two additional PSUs were included in

the sample for purposes of more

detailed representation of the United

States, for a sample of 35 PSUs in the

first year, so the number of PSUs was

35 the first year, 33 the second year, 33

the third year, and 33 the fourth year,

for a total of 110.) The national sample

of 110 PSUs, which is used over a

4-year period, includes PSUs in most

states.

From each PSU, secondary units,

called segments, were selected.

Segments are, roughly, neighborhoods or

groups of adjacent blocks. In each

selected segment, one of two procedures

has been used to obtain a housing unit

sample. In most segments, segment

addresses were obtained from a

commercial source or from prior Cycle

6 listed addresses, visited by interviewers to check and correct list

accuracy, and selected by central office

staff at ISR for screening and

interviewing. In other segments,

interviewers visited the segment and

listed all housing units (‘‘from

scratch’’). A sample of addresses is

selected from the interviewer list by ISR

central office staff. More details on the

stages of sample design can be found in

the ‘‘Detailed Sample Design’’ section.

The sample housing units were then

contacted, and a ‘‘screener’’ interview

attempted, in which the persons living at

that address (including persons living

away from the household in a college

dormitory, sorority, or fraternity) were

listed. If more than one eligible person

15–44 years of age was living at the

address, one person was randomly

selected and asked to do an interview.

The NSFG sample design consisted

of five stages of selection to choose

eligible sample persons. Women, teens

15–19 years of age, and black and

Hispanic persons are selected at higher

rates, yielding an oversample of such

persons.

The 2006–2010 NSFG sample

design started with the same national

sample of PSUs used in the 2002

(‘‘Cycle 6’’) NSFG national sample

design. The same sample of PSUs was

used in 2002 and in 2006–2010 because

it reduced the cost of sample selection for

the NSFG, and because gains in precision

could be achieved when comparing Cycle

6 to continuous NSFG findings when the

same PSUs were used.

The PSU selection in the

2006–2010 NSFG began with using the

U.S. Census Bureau division of the

entire land area of the United States into

3,141 counties and county-equivalent

units. These units were either used

individually or grouped to form 2,402

PSUs. In most cases, a PSU was a

single county. But for large metropolitan

areas, counties were grouped together by

the U.S. Census Bureau to form what

the Bureau calls metropolitan statistical

areas. In the 2002 and the 2006–2010

NSFG sample design, then, a county in

a metropolitan area was grouped with

other counties in the same metropolitan

area to form a PSU. These 2,402 PSUs

were based on the 2000 Census

definitions of the units.

Following the creation of the PSUs,

a process called stratification was used

to partition the PSUs into three major

groups or strata: 28 large metropolitan

areas, 290 other metropolitan areas, and

2,084 nonmetropolitan areas. The 28

large metropolitan areas are referred to

as self-representing (SR) areas. SR areas

are those that have such large populations that a national sample of the size

used for continuous NSFG virtually

required that they be represented. As

such ‘‘certainty’’ selections, the sample

from each of these areas represents only

those areas. That is, the sample from

these 28 PSUs represents only the

population of that area. Hence, in the

sampling literature, these types of units

are referred to as representing only

themselves, or ‘‘self-representing.’’

The remaining 2,374 PSUs are

called non-self-representing (NSR)

areas. A sample of the NSR PSUs was

selected so that each sample PSU

represented itself and other NSR PSUs

of a similar nature. In order to make the

representation more complete, the NSR

PSUs were further grouped by

geography and population size into 82

sets or strata. Each NSR stratum had

two or more PSUs, and some strata had

more than 100 PSUs. The number of

PSUs in a stratum varied because the

strata were created to have approximately equal 2000 Census population

across the PSUs.

In total, the 2006–2010 NSFG

consisted of 2,402 PSUs grouped into

110 strata. Twenty-eight of the strata

contained a single PSU—the SR strata

(with one PSU each). The remaining 82

strata contained two or more PSUs each,

one of which was selected. (In other

words, in the SR strata the single PSU

was certain to be selected). In the NSR

strata, one PSU was selected from those

within the stratum to represent the entire

stratum.

The selection of the 82 NSR PSUs

from the NSR strata was, for purposes

of sample size control, selected with a

technique called ‘‘probability

proportional to size.’’ This technique

uses information about the number of

housing units or number of persons to
Page 6 [ Series 2, No. 150

select PSUs. In particular, PSUs with

larger populations have higher chances

of selection, and those with smaller

populations have lower chances. Within

a PSU, the sample is selected with

chances that are inversely proportionate

to those used to select the PSU. The

PSU and within PSU chances are

carefully calibrated in the sample

selection to eliminate any over- or

under-representation of the population.

This result is referred to as equal chance

selection, or a self-weighting sample.

A single PSU was selected from

each of the 110 strata in the 2006–2010

NSFG national sample. The sample of

110 PSUs was then divided into four

fully representative national samples to

allow new samples to be introduced

each data collection year, to reduce

interviewer and field staff workload; to

control costs more effectively; and to

allow accumulation of sample and data

across years to yield larger sample sizes

across longer time periods.

Each annual national quarter

sample consists of the eight largest SR

metropolitan areas (referred to as the

‘‘super eight PSUs’’) that were in the

sample in each of the 4 years; 5 of the

20 SR metropolitan areas, and 20

(or 22) NSR metropolitan and

nonmetropolitan PSUs from the 82 NSR

PSUs. One national quarter sample

was chosen at random and without

replacement for each year of

interviewing. Interviewing began

effectively July 1, 2006 (a few

interviews were conducted in late

June).

In the second stage of selection,

census blocks were stratified into four

domains within each PSU, and the

housing units on those blocks were

listed. The four domains were defined to

obtain oversamples of black and

Hispanic persons. They were:

1.	 Nonminority

2.	 More than 10 percent black persons

but less than 10 percent Hispanic

persons

3.	 More than 10 percent Hispanic

persons but less than 10 percent

black persons

4.	 More than 10 percent black persons

and more than 10 percent Hispanic

persons.

To select the national sample for

each data collection year, blocks were

chosen within each domain with

probabilities proportionate to an

estimated number of Census 2000

occupied housing units. Small blocks

with only a few occupied housing units

were linked with other blocks to form

‘‘segments’’ large enough to support

efficient data collection workloads. In

NSR PSUs, 12 segments were selected

for a single year of data collection.

Larger numbers of segments were

selected in most of the SR PSUs, where

a larger number was needed from larger

PSUs. Within both SR and NSR PSUs,

the segments selected for a given data

collection year were randomly divided

into four sets. One set was released and

completed in each calendar quarter

(beginning July 1, October 1, January 1,

and April 1).

The housing units on the selected

blocks were listed. (The process of

obtaining lists of addresses is described

in detail in the section ‘‘Field listing of

segments.’’) Then the housing unit lists

were uploaded to ISR and checked by

central office staff for consistency and

accuracy.

The third stage of selection

chooses housing units from the list of

addresses available in each sample

segment. Housing units in census blocks

in which more than 10 percent of the

population was black or Hispanic

persons (domains 2, 3, and 4) are

selected at higher rates to increase the

number of black and Hispanic persons

in the sample. Data for selected housing

units are downloaded to the

interviewers’ laptop computers for the

next stage of sampling, and

interviewing.

The fourth stage of sampling is

the selection of eligible persons within

sample households. Interviewers visit

housing units selected in the third stage,

and when the housing unit is found to

be occupied, attempt to list all persons

living there. One eligible person is

chosen randomly in every household

containing one or more eligible persons.

The within-household selection used

measures of size that are pre-assigned in

the sample design process. These

measures of size vary by gender, age

group, and race and ethnicity. Teenagers

15–19 years of age and females receive

larger measures of size, and thereby

increased chances of selection within the

household, in order to yield larger

sample sizes required for the target

sample sizes for these groups. Persons

within households are selected with a

probability proportionate to a measure

of size. An example of this unequal

probability sampling procedure is

described in the section on ‘‘Detailed

Sample Design.’’

Persons living away from home in

college or university dormitories,

sororities, and fraternities are considered

to be part of their parent’s household

and listed in them. If a college student

is chosen, she or he is transferred to an

interviewer working in the nearest PSU

to the college or university for

interviewing. In many cases, college

student interviews are obtained at the

sample household at times when

selected students return home for

summer or holidays.

The fifth stage in sample selection

occurs in each 12-week ‘‘quarter’’ of

interviewing: the selection of the

‘‘double sample’’ (because it is a sample

of a sample). After 10 of the 12 weeks

of data collection in each 12-week

quarter, a set of selected housing units

has not been successfully screened or, if

successfully screened, the sampled

person has not been interviewed yet.

Nearly all such housing units have been

contacted, but not all have been

screened. To increase representation of

these types of housing units in the final

sample, a sample of about one-third of

these remaining addresses was selected

for interviewing at the end of the 10th

week. For the last 2 weeks of the

quarter, interviewer assignments were

reduced to housing units in the second

phase sample only. Interviewers

concentrated on a smaller number of

housing units for the final 2 weeks of

data collection.

Detailed Sample Design

The following sections describe the

sampling process in the 2006–2010

NSFG in more depth. The steps are

shown graphically in Figures 2 and 3.
Series 2, No. 150 [ Page 7

20 SR PSUs 

NSR MSA 

PSUs (290) 

Non-MSAs 

(2,084) 

Create nonMSA strata 

(30) 

NSR MSA 

PSUs (50) 

NSR non-MSA 

PSUs (32) 

NSFG PSUs 

(110) 

Select 1 

PSU per 

stratum (28) 

Select 1 

PSU per 

stratum 

Select 2 

linked PSUs 

in one stratum 

Create NSR 

MSA strata 

(50) 

20 Next 

largest 

MSAs 

Super 8 

PSUs (8) 

NOTE: PSU = primary sampling unit; MSA = metropolitan statistical area; SR = self-representing; NSR = non-self-representing; and super 8 = 8 largest 

PSUs. 

SR PSUs 

(28) 

PSU frame 

(2,402 PSUs) 

8 Largest 

MSAs 

Figure 2. National Survey of Family Growth 2006–2010: selection of primary sampling units

First stage: Selection of

primary sampling units

Sampling frame

The U.S. Census Bureau in 2000

divided the land area of the United

States into 3,141 counties and county

equivalents (Louisiana parishes; Alaska

boroughs and census areas; independent

cities in Maryland, Virginia, Missouri, and

Nevada; and the District of Columbia).

The ISR grouped these counties into 2,402

PSUs. These PSUs served as the sampling

frame for the first stage selection in the

2006–2010 NSFG.

The U.S. Census Bureau also

designates counties as part of metropolitan areas. A total of 1,057 counties

were grouped into 318 MSAs consisting

of two or more geographically adjacent

counties. The ISR PSU sample

designated these MSAs as separate

metropolitan sampling units. The

remaining 2,084 nonmetropolitan

counties are treated as individual

sampling units. This yields a set of

2,402 PSUs in the ISR sample.

The same set of PSUs was used for

the 2002 NSFG and the 2006–2010

NSFG. This was done to increase

quality and reduce costs of data

collection by employing interviewers in

both the 2002 and the 2006–2010

NSFG, and using housing unit lists that

had been prepared for the third stage of

selection in 2002 that had not been

selected in the 2002 NSFG in the

2006–2010 NSFG. In addition, the

overlap of PSUs between the 2002 and

2006–2010 NSFG allows gains in

precision when comparing results from

the two surveys.

The PSU selection for the 2002

NSFG thus serves as the PSU sample

for the 2006–2010 NSFG. One potential

disadvantage of this approach concerns

the timing of PSU selection for the 2002

survey. The PSUs for the 2002 NSFG

had to be selected in 2001, before

detailed 2002 data collection planning

began. ISR staff preferred to use 2000

census counts for occupied housing

units in the selection of the PSUs. Such

counts were not available at the time of

the selection. Rather than delay the start

of 2002 data collection, 1990 census

counts were used to select the PSUs.

Subsequent investigation indicated that

the use of the more out-of-date counts

in the selection led only to minor losses

in precision (and no bias) in estimates

from the 2002 NSFG. Thus, the

importance of timely data collection
Page 8 [ Series 2, No. 150

necessitated the use of 1990 census

counts in the 2002 NSFG. These counts

were the basis as well for the PSUs in

the 2006–2010 survey. There is a minor

loss in precision (or increase in

variance) for both surveys because there

were changes in the U.S. population,

particularly for the Hispanic population,

between 1990 and 2006. However, these

losses in precision were compensated in

part through the stratification and

allocation of segments across domains

within PSUs (domains were defined by

percent black or Hispanic; see ‘‘Second

stage: Selection of segments’’), where

domains are defined by the proportion

of the segment’s population that is

Hispanic or black. The allocation within

PSUs was used to obtain larger samples

of Hispanics, and improved the

efficiency of the stratification of PSUs

by using more up-to-date measures of

size.

The PSUs varied substantially in

size, from as few as 25,000 to as many

as 3.2 million 1990 occupied housing

units. The 28 largest MSAs each had

more than 600,000 occupied housing

units, and were so large that they were

chosen with certainty for the sample.

These 28 MSAs constituted 28 strata in

the ISR national PSU sample. Each

contained exactly one PSU, and that

PSU was ‘‘selected’’ into the sample

with a probability of 1. Since these

PSUs are the only PSUs in each of their

strata, they represent only themselves,

and are referred to as SR units.

The remaining 2,374 PSUs were

designated as NSR because a sample of

82 PSUs was to be selected from among

them. Thus, each one of the 82 NSR

PSUs would represent itself and other

PSUs from the same stratum, in the

national sample.

The 2,374 NSR PSUs consisted of

290 metropolitan and 2,084

nonmetropolitan PSUs. These PSUs

were grouped into the nine census

geographic divisions defined by the U.S.

Census Bureau state groups. Within

divisions, PSUs were further grouped by

ISR into 52 MSA and 30 non-MSA

strata on the basis of population size

and geography. For example, in the East

North Central division, MSA PSUs were

grouped into 10 strata, ranging from

large MSAs in one state to small MSAs

in several states. The remaining

non-MSA PSUs (all individual counties)

were then grouped into four

geographically contiguous sets

consisting of 60–80 PSUs each. From

the 82 such MSA and non-MSA strata, a

single PSU was selected. Figure 2

summarizes the national PSU sample

selection.

The 110 PSUs selected for the

2006–2010 NSFG national sample were

for purposes of identification grouped

into four types:

1.	 The eight largest metropolitan areas

among the 28 SR strata.

2.	 The remaining 20 largest SR

metropolitan areas.

3.	 Fifty-two NSR but also

metropolitan areas.

4.	 Thirty NSR but nonmetropolitan

areas.

This grouping was then used to

divide the sample of 110 PSUs into four

fully representative national samples for

the 2006–2010 NSFG. The four annual

national samples allow new samples to

be introduced in each of 4 consecutive

years of data collection. The smaller

national samples provided opportunity to

more effectively monitor field data

collection and cost, and operate with a

smaller central office staff. These four

national samples have allowed ISR to

make changes, as required, to data

collection, survey questions, and other

design features once each year, and have

nationally representative samples that

could be combined across years. The

national samples also can be accumulated across years to yield larger sample

sizes across longer multiyear time

periods.

Each annual national quarter

sample consists of:

+	 All eight of the largest SR

metropolitan areas, referred to as the

‘‘super eight PSUs,’’ that were,

because of size, always in the

sample.

+	 Five of the remaining 20 SR

metropolitan areas selected carefully

to represent the full set of 20 in

each year.

+	 Twenty (or 22, in the first year)

NSR metropolitan and

nonmetropolitan areas selected to

represent the full set of 82 NSR

PSUs in each year.

One national quarter sample was

chosen at random for the first year of

interviewing, which began about July 1,

2006. A second annual (quarter) sample

was selected, without replacement, for

the year beginning July 1, 2007, a third

for the year beginning July 1, 2008, and

a fourth for the year beginning July 1,

2009.

Second stage: Selection of

segments

Stratification of blocks

The second-stage sampling units

were census blocks, or linked

combinations of census blocks that had

sufficient numbers of households to

sustain efficient survey data collection.

For each of the 110 PSUs in the NSFG

sample, a list of blocks was obtained

from 2000 census data (which were

available for the 2006–2010 NSFG and

for the 2002 NSFG sample). Census

blocks have varying numbers of

occupied housing units—some blocks

having few, if any, occupied housing

units. These small blocks pose problems

for efficient screening and interviewing.

In order to obtain units of sufficient size

for efficient data collection, larger

blocks or smaller blocks linked to one

another to comprise larger units were

designated in each PSU. These units are

called segments. The minimum size for

a segment was 75 occupied housing

units in metropolitan areas or 50 in

nonmetropolitan areas. Linked blocks

were always within the same block

group boundaries to reduce the chance

that geographically widespread blocks

were joined together. The segments were

the sampling units for the second stage

of selection.

Segments were grouped within each

PSU into four strata or domains based

on the percentage of black or Hispanic

occupied housing units in the segment

(see Figure 3). Domain 1, the

nonminority stratum, consisted of those

segments with less than 10 percent black

or 10 percent Hispanic persons in the

2000 census. Domain 2 contained

segments with at least 10 percent black

occupied housing units, but less than
Series 2, No. 150 [ Page 9 

Calendar quarter 

sample 1 

(approximately 3 

segments) 

Select 

approximately 12 

segments per PSU 

Interviewer ‘update’ 

or ‘scratch’ listing 

of addresses 

Select housing 

units 

Select eligible 

persons 

Screening 

Main interviewing 

Domain 1 

(nonminority) 

Domain 2 

(>10% Black 

population) 

Domain 3 

(>10% Hispanic 

population) 

Domain 4 (>10% 

Black and Hispanic 

population) 

NSFG PSUs (110) 

National quarter 

sample 

1 (35 PSUs) 

NOTE: PSU is primary sampling unit. 

