National Survey of Family

Growth, Cycle 6: Sample

Design,Weighting,

Imputation,andVariance

Estimation

Series 2, Number 142 July 2006
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Suggested citation

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.

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National Survey of Family Growth, cycle 6 : sample design, weighting,

imputation, and variance estimation.

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

research ; no. 142) (DHHS publication ; no. (PHS) 2006–1342)

‘‘July 2006.’’

Includes bibliographical references.

ISBN 0–8406–0608–7

1. Family size—Statistical methods. 2. Fertility, Human—Statistical methods.

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(U.S.) II. Series. III. Series: DHHS publication ; no. (PHS) 2006–1342

[DNLM: 1. National Survey of Family Growth (U.S.) 2. Research Design—

United States. 3. Data Collection—methods—United States. 4. Family

Characteristics—United States. 5. Parenting—United States. 6. Reproductive

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Series 2, Number 142

National Survey of Family

Growth, Cycle 6: Sample

Design,Weighting,

Imputation,andVariance

Estimation

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

July 2006

DHHS Publication No. (PHS) 2005-1342
National Center for Health Statistics

Edward J. Sondik, Ph.D., Director

Jennifer H. Madans, Ph.D., Acting Co-Deputy Director

Michael H. Sadagursky, Acting Co-Deputy Director

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

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

and Legislation

Michael H. Sadagursky, Associate Director for

Management and Operations

Lawrence H. Cox, Ph.D., Associate Director for Research

and Methodology

Margot A. Palmer, Director for Information Technology

Margot A. Palmer, Acting Director for Information Services

Linda T. Bilheimer, Ph.D., Associate Director for Analysis

Epidemiology, and Health Promotion

Charles J. Rothwell, Director for Vital Statistics

Jane E. Sisk, Ph.D., Director for Health Care Statistics

Jane F. Gentleman, Ph.D., Director for Health Interview

Statistics

Clifford L. Johnson, Director for Health and Nutrition

Examination Surveys

Division of Vital Statistics

Charles J. Rothwell, M.S., Director

James A. Weed, Ph.D., Deputy Director

Stephanie J. Ventura, M.A., Chief, Reproductive Statistics

Branch

Nicholas F. Pace, Systems Programming and Statistical

Resources Branch
Acknowledgments This report describes the sample

design, associated estimation

procedures, and variance

estimation methods used in Cycle6of

the National Survey of Family Growth.

The survey was designed and conducted

by the National Center for Health

Statistics (NCHS), in cooperation with

the survey contractor, the Institute for

Social Research (ISR), University of

Michigan, Ann Arbor, Michigan. The

sampling plan was developed by Robert

M. Groves, Steven G. Heeringa, and

James M. Lepkowski, Institute for

Social Research (ISR), in consultation

with

William Mosher and Karen Davis

of NCHS. The information in this report

is based on survey design

documentation prepared by staffof ISR

and on internal NCHS memoranda.

Cycle6of the National Survey of

Family Growth (NSFG) was jointly

planned and funded by the following

programs and agencies of the U.S.

Department of Health and Human

Services: + The National Institute of Child

Health and Human Development

+ The Office of Population Affairs + The CDC’s National Center for

Health Statistics (CDC/NCHS)

+ The CDC’s National Center for HIV,

STD, and TB Prevention

+ The CDC’s Division of

Reproductive Health

+ The CDC’sOffice ofWomen’s

Health

+ The Office of Planning, Research,

and Evaluation of the Administration

for Children and Families (ACF)

+ The Children’s Bureau of the 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.

Steven Heeringa andTerry Adams of the

University of Michigan revieweda

preliminary draft of this report and

provided useful comments.Two

independent referees also reviewed this

report: Christopher L. Moriarity

,

formerly of the Government

Accountability Office (GAO), and

Steven R. Machlin of the Agency for

Healthcare Research and Quality. Their

suggestions have been incorporated into

the report; any remaining errors are the

responsibility of the authors and not the

reviewers.

This report was prepared under the

general direction of Charles J. Rothwell,

Director of the Division ofVital

Statistics (DVS), James A.Weed,

Deputy Director of DVS, and Stephanie J. Ventura, Chief of the Reproductive

Statistics Branch (RSB). The report was

edited by GailV. Johnson of the

Information Design and Publishing

Staff, Office of Information Services,

NCHS; typesetting was done by Zung

Le and graphics were produced by

Jarmila G. Ogburn, CoCHIS/NCHM/

Division of Creative Services.

iii
Contents

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

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

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

Sample Design.......................................................................................... 2

Responsive Design Features ............................................................................... 2

Sampling Weights ....................................................................................... 2

Item Imputation ......................................................................................... 3

Variance Estimation...................................................................................... 3

Conclusion ............................................................................................. 4

Background .............................................................................................. 4

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

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

Summary of Sample Design ............................................................................... 5

Sample Size Requirements ................................................................................ 8

First-Stage Selection of Primary Sampling Units .............................................................. 9

Second-Stage Selection of Segments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Third-Stage Selection of Housing Units. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Fourth-Stage Selection of Sample Persons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Sample Release . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Sampling in Three Phases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Sample Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Response Rate Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Nonresponse Bias and Phase 3 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Cost-Efficiency and Response Rates in the Phase 3 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Responsive Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Sample Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Weighting Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Inverse Probability Selection Weighting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Nonresponse Adjustment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Poststratification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Weight Trimming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Final Weight Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Estimating Equation for Totals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Imputation Procedures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Background on Imputation for Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Sequential Regression Imputation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Multiple Imputation Using Sequential Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Regression Imputation in NSFG Cycle 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Variance Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Summary of Variance Estimation Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

v
Variance Estimation Software .............................................................................. 38

Application to the NSFG ................................................................................. 39

References ............................................................................................... 44

Appendix I............................................................................................... 46

Definition of Terms...................................................................................... 46

Appendix II .............................................................................................. 49

Variables Imputed and Types of Imputation for Female, Male, and Pregnancy Files .................................. 49

Appendix III ............................................................................................. 55

Distribution of Female, Male, and Pregnancy Records, by Stratum and Sampling Error Computing Units in the

National Survey of Family Growth, Cycle 6 .................................................................. 55

Appendix IV ............................................................................................. 63

Variance Estimates Using SAS, SUDAAN, and STATA, for a Percentage .......................................... 63

Appendix V .............................................................................................. 68

Variance Estimates Using SAS, SUDAAN, and STATA, for a Mean .............................................. 68

Appendix VI ............................................................................................. 73

Variance Estimates Using SAS, SUDAAN, and STATA, for a Regression ......................................... 73

Appendix VII............................................................................................. 76

Variance Estimates SAS, SUDAAN, and STATA for a Logistic Regression ......................................... 76

Appendix VIII ............................................................................................ 80

Median Design Effect for Female, Male, and Pregnancy File Means and Proportions................................. 80

Figures

1. History of the National Survey of Family Growth ......................................................... 5

2. National Survey of Family Growth Cycle 6 selection of primary sampling units and segments..................... 6

3. Selection steps in sample implementation for Cycle 6 of the National Survey of Family Growth ................... 7

4. Within-household screener and household roster .......................................................... 8

5. Within-household measures of size and illustration of within-household selection ............................... 9

6. Response rates for Phases 1 and 2 and overall for 18 subpopulations ......................................... 20

7. Response rates for Phases 1 and 2 and overall (reflecting subsampling of nonrespondents) by age group; National

Survey of Family Growth Cycle 6, 2002 ................................................................ 20

8. Estimated proportion of females who have ever had sexual intercourse in Cycle 6 of the National Survey of Family

Growth: Cumulative estimate; estimate by call number of interview, and estimate from Phase 3 interviewing......... 21

9. Cumulative ratio of interviewer hours to number of main interviews completed by day of the data-collection period:

National Survey of Family Growth Cycle 6, 2002 ......................................................... 24

10. SAS 9.1 program code for illustration 1 ................................................................. 40

11. SAS 9.1 output from illustration 1 for estimation of the percent of women using the oral contraceptive pill in each

of six 5-year age groups, National Survey of Family Growth Cycle 6, 2002.................................... 41

12. SUDAAN program code for illustration 1 ............................................................... 42

13. SUDAAN 8.0.2 output from illustration 1 for estimation of the percent of women using the oral contraceptive pill in

each of six 5-year age groups, National Survey of Family Growth Cycle 6, 2002 ............................... 43

14. STATA 8.0 program code for illustration 1 ............................................................... 44

15. STATA output from illustration 1 for estimation of the percent of women using the oral contraceptive pill in each of

six 5-year age groups, National Survey of Family Growth Cycle 6, 2002 ...................................... 44

16. Wesvar table specification window for illustration 1 for estimation of the percent of women using the oral

ontraceptive pill in each of six 5-year age groups, National Survey of Family Growth Cycle 6, 2002 ............... 45

Text Tables

A. Allocation of sample segments and sampling rates within domains: National Survey of Family Growth, Cycle 6 ...... 12

B. Expected and observed eligibility rates by sex, age, and race or ethnicity: National Survey of Family Growth, Cycle 6 ..... 14

C. Number of Phases 1 and 2 segments by number of active incomplete cases in the segment at the end of Phase 2, and

the estimated total propensity of active cases to complete an interview on the next visit: National Survey of Family

Growth, Cycle 6 .................................................................................... 16

vi
D. Phase 3 segment sampling fractions, by number of active cases in the segment, and the estimated total propensity

of active cases in the segment to complete the interview on the next visit: National Survey of Family Growth,

Cycle 6 ........................................................................................... 16

E. Response rates, weighted only for subsampling in Phase 3, but not for other probabilities of selection, using two

estimates (0.64 and 0.58) of the proportion of nonscreened households that had an eligible respondent: National Survey

of Family Growth, Cycle 6 ........................................................................... 18

F. Fully weighted response rates, adjusted for all unequal probabilities of selection, using two estimates (0.64 and 0.58)

of the proportion of nonscreened households that had an eligible respondent: National Survey of Family Growth,

Cycle 6 ........................................................................................... 19

G. Mean number of calls per active screener case by number of active cases and total propensity strata in Phase 3:

National Survey of Family Growth, Cycle 6 ............................................................. 21

H. Mean number of calls per active main interview case by number of active cases and estimated total propensity of

cases in the segment to respond on the next visit: Phase 3 of National Survey of Family Growth, Cycle 6 ........... 22

J. Mean screener response rates for segments in Phase 3 by number of active cases and total propensity strata:

National Survey of Family Growth, Cycle 6 ............................................................. 22

K. Mean main interview response rates for segments during Phase 3 by number of active cases at the beginning of

Phase 3, and the total propensity of cases in the segment to respond on the next visit: National Survey of Family

Growth, Cycle 6 ................................................................................... 23

L. Mean ratio of number of screener interviews to number of total calls on screener cases by number of active cases

in the segment, and by the estimated total propensity of cases in the segment to respond on the next interviewer

visit: National Survey of Family Growth, Cycle 6 ......................................................... 23

M. Mean ratios of number of main interviews to number of total calls on screener cases by number of active cases and

total propensity strata: National Survey of Family Growth, Cycle 6........................................... 23

N. Number of completed interviews by age group, sex, and race or ethnicity: National Survey of Family Growth,

Cycle 6 ........................................................................................... 25

O. Mean, minimum, and maximum untrimmed base weight, and potential increase in variance due to weighting (1+L),

by age group, sex, and race or ethnicity: National Survey of Family Growth, Cycle 6............................ 27

P. Logistic Regression Model Predicting Response to the Screener, with 18 predictors (Phases 1–3): National Survey of

Family Growth, Cycle 6.............................................................................. 29

Q. Nine logistic regression models predicting response to the main interview (Phases 1–3): National Survey of Family

Growth, Cycle 6 .................................................................................... 32

R. Mean, minimum, and maximum combined nonresponse adjustments, and potential increase in variance due to

weighting, by age group, sex, and race or ethnicity: National Survey of Family Growth, Cycle 6 .................. 33

S. Poststratification adjustment factors for 36 groups, by age, sex, and race or ethnicity: National Survey of Family

Growth, Cycle 6 .................................................................................... 34

T. Mean, minimum, and maximum final (trimmed) weight, and potential increase in variance due to weighting, by

age group, sex, and race or ethnicity: National Survey of Family Growth, Cycle 6 .............................. 35

Appendix Tables

I. Imputed case counts in the female file .................................................................. 49

II. Imputed case counts in the male file .................................................................... 53

III. Imputed case counts in the pregnancy file ............................................................... 54

IV. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the

combined female and male file ........................................................................ 55

V. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the

pregnancy file, NSFG Cycle 6 ........................................................................ 59

VI. Median design effects for 10 female respondent file variables, by race and ethnicity and demographic characteristics:

National Survey of Family Growth, Cycle 6 ............................................................. 80

VII. Median design effects for six female pregnancy file variables, by race and ethnicity and demographic characteristics:

National Survey of Family Growth, Cycle 6 ............................................................. 81

VIII. Median design effects for five male respondent file variables, by race and ethnicity and demographic characteristics:

National Survey of Family Growth, Cycle 6 ............................................................. 82

vii
Objectives

Cycle 6 of the National Survey of

Family Growth (NSFG) was conducted

by the National Center for Health

Statistics in 2002 and early 2003. This

report describes how the sample was

designed, shows response rates for

various subgroups of men and women,

describes how the sample weights were

computed to make national estimates

possible, shows how missing data were

imputed for a limited set of key

variables, and describes the proper

ways to estimate sampling errors from

the NSFG. The report includes both

nontechnical summaries for readers

who need only general information and

more technical detail for readers who

need an in-depth understanding of

these topics.

Methods

The NSFG Cycle 6 was based on an

independent, national probability

sample of men and women 15–44

years of age. It was the first NSFG ever

to include a national sample of men

15–44 as well as a sample of women.

Fieldwork was carried out by the

University of Michigan’s Institute for

Social Research (ISR) under a contract

with NCHS. In-person, face-to-face

interviews were conducted by

professional female interviewers using

laptop computers. In all, 12,571 women

and men—7,643 females and 4,928

males—were interviewed, the largest

NSFG ever done.

Results

Analysis of NSFG Cycle 6 data

requires the use of sampling weights

and estimation of sampling errors that

accounts for the complex sample

design and estimation features of the

survey. Examples of how to use several

available software packages that

incorporate complex design features in

estimation, such as SAS, SUDAAN,

and STATA, are presented.

Keywords: survey methodology c

response rates c imputation c

variance estimation c National

Survey of Family Growth

National Survey of Family

Growth, Cycle 6: Sample

Design, Weighting, Imputation,

and Variance Estimation

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; and Robert M.

Groves, Ph.D., John van Hoewyk, Ph.D., and Jennifer Willem, M.S.,

Institute for Social Research, University of Michigan

Executive Summary

Cycle 6 of the National Survey of

Family Growth (NSFG) obtained

detailed information on factors

affecting childbearing, marriage, and

parenthood from a national probability

sample of 12,571 men and women

15–44 years of age. This Series 2 report

is the technical documentation of the

procedures used to select the sample,

develop the sampling weights, impute

missing data, and estimate sampling

errors. The report will be useful to at

least two types of readers: first, data

analysts who intend to use the NSFG

and need to understand the sample

design, weighting, imputation, and

variance estimation; and second, those

interested in survey methodology who

may wish to compare their procedures

to those used in the NSFG. Parts of this

report contain a great deal of technical

detail. For readers seeking a general

understanding of the survey procedures,

this section provides a summary of the

procedures used.