National quarter 

sample 

2 (33 PSUs) 

Randomly 

selected 

quarter sample 

National quarter 

sample 

3 (33 PSUs) 

National quarter 

sample 

4 (33 PSUs) 

Calendar quarter 

sample 2 

(approximately 3 

segments) 

Calendar quarter 

sample 3 

(approximately 3 

segments) 

Calendar quarter 

sample 4 

(approximately 3 

segments) 

Figure 3. National Survey of Family Growth 2006–2010: sample allocation summary
Page 10 [ Series 2, No. 150

10 percent Hispanic; domain 3 contained

segments with at least 10 percent

Hispanic but less than 10 percent black

occupied housing units; and domain 4

contained segments with at least

10 percent black and at least 10 percent

Hispanic occupied housing units. The

race and ethnicity prevalence rates were

computed at the census block group

level to reduce classification of

geographically contiguous census blocks

into different domains.

Black and Hispanic persons were to

be oversampled among the final sample

of persons in the 15–44 years of age

range. Probabilities of selection of

segments were varied across domains to

increase the chances, and the sample

sizes, of black and Hispanic persons in

the sample. Higher rates were used for

domains 2, 3, and 4, yielding a potential

increase in sampling variance, or loss of

effective sample size, through weighting

to compensate for the unequal sampling

rates.

After simulation of design

alternatives and examination of sample

sizes in target groups and effective

sample sizes, sampling rates for domains

2, 3, and 4 were set 200–250 percent

higher than the rate for domain 1.

In the 2006–2010 NSFG, exactly 12

segments were selected in 80 of the

nonmetropolitan NSR sample PSUs. In

the remaining two PSUs, slightly fewer

segments were selected, based on the

size of these PSUs. The 28 largest PSUs

received an allocation of segments that

was proportionate to size, with the

smallest receiving approximately 12

segments and the largest more than

twice as many. (The allocation of the

number of segments within a PSU and

within a domain is described in the

following text.)

Within a PSU, one-quarter of the

segments allocated to each PSU in the

yearly sample were selected in each

12-week data collection quarter. If there

were exactly 12 segments in each of the

33 PSUs in a yearly sample, there

would be 3 segments in each PSU in

each quarter, or an expected 99

segments in a calendar quarter sample.

However, since the eight largest and the

five metropolitan SR PSUs had on

average more than 12 segments because

of their larger populations, the number

of segments in a calendar quarter is

approximately 110 segments. Over the

entire year, approximately 440 segments

were in the 2006–2010 NSFG sample.

Over the 4 years, some ‘‘super 8’’

segments that have large numbers of

housing units in a block within the

segment were selected more than

once across the years. Across the

2006–2010 period, somewhat less than

4 × 440 = 1,760 segments are being

selected. The final number of segments

was approximately 1,600 over the 4-year

data collection period.

Segment domain allocation

The number of segments to be

selected from each domain is

determined prior to the national quarter

sample release each data collection year.

The number of segments in a domain in

a PSU depends on the distribution of

occupied housing units in the PSU

across the ‘‘super 8,’’ other SR

metropolitan PSUs, and the NSR

metropolitan and nonmetropolitan PSUs.

It also depends on an allocation for the

stratum from which the PSU was

selected that is proportionate to the

number of occupied housing units in the

stratum in each segment domain.

For example, suppose a given NSR

PSU a is in stratum h, and that the

proportion of occupied housing units in

the stratum across domains 1, 2, 3, and

4 is 0.75, 0.15, 0.05, and 0.05,

respectively. If the 12 segments to be

selected from stratum h (and PSUa)

were allocated proportionately to these

sizes, the allocation would be 9, 1.8,

0.6, and 0.6. However, the sample from

domains 2, 3, and 4 must be selected at

a rate that is, say, 1.5 times larger than

that from domain 1 in order to achieve

target sample sizes by race and

ethnicity. The allocation must thus be

adjusted to account for the differences

in the relative sampling rates across the

domains. The allocation must thus be

made proportionate to occupied housing

unit proportions and a domain sampling

rate. In this illustration then, the

allocation becomes 9 × 1 = 9;

1.8 × 2.5 = 4.5; 0.6 × 2.5 = 1.5; and

0.6 × 2.5 = 1.5, respectively, across the

four segment domains. The segment

counts of 9, 4.5, 1.5, and 1.5 are now in

the correct proportion to achieve the

expected sample distribution, but they

total 16.5 instead of the desired 12.

The segment domain counts are

then adjusted to sum to 12, or

9 × (12/16.5) = 6.54; 4.5 × (12/16.5) =

3.27; 1.5 × (12/16.5) = 1.09; and

1.5 × (12/16.5) = 1.09, respectively.

These final noninteger values are

rounded to integer values across PSUs

to maintain the distribution required for

target sample sizes. In this illustration,

7, 3, 1, and 1 are to be selected from

domains 1, 2, 3, and 4.

In some PSUs, there was not a

sufficient number of segments available

to support the target sample size in a

particular domain, because the PSU in

the sample from the stratum had a lower

proportion of domain population than

did all the PSUs in the stratum. In most

cases when this occurred, the number of

allocated segments in a PSU differed

from the target number by one. This

allocation was adjusted in a PSU in

another stratum from the same region

and of the same size (metropolitan or

nonmetropolitan). The allocation for a

PSU with insufficient numbers of

segments in a given domain was

decreased by one in the domain, while

the allocation in the PSU from a similar

region and size stratum was increased

by one in the same domain.

Selection of segments

Once blocks are linked and segment

sample sizes determined for a PSU, a

sample of segments is selected from

each PSU. Within a PSU domain,

census blocks are ordered

geographically by tract, block group,

and block number. Segments were

selected within a PSU separately for

each domain, systematically with

probabilities proportional to the relative

number of 2000 census occupied

housing units in each segment (see the

following text), a method used widely in

survey samples to select ‘‘clusters’’ of

unequal size (see, for example, Chapter

7 in reference 12).

Since the same PSUs were being

used for the 2006–2010 NSFG as were

used for the 2002 NSFG, another

opportunity to reduce costs was used at

the segment selection stage. In many

PSUs listed housing units were available

for segments selected for the 2002
Series 2, No. 150 [ Page 11

survey. Using these listed housing units

was expected to reduce the cost of

listing for the 2006–2010 NSFG. The

segment-level selection method

described in the following text was

modified to allow the maximum overlap

between the 2002 and the 2006–2010

NSFG segments within a PSU (13). This

overlap reduced the number of segments

that required completely new lists of

housing units to be prepared.

Within a domain in each PSU,

census blocks are ordered

geographically by tract, block group,

and block number. Segments are

selected within a PSU separately for

each domain. The selection is systematic

with probabilities proportional to the

relative size of each segment, the

Census 2000 count of occupied housing

units in a segment. For each segment in

an ordered list of segments in a domain

within a PSU, the cumulative number of

occupied housing units is calculated for

each segment, from the beginning to the

end of the list. A selection interval for

the systematic selection is computed by

dividing the sum of Census 2000

occupied housing units for the domain

within the PSU by the number of

segments to be selected from the

domain.

A random number is generated

between 0 and the selection interval.

The first selected segment is identified

by finding the segment in the ordered

list with a cumulated size that exceeds

the random number. To obtain the

second selection, the sampling interval

is added to the random number used to

make the first selection, and the next

segment in the list with a cumulative

size that exceeds this sum is selected.

For the third selection, the sampling

interval is added to the sum for the

second selection, and the next segment

in the list with cumulative size

exceeding this new sum is selected. The

selection continues, adding the sampling

interval to the last sum to find the next

selection, until the domain allocation of

segments is achieved.

For example, suppose that four

segments are to be selected in a domain

in a particular PSU, and that the Census

2000 count of occupied housing units

for all the segments (blocks) in the

domain is 100,000. The segments (and

blocks) in the domain are sorted

geographically by county and tract

within the PSU, and a cumulative count

of occupied housing units assigned to

each segment from first to last. Suppose,

for instance, that in the sorted list of

segments, the first segment has 75

occupied housing units in the 2000

census, the second has 100, the third

150, the fourth 50, and the fifth 100.

Then the cumulative size for each is

75, 75 + 100 = 175, 175 + 150 = 325,

325 + 50 = 375, and 375 + 100 = 475,

respectively. The accumulation continues

through the sorted list until all segments

in the domain have a cumulated count

assigned. The last segment in the list

has a cumulated count of 100,000.

The sampling interval for the

domain is computed as 100,000/4 =

25,000. A random number between 0

and 25,000 is generated, say 425. Then

the first selected segment is obtained by

finding the first segment with a

cumulated count that exceeds 425. In

the example, that’s segment 5 (with 100

occupied housing units at Census 2000,

and cumulated count 475). The interval

25,000 is then added to the random

‘‘start’’ of 425: 25,425. The segment

with cumulated count that first exceeds

25,425 is then selected as the second

segment from the domain. The interval

is added to 25,425 again, and the

segment with cumulated count

exceeding 50,425 is selected. And

finally, the interval is added one more

time, and the segment within the

cumulated count exceeding 75,425 is

selected. Four segments have then been

selected, each with probability

proportionate to the Census 2000 count

of occupied housing units in the

segment. Only four selections will be

made with this procedure, since in the

example, for the random start of 425,

and for all other random starts, adding

the interval to the fourth or last sum

produced a number that was greater than

the final overall cumulated count of the

domain in the PSU, 100,000 in the

example.

As noted previously, this selection

procedure was modified to maximize the

overlap between sample segments in the

2002 and 2006–2010 samples to take

advantage of the already available

listings in some segments used in 2002.

Details of the modification are given in

reference 13. This selection procedure

chose about 35 percent of the segments

in the 2006–2010 NSFG from segments

listed in 2002.

The combination of the geographic

order and systematic selection produced

a sample with a desirable property: the

selected segments ‘‘represented’’ the

entire geographic region of all segments

in that domain in the PSU. That is, this

process effectively gave the NSFG a

geographic stratification of segments

within each PSU and domain. And

stratification in sample selection,

when done in this manner, can reduce

sampling variance for estimates obtained

from the final sample results. Reduced

sampling variance was expected in final

sample estimates because of the

stratification produced by the systematic

selection of segments from the

geographically ordered list.

Segments were created and selected

the year before the sample PSU was to be

used in the national sample. Selected

segments were divided into four

approximately equal-sized groups, one

each for the calendar quarters beginning

July 1, October 1, January 1, and April 1.

Field listing of segments

Housing unit lists were prepared for

each selected segment. These segment

lists came from one of three sources. In

about 35 percent of the segments,

unused housing unit listings from the

2002 survey were available. These were

sent to the field for an ‘‘update.’’ In the

remaining 65 percent of the segments, a

list of addresses for housing units was

obtained from a commercial vendor of

the U.S. Postal Service’s Delivery

Sequence File (DSF). Not all segment

address lists from the commercial

vendor could be efficiently used. In

about 15 percent of all the segments

(about 20 percent of the segments where

addresses were purchased from the

vendor), the number of addresses was so

low that the addresses were insufficient

for subsequent field checking. These

were typically in rural areas where

either Post Office boxes or rural

delivery addresses (without street

number and street name) were present.

For these segments, an interviewer was

sent to do an original ‘‘scratch’’ listing
Page 12 [ Series 2, No. 150

(see the following text). Finally, for the

remaining 50 percent of all segments,

sufficient address counts were obtained

from the commercial vendor. The

addresses in these segments were sent

for an ‘‘update.’’

Maps were created to guide an

interviewer to the exact location of the

blocks in the segment. Maps were

generated through commercial software

using the Census Bureau’s TIGER

(Topologically Integrated Geographic

Encoding and Referencing system) files

to delineate block boundaries. Typically

three levels of maps were created for

each segment: a large-scale view

showing the location of the segment

relative to major highways and streets;

an intermediate-scale view showing the

segment relative to major streets; and a

detailed smaller-scale view showing

individual blocks in each segment.

Instructions were prepared for either

updating or ‘‘scratch’’ listing each of the

blocks in a segment, including a starting

place to begin the listing and a direction

to proceed around the block.

The available addresses in a DSF or

housing units in segments listed (but not

used) for the 2002 NSFG were sorted in

a clockwise order, starting in the

northwest corner of each block in each

segment. Maps and the sorted address

list were loaded into a listing application

on the interviewers’ laptop computer.

Interviewers visited the addresses in the

calendar quarter before the segment was

scheduled to be used in the sample.

Interviewers determined whether the

housing units or addresses in the sorted

list were physically present in the

segment. If an address was not present,

interviewers deleted it from the list.

Interviewers also added housing units to

the sorted list when found in the

segment but not appearing on the list.

Interviewers also reordered the list when

they found that the list order did not

match the physical order of the

addresses in the segment.

For the remaining 20 percent of the

segments, where the DSF addresses

were too few to justify preparation of an

update, a ‘‘scratch’’ listing was done.

Interviewers were sent to visit the

segment with the segment maps and a

blank listing provided on their laptop

computer. Interviewers visited the

segment and listed all housing units

within the segment in the blank listing.

They followed the same order as used

for the update listing, starting in the

northwest corner of each block in the

segment and moving around the block

in a clockwise manner.

The update and ‘‘scratch’’ listings

were downloaded to ISR as completed.

At this point, all listings were

considered to be housing units (as

opposed to DSF addresses that are not

housing units until verified in the field

listing). Each segment is checked for

completeness and consistency before the

third-stage selection of housing units.

Third stage: Selection of

households

The third-stage random selection of

housing units is made from the segment

housing unit list. A listing of an

individual housing unit at this point is

sometimes referred to as a sample

‘‘line.’’ In order to sample lines and

assign them to field data collection, a

within-domain sampling rate was

determined to meet allocation for the

interviewer’s monthly sample.

Screening and missed housing units

The selection of housing units is

continued in the household screening

operation in the field. Screening consists

of a short questionnaire administered at

the doorstep of every housing unit

selected for the sample.

Based on ISR experience with listed

housing units, 15 percent are expected to

be unoccupied or not actually housing

units (for example, a housing unit

converted to a commercial building). In

addition, among occupied housing units,

the March 2004 and 2005 Current

Population Survey (CPS) indicated that

38 percent were expected to have no

eligible persons ages 15–44 years of

age.

Screening was the process to

determine whether the selected housing

units were occupied, and then whether

any eligible persons resided in the

occupied housing unit. On average, an

interviewer had 15 selected housing

units from every 100 that were

unoccupied, and among the 85 occupied

housing units she was expected to find

62 percent (about 53 of the 100 selected

housing units) with one or more persons

ages 15–44 years.

During the screening phase of the

survey, interviewers were trained to

check again for housing units that may

have been missed in the updating or

‘‘scratch’’ listing process. Such missed

housing units may occur when an

interviewer overlooked a structure with

one or more housing units, or missed a

part of a structure that was a separate

housing unit. Missing units may also

occur if a housing unit is constructed

since the listing took place.

Interviewers were equipped with a

sample management system

(SurveyTrak) on their laptop computers

that contained all housing units in each

segment. The procedure for handling

missed housing units in the field was as

follows: at each sample housing unit

designated in the SurveyTrak listing,

interviewers checked to be sure that all

housing units following the selected

housing unit on the list were present,

and checked for mail boxes, doors, or

utility meters that might indicate a unit

that was not listed. They were instructed

to ask screener respondents about any

additional housing units in the structure.

If an additional housing unit not on the

list were discovered between a sample

housing unit and the next listed unit, the

interviewer added it to the SurveyTrak

list, and then attempted a screening

interview with the additional units.

When more than two housing units

were missing from the SurveyTrak

listing, interviewers were instructed to

suspend work for that sample housing

unit, including contact with the

household, and to call the ISR’s

sampling unit to receive further

instructions. Before calling, interviewers

were to obtain a list of all additional

housing units associated with the sample

housing unit. The ISR central office

staff then subsample the original and the

additional housing units. This creates

unequal probabilities of selection of

housing units within each domain, and

so weighting adjustments to account for

the missed housing unit subsampling are

incorporated into final weights.
Series 2, No. 150 [ Page 13

Fourth stage: Selection of

eligible persons

The last stage of sample selection

was conducted within the household

during the screening activities. An adult

member of the household was asked to

provide a list of all persons living in the

household. Information on the gender,

age, and race and ethnicity of each

person was recorded in the screening

portion of the interview (see Figure 4).

Interviewers asked additional questions

to be sure no one was missed,

particularly college students living away

from home at a dormitory, fraternity, or

sorority. (College students living away

from home in their own apartment or

housing unit were covered by the

household frame, and were not

considered to be part of their parents’

household.) Dormitory residents were

indicated on the household listing.

If no one in the household was

between the ages of 15 and 44 years,

the household was not eligible to be part

of the NSFG sample. If one or more

eligible persons were found, the

computer-assisted screening system

made a selection of one eligible person

in the household. That is, an eligible

person was selected within each

household that had an eligible person.

. .

Figure 4. Within-household screener and household roster
Age

and

sex

Female:

15–19

20–24

25–44

Male:

15–19

20–24

25–44

Illustration:

Black

1.00

0.94

0.87

1.00

0.90

0.68

Hispanic

1.00

0.94

0.84

1.00

0.57

0.47

White

or

other

0.84

0.84

0.28

0.84

0.47

0.12

Race

and

ethnicity Sex Age

Measure

of size

Cumulative

measure

of size

Random

number

(from 0 to

3.45)

Black Female

Black Female

Black Female

Black Female

Black Female

Black Female

6

15

40

10

21

42

0.00

1.00

0.87

0.00

0.90

0.68

- - ­

1.00

1.87

- - ­

2.77

3.45

← 0.95

- - -Category not applicable.