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, infertility, and

sources of family planning services

+ Factors affecting marriage, divorce,

cohabitation, and adoption

+ What women and men do to raise

their children

+ Their attitudes about sex,

childbearing, and marriage

For Cycle 6, interviewing and data

processing were conducted by the

University of Michigan’s Institute for

Social Research (Robert Groves, Project

Director), under a contract with NCHS.

A national probability sample of

12,571 men and women 15–44 years of

age living in households in the United

States were interviewed between

mid-March 2002 and the end of

February 2003. The interviews were

conducted in person by trained female

interviewers using laptop, or notebook,

computers, a procedure called

computer-assisted personal interviewing

(CAPI). The interviews for women

averaged 85 minutes; the interviews for

men averaged about 60 minutes.

For women, data were collected on

each pregnancy (if any); contraceptive

use; her ability to bear children; the use

of medical services for contraception,

Page 1
Page 2 [ Series 2, No. 142

infertility, and prenatal care; marriage

and cohabitation history; and a variety

of demographic and economic

characteristics. For men, data were

collected on marriage histories;

contraception and children fathered;

fatherhood involvement measures; and

demographic and economic

characteristics. In a self-administered

part of the interview, women and men

were also asked questions on behaviors

related to the risk of sexually

transmitted diseases, including the

human immunodeficiency virus (HIV).

This summary describes briefly how

the sample was designed and selected,

how sampling weights were computed

and adjusted to compensate for

nonresponse, how sampling errors were

estimated, and how missing data were

imputed for some data items.

Sample Design

A total of 12,571 men and women

were selected using probability sampling

methods from 121 primary sampling

units (PSUs). A PSU is a metropolitan

area, a county, or a group of adjacent

counties. PSUs were located in nearly

every State, and included all of the

largest metropolitan areas in the United

States.

From each PSU, secondary units,

called segments, were selected.

Segments are, roughly, neighborhoods or

groups of adjacent blocks. In each

selected segment, addresses were listed,

and a sample of the addresses was

taken. The sampled addresses were

contacted, and a ‘‘screener’’ interview

was 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.

Responsive Design

Features

Surveys with high response rate

goals and limited budgets such as Cycle

6 of the NSFG, need a way to stay

informed on how much interviewer

labor and money are being spent on data

collection; what areas and interviewers

are having good results and poor results;

and what types of nonresponse are most

prevalent in each area. The Institute for

Social Research (ISR) used a system

called SurveyTrak to provide that

information each working day.

Because the NSFG budget for data

collection was limited, and significant

changes to the questionnaires and

procedures had to be made between the

pretest and the main study, it was

important to determine how well main

study interviewers could perform the

tasks they had been trained to do.

SurveyTrak allowed study staff to

monitor how many hours of effort were

required to obtain interviews, whether

interviewers worked as many hours as

expected, and response rates. Project

staff used this and other information to

manage study data collection to keep it

within budget. The management process

led to modification of study procedures

throughout the field period. Three

phases of fieldwork occurred.

Phase 1 of fieldwork—In March

2002, an initial group of interviewers

was trained and started fieldwork in a

subsample of areas. Monitoring through

the SurveyTrak system made it possible

to make better estimates of interviewer

production and survey costs for the rest

of the main study.

Phase 2 of fieldwork—The full

study sample was released June 2002

through January 2003. Interviewers

recorded characteristics of neighborhoods (sample segments) and sampled

households. The contractor collected and

analyzed these data during fieldwork

and used the data to allocate interviewer

labor during the study.

Phase 3 of fieldwork—SurveyTrak

data were used most effectively in the

last month of fieldwork—February

2003. Approval was obtained to use

somewhat larger incentives to encourage

participation. Prediction models based

on SurveyTrak data were used to select

segments that had the largest number of

people who were most likely to

complete the interview. Field resources

in the last month of the survey were

focused on the selected Phase 3

segments. A large number of interviews

were obtained and contributed to higher

weighted response rates across all three

phases of data collection. The Phase 3

interviews also provided indirect

evidence that nonresponse bias was

reduced by the addition of Phase 3

interviews.

A more complete description of

these design phases and the generation

and use of these data are reported in the

NSFG Series 1 report. The procedures

to produce and use these data to manage

fieldwork are called ‘‘responsive

design’’ features and were developed to

yield higher response rates in key

demographic groups in the sample than

would have occurred without it.

Sampling Weights

A simple random sample in which

response rates and coverage were the

same in every subgroup would be a

‘‘scale model’’ of the population.

However, many survey samples are not

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

model’’ of the population were 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 select groups at different rates

deliberately 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 men and women

were chosen to account for 19.6 percent

of all respondents in the sample, even

though they are 12.9 percent of the

population 15–44 years of age. In

addition to black men and women,

Hispanic men and women, and teenagers

of all races were also sampled at higher

rates than non-Hispanic white adults in

this cycle of the NSFG. ‘‘Sampling

weights’’ adjust for these different

sampling rates, response rates, and

coverage rates so that unbiased national

estimates can be made from the sample.

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
Series 2, No. 142 [ Page 3

represents 8,000 women in the

population. For the NSFG, the fully

adjusted sampling weights were

assigned to each respondent and

consisted of three factors. The first

factor is the inverse of the probability

that the case was selected. For example,

if the probability of selection is 1 in

6,000, then the initial sampling weight

is 6,000. The second factor is an

adjustment for nonresponse, which was

calculated separately based on the

probability of completing a screener,

and the probability that a completed

screener would result in a completed

interview. The third factor is an

adjustment to control totals of the

number of persons by age, gender, race,

and Hispanic origin, provided by the

U.S. Census Bureau (and, for military

personnel living in a household outside

of a military facility, by the Defense

Manpower Data Center). This process is

called poststratification.

Item Imputation

In any survey, not every question is

answered by every person interviewed.

Sometimes a respondent cannot

remember the fact asked for in a

question; sometimes he or she may

refuse to answer. Other times, the

answer that the respondent gives is

clearly inconsistent with other

information in the interview, so one or

more of the inconsistent answers are set

to ‘‘missing.’’ Such ‘‘missing data’’

create inconsistencies in estimates,

which may be confusing for many users

of the data. Assigning 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 have to be

dropped from an analysis. It also allows

all of the collected data to be used in

analysis. In an analysis involving several

different variables, cases may be

dropped because they are missing for

one variable, even though there are

nonmissing values for the other

variables available for those cases. In

effect, the analyst discards collected data

by deleting cases with item missing

values. Finally, 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

a value that is a prediction from other

variables and not just the average of the

same variable from cases without

missing values.

In Cycle 6 of the NSFG, there are

thousands of variables in the data file.

Of these many variables, 356 recoded

variables (called ‘‘recodes’’) were

selected because they are used

frequently in analysis. Missing data for

these recodes could create

inconsistencies among survey estimates

and confusion among data users of both

the published data and the microdata

file, so these variables were imputed.

Selecting, editing, and imputing these

variables was one way to decide which

variables should be examined carefully

to ensure high-quality data, without

unduly delaying the release of the data

file.

The frequency of missing values for

the recoded variables in Cycle 6 was

low, in part because CAPI requires the

interviewer to enter an acceptable

response and then goes automatically to

the next appropriate question. A total of

2.0 percent of the values of all recoded

variables, in male, female, and

pregnancy files combined, were missing

and subsequently imputed. The program

also performed range and consistency

checks to help prevent logically

impossible answers. The two imputation

techniques used in Cycle 6 were:

+ Logical imputation

+ Regression imputation

Logical imputation involves having

a substantive expert (usually at NCHS)

look at the case, and by examining

related variables, assign a value for a

missing value that is essentially an

educated guess of the true value.

Regression imputation, as used for

NSFG Cycle 6, used software that

imputes a missing value using all other

variables in the data set as predictors. A

major part of the work of imputation

involves making certain that the values

imputed are within acceptable ranges,

and are consistent with other data

reported by the respondent. Slightly over

90 percent of the imputed values were

imputed by regression imputation.