Figure 5. Within-household measures of size and illustration of within-household

selection

Page 14 [ Series 2, No. 150

Subsampling by age, gender, and race

and ethnicity

The sampling rates used within

households for eligible persons varied

by age, gender, and race and ethnicity,

since the selection is carried out by a

computer application. This system

allowed SRC and NCHS staff to achieve

target sample sizes more precisely in the

face of uncertainty about rates of

eligible persons in the population.

The within-household selection

procedure assigned a ‘‘measure of size’’

to each age-eligible person in the

household. The measure of size was 1

of 12 values, 1 for each of 12 age,

sex, and race and ethnicity subgroups of

interest (see Figure 5 for an illustration).

The measures were numbers between

zero and one. Larger numbers assigned

to a subgroup increased the chances that

persons in that subgroup would be

selected for interviewing. Thus, larger

numbers were assigned to teenagers

15–19 years of age so that larger

numbers of teenagers would be selected

for interviewing, and sample sizes

increased. Slightly larger numbers are

also assigned to females to increase the

number of females relative to males in

the final sample.

The specific measures of size values

were developed through a simulated

sample selection from concatenated CPS

data for 2004–2005 to generate the

desired sample sizes by subgroups by

age and gender. For each member of a

household in the concatenated CPS data

file, measures of size were assigned and

a subject selected. The measures were

adjusted, subgroup by subgroup, to

achieve target sample sizes. The rates

were then applied to the continuous

NSFG within-household selection.

Once each eligible person was

assigned a measure of size, the sizes

were cumulated. A random number from

zero to the sum of the measures in the

household was generated by the

computer-assisted sample screener

application. The first listed person

whose cumulative measure of size

within the household exceeds the

random number is selected. The chance

of selection of the person is thus

proportionate to their measure of size in

the household.

The lower panel of Figure 5

illustrates how the selection would be

executed in a hypothetical household

with six persons: the black female age

15 has a measure of size of 1.0, which

exceeds the random number of 0.95, so

she was chosen to be interviewed. By

assigning larger measures to teens (i.e.,

those ages 15–19 years) and females

within the household, larger samples of

these individuals are achieved. The

selection probabilities within the

household depend on the distribution of

measures of size for other eligible

persons in the household.

Implementing the Sample

in a Responsive Design

Context

Surveys with high response rate

goals and limited budgets such as the

NSFG need a way to stay informed on

the level of key survey design

parameters such as completed

interviews, eligibility rates, response

rates, expenditures, and interviewer

productivity. In most surveys, the

information systems that provide such

data are designed to provide some data

daily and other data at the end of the

data collection period. There is seldom

an opportunity to make changes to a

survey design based on this information

between the start and end of data

collection.

In contrast, the NSFG’s information

systems allowed survey design changes

throughout the data collection. The

information systems provided daily data
Series 2, No. 150 [ Page 15

on such features as how many

interviewer hours and how much money

were being spent on data collection,

what areas and interviewers were having

good and poor results, and what types of

nonresponse were most prevalent in an

area. ISR uses a system called

SurveyTrak to provide this daily

information.

NSFG project staff used SurveyTrak

and other information systems to

manage data collection to keep within

budget and meet survey data quality

targets. The key variables in

implementing responsive design are the

information systems that supply the data

on a daily basis and the trained staff to

use them. Thus, any survey done at any

pace could implement responsive design.

A continuous interviewing system,

which spreads the data collection tasks

over time, however, offers more

opportunities to use information systems

to adapt sample size and data collection

procedures to survey conditions.

The 2006–2010 NSFG design relied

on a set of five sampling levels: the

PSU, the segment, the housing unit, the

person within the housing unit, and the

second-phase (or ‘‘double’’) sample. The

sample levels permitted design changes

at regular intervals throughout data

collection, where sample sizes could be

varied depending on survey information

about eligibility rates, response rates,

and interviewer performance. These

features constituted most of the design

elements of the survey’s ‘‘responsive

design’’ that were used to control

sample size and response rates.

The first step of the responsive

design process was to set sample size at

the first level (the PSU) on the basis of

an annual review of SurveyTrak data.

Each annual quarter sample was fielded

in one data collection year starting on

about July 1 of 4 successive years.

These national samples could have

sample size targets computed based on

the most recent data available on

expected interviewer workload and past

interviewer performance in the same

PSU, or a similar PSU, PSU-specific

eligibility rates, and past or estimated

PSU-level response rates.

Within an annual national sample,

there was a further selection at the

second level within the selected PSUs:

the segment selection. As described

previously in more detail, census blocks

within the PSU from the 2000 Census

of Population and Housing are divided

into four groups, or domains, on the

basis of the concentration of black and

Hispanic populations within each block.

Separate samples were selected from

each domain to allow 12 or more

segments to be selected in most PSUs.

(The eight largest PSUs often had

samples greater than 12 segments, and

some smaller nonmetropolitan PSUs had

fewer.) These 12 segments were selected

according to probability sampling

procedures. The 12 segments were

randomly allocated into four sets of

approximately three segments each, and

each set was assigned to a sample for

one 12-week quarter, released starting

July 1, October 1, January 1, or April 1

in each data collection year. As for the

annual national quarter samples, the size

of sample from each segment in each

quarter could be adjusted based on

SurveyTrak data to reflect interviewer

workload and performance, expected

eligibility rates, and expected response

rates.

The third level sample selection

was a housing unit sample within the

segments selected for use in a given

calendar quarter. In each selected

segment, one of two procedures was

used to list housing units before

selection. In the ‘‘update’’ segments,

addresses were obtained from a

commercial source or an unused list

prepared for the 2002 NSFG, visited by

interviewers to check and correct list

accuracy, and selected by central office

staff at ISR for screening and

interviewing. In the ‘‘scratch’’ segments,

interviewers visited the segment and

listed all housing units, and a sample of

the updated or listed addresses was

selected from the interviewer list by ISR

central office staff. The sample selection

rates and cluster sizes could be varied

across segments depending on the

housing unit yield of the listing

operation in order to yield a number

of housing units in each interviewer’s

assignment for the calendar quarter to

match interviewer efficiency (hours

per interview, described in the

following text) as well as expected

response, occupancy, and eligibility

rates. The determination of selection

rates within PSUs, and thereby within

segments, is described in detail in the

next section.

The fourth level of selection was

the random choice of one person 15–44

years of age within the household.

Interviewers visited selected housing

units in assigned segments starting at

the beginning of the calendar quarter. A

household roster was generated

containing a list of all persons who

usually resided in the household. If one

or more of these was 15–44 years of

age, one age-eligible person was

selected with varying probabilities in

each of these households in the person

level sample. If the selected person was

present at the time of this screening

visit, the interviewer attempted to

interview him or her. Otherwise,

interviewers collected information to set

a time to revisit the household to

interview the selected person. Sample

persons were offered $40 as a token of

appreciation at the time of selection.

The within-household selection

probabilities could be varied from one

calendar quarter sample to the next to

achieve target sample sizes for key

subgroups defined by age and gender.

A fifth level of selection that

provided an opportunity to respond to

design circumstances as well occurred at

the 10th week in each calendar quarter.

At that time, some selected housing

units had not been successfully screened

or had been screened but a completed

main interview has not been obtained. A

sample, typically about one-third, of

these remaining addresses were selected

for Phase 2 sample interviewing during

the last 2 weeks of the calendar quarter.

Effectively, interviewer assignments

were reduced so that interviewers

concentrated on a smaller number of

housing units and selected persons for

the final 2 weeks of data collection in

the 12-week quarter. This within-quarter

level of selection was particularly useful

to control final response rates and costs

for the overall sample in the calendar

quarter.

SurveyTrak data were again used to

adjust the sample size in each segment.

Prediction models, based on SurveyTrak
Page 16 [ Series 2, No. 150

data generated daily, were used to group

housing units that had not been

completed by the end of week 10 into

sets with varying levels of predicted

propensity to respond. Housing units

were selected from each group, and the

selected housing units constituted the

Phase 2 sample released to each

interviewer at the beginning of the 11th

week of data collection. Somewhat

larger tokens of appreciation were used

in Phase 2 to encourage higher levels of

participation. At the end of the quarter, a

weighted response rate was computed

across the two phases of data collection

and data generated to allow assessment

of the extent to which nonresponse bias

was being reduced by the addition of

Phase 2 interviews. A more detailed

description of the Phase 2 design and

procedures can be found in the

following text, in the section,

‘‘Sampling in Two Phases.’’

Sample Release Design

With each annual and each calendar

quarter sample released, key design

parameter estimates were altered to

change sample allocation across

segments for each calendar quarter. The

sample sizes were varied by PSU and

by quarter to respond to changing

survey conditions. Thus, the sample size

of lines or persons in each quarter was

not fixed—it could be adjusted based on

the most recent available data.

The primary parameter in the

allocation at the PSU and segment level

was the interviewer workload.

Interviewers were recruited and hired to

work an average of 30 hours per week,

or approximately 360 hours in a

12-week quarter. This management

model required an allocation of sample

across PSUs and segments that was not

standard. In many standard survey

allocations, the number of sample lines

to be chosen from a segment and a PSU

is determined on average across all

PSUs, or PSUs of a similar type (such

as NSR). In the 2006–2010 NSFG, this

standard practice would have allocated

approximately the same number of

housing units to be selected across

segments within the same domain.

Interviewers worked to complete the

sample lines, regardless of expected

response and eligibility rates and their

expected efficiency.

In the 2006–2010 NSFG, however,

a more efficient management model was

used for interviewer employment.

Interviewers were recruited to work an

average of 30 hours per week

throughout 48 weeks of the year.

(Interviewers in nonrotating ‘‘super 8’’

PSUs are promised 30 hours per week

for as long as their performance met

ISR standards, since the ‘‘super 8’’

PSUs were in the sample for all 4 years

of the design.) This hiring, recruiting,

and employment model forced a

nonstandard method of allocating lines

across segments. The management target

is a fixed number of hours of work in a

calendar quarter, instead of a fixed

number sample housing units to be

visited.

Within an expected 360 hours in a

12-week period, interviewers update or

prepare ‘‘scratch’’ listings for the

segments allocated in the next calendar

quarter, screen selected lines, and

conduct main interviews. Interviewers

had in their work assignments varying

survey conditions that could make them

more or less efficient within the 360

hours. The conditions varied by the

nature of the communities in which they

work, which in turn affects parameters

such as the number of hours required to

complete an interview (i.e., the hours

per interview for PSU α at calendar

quarter t, HPIαt

); the housing unit

occupancy rate (O|αt); the proportion of

occupied housing units with one or

more persons ages 15–44 (the eligibility

rate, E|αt

); and the combined screener

and main interview response rate (R|αt

).

Prior to each calendar quarter, study

staff asked interviewers to provide an

estimate of the number of hours to be

worked in the next quarter. The sample

line assignment process then allocated

hours equally over the segments that

would be assigned to the interviewer in

the quarter, say Hαt for the αth PSU at

calendar quarter t. Estimates HPIαt

, O| αt,

E|αt

, and R|αt are prepared for each PSU

as well. The estimation took into

account variation in the achieved values

of these parameters by region (that is,

Northeast, South, Midwest, and West)

and by segment domain, generating 16

values of these parameters. For each

PSU in the sample, the number of

housing units or lines to be allocated for

the forthcoming calendar quarter t was

computed as

(Hαt / HPIαt

)

L . αt = (E|αt

× O| αt× R|αt)

These housing unit or line estimates Lαt

were adjusted after review by study staff

to account for interviewer or PSU

conditions that departed from

expectations for region and domain.

This allocation led to variation in

probabilities of selection of housing

units across segments within and among

PSUs. The variation was compensated

for in the weighting process (described

in the following text), although the

added variability in sample weights

from varying line probabilities at the

segment level has the potential to

increase the variability of survey

estimates.

Thus, for the sake of management

efficiency, the NSFG sample was

designed around interviewer

productivity. The calendar quarter

allocation of sample lines per segment

allows study staff to adjust sample size

to account for variation in eligibility,

occupancy, and response rates, as well

as hours per interview, each calendar

quarter.

Sampling in Two Phases

Each NSFG calendar quarter

consisted of two phases. In the first 10

weeks of the quarter, interviewers had to

screen selected lines in assigned

segments, conduct main interviews in

households with eligible persons, and

update or prepare ‘‘scratch’’ listings for

the segments allocated in the next

calendar quarter. After 10 weeks of data

collection, sample lines (or cases)

remained that had not been successfully

screened and sample persons who had

not yet completed the interview. If the

data collection was halted at the end of

10 weeks, these unscreened households

and noninterviewed persons would

contribute to nonresponse bias. A

‘‘double or two-phase sample

design’’ (14) was instituted for the
Series 2, No. 150 [ Page 17

remaining 2 weeks of the quarter as a

device to reduce the nonresponse bias in

survey statistics. The elements of a

‘‘two-phase design’’ (as Hansen and

Hurwitz in (14) coined the term) are:

A.	 The design and implementation of

a survey design on a large sample

(labeled the ‘‘first phase’’).

B.	 The selection of a probability

sample of the nonrespondents from

the first-phase sample.

C.	 The use of a different participation

protocol for the second phase.

There are two impacts of a

two-phase design. First, if the secondphase protocol is successful in

measuring (i.e., interviewing) 100

percent of the sampled nonrespondents

from the first phase, nonresponse bias is

eliminated. In practice, no subsample of

nonrespondents attains a 100 percent

response rate and thus some nonresponse bias remains, but the bias is

expected to be reduced by the capture of

data from the first phase nonrespondents. Second, the cases sampled into

the second phase that are successfully

interviewed are assigned new selection

weights (reflecting the fact that they

must ‘‘represent’’ the nonselected

nonrespondents). This additional weight

component generally increases the

variance of the estimates.

Two-phase designs are increasingly

attractive to survey researchers because

they offer a way to control the costs at

the end of a data collection period,

while addressing concerns about

nonresponse rates and errors. In

face-to-face surveys, at the end of the

data collection period, large costs are

incurred for travel to sample segments

to visit only one or two sample units,

usually those extremely difficult to

contact in prior visits or repeatedly

displaying some reluctance to grant the

survey request. By restricting these

expensive visits to a sample of the

nonrespondents at the end of the study,

a more cost-effective method limits

costs while addressing the need to

increase response rates.

In the 2006–2010 NSFG design, a

subsample of nonrespondents was

chosen for weeks 11 and 12 based on

study of the history of the first 10

weeks’ sample. Study staff developed

response propensity models to predict

the probability that a given case yields a

completed screening interview or a

completed main interview (see reference

11 for details of the propensity models).

Within a PSU, one of the three

segments was deleted at random. The

active nonresponse cases in the two

remaining segments were grouped by

type (screener or main interview) and

four categories based on their estimated

propensity as of the conclusion of the

first 10 weeks. A disproportionately

allocated sample of nonresponse cases

was selected across these four strata,

with higher probabilities of selection

from strata with higher propensities.

These selected lines and persons were

then released to interviewers for Phase 2

data collection in the last 2 weeks of the

calendar quarter.

Under a responsive design

theoretical perspective (15) that guided

the NSFG’s design and fieldwork, study

staff sought a Phase 2 interview

recruitment protocol that was distinctive

from that used in Phase 1. Such a

distinction is necessary (but not a priori

sufficient) to attract sample persons who

did not find the Phase 1 protocol

effective, and thus to increase response

rates and reduce bias in the sample data.

With the approval of two Institutional

Review Boards and the Office of

Management and Budget, the Phase 2

recruitment protocol involved the

following components:

A.	 Limited use of proxy respondents

(such as neighbors) for the

screening interview was allowed in

weeks 11–12 of each quarter.

B.	 A prepaid $5 token of

appreciation payment (versus

none) for cases that had not yet

completed the screening

interview.

C.	 A prepaid $40 token of

appreciation for the main

interview (versus a $40 token

provided after the informed

consent was signed).

D.	 A promised additional $40 token of

appreciation for a completed main

interview.

Sample Weighting 

Overview of Sample

Weighting

A simple random sample in which

response rates and coverage are the

same in every subgroup would create a

‘‘scale model’’ of the population.

However, many survey samples are not

‘‘scale models’’ in that sense. If a ‘‘scale

model’’ of the population is selected,

smaller groups in the population would

have too few observations in the sample

to provide adequate precision for

characteristics of interest for those

groups. As a result, survey samples

often deliberately select groups at higher

and lower rates to over represent smaller

groups in the sample. This allows

analysts the opportunity to answer key

survey questions for the total population

and for those small but often important

groups of the population.

For example, in the NSFG,

non-Hispanic black women and men

were sampled at a rate that made them

about 20 percent of all respondents in

the sample, even though they are

approximately 13 percent of the

population 15–44 years of age. Hispanic

women and men and teenagers of all

races were also sampled at higher rates

in the 2006–2010 NSFG yielding about

20 percent of the sample interviewed

from each group. ‘‘Sampling weights’’

adjust for these different rates.

A respondent’s sampling weight can

be interpreted as the number of persons

in the population that he or she

represents. For example, if a woman’s

sampling weight is 8,000, then she

represents 8,000 women in the

population. For the NSFG, the fully

adjusted sampling weights are assigned

to each respondent and consist of three

factors. The first factor is the inverse of

the probability that the case was

selected. For example, in NSFG the first

stage of the weight construction is the

probability of selection for each sample

person. If the probability is 1 in 6,000,

then the initial sampling weight is

6,000. The second factor is an

adjustment for nonresponse, calculated

separately and based on the probability
Page 18 [ Series 2, No. 150

of completing a screener and the

probability that a completed screener

results in a completed interview. If that

response rate is 75 percent, or 0.75, then

6000/.75 = 8,000, so the nonresponse

adjusted weight would be 8,000.

The third factor is termed

‘‘poststratification’’ and is an adjustment

to control totals of the number of

persons by age, sex, race and Hispanic

origin, provided by the U.S. Census

Bureau (and, for military personnel

living in a household outside of a

military facility, the Defense Manpower

Data Center). If, for example, it is

necessary to increase the weights for

cases in our example cell by 5 percent

to agree with the poststratification totals,

then the poststratified weight would be

(8,000) × (1.05) = 8,400.

More thorough discussion of

weighting procedures appears in the

‘‘Detailed Weighting Procedures.’’ The

use of weights in estimation requires

that standard error estimates reflect

these weights; see ‘‘Overview of

Variance Estimation.’’

Detailed Weighting

Procedures

The 2006–2010 NSFG was

designed to estimate the number of

women and men with particular

characteristics in the U.S. household

population. For example, the NSFG can

provide estimates of the number of

women who have ever had sexual

intercourse for the total population and

for groups such as teenagers 15–19

years of age, men 20–24 years of age,

or Hispanic women 25–44 years of age.

The 2006–2010 NSFG deliberately

oversampled specified subgroups of the

population. It was also subject to

nonresponse and noncoverage error.

Sampling weights were assigned to

every person in the public-use data files.

These weights must be used to

compensate for unequal probabilities of

selection and for under-representation of

subgroups due to nonresponse or

noncoverage. That is, the sampling

weights are designed to produce

estimates from the sample that correct

for oversampling, nonresponse, and

noncoverage.

The weights were constructed in

four steps. In the first step, a set of

weights is constructed to account for

deliberate or designed over- and undersampling features of the NSFG sample.

These weights are combined into a

single ‘‘base weight’’ to adjust for

unequal probabilities of selection across

all such sampling features. This is

sometimes called a ‘‘probability-ofselection weight.’’

In the second step, an extensive set

of nonresponse models was developed

to predict the probability of response to

the screener for all households that

contained eligible persons and for all

selected eligible persons within

successfully screened households. These

models used predictors obtained from

the 2000 Census of Population and

Housing, from the housing unit listing

process, from interviewer observations

collected at each contact with the

household, from household call records,

from household composition, and from

several additional household and

selected person characteristics. The

inverse of the predicted probabilities

from the screener and main interview

nonresponse models were used as

adjustment factors applied to the base

weights.

Third, nonresponse adjusted base

weights were summed within each of 36

groups—six age, two gender, and three

race and ethnicity categories. The

cumulated weights were compared with

counts that combine independent Census

Bureau population projection estimates

for each of the 36 groups and U.S.

Department of Defense counts of

military personnel in each of the 36

groups living off-base. Poststratification

weights are computed for each cell as

the ratio of Census plus Defense

Department counts to the cumulated

sample weights in each subgroup. The

poststratification factors are applied to

the individual nonresponse adjusted base

weights.

Fourth, the poststratified weights

were inspected to detect extremely large

or small weight values. A small number

of large or small weight values are

‘‘trimmed’’ to reduce the overall

variability of the weights. The trimming

procedure is designed to minimize the

impact on weighted estimates, while at

the same time reducing the variability of

the weights as much as possible. The

poststratification factors for the respective

cells of each trimmed weight were

recomputed after trimming. The weights

will be released with the data files.

Inverse probability selection

weighting

The NSFG sample management

system preloaded components of the

probability of selection of each sample

line onto the SurveyTrak record. Each

sample person was selected through

PSU, segment, housing unit, and person

within selected household selection

procedures. In addition, the Phase 2

sample selects a subset of the remaining

nonrespondent cases for intensive

follow-up for the last 2 weeks of

fieldwork.

Primary sampling units

Each of the 110 PSUs was selected

with probabilities proportionate to the

number of occupied housing units. The

probabilities were computed at the time

of selection and stored on the

SurveyTrak record for each sample line.

Segment selection

Blocks were selected within each

domain within PSUs with probabilities

proportionate to the Year 2000 census

count of the number of occupied

housing units in the block. In all PSUs,

segments in domains 2, 3, and 4 are

chosen with higher probabilities of

selection than for those in domain 1.

Probabilities of selection for segments

within domains are proportionate to the

estimated number of households in the

segment. The probability of selection of

each block is computed at the time of

selection and stored in the SurveyTrak

record for each line for later use.

Housing unit selection

Not all housing units in sample

segments were selected for the NSFG

sample. Segment housing units were

subsampled in Phase 1 to achieve a

target number of sample housing units

or lines. The subsampling probabilities

of selection for housing units vary by

segment and are recorded on the

SurveyTrak record.
Person selection

Once sample housing units or lines

were selected and released through the

ISR sample management system to

interviewers in each PSU, interviewers

visit sample housing units to determine

if any eligible persons reside there. The

interviewer completed a household

roster in the Blaiset instrument,

recording age, gender, and race and

ethnicity for each member of the

household. If one or more persons

15–44 years of age lived there, a

random selection was made of one

eligible person per household—with the

chances of selection varied to increase

the selection of teens (ages 15–19),

women, and blacks and Hispanic

persons. The household roster and

chances of selection were recorded in

the Blaiset household record.

Phase 2 sample design

A final component of sample

selection was introduced in Phase 2

sampling in the final 2 weeks of the

calendar quarter data collection. On

average, a sample of two or three

segments in a calendar quarter was

chosen in each PSU. Nonresponding

housing units in the selected segments

(those which have not reached a final

disposition after 10 weeks of data

collection) were divided into strata on

the basis of type (screener or main) and

predicted probabilities of obtaining a

completed interview. A Phase 2 selection

of segments and of nonresponding

housing units was chosen, with higher

chances of selection assigned to those

nonresponding housing units with higher

estimated response propensities. The

varying chance of selection for segments

and housing units in the second phase

selection were retained for subsequent

weighting.

Probability of selection and weight

The probability of selection of each

sample person can thus be computed

using the probabilities of selection for

PSUs, segments, sample line, withinhousehold selection, and Phase 2

subsampling of replicates.

Let Mhα denote the size measure of

the αth PSU in stratum h, the number of

occupied households in the PSU in

1990. Let Mhαβ denote the size for the

βth segment in the (hα)

th PSU, where

the size measure for each segment is the

number of occupied housing units in the

2000 Census. Also, let Ldhα denote the

desired number of sample lines for the

dth domain in the (hα)

th PSU. Finally,

let π2,hαβ denote the combined Phase 2

within-household selection probability

for the (hαβγ)

th sample person.

The probability of selection of the

(hαβγ)

th eligible person (where γ 

denotes the γ

th sample person in the

(hαβ)

th segment) is computed as

( ) ( ) ( ) Mhα Mhαβ Ldhα 7hI�1 = • • • π2,hαβ ah bhα Mhαβ

∑Mhα ∑Mhαβ

α=1 β=1

The base weight compensating for

unequal chances of selection for the

(hαβγ)

th eligible person is the inverse of

this probability of selection,

–1 whαβγ = w1i = πhαβγ .

This variation in base weights due to the

over- and under-sampling within

households has the potential to increase

the variance of estimates. A summary

factor of the potential increase in

variance, computed under the

assumption that the weights are

uncorrelated with the values of the

characteristics being estimated, is

estimated for each of the 12 age-genderrace and ethnicity subgroups of greatest

interest. The factor 1+L is a measure of

the relative variance of the weights

themselves, and is computed as

n

2 ∑w ( 1i i=1 )

1+L = n .

(∑ 

n

)

2 w1i

i=1

This factor is an approximate potential

relative increase in the variance of

estimated means that can be attributed

to the distribution of the weights. The

full effects of these increases are

reduced later in the weighting process

through trimming of the largest final

weight values.

Series 2, No. 150 [ Page 19

Nonresponse adjustment

Nonresponse in the 2006–2010

NSFG occurred at both screening to

identify sample eligible persons in

sample households and at the main

interview among selected eligible

persons. If there was nonresponse at the

initial contact for the screener interview,

there was little or no information about

the address. The main interview

nonresponse occurred any time after the

conclusion of screening—that is, after a

sample person had been selected. The

main interview nonresponse therefore

had information about household

composition and race and ethnicity of

the selected person, as well as a few

additional variables that were obtained

during household screening.

Nonresponse adjustment for the

NSFG was implemented under an

assumption widely used in the

adjustment of survey data, missing at

random (MAR). That is, within

subgroups of sample units (housing

units in the screener, selected eligible

persons in the main interview), it is

assumed that the nonrespondents are a

random sample from all the units in the

sample. A nonresponse weighting

adjustment developed under this

assumption is computed as the inverse

of an estimated response rate within a

subgroup. This sample-based weight

represents an adjustment that, under the

MAR assumption, substitutes for a

probability of selection in the response

process. Thus, as for unequal probability

weighting, the inverse of the predicted

probability of response serves as an

adjustment factor.

There are many methods for

estimating response rates (16,17). Most

are alternative ways of estimating

probabilities of responding under MAR.

The weighting class method divides the

sample into weighting classes across

which response rates are expected to

vary, and across which the characteristics of sample persons are expected

to vary. An alternative method is to

estimate probabilities of response

through a propensity model, such as

in a logistic regression model. The

2006–2010 NSFG uses this latter

logistic regression method, computing
Page 20 [ Series 2, No. 150

separate models for screening and main

interview nonresponse.

Let Si denote a zero-one indicator

for whether a sample address is

successfully screened to determine

whether eligible persons lived in the

household, where 1 denotes successful

screening. (Si is not defined for sample

addresses that were not occupied.) The

screening logistic regression model for

all occupied sample addresses is

' �si = Pr(Si

=1|Xi)=(1+exp(–Xi β))–1

where Xi is a vector of predictor values

for the i

th occupied housing unit and β 

is a vector of logistic regression

coefficients. The coefficient values are

estimated through standard maximum

likelihood methods, and predict the logit

�

^ ' = (1+exp (Xi β|))–1 si .

This logit in turn is used to obtain a

predicted probability of successful

screening as

^ exp(–λ ^ si) 7 = . si	 ^ (1+exp(–λsi))

Similarly, let Ri denote a zero-one

indicator for main interview response

for the i

th successfully screened

occupied housing unit, equal to 1 when

the selected eligible person has a

completed interview, and 0 for selected

eligible persons who did not complete

an interview. (Ri is not defined for

sample addresses that are not occupied

or are not successfully screened.) The

main interview logistic regression model

for selected eligible persons is

�mi = Pr(Ri=1 | Si=1, Zi) = (1+exp(–Zi

'

γ))–1

where Zi is a vector of predictor values

for the i

th selected eligible person and γ 

is a vector of logistic regression

coefficients. Standard maximum

likelihood methods yield

�

^ ' = (1+exp (Zi γ| ))–1 mi .

The predicted probability of successful

screening is	 

^ exp (–λmi) ^

7mi =

(1+exp (–λ ^

mi))

.

A model is fit to existing data

separately for screening and for main

interviews among successfully screened

addresses. The overall response

propensity is then estimated for each

housing unit based on the product

of two predicted probabilities,

|πri = |πsi × |πmi. This predicted probability

is computed for all selected eligible

persons.

The nonresponse weight is the

inverse of the predicted probability |πri,

but only for completed interview cases.

In particular, w = π| –1

ri ri if Ri = 1 and

Si = 1, and wri = 0 otherwise.

Unlike the 1995 NSFG, which had

the National Health Interview Survey

data set for each sample person to use

as potential predictors in response

propensity models, the NSFG now has a

more limited set of geographic and

operational variables to use as predictors

for the screening and main interview

models. Preliminary modeling of

screener and conditional main interview

response propensity was performed in

the estimation of segment level expected

completed interview counts for the

Phase 2 sample selection (see reference

11). The preliminary models employed a

number of predictors:

1.	 Counts and rates for the segment

from which the housing unit is

selected, derived from 2000 census

data for the blocks in the segment.

2.	 Data obtained from observations

made at two levels for each housing

unit: characteristics of the segment

and housing unit recorded by the

interviewer listing the segment.

3.	 Respondent behavior recorded by

the interviewer at each contact with

anyone within the housing unit.

4.	 Operational measures, such as

number of calls to a housing unit,

number of calls to the sample

person, and interviewer response

rate.

5. For the main interview propensity

model, data drawn from the

household roster and other data

collected in the screening interview.

These sets of variables were used as

predictors in the response propensity

models for screener and main interview.

The census variables, segment and

housing unit observations, screener 

contact observations, and operational

variables are being examined in

stepwise logistic regressions for the

screening indicator variable Si

. The

same predictor variables plus main

interview contact observations,

household composition, and other

variables available from the screening

interview are being examined in

stepwise logistic regressions for the

main interview response indicator Ri

.

Screener response propensity model

The predictors in the screener

propensity model are a set of variables

theoretically important for response

propensity. The set of variables to be

used in the screener propensity model is

shown in Table B.

Substantial variation in predicted

probabilities generates considerable

variation in response propensity weights.

A common practice in survey estimation

is to reduce this variation by grouping

predicted values into classes, and then

using some middle value to represent

the entire group’s predicted values. This

method effectively imposes a constraint

on the underlying propensity model. The

predicted probabilities are grouped by

deciles of the predicted probabilities. All

completed screener cases in the lowest

predicted probability decile are assigned

the value of the median of the predicted

probabilities in the decile. The

remaining 90 percent of the values are

not changed. This response propensity

revision reduced the variation to a range

from 0.39 to 0.98. The main interview

nonresponse weight Wmi is computed as

the inverse of the predicted probability,

or the inverse of the median of the

lowest decile.

Main interview response propensity

model

The main interview response

propensity model development has more

predictor variables than the screener

propensity model because a larger

number of predictors is available, and a

broader theoretical framework can be

used for weight development. The

stepwise logistic regression does not

select such variables as age, gender,

race, and ethnicity as predictors of

response propensity for the main

interview model. However, all are
Series 2, No. 150 [ Page 21

known to be predictors of the values of

the fertility and other characteristics

observed in the interview.

Theoretically, there are two general

approaches to developing weights to

adjust for nonresponse. The response

propensity approach seeks to find a set

of subjects whose predicted probabilities

of response are identical, or nearly

identical, and then assign to the

respondents in the set a common weight

that compensates for the nonrespondents

in the set. Because there is no variation

among the cases in the set on the

propensity to respond, there is no

association between characteristics of

interest and response propensity. That is,

nonresponse is independent of the

characteristics of interest. At the same

time, individuals with different

propensities (that is, in different sets)

who also have different values of the

characteristic will generate association

between propensity and substantive

measures. The inverse propensity

weights thus allow the association

between propensity and substantive

values to be used to adjust the estimates.

A second approach is the predictive

approach. If a group of sample persons

with the same or similar propensities

has the same value of a substantive

measure, nonresponse is independent of

the substantive measure. Models that

predict substantive measures can be

used to group respondents together

based on predicted values of substantive

variables. Inverse propensity weights

within these groups will then allow the

association between propensity and

substantive measures to be used to

adjust estimates.

The predictive approach is difficult

to implement in practice because good

predictors of substantive measures are

not usually available for nonresponse

adjustment for both responding and

nonresponding cases. However, in the

NSFG, age, sex, race, and ethnicity are

known for all selected sample persons

and are important predictors of

substantive measures. Thus, while the

response propensity model stepwise

regression screening may not select

these predictive variables, it is important

to include them in some form in the

propensity models. That is, a

combination of response propensity and

predictive models were used for the

main interview nonresponse adjustment.

In addition, interviewers were

required to provide subjective

assessments of several household or

personal characteristics at the first visit

to the sample housing unit. One of

these, an interviewer’s assessment of

whether there is an active sexual

relationship in the household (i.e., a

married or cohabiting couple, as

indicated by a screener informant

referring to ‘‘my husband’’ or ‘‘my

partner’’), has proven to be correlated

with key variables and thus useful in a

predictive model.

A number of different models fit to

the data will employ a reduced set of

propensity predictors in subclasses

defined by age and interviewer-assessed

active sexual relationship (Table C).

Within these groups, separate models

with similar predictors are to be used in

each subgroup propensity model. The

level of change in the logarithm of the

likelihood from the ‘‘null model’’

(containing only an intercept as the

predictor to the model) will be observed

to determine if these models explain

substantial amounts of variability in

propensity. Thus, the main interview

propensity estimates will most likely be

based on subgroup models selected

through stepwise regression.

Combined nonresponse weight

As a final step in the construction

of the nonresponse adjustment weight,

the screener nonresponse weight wsi is

multiplied by the main interview

nonresponse adjustment wmi to obtain

the final nonresponse adjusted weight,

wri = wsi× wmi. No trimming of this final

weight value is expected to be needed.

Poststratification

The final stage in the weighting

process is the adjustment of weighted

sample values to outside distributions.

Preliminary study indicated that there

are few external data to which the

NSFG data could be benchmarked in a

calibration approach.