Except when a reported value was

obviously incorrect, actual reported data

were never replaced by an imputed

value. For each recoded variable in the

database, an imputation flag identifies

whether the value of that variable was

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.

Variance Estimation

The sampling variance is a measure

of the variation 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

NSFG, the sampling error measures the

variation caused by interviewing 12,571

men and women in the NSFG instead of

all the 125 million men and women

15–44 years of age.) It 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 a function of

sampling design and the population

parameter being estimated, and it is

called the design-based sampling

variance. The NSFG data file contains a

final weight and information necessary

to estimate the sampling variance for a

statistic. Many statistical software

packages, such as SAS and SPSS, will

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 becoming more and

more common, and is easier to use and

obtain. Examples of how to use such

software to estimate sampling errors for

the NSFG are included in this report,

and further examples are shown on the

Internet at: http://www.cdc.gov/nchs/

about/major/nsfg/nsfgvar.htm.
Page 4 [ Series 2, No. 142

Conclusion

This report is designed in part to

give researchers using the NSFG the

knowledge they need to conduct their

research in a statistically valid way.

Because of the complex sample design

of the NSFG, analysts should use the

weights in analysis and use software

that will compute ‘‘design-based’’

estimates of sampling errors. Failure to

use the weights and accurate variance

estimates may lead to biased or

inaccurate findings and conclusions.

Background

The National Survey of Family

Growth (NSFG) is an ongoing

series of sample surveys designed

to provide current information about

childbearing, contraception, and related

aspects of maternal and child health for

the United States. Established in 1971

by the National Center for Health

Statistics, the NSFG is a periodic

survey. The first cycle was conducted in

1973, and subsequently conducted in

1976, 1982, 1988, and 1995. Cycle 6

was conducted in 2002–03.

The NSFG target population has

been expanded over time. For example,

the target population for Cycles 1 and 2

was the civilian household population of

women ages 15–44 years living in the

48 coterminous States and the District

of Columbia. These women were

currently or previously married, or never

married mothers with children living

with them at the time of interview (1,2).

The population for Cycle 3 included

women of all marital statuses and added

women living in group quarters. Thus,

Cycle 3 included all women ages 15–44

years in the civilian noninstitutionalized

population (3). Similarly, Cycles 4 and 5

targeted all women ages 15–44 years in

the civilian noninstitutionalized

population, but drew the NSFG sample

from a larger NCHS survey, the

National Health Interview Survey, and

included Alaska and Hawaii in the

sample (4–6).

The target population for NSFG

Cycle 6 differs from previous cycles in

two important ways. First, it includes

both men and women 15–44 years of

age in all 50 States and the District of

Columbia. This is the first time men

have been included in the NSFG.

Second, the target population is

restricted to the household population

(including persons living away from

home in college dormitories, sororities,

or fraternities), but it does not eliminate

all members of the military, as in past

cycles. If a member of the military

resided in housing outside a military

base or installation, they were eligible to

be included in the sample. Members of

the military residing in military

installations were not eligible. Cycle 6

thus includes for the first time men and

military personnel residing in civilian

housing.

In all cycles, data collection has

been conducted in person. In Cycles

1–3, interviews lasted an average of 1

hour; in Cycle 4, the average interview

length was 70 minutes; and in Cycle 5,

100 minutes (figure 1). Cycle 6

interviews were designed to last an

average of 1 hour for males and 80

minutes for females. Across the six

cycles, data are available on trends and

changes in fertility, contraception,

breastfeeding, use of family planning

services, and maternal and child health.

The sample design and data

collection for NSFG have been

conducted by several different survey

research organizations. For example,

Cycle 1 was conducted by the National

Opinion Research Corporation of the

University of Chicago. Cycles 2, 3, and

4 were conducted by Westat, Inc. of

Rockville, MD. Cycle 5 was conducted

by the Research Triangle Institute of

North Carolina, while the present cycle

was conducted by the ISR at the

University of Michigan. Descriptions

of sample design and data collection

procedures for Cycles 1 through 5 are

available in reports produced by

NCHS (1–6).

Cycle 6 obtained 12,571 completed

interviews, 7,643 with eligible women and

4,928 with eligible men. Interviews were

conducted between March 2002 and

February 2003, with the midpoint of the

survey in September 2002. This report

describes the sample design developed to

select the sample of men and women,

procedures to make national estimates

from the sample data, and methods used

to estimate the precision of those

estimated population parameters.

Design Specifications

Cycle 6 employed a stratified

multistage probability sample of

households and eligible persons.

The decision to use this kind of a design

was based on the following

specifications:

+ The target population for Cycle 6

was to be the household population

of men and women ages 15–44

years living in households in the 50

States and the District of Columbia.

+ The sample was to consist of

approximately 7,500 female and

4,800 male respondents from a

probability sample of households.

The sample was designed to yield at

least 2,000 completed interviews

with each of three groups: black

persons 15–44 years of age,

Hispanic persons 15–44 years of

age, and teens 15–19 years of age.

+ Screening interviews were to be

conducted in each sample

household, to determine eligibility

and to select one person from each

household that contained one or

more eligible persons using a

random selection procedure applied

to the list of persons living in the

household.

+ Data collection was to be conducted

by personal interview conducted in

sample households. Interviews were

expected to last an average of 1

hour for men and 80 minutes for

women. No proxy reports or data

were allowed.

+ All interviewers were to be female

because of the sensitivity of the

content of the interview. That is,

men and women were to be

interviewed by a female interviewer.

+ Data collection was to be completed

using Computer-Assisted Personal

Interviewing (CAPI). Survey

instruments were to be programmed

on laptop computers meeting

specified requirements. Data

collection was to be by personal
Series 2, No. 142 [ Page 5

Figure 1. History of the National Survey of Family Growth

interview for most items, but data

were to be collected for a selected

set of sensitive items using audio

computer-assisted self-interview

(ACASI) in which the respondent

listened to a prerecorded audio

reading of the questions via a headset

connected to the laptop and entered

responses directly into the laptop.

+ Signed informed consent was to be

required for every selected eligible

respondent. Minors 15–17 years of

age were to be required to have both

the signed consent of a parent or

guardian and their own signed

assent. Interviews obtained without

signed consent were not to be

allowed. (The interviewer was to be

permitted to sign for an adult

respondent 18–44 years of age, but

this was to be done infrequently.)

+ Interviewers were to ask questions

about fertility, contraceptive use,

sources and types of family planning

services, and maternal and child

health using structured questions

read by the interviewer from the

laptop display or from audio

recordings heard on a headset.

+ The target interview completion rate

for females was to be 80 percent of

the expected number of selected

eligible women and 75 percent of the

expected number of selected eligible

men, where the interview completion

rate is the product of household

screener response rates and the main

interview response rate.

+ The contractor, in cooperation with

NCHS, was to design and

implement procedures for measuring

and controlling the quality of data

collection and preparation

procedures. These included

verification of a sample of

interviews.

Sample Design

Summary of Sample

Design

This section summarizes the sample

design and sample release features of

NSFG Cycle 6. A more detailed account

of the sample design follows this

summary. The NSFG Cycle 6 sample

design consisted of four stages of

selection to choose eligible sample

persons. Women, teenagers 15–19 years

of age, and black and Hispanic persons

were selected at higher rates, yielding an

oversample of such persons in Cycle 6.

The sample design is summarized in

figures 2 and 3.

The entire land area of the United

States was divided in the 1990 census

into 3,141 counties and county
Page 6 [ Series 2, No. 142

Figure 2. National Survey of Family Growth Cycle 6 selection of primary sampling units and segments

equivalent units. The metropolitan

counties and other units were grouped to

form 2,402 PSUs. These PSUs were

partitioned into three major strata—large

metropolitan areas, other metropolitan

areas, and nonmetropolitan areas. The

PSUs within each major stratum were

further grouped by geography and

population size.