A calibration to external population

estimates by age, gender, and race and

ethnicity adjusts the nonresponse

adjusted weight w2i to an estimate of the

civilian noninstitutionalized population

in the group provided by the Census

Bureau plus counts for military

Table B. Screener propensity model predictors, 2006–2010 National Survey of Family

Growth

Predictor name Predictor description

LRESIDENTIAL ............ All housing units in sample segment are residential (yes/no)

MANYUNITS ............. Address in a structure with multiple housing units (yes/no)

EVERQUEST ............. Informants at address asked one or more questions during one or more

contacts (yes/no)

EVERRESIST ............. Informants at address made statements indicating reluctance to be

interviewed (yes/no)

NCALLINFOLD ............ Total number of calls made to address

NCONTACT .............. Total number of contacts with household

EVENCALLPER............ Percentage of calls made during evening hours

EVERSTATE.............. Informants at address made statements delaying interview (yes/no)

CHILD_LT15.............. Interviewer assessment of whether anyone in the household is under

15 years of age (yes/no)

IWER_EXP .............. Number of days since interviewer was hired by UM-ISR

BILINGUAL .............. Interviewer speaks English and Spanish (yes/no)

REGION ................ Geographic region (Northeast, Midwest, South, West)

PSU_TYPE .............. Type of PSU (Super eight, other large metropolitan, and smaller

metropolitan or nonmetropolitan)

DOMAIN ................ Segment has 1) more than 10% population Black, 2) more than 10%

Hispanic, 3) more than 10% Black and Hispanic, or 4) all other

QUARTER ............... Calendar quarter of data collection (1 to 16, where quarter 1 is

approximately July 1 to September 30, 2006, quarter 2 October 1 to

December 31, 2006, etc.)

SCRNPROB .............. Interviewer assessment of probability (high, medium, low) of obtaining

screener interview made at week 7 of each quarter

SCRN_PHASE2 ........... Address selected for Phase 2 sample as a screener case (yes/no)
Page 22 [ Series 2, No. 150

personnel living off base obtained from

the Defense Manpower Data Center.

Across 36 age, gender, and race and

ethnicity cells, a poststratification weigh

to

s

n

in

,

s

.

at

ce

t

s

c

u

r

c

L

L

P

U

N

N

S

C

S

B

I

R

B

P

D

Q

M

B

w3i is computed as the ratio of the

combined Census and Defense counts

the sum of the nonresponse adjusted

weight w2i in each cell. A preliminary

final weight w4i = w3i

× w2i is being

computed for each of the completed

interview cases.

Weight trimming

The distribution of the preliminary

final weight is being examined in each

of the primary 12 subclasses as well a

by groups formed by age, gender, race

and ethnicity, and two-way combinatio

of these variables. Potential increases

variance (the factor 1+L presented

previously) greater than 2.0 may be

observed in some of the 12 subclasses

suggesting that individual weight value

may be contributing to a substantial

variation in the weights in the subclass

Considerable reduction of the

variability in the weights can be

achieved by a reduction of a few

extremely large weight values. These

kinds of ad hoc weight trimming

procedures are used on occasion in

many surveys under the assumption th

reduction in weight variation will redu

standard errors of survey estimates

while at the same time leading to

inconsequential changes in the survey

estimates themselves. The trimming

process first trimmed the second-phase

probability weight to be no more than

2.0. Then individual weight values and

their components (that is, w1i

, w2i, w3i,

w4i) for each of the 12 subclasses were

listed for those cases where the

preliminary final weight value is more

than two times the mean weight value

for the subclass. The nonresponse

adjusted base weight for cases with the

largest weight is trimmed to the next

largest nonresponse adjusted base

weight value, and poststratification

weights recomputed following this

trimming. The potential increase in

variance factor 1+L is recomputed, and

weighted estimates for as many as 20

key variables (variables monitored

during data collection to assess changes

in value as data are collected) are also

omputed using the trimmed and

ntrimmed poststratified weights. The

elative change in the 1+L value is

ompared with the relative change in the

weighted estimates for the cell where

trimming occurred. If the change 1+L

from untrimmed to trimmed weights is

large, and the relative change in the

trimmed and untrimmed weighted

estimates is small, the trimming is

accepted as satisfactory. Trimming stops

in subclasses when the relative change

in weighted estimates becomes large

from one trimming step to the next, and

the change in the factor 1+L is small.

Estimating equation for totals

The NSFG estimator of the number

of men or women with a particular

characteristic can be computed as w I

^

N = w ∑
 5i Ii i

where w5i is the final trimmed weight

for the i

th sample person and Ii = 1 if the

person has the characteristic and Ii = 0

otherwise.

To estimate the total number of

events or the total for a particular

variable, such as the total number of

live births, for persons in a particular

subgroup identified by the indicator Ii is

computed as

^

Y = ∑
w5i Ii yi i

where yi is the value of the characteristic of interest. The variable yi may be

a count such as number of live births or

a continuous measure such as income.

Estimates of means or proportions

can be constructed from these estimated

totals. For example, the proportion of

the population with a characteristic of

interest can be computed as the ratio

estimator

∑
 5i i

i

p =

∑
w5i

i

Alternatively, the mean for a variable yi

can be computed as the ratio estimator

Table C. Main interview propensity model predictors, 2006–2010 National Survey of Family

Growth

Predictor name Predictor description

RESIDENTIAL ............

SAFECON ..............

HYSIMPED .............

RBAN .................

CALLINFOLD ............

CONTACT ..............

CR_SINGLEHH ...........

HILD_LT15..............

EXUALLY_ACTIVE .........

AGE ...................

HISPANIC ...............

LACK .................

WER_NONWHITE..........

EGION ................

ILINGUAL ..............

SU_TYPE ..............

OMAIN ................

UARTER ...............

AINPROB ..............

OTH_PHASE2............

All housing units in sample segment are residential (yes/no)

Interviewer noted safety concerns about segment during segment listing

or updating procedure (yes/no)

Interviewer observed physical impediments to entry, such as locked

door, community gate, etc. (yes/no)

Address in an urban location (yes/no)

Total number of calls made to address

Total number of contacts with household

Screener interview data indicates single person household (yes/no)

Interviewer assessment before screener of whether anyone in

household under 15 years of age (yes/no)

Interviewer assessment before screener of whether anyone in

household is married or cohabiting (yes/no)

Selected person age

Selected person ethnic origin (Hispanic, not Hispanic)

Selected person race (black, not black)

Interviewer nonwhite (yes/no)

Geographic region (Northeast, Midwest, South, West)

Interviewer speaks English and Spanish (yes/no)

Type of PSU (super eight, other large metropolitan, and smaller

metropolitan or nonmetropolitan)

Segment has in Census 2000 1) more than 10% population black,

2) more than 10% Hispanic, 3) more than 10% black and Hispanic, or

4) all other

Calendar quarter of data collection (1 to 16, where quarter 1 is

approximately July 1 to September 30, 2006, quarter 2 October 1 to

December 31, 2006, etc.)

Interviewer assessment of probability (high, medium, low) of obtaining

main interview made at week 7 of each quarter

Address selected for Phase 2 sample as a main interview case (yes/no)
∑ w5i Ii yi

i

y =

∑ w5i Ii

i

Series 2, No. 150 [ Page 23

Sampling variances for these estimators

must be computed taking the stratified

multistage sample design into account.

Variance estimation procedures are

described in the last section.

Item Imputation

Overview of Item

Imputation

In any survey, not every question is

answered by every person interviewed.

Sometimes a respondent cannot

remember a fact asked for in a question;

sometimes he or she refuses to answer.

Other times, the answer that the

respondent gives is clearly inconsistent

with other information in the interview;

one or more of the inconsistent answers

is then set to ‘‘missing.’’ Such ‘‘missing

data’’ create inconsistencies in estimates,

which may be confusing for many users

of the data. Assigning predicted values

to these missing items is called

‘‘imputation.’’

Imputation has several advantages.

It makes the data more complete and

easier to use, eliminating the sometimes

confusing decreases in sample size when

cases with missing values are dropped

from an analysis. It also allows all of

the collected data to be used in analysis.

In an analysis involving several different

variables simultaneously, entire cases are

dropped because they are missing for

one variable, even though there are

nonmissing values for the other

variables for the case. The analyst thus

discards a great deal of collected data

by deleting cases with item-missing

values. Discarding cases with missing

values implicitly assigns a value to the

missing items. Effectively, discarding

cases assigns the average of the

nonmissing values to each of the

missing values. Imputation is a

procedure that attempts to improve on

this assignment by assigning a

replacement value for each item-missing

value that is a prediction from other

variables, and not just an average of the

same variable from cases without

missing values.

Imputation is essentially based on a

statistical (usually regression) model.

Some analysts would prefer to use a

different model than the one used for

imputation. For example, an analyst may

believe that a more statistically

sophisticated model than the one

employed in the imputation of the

public-use data would provide a better

fit and thus better imputed values.

Other analysts are concerned that

standard errors using imputed values

may underestimate standard errors of

estimates. Thus, these statisticians urge

analysts to use recently available

methods for computation that account

for imputed values in variance

estimation.

Finally, there is a view that

imputation is somehow a ‘‘fabrication’’

of data, and therefore that imputed

values should not be used. However,

imputation predicts values using the

same methods analysts use to develop

statistical models that summarize the

associations present in the data. Given

the low levels of imputation on most

imputed values in the NSFG, however,

these concerns are not likely to have

any practical effect on most NSFG

analyses, and they still have the

advantages listed above—increasing

sample size, preserving other reported

data, and reducing bias.

There are thousands of variables in

the NSFG data files. Of these,

approximately 600 variables were

selected for imputation because they are

used frequently in analysis. These

variables are referred to here as

‘‘recodes’’ or ‘‘recoded variables’’; some

surveys use terms such as ‘‘constructed

variables’’ or ‘‘generated variables.’’

Missing data for these recodes could

create inconsistencies among survey

estimates and confusion among data

users about both the published data and

the microdata file. Selecting, editing,

and imputing this more limited set of

variables was a way to ensure

high-quality data for the variables

used most often by NCHS and many

other data users. Release of the data

file could have been delayed

significantly if missing data for all

variables in the data file had been

imputed.

The frequency of missing values for

the recoded variables in the NSFG data

was, as in previous NSFGs, low—in

part because CAPI requires the

interviewer to enter an acceptable

response and then goes automatically to

the next appropriate question. The CAPI

program performs range and consistency

checks to help prevent logically

impossible answers. Typically no more

than 1–2 percent of the values of all

recoded variables, in male, female, and

pregnancy files combined, are missing

and subsequently being imputed.

The two imputation techniques used

in the NSFG are:

+ Logical imputation

+ Regression imputation

Logical imputation involves having

a substantive expert (usually at NCHS)

look at a missing value, examine related

variables, and assign a value to the

missing value that is essentially an

educated prediction of the true value.

Regression imputation, as used for

the NSFG, uses software that imputes a

missing value using potentially all other

variables in the data set as predictors. In

practice, logical imputation is rarely

used in the NSFG. The vast majority of

imputed values are imputed by

regression imputation.

A major part of the work of

imputation involves making certain that

the values imputed are within acceptable

ranges, and are logically consistent with

other data reported by the respondent.

Except when a reported value is

obviously incorrect, actual reported data

are never replaced by an imputed value.

For each recoded variable in the

database, an imputation flag identifies

whether the value of that variable is

imputed or not. Using the imputation

flag, a researcher can identify the

observations with an imputed value and

the specific type of imputation

procedure used for each specific recoded

variable.
Page 24 [ Series 2, No. 150

Detailed Item Imputation

Procedures

In every survey some values are

missing for some variables. These

missing values create inconsistencies in

reported results, since an analysis using

different variables with different

numbers of missing values will typically

have unequal sample sizes. Item-missing

values may also contribute to bias if the

analysis is restricted to complete cases,

those with valid values. If missing

values differ on average from those

observed in the valid cases, an analysis

that only uses cases with complete data

may yield biased estimates.

There are several remedies for

item-missing values, including

weighting specific to each analysis, and

imputation—replacing missing values

with predicted values (see reference 18).

The latter, imputation, makes analyses

more consistent and can reduce bias due

to item nonresponse, depending on the

predictive power of the imputation

procedure.

As in previous NSFG cycles,

imputation was used only for a subset of

variables, those to be used in the

primary tabulations and analyses

released by NCHS. Three files required

imputation for some variables: female,

male, and pregnancy. Each has

somewhat different sets of variables.

There are approximately 600 variables

(called ‘‘recodes’’ in the NSFG data

files) identified for imputation across the

three data files in the NSFG.

For all variables with imputed

values, a set of indicator variables was

generated in the female, male, and

pregnancy files that designated which

cases received imputation for each

variable. These ‘‘imputation flags’’ may

be used by the analyst to replace the

imputed values in the data set with

values predicted from a model or

procedure specified by the user.

There is a considerable range of

methods used to impute for itemmissing values in survey practice

(18,19). In some instances, values can

be replaced through a logical derivation

based on the relationship among

variables. In most cases, though,

imputation must be based on a form of

prediction. The imputation procedures

used in survey practice range from

simple cell mean procedures to

sequential hot deck, flexible matching,

and regression imputation (19).

Previous NSFG cycles have used a

combination of logical, hot deck, and

regression imputation procedures. In the

2006–2010 NSFG, most imputations are

being performed using a regression

procedure. The procedure is

programmed to impute all recoded

variables within a data file. A large

number of potential predictors was used

in the regression procedure. The

potential predictors for any given

recoded variable consist of all recoded

variables and all variables used to

‘‘construct’’ recoded variables. (In

addition, certain logical constraints were

built in when necessary.) The imputation

procedure itself employs a generalized

linear regression model, providing for

the imputation of interval, dichotomous,

ordinal, nominal, and count scales for

the variable being predicted.

Sequential regression

Regression imputation in the NSFG

is implemented using a sequential

regression method described in

Raghunathan et al (20). The method

employs both a sequential procedure to

impute multiple variables in a particular

order and Bayesian methods to perturb

regression coefficients used in predicting

values and to add residuals to imputed

values. The method allows for

alternative scales for each variable to be

imputed, including interval,

dichotomous, ordinal, nominal, and

count scales.

For several scales, predicted values

outside the range of acceptable values

could be generated. For example, for

dichotomous scales, under logistic

regression, predicted proportions

between zero and one are generated.

These values were converted to zero or

one values through a random process.

For example, suppose that the predicted

value for a dichotomous variable is 0.4.

A uniform random number from zero to

one is generated. If the random number

is greater than 0.4, the imputed value of

‘‘1’’ is assigned. Otherwise, the imputed

value of ‘‘zero’’ is assigned. Similar

random assignment of valid values

occurs for polytomous and count

variables.

Imputed values from other variables

are used in the prediction of values used

as imputations for a given variable. The

procedure is multivariate, using all

variables specified as predictors of each

variable in the imputation procedure,

and thus effectively preserves existing

covariance structure in the data.

Consider a set of p + q variables

from the NSFG, where the p variables

do not require imputation (all values

were observed, or logically imputed

prior to regression imputation).

The imputation required several

cycles. In the first, the first of the q

variables, yp+1, is regressed on all the p

variables without missing values. A

linear, logistic, multinomial, or Poisson

regression is used, depending on the

measurement scale for yp+1 (the variable

being imputed) specified by the user.

From this regression, a predicted value

yˆp+1 is generated for each missing value.

Still in the first cycle, the second

variable, yp+2, is regressed on the p

variables without missing values and

yp+1 (including its imputed values).

Predicted values for yp+2 from this

estimated regression model are assigned

to item-missing values. This cycle

continues by repeating the regression

imputation process for the variables

yp+3, ..., yp+q, imputing in each case

using all p variables with no missing

values and all imputed variables

obtained to that point.

Cycles two, three, and so on (a total

of five cycles are typically used) repeat

the regression imputation, but include

all variables, the p with no missing

values and the other q–1 with imputed

values, as predictors. Thus, yp+1 is

regressed on the set of predictors

y1, ..., yp, yp+2, ..., yp+q, and imputed

values obtained from the predicted

values under the regression model.

The predicted values from each

regression in each cycle are obtained

through a process that perturbs the

coefficients and adds a random residual.

The process is described in the appendix

of Raghunathan et al. (20).

Variables may have restrictions to

limit the set of cases to be imputed. For

example, persons who have never had
Series 2, No. 150 [ Page 25

sex should not receive an imputed value

for age at first intercourse. Restrictions

are readily programmed in the software.

Values to be imputed may be bounded

within specified limits. Bounds for

imputed values can also be set and

truncated distributions are used to assign

predicted values. In the NSFG, a major

part of the work of imputation is in

specifying appropriate bounds for

imputed values because of the extensive

inter-relationships within the data.

The sequential regression

imputation method is implemented in

the IVEware software system

http://www.isr.umich.edu/src/smp/ive/.

Variance Estimation

Overview of Variance

Estimation

Sampling variance is a measure of

the quality of a statistic (such as a

proportion or a mean) caused by having

taken a sample instead of interviewing

the full population. For example, in the

2006–2010 NSFG, the sampling

variance measures variation caused by

interviewing the continuous NSFG

sample instead of the full population—

approximately 125 million women and

men 15–44 years of age in the entire

country. Sampling variance measures the

variation of the estimated statistic over

repeated samples. The sampling variance

is zero when the full population is

observed, as in a census.

For the NSFG, the sampling variance

estimate is determined by sampling design

and the population parameter being

estimated, and it is called the design-based

sampling variance. The NSFG data files

contain a final weight and information

necessary to estimate the sampling

variance for a statistic. Many statistical

software packages by default compute

‘‘population’’ variances, which may

severely underestimate the sampling

variances.

Special software is required to

accurately estimate sampling errors in a

complex sample such as the NSFG, but

such software is increasingly common

and easy to use and obtain. For

example, by default, SAS produces

population variances, but it has Taylor

series expansion and repeated replication

procedures for complex survey estimates

in specialized ‘‘SURVEY’’ procedures.