Two separate samples of the 2,402

primary areas were selected for Cycle 6.

A national sample selected 110 PSUs

from the 50 States and the District of

Columbia. The national sample PSU

selection was based on 1990 census

counts of occupied housing units

(households) in each county or other

unit. This national sample was efficient

for obtaining a sample of black, white,

and other race households. It was not

efficient for selecting a sample of

Hispanic households.

A second national sample of

primary areas was selected to

supplement the sample of Hispanic

households in the United States obtained

in the national sample. Since the

selection of the Hispanic primary area

sample was completed after the 2000

census individual-level redistricting data

were released but before 2000 census

household data were available, the

counties were selected using counts of

Hispanic persons rather than occupied

housing units. The same 2,402 primary

areas were grouped into the same three

partitions used in the national sample,

and each partition was stratified by

geography and the number of Hispanic

persons. Within each stratum, one

primary area was selected as the PSU to

represent the stratum. The selection

procedure used a method developed by

Kish and Scott (7) to maximize the

probability of retaining a primary area

already selected as a PSU in the

national sample with probability

proportional to the year 2000 Hispanic

population of the PSUs. The selection to

maximize overlap yielded a total of 39

Hispanic sample PSUs that were already

in the national sample and 11 additional

Hispanic PSUs.

Within the 110 + 11 = 121 national

and Hispanic sample PSUs, a database

of population counts for census blocks

was obtained from the 2000 census.

Information was available on block

population by race and ethnicity.

In the second stage of selection,

census blocks were stratified into four

domains within each PSU:

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.

Blocks were then selected within each

domain with probabilities proportionate

to the estimated number of 2000 census

households.

Some blocks had only a few

housing units and were not adequate to

support subsequent required interviewer

workloads. Such small blocks were

linked together with other larger blocks,

and sometimes with other smaller

blocks, to form ‘‘segments’’ that were

large enough to support data collection

workloads. In urban areas, the minimum

size for a block before linking was

required was 75 estimated households

while in rural areas the minimum was

50. The segments thus formed consisted

of entire blocks or set of linked blocks.

A total of 1,414 segments were selected

across the 121 PSUs.

Trained household listers were sent

to each of the sample segments selected

in the second stage to list housing units

on the blocks in the segments. Housing

unit lists were returned to the ISR and

keyed into a file containing one line per

housing unit.

The third stage of selection chose

sample lines (housing units) from each

sample segment with probabilities

designed to obtain equal chances of

selection for households within segment

domains. Sample lines (sampled

households) in census blocks in which

more than 10 percent of the population

was black or Hispanic (domains 2, 3,

and 4 shown previously) were selected

at higher rates to increase the number of

black and Hispanic persons in the

sample.

The fourth stage was the selection

of eligible persons within sample

households. Interviewers visited housing

units represented by each selected

sample line or address. When the

housing unit was occupied, interviewers

attempted to list all persons living there.

One eligible person was chosen

randomly in every sample household

that contained one or more eligible

persons. The within-household selection

used preassigned measures of size that

varied by gender, age group, and race

and ethnicity. Teenagers 15–19 years

and females received larger measures of

size to increase their chances of

selection within the household and to

yield larger sample sizes required for

the target sample sizes for these groups.

Persons within households were then

selected with a probability proportionate

to the measure of size.

Persons living away from home in

college or university dormitories,

sororities, and fraternities were

considered to be part of their parents’

household and were listed in them. If a

college student was chosen, she or he

was transferred to an interviewer

working in the nearest PSU to the

college or university for interviewing. In

some cases, interviews were obtained at

the sample household when selected

students returned home for summer or

holidays.

A two-phase sampling procedure

was used to obtain the target number of

interviews within the survey budget.

Five weeks before the end of data

collection, a total of 11,721 interviews

had been completed. Not all sample

lines that had been released were

successfully screened to determine if

eligible persons resided at the address.

Additional sample lines had eligible

persons who were selected for interview,

but the interview had not been

completed. Among these remaining lines

to be screened and eligible selected

Figure 3. Selection steps in sample implementation for Cycle 6 of the National Survey of

Family Growth

Series 2, No. 142 [ Page 7
Page 8 [ Series 2, No. 142

Figure 4. Within-household screener and household roster

persons remaining to be interviewed, a

second-phase sample was chosen. The

second-phase sample consisted of

approximately 1,700 lines that had not

been screened and another 1,500

households containing eligible persons

but without main interviews. This size

was chosen to yield a target of 12,500

completed interviews without

exceeding the funds that remained to

pay for interviewer labor for data

collection. The second-phase sample

yielded an additional 850 completed

interviews. The final response rate

needs to be weighted to account for

the second-phase sampling rates. The

final weighted response rate for men

and women combined was 79 percent,

virtually the same for women

(80 percent) and men (78 percent). The

detailed description of the design

follows.

Sample Size Requirements

The final sample size for Cycle 6 of

the NSFG was determined on the basis

of the resources and interviewer hours

available at the time of sample selection.

The target sample size was set at 12,500

completed interviews. The actual sample

size was 12,571 completed interviews.

Among the 12,500 target interviews,

at least 2,000 completed interviews were

required with teenagers 15–19 years of
Series 2, No. 142 [ Page 9

age. At least 2,000 completed interviews

were required with Hispanic eligible

persons and at least 2,000 with black

eligible persons. In addition, at least

7,500 interviews were required with

female respondents.

These requirements represent

substantial oversamples of teen, black,

and Hispanic populations, and a modest

oversampling of females. To achieve

these oversample targets, three sample

design features were employed.

First, a national sample of Hispanic

households was added to a national

sample of all households to increase the

number of completed interviews with

Hispanic eligible persons in the final

sample. Second, within sample PSUs,

second-stage sampling rates were set to

increase the number of black and

Hispanic households in the sample. Four

strata of blocks were created in sample

PSUs, and different sampling rates were

set across each of the strata. Lastly,

within households, teens and females

were assigned higher probabilities of

selection. This was achieved through the

assignment of measures of size to each

eligible person in the household. A

probability proportional to a measure of

size was used to select one eligible

person in each household (see figures 4

and 5 for an illustration of the

within-household selection process).

The measures of size were larger

for individuals in target groups within

the household, such as teens 15–19

years of age or females and lower for

other groups such as individuals 25–44

years of age. For example, in the black

household shown in the second table in

figure 5, there are six persons in the

household. Two are not eligible because

they are too young. They received a

measure of size equal to zero, which

means that they cannot be selected for

the study. Among the remaining four

persons, the 15-year-old female receives

a measure of size from the first table in

figure 5 of 1.00. The 21-year-old male

receives the next highest measure of size,

0.90. The remaining two eligible persons,

a 40-year-old female and 42-year-old

male, receive correspondingly smaller

measures of size.

In the selection, the measures of

size are then cumulated, and a random

number is generated by the

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

questionnaire software between zero and

the total sum of the measures of size (in

this case, 3.45). The random number is

then compared with the cumulative sum

of measures of size to determine which

interval it falls into. For example, in the

illustration the random number 0.95

falls in the interval for the first eligible

person, the 15-year-old female. The

15-year-old female is selected for the

sample.

The measures of size were

determined before data collection began

and were the same for every household

in the survey. There were 18 possible

measures of size (see figure 5). The

original sample design specified the

number of completed interviews in each

of 18 subgroups defined by age interval

(15–19, 20–24, and 25–44), gender

(female and male), and race and

ethnicity groups (black, Hispanic, white

or other). The measures of size were

selected to yield a prespecified number

of teens in the sample through a series

of simulated sample selections applied

to data from the Current Population

Survey (CPS). The remaining sections in

this chapter describe the selection

procedures in greater detail.

First-Stage Selection of

Primary Sampling Units

Sampling frame

The Census Bureau in 1990 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
Page 10 [ Series 2, No. 142

Maryland, Virginia, Missouri, and

Nevada; and the District of Columbia).

Metropolitan areas were sampled as one

PSU, even if they included more than

one adjacent county. The counties used

for each metropolitan area were defined

by the U.S. Office of Management and

Budget in 1990 as ‘‘primary

metropolitan statistical areas’’ (MSAs).

Each nonmetropolitan county was

treated as a potential PSU.

The ISR grouped these metropolitan

and nonmetropolitan units into 2,402

PSUs in its national area probability

sample. The ISR used these PSUs as the

sampling frame for Cycle 6 of the

National Survey of Family Growth.

Data from the 2000 census were

just becoming available at the time of

NSFG Cycle 6 selection. However,

complete information needed for PSU

identification and selection were not

available at the time the sample had to

be chosen. The frame used units and

population data from the 1990 census of

population and housing for selection of

PSUs in the national NSFG Cycle 6

sample.

The national sample would not

yield a large enough sample of Hispanic

households to meet sample size goals

without substantial oversampling of

second-stage sampling units. A second

national Hispanic PSU sample was

chosen to supplement the national

sample. It used the same frame of 2,402

PSUs in the national sample, but the

stratification was altered to increase the

number of Hispanic households

available in sample PSUs.

The national sample chose a sample

of 110 PSUs using 1990 census data.

PSUs were selected with probabilities

proportional to all 1990 households,

Hispanic and non-Hispanic. The

Hispanic sample selected 50 PSUs from

the same frame using 2000 census data

on Hispanic persons. Hispanic sample

PSUs were selected with probabilities

proportional to the number of Hispanic

persons counted in the 2000 census. The

Hispanic PSU sample also used a

technique to maximize overlap with the

national sample of PSUs. The Hispanic

sample chose 39 PSUs that were already

in the national sample and another 11

PSUs that were not in the national

sample. These 11 additional PSUs were

added to the national sample to yield a

final sample size of 121 PSUs. Both

PSU samples are described in the next

two sections.

National Sample

The national sample component was

drawn from the ISR’s national sample of

primary sampling units. Metropolitan

areas vary substantially in size, from as

few as 25,000 to as many as 3.2 million

households in the 1990 census. The 28

largest MSAs each had more than

600,000 households. These MSAs were

so large that they were chosen with

certainty for the sample. These 28

MSAs constitute 28 strata in the NSFG

national sample with exactly one PSU in

each. Since they are chosen with

probability one, they are referred to as

self-representing (SR) units.

The remaining 2,374 non-selfrepresenting (NSR) PSUs were divided

into 290 metropolitan and 2,084

nonmetropolitan PSUs. Each of these

partitions was then grouped into a

combined set of 82 strata that contained

approximately the same number of

households (about 721,000 households

in a NSR stratum).

This stratification of PSUs was

based on several criteria. PSUs were

first grouped into one of nine census

geographic divisions defined by

groupings of States. In addition, the

States of Alaska and Hawaii were

separated from the Pacific division and

treated as separate strata of PSUs.

Within divisions, PSUs were grouped

into a total of 52 MSA and 30

non-MSA strata. In Alaska and Hawaii,

the distinction between MSA and

non-MSA PSU was not made, and each

State was treated as a stratum.

Within a division and MSA

category, PSUs were further stratified on

the basis of size and geography. For

example, in the Middle Atlantic

Division, MSA PSUs were grouped into

eight strata, ranging from large MSAs in

one State to small MSAs in the western

part of two States.

A single PSU was then selected

from within each of the 82 strata. For

purposes of minimizing the need to hire

new interviewers, a procedure to

maximize overlap between two

samples (7) was used to determine

which PSUs from the 1980 ISR national

PSU sample would be retained in the

1990 sample. The selection was random

and with probabilities proportional to

the 1990 households in the PSU. A

diagram summarizing the national and

Hispanic sample selection is presented

in figure 2.

Hispanic Sample

The Hispanic sample stratification

followed a procedure similar to that

used in the national sample. The

national sample’s 2,402 PSUs were the

starting point for the selection. Each

PSU was evaluated on the basis of its

2000 census Hispanic total population.

A minimum of 280,000 Hispanic

persons was established as the required

size for SR PSUs. Fourteen MSAs met

this criterion. Two adjacent PSUs met

the minimum size threshold when they

were combined. For the sake of sample

efficiency, these two PSUs were treated

as SR PSUs, resulting in a total of 16

SR PSUs.

The remaining 302 MSAs were

treated as NSR PSUs. These were

grouped into 26 strata primarily by

combining 1990 national sampling

frame strata within census divisions and

by geographic proximity. The 26 strata

were created to make the numbers of

Hispanic persons across them as equal

as possible, with populations ranging

from 378,000 to 533,000.

The 2,084 non-MSA PSUs were

grouped into eight strata, initially within

census divisions and retaining

geographic proximity. Because of the

concentration of Hispanic persons in the

south and west regions, however, there

is one non-MSA stratum covering the

entire northeast and midwest regions,

and one covering the south Atlantic and

east south central divisions. Three strata

were created in the west south central

division and three in the west region. (In

this case, the mixed Alaska and Hawaii

strata from the national sample were

combined with the Pacific coastal

non-MSA strata and treated as a

non-MSA.) Hispanic populations in

these non-MSA strata ranged from

235,000 to 462,000. Among the 16 SR

PSUs, 14 were already selected in the
Series 2, No. 142 [ Page 11

NSFG national sample. Thus, the

Hispanic sample added two new SR

PSUs for the NSFG Cycle 6 sample.

In the 26 NSR non-MSA strata, a

Kish-Scott procedure (7) was used to

select PSUs for the Hispanic sample.

The goal was to maximize the overlap

between the national and Hispanic NSR

non-MSA PSUs. In the procedure, units

in one sample (the national sample) are

first selected to determine which will be

‘retained’ in the second sample. In the

strata where multiple PSUs could be

retained from the national to the

Hispanic sample, one PSU was selected

from those eligible for retention with a

probability proportional to the 2000

census Hispanic population. Where only

one could be retained, it was selected

with certainty. Where none could be

retained, a new PSU was selected from

among all PSUs in the stratum with

probabilities proportional to the

Hispanic population. New PSU

selections not in the NSFG national

sample were made in 5 of the 26 NSR

non-MSA strata.

In the eight non-MSA and mixed

Alaska-Hawaii strata, the same

Kish-Scott procedure was used to select

PSUs. Four national sample PSUs were

retained and four new NSR non-MSA

PSUs were selected. In summary, a total

of 11 new PSUs were added to the

national sample from the Hispanic

sample. With these new PSUs, the

combined NSFG national and Hispanic

samples contained 30 SR, 57 NSR

MSA, and 34 NSR non-MSA PSUs, or

a total of 121 PSUs.

Second-Stage Selection of

Segments

Stratification of Blocks

As noted previously, the NSFG

Cycle 6 design requirements specified

substantial oversampling of black and

Hispanic men and women across the

15–44 years of age range. In order to

achieve the target oversampling,

probabilities of selection of second- and

third-stage units—segments and

households, respectively—were varied

across domains created within each

PSU.

The second-stage sampling units

were census blocks, or combinations of

census blocks that had sufficient

numbers of households to sustain

efficient survey data collection. For each

of the 121 NSFG Cycle 6 PSUs, a list

of blocks was obtained from the 2000

census data.

Census blocks have a varying

number of households and eligible

persons. Some blocks have few, if any,

households. These small blocks pose

problems for efficient screening and

interviewing in any study. The sample

design specified that in order to obtain

units of minimum sufficient size for

efficient data collection, blocks should

be linked to one another into units

called segments with at least 75

households in urban areas or 50

households in rural areas. Linking was

within block group boundaries to

increase the chance that geographically

contiguous blocks were linked to form

segments. These segments were the

second-stage sampling units.