Similarly, Stata and SPSS have the

Taylor series procedures for complex

surveys in ‘‘svy’’ commands or a

complex sample survey module,

respectively.

Examples of how to use such

software to estimate sampling errors for

Cycle 6 are on the NSFG website at

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

sr_02/sr02_142.pdf (accessed February

6, 2009; also available in reference 21).

In addition, examples of variance

estimates for the 2006–2010 NSFG will

be presented with documentation for

each data release.

Detailed Description of

Variance Estimation

Since NSFG estimates are based on

a sample rather than a complete

enumeration of the eligible population,

they are subject to sampling error, a

departure between the true population

value and the estimated value. This

difference may be due to systematic or

fixed sources of error, such as

nonresponse or noncoverage. The

difference is also attributable to variable

sources of error, including the use of a

sample to represent the population.

Probability sampling, as used in the

NSFG, allows the direct estimation of

one of these sources of error, the

variable error due to sampling. A

considerable share of the survey design

and estimation literature develops proper

procedures for estimating the sampling

variance under different sample

selection techniques. See Wolter (22) for

a review of variance estimation

techniques.

The estimation of sampling variance

for NSFG estimates requires procedures

that properly account for the principal

effects of the different sampling

techniques employed in the NSFG.

There are four principal design features

that can be accounted for:

+	 Stratification of PSUs.

+	 Selection of PSUs (cluster sample

selection).

+	 Weights that adjust for unequal

probabilities of selection and other

design features.

+	 The presence of imputed values.

One way to handle imputed values

in variance estimation is with multiple

imputation. Multiple imputation is a

process with three complementary parts.

The first part of multiple imputation

is that several (two or more) imputed or

predicted values are generated for a

missing item for each case with missing

values. Second, estimates are produced

by computing an estimate using each of

the imputed values. For example,

suppose that five imputed values are

generated for a particular variable for

each case with a missing value for the

variable. Then an estimate is computed

using the set of first imputed values and

the remaining valid or real values.

Another estimate is computed using the

second imputed values and the valid or

real values. Five estimates in total are

computed. The final estimate is the

average of the five estimates. The third

part is that the estimate is now subject

to variability from two sources, the

sample design used to select the overall

sample, and the imputation process used

to generate imputed values.

To capture these variance sources

properly, the variance of each of the five

estimates is computed using the imputed

and valid values, taking into account the

complex sample design. The five

variance values are averaged. In

addition, the variance among the five

estimates is computed. The total

variance of the overall estimate (the

average of the five estimates, one per

set of imputed values) is the sum of the

average variance of the five estimates

plus the variance among the five

estimates. This approach captures fully

both sets of variability.

These methods are becoming

increasingly more accessible in existing

statistical estimation software. However,

to implement the method, these software

systems require that there be a data set

for each set of imputed values. If there

are five imputed values, five data sets

are needed. Because of the size of the

NSFG data files, the computational

burden on users is potentially

prohibitive, even with the availability of
Page 26 [ Series 2, No. 150

procedures in software. While the

IVEware software used for the NSFG

has the capacity to generate multiplyimputed data, for public release

purposes a single imputed data set, with

a single imputed value for imputed

variables, is being released.

The remainder of this section

considers the effect of the NSFG design

(including stratification, oversampling,

and weighting) on estimated sampling

variances, the availability of software

that can estimate variances taking these

design features into account, and the

creation of codes that can be used for

variance estimation.

If software is used that does not

account for the complex design,

sampling variances will be underestimated. The consequence will be test

statistics that generate smaller

probability test values than are actually

correct, and confidence intervals that are

narrower than they should be. Both of

these types of errors can lead to

incorrect inferences. For example,

smaller probability test levels might lead

an analyst to reject a null hypothesis

incorrectly.

For this reason, all users of NSFG

data should use variance estimation

procedures discussed in this section.

They will assure that incorrect test

procedures or overly narrow confidence

intervals are not used in drawing

conclusions from the data.

Summary of Variance

Estimation Principles

There are two primary approaches

used to estimate sampling variance from

complex sample surveys: Taylor series

approximation methods and repeated

replication methods. The former are

based on an analytic treatment of

statistical estimates, while the latter are

based on computer intensive resampling

of the survey data.

Complex sample survey data

generate statistics which, through the

use of weights, can be estimated by

standard statistical software. That is, the

estimated percentages, means, regression

coefficients, and other statistics are

computed properly using weighted data.

However, for the purpose of estimating

test statistics and standard errors of

estimates, standard software treats the

data as though the sample was selected

using very simple sampling methods.

The consequence, as noted previously, is

an underestimation of standard errors,

and an overstatement of the significance

of test statistics.

The Taylor series approximation, or

linearization procedure, can be adapted

to account for the complex design

features. Taylor series linearization is a

widely used technique in statistics for

obtaining approximate variance

estimates for nonlinear statistics. The

Taylor series is an infinite series

expansion of a function that has a

valuable property: for ‘‘well behaved’’

functions, the successive terms in the

expansion rapidly approach zero. Thus,

for some functions, the first term of the

Taylor series expansion can be used to

represent the entire infinite series.

The first term is linear in the

variables in the function. Variances are

computed for the ‘‘linearized’’ function.

The properties of the Taylor series

expansion approximation variance

estimates are well understood

empirically (see reference 23 for

evidence from complex sample surveys).

It has been applied to a wide range of

statistics computed from sample survey

data, from means and proportions to

logistic regression coefficients estimated

using iterative numerical solutions.

For NSFG data, the Taylor series

method can be adapted to the sampling

error computing units (SECUs) created

to represent the stratification and

clustering in the sample. These variance

estimation procedures are widely

implemented in software systems that

implement Taylor series approximations

for complex sample survey designs.

The repeated replication procedures are based on early work of

Mahalanobis (24) and Deming (25).

They proposed and encouraged the use

of interpenetrating or replicating

sampling methods that simplified

variance estimation considerably. For

example, if a sample were comprised of

T replicate samples, each selected in the

same way, and a statistic θ

^

t was

computed from each replicate, the

sampling variance of the average

^

T 1 ^ 8 = T ∑ θt

t=1

can be computed simply as

T 1 ^ var (θ) = (θt – θ)2. T(T–1) ∑ 

t=1

The idea of replicated sampling

was extended to survey data by

McCarthy (26), who proposed selecting

replicate samples from a larger sample

by employing in the replicates selections

that accounted for principle sampling

methods used in the original sample

selection. More appropriately for NSFG

data, a ‘‘jackknife’’ replication procedure

‘‘drops one’’ SECU in a stratum, and

retains all other SECUs and their cases

in the sample. In the NSFG, the cases in

the SECU remaining in the stratum from

which the one SECU was dropped must

have their values increased (doubled) to

account for the under-representation of

the stratum.

With repeated replication, the

sampling variance for a statistic can be

estimated in a form very similar to that

given previously for replicated sampling. ~ Let θ denote the estimate of the statistic

of interest computed from the entire

sample. Then,

T ~ 1 ^ ~ var(θ) = T ∑ (θt – θ)2, t=1

where θt is computed from the t

th

replicate. For the jackknife repeated

replication procedure,

T ~ ~ ^ var(θ) = ∑ (θt – θ)2. t=1

See Rust (27) for a more detailed review

of these procedures.

Variance Estimation

Software

These variance estimation

procedures are all available in a number

of commercially available computer

software packages, or for free download

over the Internet in a few instances. In

earlier NSFG cycles, it was often

complex and difficult to obtain and use

variance estimation software, but several

software packages now allow users to
Series 2, No. 150 [ Page 27

estimate variances for means,

proportions, regression coefficients,

logistic regression coefficients, and other

statistics directly within the larger

software system. These statistical software

packages implement the Taylor series

approximation, repeated replication

methods, or both approaches for a

complex sample survey like the NSFG.

There are two basic types of

software systems available for this

purpose: stand-alone and integrated

packages. The stand-alone software

packages require users to input data into

a special format used by the system.

Integrated software for estimation from

complex sample survey data allows a

user to conduct an analysis without

converting data to another format. The

user can construct a survey data set in a

format used by a major statistical

software package, such as SAS, Stata, or

SPSS, and then compute estimates and

test statistics within those systems that

take complex design features into

account. For example, SAS version 10

has four PROCedures which properly

account for complex features such as

stratification, clustering, and weighting.

SUDAAN is a stand-alone system that

comes in a SAS compatible version that

allows SUDAAN commands to be

embedded in SAS programs as though

they were part of the SAS language.

Users with SAS data sets can thus

analyze data using either SAS or

SUDAAN, although they will find more

analysis procedures available in

SUDAAN. Stata has a larger set of

survey or ‘‘svy’’ versions of frequently

used analysis commands that account

for stratification, clustering, and

weighting in complex sample surveys.

The SPSS Complex Sample module that

can be added to the base statistics

module for complex sample surveys has

features for frequency tables and several

types of regression models.

NSFG data users can find

descriptions of these and other

software systems for estimation from

complex sample survey data, along

with detailed explanations of their

features, at a website maintained by

the Survey Research Methods Section

of the American Statistical

Association, currently located at

http://www.fas.harvard.edu/~stats/surveysoft/survey-soft.html (28). NSFG data

users are encouraged to visit this

website and become more familiar with

the features of these software systems.

Empirical comparisons of these

software packages (28) have shown that

they provide essentially identical

estimates of statistics of interest, and

that the estimated sampling variances of

the estimated statistics are virtually the

same, regardless of the computational

method used to estimate the sampling

variances. The choice among these

systems can thus be based on practical

considerations of convenience, cost of

software acquisition and maintenance,

and current data format.

Sampling Error

Computing Units

The 2006–2010 NSFG sample has

110 PSUs. Each sample PSU was drawn

from a stratum of one or more PSUs.

Sampling variance cannot be estimated

directly from this type of a design, since

there is but one selection per stratum.

The NSFG PSUs are arranged into a set

of sampling error computing units

(SECUs) that could be grouped into

pairs for variance estimation purposes.

The 28 Self-Representing (SR)

PSUs (the ‘‘super 8’’ plus 20 other

metropolitan SR PSUs) are divided into

two or four representative units each by

a systematic sample of the segments

within each PSU. For example, in a

large SR PSU the segments are

numbered in sample selection order

within each sample domain as 1, 2, 3, 4,

1, 2, 3, 4, 1, 2 .... Segments with the

same number are grouped together to

form a pseudo PSU representing the

entire MSA. The first and third such

‘‘combined units’’ are paired to form a

stratum with two combined units, or

SECUs. Thus, the large PSU in this

example has four SECUs in the data set,

grouped into two pairs. Each pair is a

pseudo-stratum. Smaller SR PSUs are

divided into two SECUs, which are in

turn grouped into a single pair as a

pseudo-stratum. This SR PSU process

generates a total of 36 stratum pairs of

SECUs.

For the remaining 80 Non-SelfRepresenting (NSR) PSUs, the strata are

inspected to identify groups of four that

were as alike as possible. A total of 20

sets of four PSUs were created for

purposes of variance estimation.

In combination with the SR PSU

SECUs, there are thus a total of 36 pairs

of SECUs and 20 sets of four, or a total

of 152 SECUs. Used in pairs and groups

of four, the total degrees of freedom for

variance estimation from the female,

male, or pregnancy file is thus

approximately 96. Two variables, SEST

and SECU, are in the data set to identify

these groupings. The codes for these

values have been randomly ordered to

mask the identity of the units.

The National Survey

of Family Growth

Continuous Design: A

Guide for Analysts

If the NSFG plan for continuous

interviewing (29) is carried out for a

period of years, it will allow the release

of the data more frequently than in the

past, as it is collected.

Analyzing the Continuous

National Survey of Family

Growth as Periods of Data

Collection

The present plan is that under the

sample design described in this report,

there would be two releases of NSFG

2006–2010 data.

+	 The first data file includes about

13,500 interviews collected during

the 2.5-year (30-month) period from

July 1, 2006–December 2008 (‘‘the

2006–2008 data’’).

+	 The second will include over 20,000

interviews collected during the

4-year (48-month) period from July

1, 2006, through June 30, 2010.

For each of these data releases,

users can treat the data much as they

might have handled previous NSFG
Page 28 [ Series 2, No. 150

cycles. That is, a user will analyze the

2006–2008 data as the next ‘‘cycle’’ of

the NSFG. The structure of the

2006–2010 NSFG data is very similar to

that of the 2002 NSFG, and there are

weight and sampling error codes that

allow the user to compute estimates for

the entire 30-month period during which

data were collected. Users can compute

estimates and fit models as they did for

the 2002 NSFG or earlier cycles.

The principal difference is simply

that in previous cycles, the data were

collected in 12 months or less, but in

the current NSFG, the data were

collected over a longer time period. In

the 2002 NSFG, for example, data were

collected over a 12-month period

(March 2002–February 2003), and users

referred to the findings as being about

the U.S. population in 2002, using a

single weight and a set of sampling

error codes.

With the first release of the NSFG

continuous design, a user could repeat

an analysis and compare results for

2002 with results for the period

mid-2006 through 2008. The 2006–2008

data set weight is ‘‘centered’’ in the

middle of this time period. The

population for which inferences can be

made will thus be the average U.S.

population over the period from

approximately July 1, 2006, through

December, 31, 2008, for the first

release—as it was in 2002, but the time

period is longer.

By interviewing about 5,500

respondents per year, the NSFG can

give data users larger sample sizes than

were available from earlier cycles (and

obtained at a lower cost per completed

interview), but the user must refer to

periods of time longer than 1 year. The

larger sample sizes make more detailed

study of the entire sample or subgroups

possible than before. The analysis is

done the same way as in earlier cycles

of the NSFG.

Questions added in 2007—Some

new questions were added to the NSFG

questionnaire in July 2007. The

variables generated by these new

questions are contained in the first (30

month) release, and were collected for

an 18-month period from July 2007,

through December 2008. Any analysis

conducted that uses these 2007 variables

will have missing or ‘‘inapplicable’’

codes for respondents interviewed

before the questions were added to the

survey. Sample sizes will be reduced

accordingly, from approximately 13,500

to about 8,000. To compensate, a

separate weight will be provided for the

variables introduced in 2007 (‘‘2007

variables’’) so that NSFG data users can

analyze them.

Analyzing the 2006–2010

National Survey of Family

Growth for shorter periods of

time

There are of course several shorter

time periods within the expected NSFG

releases that could be selected by a user.

However, as the time period becomes

shorter, such as moving to a single year

or even a calendar quarter within a year

(such as the period January through

March 2007), the sample sizes become

smaller, and estimates become less

reliable. At a certain level, the NSFG

estimates will be based on such small

sample sizes that any estimates

produced will no longer have adequate

precision.

At the present time, NSFG staff are

examining whether estimates for single

calendar years will be adequately

reliable for any purpose. A calendar year

contains approximately 5,500 completed

female and male interviews. Given the

NSFG’s oversampling of minority

groups, teenagers, and women, and its

clustered design, this sample size is

unlikely to produce statistically reliable

estimates for most purposes. For the

anticipated first release of 2006–2008

data, which contains only the two

calendar years 2007 and 2008, calendar

year estimates are not recommended. No

calendar year weights will be provided

in the first data file release.

As a result, NSFG does not

recommend that estimates be computed

for intervals shorter than 18 months.

Estimates for individual calendar years,

or individual calendar quarters, should

not be computed, and weights to allow

users to compute such estimates will not

be provided.

When the second and subsequent

data files are released from continuous

interviewing, other analytic options may

be possible, including analyzing trends

in 1 or 2-year estimates. Research is

being conducted to determine whether

and under what conditions such

estimates will be possible. If they are

determined to be feasible, weights will

be provided for them in a future data

release.

Once several calendar-year time

periods of NSFG data become available,

users may then compare estimates

across time. Guidance on how to make

comparisons of these time periods will

be provided in the user guide for the

2006–2010 release. Again, precision for

comparisons will be limited, due to the

smaller sample sizes, and differences

will have to be quite large to be

statistically significant.

Analyzing the 2006–2010

National Survey of Family

Growth for small subgroups or

rare behaviors

The continuous NSFG allows users

the opportunity to accumulate data

across several years of data collection to

study small subgroups or rare behaviors.

Accumulation is useful when a short

time period simply does not provide

enough cases for adequate levels of

precision in estimates. In the expected

2006–2010 release, users will (with the

overall larger sample size) be able to

select proportionately smaller subgroups

for analysis than possible with previous

NSFG cycles.

Yet, there will be limits to the size

of sample that should be analyzed.

Guidelines regarding the minimally

acceptable levels of precision for such

subgroup analyses have not yet been

determined. However, NSFG staff

themselves exercise caution in

publishing or releasing findings for

estimates of prevalence or averages

whenever a standard error is more than

25 percent of the size of the estimate.

Estimated proportions or means where

the standard error is larger than

25 percent of the estimate itself, or

where the numerator or denominator are

too small, must be interpreted with

caution.
Frequently Asked

Questions: Summary in

Question-Answer Format

To summarize some of the

practical issues of interest to users of

the data file, the following section

shows some Frequently Asked

Questions (FAQ’s) and their answers.

Additional information will be

released on the NSFG website

http://www.cdc.gov/nchs/nsfg.htm as it

becomes available.

1.	 Given that the 2006–2010 design

uses quarterly samples, can I

analyze the data for just one

quarter?

No. Sample sizes for a single

quarter are too small to provide

estimates with adequate levels of

precision. In the first data file,

weights will be included only for

the full 30 months of interviewing,

and for the last 18 months of

interviewing (when certain new

questions were introduced).