At the time of selection, only

limited information on population and

housing from the 2000 census was

available for blocks for all PSUs. The

2000 census blocks were used as

second-stage sampling units. Numbers

of black, Hispanic, and white or other

race households were not available at

the time of sample selection. The

number was estimated by dividing the

total black, Hispanic, and white or other

population for the segment by the

national average number of persons per

household (obtained from the March

1999 CPS) for the respective group.

This division yielded an estimated

number of households for each group in

the group in the block.

Blocks and segments within PSUs

were grouped into one of four domains

within each PSU based on the

percentage of black or Hispanic

households in the segment. Domain 1,

the nonminority stratum, consisted of

those segments with less than 10 percent

black or 10 percent Hispanic persons.

Domain 2 contained segments with at

least 10 percent black persons, but less

than 10 percent Hispanic persons.

Domain 3 contained segments with at

least 10 percent Hispanic persons but

less than 10 percent black persons.

Domain 4 contained segments with at

least 10 percent black and at least

10 percent Hispanic persons. All race

and ethnicity prevalence rates were

computed at the census block group

level to reduce classification of

geographically contiguous census blocks

into different domains.

Sampling rates for households

within each domain were determined to

achieve target numbers of completed

interviews with black and Hispanic

households. During the sample planning,

the final sample size could not be

precisely fixed. Limitations in funding

forced consideration of several different

target sample sizes, and target sizes for

various age, gender, and race and

ethnicity groups. For purposes of

second- and third-stage selection,

sampling rates within PSUs were

established before final sample size

targets were known.

Initial domain sampling rates were

established to yield sufficient sample

sizes of minority groups under several

different NSFG sample size targets.

Higher rates were needed for Domains

2, 3, and 4 in order to achieve target

subgroup sizes under all final sample

sizes considered. The higher sampling

rates in these three domains meant that

there would be a consequent increase in

sampling variance and loss of effective

sample size through the weighting

needed to compensate for unequal

sampling rates.

After simulation of many design

alternatives, and examination of sample

sizes in target groups and effective

sample sizes, sampling rates for

Domains 2, 3, and 4 were set relative to

Domain 1. The sampling rate for

Domain 2 was 80 percent higher than

the sampling rate for Domain 1, while

rates for Domains 3 and 4 were

110 percent higher (more than double)

the rate for Domain 1. These rates are

summarized in table A.

The number of segments to be

selected from each PSU and domain

depended on both the expected increase

in variance due to cluster sample

selection within the PSU and the cost of

traveling to segments within a PSU. An

analysis of the variance and cost

trade-offs led to the conclusion that a

total of 1,400 segments were to be
Page 12 [ Series 2, No. 142

Table A. Allocation of sample segments and sampling rates within domains: National

Survey of Family Growth, Cycle 6

Number of Number of

segments segments Sampling

Domain allocated listed fraction

1. Nonminority ........................... 618 614 1 in 2,572

2. More than 10 percent black ................. 269 264 1 in 1,429

3. More than 10 percent Hispanic ............... 288 287 1 in 1,225

4. More than 10 percent black and Hispanic......... 239 219 1 in 1,225

Total ................................. 1,414 1,384 1 in 1,788

units on each block. When no address

was available, a description of the

housing unit was entered onto the listing

form. These lists contained the units in

the third-stage selection of housing

units.

The listing forms were returned to

the ISR in Ann Arbor, Michigan, where

they were checked for completion and

then keyed in listing order to a

selected across all 121 PSUs.

The number of segments within a

PSU and within a domain was allocated

based on the number of households in

the domain in the PSU. However, no

households were to be selected from

Domain 1 and 2 in the 11 additional

Hispanic sample PSUs. That is, sample

segments were to be drawn from

Domains 3 and 4 only in the 11

additional Hispanic sample PSUs. Thus,

the Hispanic sample would increase the

number of Hispanic households and

eligible persons in the sample.

The allocation of segments across

domains had to be rounded to a whole

number in each domain and PSU. While

rounding could have been controlled

across the domain-PSU cells of the

allocation, it would have been a

time-consuming task with little real

benefit in terms of the final sample size.

The rounding was not controlled and a

total of 1,414 segments were allocated

across the PSUs, slightly more than the

target number.

Selection of Segments

Once minimum measures of size for

each segment and the number of

segments per domain in a PSU were

determined, a sample of segments was

selected from each PSU. Within a

domain and PSU, the census blocks

were ordered geographically by tract,

block group, and block number.

Segments were selected within a

PSU using a systematic method with

probabilities proportional to the 2000

census count of estimated households in

the segment. The cumulative number of

households for each segment in the

domain was calculated, cumulating in

list order. The sum of domain household

counts in the PSU was divided by the

number of segments allocated to the

domain to obtain a sampling interval for

selection. A random number was chosen

for each domain in each PSU that was

between zero and the sampling interval

for the domain. The first selected

segment was identified by finding the

first segment with cumulative size that

exceeded the random number. The

second selection was obtained by adding

the sampling interval to the random start

and identifying the segment with

cumulative size that first exceeded the

sum. The selection continued, adding

the interval to the last sum to find the

next selection until the domain

allocation was achieved.

The combination of the geographic

order and systematic selection produced

an implicit geographic stratification of

segments within each PSU and domain.

The implicit stratification was expected

to yield a decrease in sampling variance

for estimates obtained from the final

sample results.

Field Listing of Segments

Following segment selection, a

folder was prepared for each segment

containing maps and blank listing forms.

The maps were generated through

commercial software using the Census

Bureau’s TIGER (Topologically

Integrated Geographic Encoding and

Referencing system) files to delineate

block boundaries. Instructions were

prepared for listing each of the blocks in

a segment, including a starting place to

begin the listing and a direction to

proceed around the block.

Segment folders were sent to

experienced field interviewers for

listing. Interviewers entered onto a

segment housing unit listing sheet the

number and street name of all housing

computer file of listings. More than

140,000 addresses were listed in the

sample segments.

Because the estimated numbers of

households were used as a measure of

size in the selection of segments, there

was a large discrepancy between

estimated size and actual count for 30 of

the 1,414 segments. Several of these

segments contained institutions such as

prisons, jails, or college dormitories that

had populations but no households,

segments that could not be identified in

advance from the available 2000 census

materials. These 30 segments were

treated as foreign elements in the

segment list, and were excluded from

subsequent within-segment listing and

housing unit selection. Thus, the total

number of segments in the 121 PSUs

was 1,384.

Third-Stage Selection of

Housing Units

The third-stage selection of housing

units was made from the keyed list of

housing units. The individual listings

were referred to as sample lines. In

order to identify which sample lines

would be selected and assigned for field

data collection, a selection was required

that would achieve the specified

within-domain sampling rate for lines,

housing units, and households. A

probability of selection of the PSU and

the segment was recorded for the first

two stages of sampling. Given the target

sampling rates shown in table A, a

probability of selection for lines within

each of the 1,384 segments was

calculated. The lines were then

subsampled at the specified rate

systematically from the ordered list of

lines in the segment. The combination

of systematic selection and list order

implicitly stratified the selection of lines
to provide representation of lines across

blocks and the geographic area

represented by the segment. A total of

74,132 lines were subsampled from the

1,384 segments.

Not every line selected for the

sample was released for subsequent

screening and within-household

selection of eligible persons. In order to

control sample size and constrain costs

at or below budget, the final sample was

released in two phases. In the first

phase, operational constraints allowed a

reduced interviewer staff to be trained.

A sample of segments from a

one-quarter sample of 33 NSFG PSUs

was released. In the second phase, when

the complete interviewer staff was

trained across all PSUs, sample

segments were released across all 121

PSUs. Each phase used a sample drawn

from the available NSFG PSUs,

segments, and lines.

A third phase of sample selection

was employed at the end of the

data-collection period. The phased

sampling is described in greater detail in

a subsequent section.

Screening and Missed Housing

Units

The selection of households was

carried out through household screening

in the field. Screening consisted of a

short questionnaire administered at the

doorstep of every occupied sample line

released for the sample.