Analysts should only use time

periods for which sampling weights

are provided.

2.	 The public-use data file has both

recoded variables (‘‘recodes’’)

and raw variables. Which ones

should I use?

NCHS recommends using recodes,

when they are appropriate for a

given analysis, for two main

reasons. First, the recoded variables

have been studied for consistency

and any missing data have been

resolved through imputation. Thus,

using the recodes allows the analyst

to make use of the intensive

scrutiny given to them. Second,

NCHS uses the recoded variables in

many of the tables in NCHS official

publications, so users can check

their own results with those of

NCHS to ensure comparability. If a

recoded variable is not available for

a specific analysis, then the raw

variables or computed variables

should be used.

3.	 Were different questionnaires

used over the 4 years of data

collection (2006–2010)?

Changes occurred at the beginning

of the 2nd and 3rd years of

interviewing (no changes were

made for year 4). But over

95 percent of the questions remain

the same from year 1 to year 4 of

interviewing. Variables measured in

only some of the years are noted in

the public-use documentation of the

data sets.

4.	 How do I combine the different

quarters of data collection in my

analysis?

Sampling weights will be provided

on the data files for time periods

for which analysis is appropriate.

See the data release documentation

for full guidance on your specific

analysis.

5.	 Given that the size of the data set

can become large over several

years of interviewing, can I

analyze the data separately for

different states in the country?

Although the number of data

records in the survey can become

large when several years of data are

combined, the sample is limited to

110 PSUs or areas. These primary

areas do not fall in all states. As

long as this design is used,

estimates cannot be computed for

individual states.

6.	 Can I combine the data for males

and females?

Yes. The data files contain some of

the same variables for males and

females. Using the sampling

weights, estimates for males and

females combined can be made.

7.	 For a recoded variable, how do I

find out what questions in the

questionnaire contributed to it?

The ‘‘recode specifications’’ are

given in the public-use data file

documentation. These specifications

show how each recoded variable

was constructed. The NSFG staff

recommends using recodes when

Series 2, No. 150 [ Page 29

they are appropriate for a particular

analysis, because they have been

checked for accuracy, edited for

consistency, and imputed.

8.	 Should I obtain the same results

on birth-related statistics from

analyzing the NSFG as vital

statistics?

Birth statistics based on the

National Vital Statistics System are

derived from a complete count of

the approximately 4 million birth

certificates filed each year. The

NSFG, in contrast, estimates these

births with a sample of a few

hundred births each year to women

in the continuous NSFG sample.

Therefore, NSFG estimates will not

match those from the birth

certificates exactly, primarily

because of sampling error. But there

are also some differences between

the coverage, nonresponse, and

measurement features of the two

approaches to estimation.

9.	 What format is used for the

public-use data set?

The data sets are released in ASCII

format and are compatible with

several major statistical software

systems that permit analysis of

complex survey data, including

SAS, SPSS, and Stata.

10.	 How is the data set any different

from analyzing any other sample

survey?

For the most part, analyzing the

NSFG data is no different than

analyzing a previous NSFG cycle,

at least in terms of the application

of standard survey estimation

software to the data file. Each of

the data files released from the

2006–2010 NSFG data can be

thought of and treated as a single

cycle of the NSFG, but the time

period to which the results apply

must be described accurately.

Previous NSFG cycles allowed the

user to report estimates for a

single year. The continuous NSFG

will allow the user to report

estimates for a period of a few

consecutive years. For example,
Page 30 [ Series 2, No. 150

the proportion of women 15–44

years of age who are currently

using the oral contraceptive pill

may be reported for the period

from 2006 to 2008. Once the

2009–2010 data are released, the

same statistic can be computed for

2006–2008, 2009–2010, or

2006–2010. The third period

(2006–2010) allows the user to

report the rate across a longer time

interval, but with greater precision,

since the sample size of the pooled

data will be larger.

In all these estimates, just as in

previous NSFG files, the analyst

must use appropriate weights and

variance estimates. The appropriate

weights and sampling error codes

will be provided with each release

of the NSFG that allow proper

estimation for different time

periods of interest. The presence

of alternative time period weights

will require users to choose the

time period of estimation and the

appropriate weight to apply, but

the benefits include greater

analytical flexibility and larger

sample size.

References

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Growth, Cycle 1: Sample design,

estimation procedures, and variance

estimation. National Center for Health

Statistics. Vital Health Stat 2(76). 1978.

2.	 Freedman R, Whelpton PK, Campbell

AA. Family planning sterility and

population growth. New York:

McGraw-Hill. 1959.

3.	 Whelpton PK, Campbell AA, Patterson

JE. Fertility and family planning in the

United States. Princeton, NJ: Princeton

University Press. 1966.

4.	 Ryder NB, Westoff CF. Reproduction in

the United States, 1965. Princeton, NJ:

Princeton University Press. 1971.

5.	 Westoff, CF, Ryder NB. The

contraceptive revolution. Princeton, NJ:

Princeton University Press. 1977.

6.	 Grady, WR. National Survey of Family

Growth, Cycle 2: Sample design,

estimation procedures, and variance

estimation. National Center for Health

Statistics. Vital Health Stat 2(87). 1981.

7. Bachrach CA, Horn MC, Mosher WD,

Shimizu I. National Survey of Family

Growth, Cycle 3: Sample design,

weighting, and variance estimation.

National Center for Health Statistics.

Vital Health Stat 2(98). 1985.

8. Judkins DR, Mosher WD, Botman S.

National Survey of Family Growth:

Design, estimation, and inference.

National Center for Health Statistics.

Vital Health Stat 2(109). 1991.

9. Potter FJ, Iannacchione VG, Mosher

WD, Mason RE, Kavee RD. Sample

design, sampling weights, imputation,

and variance estimation in the 1995

National Survey of Family Growth.

National Center for Health Statistics.

Vital Health Stat 2(124). 1998.

10. Kelly JE, Mosher WD, Duffer Jr. AP,

Kinsey SH. Plan and operation of the

1995 National Survey of Family

Growth. National Center for Health

Statistics. Vital Health Stat 1(36). 1997.

11. Groves RM, Benson G, Mosher WD,

Rosenbaum J, Granda P, Axinn W,

Lepkowski JM, Chandra A. Plan and

operation of Cycle 6 of the National

Survey of Family Growth. National

Center for Health Statistics. Vital

Health Stat 1(42). 2005.

12. Kish, L. Survey sampling. New York:

J. W. Wiley and Sons. 1965.

13. Kish L, Scott A. Retaining units after

changing strata and probabilities. J Am

Stat Assoc 66:461–70. 1971.

14. Hansen MH, Hurwitz WN. The

problem of nonresponse in sample

surveys. J Am Stat Assoc 41:517–29.

1946.

15. Groves RM, Heeringa S. Responsive

design for household surveys: Tools for

actively controlling survey errors and

costs. J Royal Stat Soc A 439–57.

2006.

16. Groves RM, Couper MP. Nonresponse

in household surveys. New York: John

Wiley and Sons. 1998.

17. American Association for Public

Opinion Research. Standard definitions:

Final dispositions of case codes and

outcome rates for surveys. Available

from: http://www.aapor.org On-line

edition 3.1. Revised 2008.

18. Groves RM, Dillman DA, Eltinge JL,

Little RJA, editors. Survey nonresponse, New York: Wiley and Sons.

2001.

19. Kalton G, Kasprzyk D. The treatment

of missing survey data. Survey

Methodology 12:1–16. 1986.

20. Raghunathan TE, Lepkowski JM, Van

Hoewyk J, Solenberger P. Sequential

regression imputation for survey data.

Survey Methodology 27:85–96. 2001.

21. Lepkowski JM, Mosher WD, Davis

KE, et al. National Survey of Family

Growth, Cycle 6: Sample design,

weighting, imputation, and variance

estimation. National Center for Health

Statistics. Vital Health Stat 2(142).

2006.

22. Wolter KM. Introduction to variance

estimation. New York: Springer-Verlag.

1985.

23. Kish L, Frankel MR. Inference from

complex samples. J Royal Stat Soc B

36:1–37. 1974.

24. Mahalanobis PC. On large-scale sample

surveys, Philosophical transactions of

the Royal Soc B 231:329–451. 1944.

25. Deming WE. On simplifications of

sampling design through replication

with equal probabilities and without

stages. J Am Stat Assoc 51:24–53.

1956.

26. McCarthy PJ. Replication. An approach

to the analysis of data from complex

sample surveys. National Center for

Health Statistics. Vital Health Stat

2(14). Washington, DC: U.S.

Government Printing Office. 1966.

27. Rust K. Variance estimation for

complex estimators in sample surveys.

J Official Stat 1:381–97. 1985.

28. Survey Research Methods Section,

American Statistical Association.

Summary of survey analysis software.

Available from: http://www.fas.harvard.

edu/~stats/survey-soft/survey-soft.html.

Accessed July 7, 2009.

29. Groves RM, Mosher WD, Lepkowski

JM, Kirgis NG. Planning and

Development of the Continuous

National Survey of Family Growth.

National Center for Health Statistics.

Vital Health Stat 1(48). 2009.
Series 2, No. 150 [ Page 31

Appendix I. Glossary 

Audio computer-assisted selfinterviewing (ACASI)—An interviewing

technique in which the respondent uses

a laptop computer to complete a

questionnaire. The respondent uses

earphones, which deliver an audio

recording of the questions, and reads the

question text on the laptop monitor. The

respondent chooses a desired response

option to each question, using the

laptop’s keyboard. The software directs

the respondent to the next appropriate

question based on the answers entered.

The respondent performs these steps out

of the sight of the interviewer, in an

attempt to offer the respondent as much

privacy as possible. ACASI is offered in

both English and Spanish in the

continuous NSFG.

Blaiset—A software system that

presents the questions in a questionnaire,

such as the NSFG. Blaiset is programmed to route the respondent to the

next appropriate question, store the

respondent’s answers, and check the

consistency of one answer with answers

to other related questions. Blaiset was

used in the 1995, 2002, and 2006–2010

NSFG.

Computer-assisted personal

interviewing (CAPI)—An interviewing

technique in which the interviewer

uses a laptop computer in the

interview. The laptop displays question

text for the interviewer to read and

provides any other necessary

instructions to the interviewer.

Interviewers record the respondent’s

answers using the keyboard. Software

directs the interviewer to the next

appropriate question based on the

answers entered.

Contact rate—At the screener stage,

the contact rate is the percentage of

sample households where an interviewer

talked with someone at the household

(i.e., the screener contact rate). At the

main interview stage, the contact rate is

the percentage of sample persons who

met with the interviewer on one or more

visits to the household by the

interviewer (i.e., the main interview

contact rate).

Cooperation rate—The percentage

of sample households that were

contacted and granted a screener

interview (i.e., screener cooperation

rate); or the percentage of sample

persons contacted who granted a main

interview (i.e., main interview

cooperation rate).

Coverage error—Deviations

between the characteristics (for example,

values of estimated population

characteristics) of the sampling frame

and the desired target population.

Coverage errors arise from the failure to

include some households containing

eligible persons in the list of households

within segments and failure to list some

eligible persons within sample

households on the sampling frame.

Delivery Sequence File (DSF)—The

DSF from the U.S. Postal Service lists

all addresses to which mail is currently

delivered by the Postal Service. In most

areas, the DSF is the basis for a list of

housing units from which listing for the

NSFG is done.

Domain—A stratum; a group of

sampling units (such as blocks) placed

in the same subset from which a sample

of units was selected.

Double (or two-phase) sample—A

subsample of nonrespondent sample

cases selected after the completion of a

phase of data collection. NSFG used

such a subsample in Cycle 6 (2002) and

in 2006–2010.

Eligible household—A household

containing at least one person who was

eligible for the NSFG—that is, males or

females 15–44 years of age at the date

on which the screener was completed,

and living in the household population

of the United States (all 50 states and

the District of Columbia). Whether a

selected household has an eligible

person is not known until the household

screener is conducted. If a household

has two or more persons 15–44 years of

age, one person is selected randomly.

Eligibility rate—The percentage of

sample cases that are members of the

target population. In the NSFG the

eligibility rate is the percentage of

households that contain a person aged

15–44.

Equal probability selection method

(Epsem)—A sample design that gives all

sample units an equal chance of

selection.

Institute for Social Research,

University of Michigan—The Institute

for Social Research (ISR) at the

University of Michigan conducts the

fieldwork and data processing for the

2006–2010 National Survey of Family

Growth (NSFG) under a contract with

NCHS. ISR has several centers that

participated in the NSFG: the Survey

Research Center provides overall

coordination and is responsible for data

collection, weighting, and variance

estimation; the Interuniversity

Consortium for Political and Social

Research processes data and develops

documentation and web-based systems;

and the Population Studies Center

provided substantive expertise on

demography and family growth.

Institutional Review Board

(IRB)—A committee of peer and

community reviewers of research

procedures involving human subjects

that weighs the benefits of the research

relative to the risks of harm to human

subjects. The NSFG was reviewed and

approved by the NCHS IRB, which

NCHS calls a ‘‘Research Ethics Review

Board,’’ or RERB.

Item imputation—The process of

assigning answers to cases with missing

data (‘‘don’t know,’’ ‘‘refused,’’ or ‘‘not

ascertained’’). In the NSFG, item

imputation is only performed on

approximately 600 ‘‘recoded variables,’’

or ‘‘recodes’’ (defined in the following

text, under ‘‘recodes’’), rather than all of

the thousands of variables in the data

set. The purposes of imputation are to

make the data more complete, more

consistent, easier to use, and, most

importantly, to reduce bias caused by

differential failure to respond. For

example, if a respondent’s educational

level is missing and a value of ‘‘high

school graduate’’ is assigned, education

is imputed. Imputation is done in two

ways in the 2006–2010 NSFG, logical

and regression imputation. Regression

imputation uses a regression equation to

estimate a value for a case with missing

data. Regression imputation was used to

assign most of the imputed values.

Occasionally, however, logical

imputation is used: logical imputation

uses a subject-matter expert to assign a

value based on the value of other

variables for the case with missing data.
Page 32 [ Series 2, No. 150

For nearly all of the recoded variables

for which imputation is done in the

continuous NSFG, less than 2 percent of

the cases received an imputed value.

Life history calendar—A visual

presentation of a calendar covering the

reference period of various questions. A

life history calendar is used to help the

respondent record the dates of events,

which are used as ‘‘anchors’’ to cue

memories of the dates of events

measured in the survey. In the

2006–2010 NSFG, the female interview

used a life history calendar as a recall

aid for the pregnancy and contraceptive

history portion of the interview.

Main interview—An interview

sought within sample households

containing an eligible target population

member. If the screening interview

reveals that the household contains one

or more persons 15–44 years of age, a

main interview is requested from one of

those persons. If there are two or more

persons 15–44 years of age, one such

person was selected at random for the

main interview.

Measure of size—A value assigned

to every sampling unit in a sample

selection. Typically measures of size are

a count of units associated with the

elements to be selected. For example,

measures of size for NSFG PSUs are

the count of occupied housing units

obtained in the 2000 Census of

Population and Housing, since sample

selection with the PSU would have

selected housing units.

Measures of size are also used in

the selection of eligible persons within

the household (see Figure 5) to increase

the chances of selection of such groups

as teenagers 15–19 years of age, black

and Hispanic persons, and females. Each

person in the household is assigned a

measure of size between zero and one,

where the measures are predetermined

values for each age by gender by race

and ethnicity group. The measures of

size are cumulated across eligible

persons, a random number from zero to

the sum of the measures generated, and

an individual selected based on the

cumulated measures of size.

Multiphase design—A survey

design that changes its sample design or

recruitment protocol over different sets

of sample cases or over time periods of

the survey to obtain an optimal balance

of costs and quality of survey estimates.

National Center for Health

Statistics (NCHS)—NCHS is the

Nation’s principal health statistics

agency. It designs, develops, and

maintains a number of systems that

produce data related to demographic and

health concerns. These include data on

registered births and deaths collected

through the National Vital Statistics

System, the National Health Interview

Survey (NHIS), the National Health and

Nutrition Examination Survey

(NHANES), the National Health Care

Surveys, and the National Survey of

Family Growth (NSFG), among others.

NCHS has conducted the NSFG since

1973. NCHS is one of the ‘‘Centers’’ for

Disease Control and Prevention (CDC),

which is part of the U.S. Department of

Health and Human Services.

Office of Management and Budget

(OMB) Clearance—OMB reviews

survey materials and questionnaires

proposed for use by government

agencies under the provisions of the

Paperwork Reduction Act. The review is

conducted by the OMB’s Office of

Information and Regulatory Affairs. The

NSFG was reviewed by this office of

OMB.

Paradata—Information collected

via computer software or interviewer

observations describing the sample unit,

interactions with sample household

members, or features of the interview

situation. The NSFG used observations

of characteristics of sample housing

units to reduce the number of callbacks;

used statements made by household

screener informants to diagnose their

concerns about the survey; used call

record data to model the probability of

obtaining an interview on the next visit;

and used observations of the respondent

during ACASI for measurement error

modeling. Some paradata are labeled as

‘‘process data.’’

Phase—A period of data collection

during which the same set of sampling

frame, mode of data collection, sample

design, recruitment protocols, and

measurement conditions are used. In the

2006–2010 NSFG there are two phases

in each 12-week quarter: first, in weeks

1–10, the standard protocol is used,

although paradata are used to optimize

the efficiency of the interviewers;

second, in weeks 11–12, a subsample of

nonrespondents from Phase 1 is offered

higher incentives and certain other rules

are changed. (See text for details.)

Public-use file—An electronic data

set containing respondent records from a

survey with a subset of variables

collected in the survey that have been

reviewed by analysts within NCHS to

assure that the identities of the

respondents are protected. This file is

disseminated by NCHS to encourage

widespread use of the survey.

Primary sampling unit (PSU)—The

first-stage selection unit in a multistage

area probability sample. In the NSFG,

PSUs are counties or groups of counties

in the United States; there were 110

PSUs selected into the NSFG sample for

2006–2010.

Race and ethnicity—Race and

ethnicity is used in this report as it was

used to design and select the NSFG

sample. Three categories were used:

Hispanic, non-Hispanic black, and all

other. Hispanic and non-Hispanic black

men and women are selected at higher

rates than others in the NSFG to obtain

adequate numbers of Hispanic and black

persons to make reliable national

estimates for these groups. Thus, in this

report, tables showing ‘‘race and

ethnicity’’ show the three categories

used to design and select the sample. In

contrast, in reports that are designed to

present substantive results, the ‘‘all

other’’ category is often split into

‘‘non-Hispanic white’’ and ‘‘nonHispanic other’’ categories.

Recodes or recoded variables—It is

not possible to edit or impute all of the

variables in the continuous NSFG data

file. NSFG staff selected about 600

variables from the NSFG data file that

were constructed, edited, and imputed.

These are called recodes or recoded

variables. Recodes are variables that are

likely to be used frequently by NCHS

and other data users. They are edited for

consistency, and missing values are

imputed. Many (but not all) of these

recoded variables are constructed from

other variables in the NSFG; some are

constructed from a large number of

other variables. Other variables in the

data file are not edited or imputed in

this way.
Series 2, No. 150 [ Page 33

Replicate—A probability subsample

of the full sample design. The complete

sample consists of several replicate

subsamples, each of which is a small

national sample of housing units.

Replicate samples are released over the

data collection to control the workflow

of the interviewers. In responsive

designs, early replicates are used to

measure key cost and error features of a

survey.

Respondent—A person selected into

the sample who provides an interview.

In the 2006–2010 NSFG, the

‘‘respondents’’ are the approximately

5,500 men and women 15–44 years of

age who completed the NSFG interview

each year.

Response rate—Respondents to a

survey divided by the number of eligible

persons in the sample. In this report, the

response rate is the number of

respondents (15–44 years of age)

divided by the number of eligible

persons (15–44 years of age). Given that

not all screeners were completed, the

number of eligible persons is not known

precisely, so this number is estimated.

Responsive design—Responsive

survey designs pre-identify a set of

design features that could affect costs

and errors of survey statistics; identify a

set of indicators of the cost and error

properties of those features; monitor

those indicators in the initial phases of

data collection; alter the active features

of the survey in subsequent phases

based on cost and error tradeoff decision

rules; and combine data from the

separate design phases into a single

estimator.

Sample line—‘‘Sample line’’ is a

‘‘hold-over’’ term from an era in which

interviewers were sent to selected area

segments (blocks or linked groups of

blocks) to list all housing units. The

listing was done on paper, and later

keyed to a master list. The sample for

any given survey was selected from the

master list. The housing units listed

were ‘‘lines’’ on the listing sheet, and

the terminology was applied to the

electronic records in the master list.

The current design primarily uses

U.S. Postal Service DSF addresses

obtained from a commercial firm in

each segment. In segments where the

commercial firm cannot provide

adequate numbers of addresses (for

example, in rural areas where rural

delivery routes are used and no house

numbers or street names are available in

the DSF), listing is done ‘‘from

scratch.’’ Interviewers visit these

segments and list all housing units

directly into a laptop. Listed addresses

are uploaded to the central office at

the end of each day of listing. The

‘‘master file’’ contains addresses from

the DSF and from scratch listings. On

occasion the term ‘‘sample lines’’ is

used to refer to the electronic records

in this file. Thus, sample lines are

addresses and not necessarily housing

units. They become sample housing

units once selected and households

when the interviewer visits and finds

the housing unit occupied.

Sampling variance—The sampling

variance is a measure of the variation of

a statistic, such as a proportion or a

mean, which is due to having taken a

random sample instead of collecting

data from every person in the full

population. It measures the variation of

the estimated proportion or mean over

repeated samples. The sampling variance

is zero when the full population is

observed, as in a census. For the NSFG,

the sampling variance estimate is a

function of the sampling design and the

population parameter being estimated

(for example, a proportion or a mean).

Many common statistical software

packages compute ‘‘population’’

variances by default; these may

underestimate the sampling variance.

Estimating the sampling variance

requires special software, such as those

discussed in this report.

Sampling weight—For a respondent

in the NSFG, the ‘‘sampling weight’’ is

the estimated number of persons in the

target population that he or she

represents. For example, if a man in the

sample represents 12,000 men in his age

and race and ethnicity category, then his

‘‘sampling weight’’ is 12,000. The

NSFG sampling weights adjust for

different sampling rates (of the age and

race and ethnicity groups), different

response rates, and different coverage

rates among persons in the sample, so

that accurate national estimates can be

made from the sample. Because it

adjusts for all these factors, it is

sometimes called a ’’fully adjusted’’

sampling weight.

Screening interview—Sometimes

called a ‘‘screener,’’ a screening

interview is a (usually short) set of

questions asked of a household member

to determine whether the household

contains anyone eligible for the survey.

In the NSFG, the screening interview

consisted of a household roster,

collecting age, race, ethnicity, and

gender. Those households having one or

more persons 15–44 years of age were

eligible for a main interview. In the

NSFG, only persons 18 and over can

answer the screener.

Self-representing area—A county or

group of counties forming a primary

sampling unit with population counts

sufficiently large to be equal to or

greater than the typical stratum size in

the U.S. national sample. Such PSUs are

thus represented in all draws of a

national sample using the design. The

sampling probabilities for persons in

such areas are designed to be equal to

that applicable in smaller PSUs, called

nonself-representing areas.

Segment—A group of housing units

located near one another, all of which

were selected into the sample.

Simple random sample—A sample

in which all members of the population

are selected directly and have an equal

chance to be selected for the sample.

The NSFG sample is not a simple

random sample. The NSFG sample was

stratified, selected in stages, and

employed unequal chances of selection

for the respondents, by age, race,

ethnicity, and gender. Such designs are

referred to as ‘‘complex’’ and require

special software to estimate the variance

of statistics computed from a sample

with a complex design.

Strata; Stratification—Stratification

is the partitioning of a population of

sampling units into mutually exclusive

categories (strata). Typically,

stratification is used to increase the

precision of survey estimates for

subpopulations important to the survey’s

objectives. In the 2006–2010 NSFG,

those groups include teenagers (15–19

years of age), Hispanic men and

women, and non-Hispanic black men

and women. To obtain larger and more

reliable samples of these groups, the
Page 34 [ Series 2, No. 150

NSFG sample was stratified: in the first

stage of selection, PSUs were stratified

using socioeconomic and demographic

variables; in the second stage of

selection, segments within each PSU

were stratified by the proportion of the

population that was black or Hispanic.

SurveyTrak—A software-based

sample administration system that was

used in the 2002 and 2006–2010 NSFG.

The system is used by interviewers on

laptop computers to document their

sample assignment, to organize the

activities of their workday, to prompt

them for appointments to be kept, to

record results of each call attempt, to

record observations of the sample

housing unit, and otherwise keep track

of their job duties.

Target population—The population

to be described by estimates from the

survey. In NSFG the target population

was the household population of the

United States, which refers to the

civilian noninstitutionalized population, plus active-duty military who

are not living on military bases.

‘‘Noninstitutionalized’’ refers to the

omission of prisons, hospitals,

dormitories, and other large residences

under central control. College students

living in dormitories were interviewed

but sampled through their parent or

guardians’ households.

UM-ISR—the University of

Michigan Institute for Social Research.

WEBDOC—A software-based

presentation of metadata and other

survey documentation used for the

NSFG. The NSFG Webdoc shows the

variable names, category labels, and

frequencies for each variable in the data

file. The NSFG Webdoc is accessible at:

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

Weight—See ‘‘Sampling Weight.’’
Series 2, No. 150 [ Page 35

Appendix II. Degrees

of Freedom in

Estimates from the

National Survey of

Family Growth

As noted earlier in the report, the

NSFG design is based on a national

sample containing 110 primary sampling

units (PSUs). For purposes of variance

estimation, these 110 PSUs are

rearranged to create sampling error

computing units (SECUs) that parallel

the sample design, mask the identity of

geographic areas representing the PSUs

to protect respondent confidentiality, and

produce variance estimates for the

stratified multistage design that are quite

close to those that would be obtained if

the 110 PSUs were used directly. These

SECUs will be more numerous than the

PSUs, as noted in the estimation section

of this report.

For the expected NSFG 2006–2010

release, all 110 PSUs will be included.

This release thus includes eight ‘‘super

8,’’ 20 other self-representing (SR), and

82 nonself-representing (NSR) PSUs.

These are assembled into a set of 152

SECUs: 32 from the super 8, 40 from

the SR PSUs, and 80 from the NSR

PSUs, or a total of 152 SECUs. (4 of

the 82 NSR PSUs will be combined to

form two SECUs, thus creating 80

SECUs from 82 PSUs.)

These SECUs are grouped into 56

sampling error strata: 16 for the ‘‘super

8’’ PSUs containing two SECUs each;

20 for the SR PSUs containing two

SECUs each; and 20 for the NSR PSUs

containing four SECUs each. Thus,

there are 16 + 20 pairs of units for the

super 8 and SR part of the sample, and

20 ‘‘quads’’ for the NSR portion of the

sample.

The NSR grouping into strata with

four SECUs is done because of the need

to provide calendar year estimates. Any

one calendar year spans two national

samples, and thus uses 20 NSR PSUs

from each calendar year. Pairs could be

formed for any two successive data

collection years to group the 40 total

NSR PSUs into strata for variance

estimation purposes. However, a NSR

SECU in one data collection year would

have to be paired with an NSR SECU in

the year before and the year following.

This would require multiple sets of

variance estimation strata and SECUs

that users would then have to select on

the basis of what data collection and

calendar years are being used in a

particular analysis. This is too

complicated a scheme to impose on

NSFG users. Instead, the ‘‘quad’’

grouping of strata provides a

nonstandard but statistically sound way

to provide a single set of sampling error

strata and SECUs for any release or

calendar year estimate.

The ‘‘quad’’ grouping does incur a

slight loss in precision. ‘‘Quad’’

grouping requires a higher level of

collapsed strata than ‘‘pairs.’’ For

example, there are 80 total SECUs in 80

total strata in the sample of NSR PSUs.

In a pairwise collapsing, the 80 stratum

would be collapsed into 40 and 40

stratum boundaries ignored. This

collapsing leads to a slight overestimation of variance. On the other

hand, 20 ‘‘quads’’ ignore an additional

20 stratum boundaries over the pairwise

collapsing by collapsing four strata into

one sampling error stratum. By ignoring

the additional stratum boundaries, there

is a further increase in estimated

variance. This additional increase has

been examined empirically, and

preliminary analysis suggests that the

increase in variance is in the range of 7

to 8 percent for means and proportions,

or 3 to 4 percent increases in estimated

standard errors. Such modest losses can

be tolerated for many NSFG estimates.

Therefore, for user convenience of a

single set of strata to be used across all

possible analyses, the NSFG uses a

grouping of four SECUs in the NSR

portion of the sample.

The degrees of freedom for an

analysis depend on the number of

SECUs and the number of strata used in

the analysis. The full 4-year sample in

the 2006–2010 release will contain 152

SECUs created from the 110 PSUs

grouped into 56 strata (16 from the

super 8, 20 from the SR, and 20

‘‘quads’’ from the NSR). The degrees of

freedom can be computed approximately

as the number of SECUs minus the

number of strata, or a total of

152 – 56 = 96 degrees of freedom. This

degrees of freedom count will only be

available for analyses of the complete

2006–2010 release. Shorter time periods,

such as the 2-year 2007–2008 calendar

year period will have fewer degrees of

freedom.

For example, a single data

collection year will have the full set of

32 variance computation units formed

for the super 8 PSUs. There will be five

rotating SR SECUs each data collection

year generating 10 SECUs, and there

will be 20 rotating NSR SECUs. Thus, a

single data collection year from July in

the first year through June in the second

will have 62 SECUs. Since at present

there are no plans to produce data

collection year estimates, these units

will not need to be grouped into

sampling error strata, and degrees of

freedom are not relevant.

However, in a single calendar year,

which spans two data collection years,

there will be 62 SECUs from the first

data collection year plus 30 new SECUs

from the following data collection year,

a total of 92 units. Average counts per

unit indicate cluster sizes for female or

male data sets for a calendar year will

be small (20 males and 25 females per

unit) reducing design effects for

calendar year estimates. With respect to

degrees of freedom, the 32 super 8, 20

SR SECUs, and 40 NSR SECUs will be

grouped into 46 strata. Thus, an annual

calendar year estimate will have

92 – 46 = 46 degrees of freedom.

For a two calendar year estimate,

there will 16 degrees of freedom from

the super 8 PSUs, 15 degrees of

freedom from the 15 SR SECUs in three

data collection years spanning the two

calendar year period, and 60 – 20 = 40

degrees of freedom from NSR SECUs,

or a total of 71 degrees of freedom.

Finally, research continues

concerning the reliability of estimates

for the new variables added in 2007. In

the 2006–2008 release, a weight will be

provided for the 18-month period during

which the new variables were collected.

The degrees of freedom for this

18-month sample will be based on

interviews in all eight super 8 PSUs, 10

SR PSUs, and 40 NSR PSUs. For 5 of

the SR and 20 of the NSR SECUs in
Page 36 [ Series 2, No. 150

such an 18-month period, the number of

interviews will be one-half as large as

those from the other 5 SR and 20 NSR,

because only 6 months of interviewing

will be represented. Nonetheless, the

degrees of freedom for data estimates

from such an 18-month sample will be

the same as those for a two calendar

year estimate, or 46 degrees of freedom.
Vital and Health Statistics

Series Descriptions

ACTIVE SERIES

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

describes the data collection programs of the National Center

for Health Statistics. Series 1 includes descriptions of the

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

other material necessary for understanding the data.

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

report concerns statistical methods and includes analytical

techniques, objective evaluations of reliability of collected

data, and contributions to statistical theory. Also included are

experimental tests of new survey methods, comparisons of

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

2009, studies of cognition and survey measurement, and final

reports of major committees concerning vital and health

statistics measurement and methods.

Series 3.	 Analytical and Epidemiological Studies—This type of

report presents analytical or interpretive studies based on vital

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

studies based on surveys that are not part of continuing data

systems of the National Center for Health Statistics and

international vital and health statistics reports.

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

type of report contains statistics on illness; unintentional

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

services; and a wide range of special current health topics

covering many aspects of health behaviors, health status, and

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

this continuing national household interview survey.

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

National Health and Nutrition Examination Surveys, and

the Hispanic Health and Nutrition Examination Survey—

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

and measurement on representative samples of the civilian

noninstitutionalized population provide the basis for (1)

medically defined total prevalence of specific diseases or

conditions in the United States and the distributions of the

population with respect to physical, physiological, and

psychological characteristics, and (2) analyses of trends and

relationships among various measurements and between

survey periods.

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

report contains statistics on health resources and the public’s

use of health care resources including ambulatory, hospital,

and long-term care services based on data collected directly

from health care providers and provider records.

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

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

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

demographic variables, and geographic and trend analyses

are included.

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

report contains statistics on natality, marriage, and divorce

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

Special analyses by health and demographic variables and

geographic and trend analyses are included.

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

reports contain statistics on factors that affect birth rates,

including contraception and infertility; factors affecting the

formation and dissolution of families, including cohabitation,

marriage, divorce, and remarriage; and behavior related to

the risk of HIV and other sexually transmitted diseases.

These statistics are based on national surveys of women and

men of childbearing age.

DISCONTINUED SERIES

Series 4.	 Documents and Committee Reports—These are final

reports of major committees concerned with vital and health

statistics and documents. The last Series 4 report was

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

in Series 2 or another appropriate series, depending on the

report topic.

Series 5.	 International Vital and Health Statistics Reports—This

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

those of other countries or presents other international data of

relevance to the health statistics system of the United States.

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

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

depending on the report topic.

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

uses methods of cognitive science to design, evaluate, and

test survey instruments. The last Series 6 report was

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

in Series 2.

Series 12.	 Data From the Institutionalized Population Surveys—

The last Series 12 report was published in 1974. Reports

from these surveys are included in Series 13.

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

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

health resources are included in Series 13.

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

statistics on health and health-related topics collected in

special surveys that are not part of the continuing data

systems of the National Center for Health Statistics. The last

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

based on these surveys are included in Series 3.

Series 16.	 Compilations of Advance Data From Vital and Health

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

All reports are available online, and so compilations of

Advance Data reports are no longer needed.

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

The last Series 22 report was published in 1973. Reports

from these sample surveys, based on vital records, are

published in Series 20 or 21.

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

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

All reports are available online, and so compilations of reports

are no longer needed.

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

in these series, contact:

Information Dissemination Staff

National Center for Health Statistics

Centers for Disease Control and Prevention

3311 Toledo Road, Room 5412

Hyattsville, MD 20782

1–800–232–4636

E-mail: cdcinfo@cdc.gov

Internet: http://www.cdc.gov/nchs
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DHHS Publication No. (PHS) 2010–1350, Series 2, No. 150