Based on ISR experience with listed

addresses, 15 percent of sample lines

were expected to be unoccupied or not

actually housing units (for example, a

housing unit converted for a commercial

purpose). Based on estimates obtained

from the March 1999 CPS, an additional

38 percent of households was expected

to have no eligible persons. Screening

was the process used to determine

whether the sample line contained a

household and whether any eligible

persons resided in the household.

During the screening phase of the

survey, interviewers were trained to

check for housing units that were not

found during listing. Missed housing

units may occur when a listing

interviewer overlooked a structure with

one or more housing units or a part of a

structure that was a separate housing

unit. Missing units may also have

occurred if a housing unit was

constructed since the listing took place.

Interviewers were equipped with a

laptop computer and a sample

management system that contained all

sample lines in the segment and

highlighted sample lines they were to

attempt to screen within the segment.

The procedure for handling missed

housing units in the field was as

follows:

+ At each sample line, interviewers

checked to be sure that all housing

units in the structure were on the

sample list. Interviewers were to

check mail boxes, doors, or utility

meters that might indicate a unit

present that was not listed. They

were also instructed to ask screener

respondents about any additional

housing units in the structure where

they were living.

+ If an additional housing unit was

discovered, and it was between the

sample housing unit and the next

listed unit, the interviewer was to

add the additional unit to the online

system and attempt a screening

interview with the additional unit.

+ When more than two housing units

were missed between the sample

line and the next listed housing unit,

interviewers were instructed to

suspend work, including contact

with the household, and to call the

ISR’s sampling unit to receive

further instructions. Before calling

the sampling unit, the interviewers

were to obtain a list of all additional

housing units associated with the

sample line.

If a large number of additional

housing units were found, a subsample

of the original sample line and the

additional housing units was selected.

This subsampling disrupts the equal

probability of selection of households

within each domain. Weights to account

for the missed housing unit subsampling

were incorporated into compensatory

weights to adjust for unequal

probabilities of selection.

Series 2, No. 142 [ Page 13

Fourth-Stage Selection of

Sample 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 screening interview was terminated.

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.

Subsampling by Age, Gender,

and Race and Ethnicity

The sampling rates used within

households with eligible persons varied

by age, gender, and race and ethnicity.

Since the selection was carried out by a

computer application, a selection

procedure that allowed person selection

rates to be varied across sample releases

could be implemented. This flexibility

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 original sample design

specified the number of completed

interviews in each of 18 subgroups

defined by age interval (15–19, 20–24,

and 25–44), gender (female and male),

and race and ethnicity groups (black,

Hispanic, white, or other). Figure 4

illustrates the household listing

procedure and the upper panel of
Page 14 [ Series 2, No. 142

figure 5 illustrates the configuration of

the 18 cells.

To achieve the target sample sizes

across these cells, varying rates of

selection had to be set for each cell to

account for expected eligibility rates for

each cell. Table B presents expected and

achieved eligibility rates for each of the

18 target subgroup cells. Expected

eligibility rates were computed as the

product of household eligibility rates

and within-household rates by age and

race and ethnicity as determined from a

concatenated March 1998, 1999, and

2000 CPS data set.

As shown in the ‘‘Total’’ column of

table B, approximately 62 percent of the

households were expected to have one

or more eligible persons. Expected

eligibility for some cells—for example,

black males 15–19 and 20–24—was

quite low, less than 1 percent. Given

these low rates, study staff were

concerned that slight departures in

observed rates from the expected rates

could mean that target sample sizes

might not be achieved. An additional

concern was that response rates across

these 18 cells might vary.

A within-household selection

procedure was devised that, when

implemented in the computer-assisted

screener application, could be modified

during data collection to refine

within-household selection rates to

adjust for observed eligibility and

varying response rates across cells. The

selection procedure involved assigning a

‘‘measure of size’’ to each of the 18

cells. Each person in the household

would then be assigned a measure of

size based on their age group, gender,

and race and ethnicity. The measures of

size were developed through a simulated

sample selection from the concatenated

CPS data used to generate the expected

eligibility rates. For each member of a

household in the concatenated CPS data

file, a measure of size was assigned, and

a subject was selected. The average

probabilities of selection for persons in

each cell were then computed and used

to determine expected sample sizes by

cell. The measures were adjusted cell by

cell in order to achieve target sample

sizes by gender, age, and race and

ethnicity group. Figure 5 presents the

size measures used in the initial sample

release for each cell.

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 of size

in the household was generated by the

computer-assisted sample screener

application. The first listed person

whose cumulative measure of size

within the household exceeded the

random number was selected. The

chance of selection of the person was

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 is chosen to be interviewed. By

assigning larger measures to teens

(that is, those ages 15–19 years) and

females within the household, larger

samples of these individuals could be

achieved. The selection probabilities

within the household depend on the

distribution of measures of size for

other eligible persons in the household

as well.

Sample Release

The NSFG Cycle 6 sample was not

released at one time. Due to concerns

about the project budget and about

interviewer attrition over the course of

data collection, interviewers were

recruited and trained in three groups

during the data-collection period. The

first cohort of interviewers was trained

in March 2002, the second in June

2002, and the last in September 2002.

As each of the first two groups of

interviewers completed training, they

were assigned samples from two

different releases. Each release consisted

of replicate samples drawn from the

overall sample of PSUs, segments, and

lines that were available for the NSFG

Cycle 6 sample. Each release was a

replicate of the overall sample design,

selected with known probabilities of

selection from the available listings. It is

thus possible to construct overall

probabilities of selection for each

sample line in the NSFG sample (see

Weighting Procedures).

In addition, in the last month of

study data collection, there were sample

lines that had not been assigned a final

result. These lines had either not been

successfully screened (that is, it was not

known whether any eligible persons

lived in the housing unit) or had been

screened, but no interview had been

completed with a selected respondent.

During the last month of data collection,

these lines were subsampled to allow for

a more intensive data collection effort

for a sample of lines. This subsampling

Table B. Expected and observed eligibility rates by sex, age, and race and ethnicity: National Survey of Family Growth, Cycle 6

Black Hispanic White or other Total

Sex and age Expected Observed Expected Observed Expected Observed Expected Observed

Female:

15–19 years ...........................

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

25–44 years ...........................

0.007

0.009

0.039

0.010

0.010

0.048

0.010

0.011

0.044

0.010

0.013

0.048

0.036

0.038

0.163

0.032

0.040

0.144

0.053

0.058

0.246

0.052

0.063

0.240

Male:

15–19 years ...........................

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

25–44 years ...........................

0.007

0.006

0.025

0.009

0.006

0.027

0.011

0.009

0.034

0.010

0.010

0.032

0.037

0.027

0.106

0.032

0.028

0.083

0.055

0.042

0.165

0.051

0.044

0.142

Total ................................. 0.093 0.110 0.119 0.123 0.407 0.359 0.619 0.591
Series 2, No. 142 [ Page 15

and intensive data collection activity is

referred to as the third phase of sample

release.

The NSFG Cycle 6 national sample

of PSUs was divided into subsets that

would allow replicate samples consisting

of one-quarter, one-half, two-thirds, and

all PSUs. In addition, each of the 1,384

segments was assigned to 1 of 12

replicates through a systematic

procedure within each domain. Lastly,

the selected lines in each segment were

also assigned systematically to 1 of 24

replicates. These three replication

features allowed more refined

management of sample releases

throughout the study in order to achieve

target sample sizes and avoid field costs

exceeding available budget.

In the phase-one release,

interviewers were recruited from PSUs

in one of the four quarter-samples

available for the national sample. The

first training session was limited in size

because only a quarter-sample in a

limited number of PSUs was being

released at first. The first phase

consisted of a sample of segments and

lines drawn from available listings in

the one-quarter sample of PSUs.

The PSU quarter-sample contained

33 PSUs: the 8 largest SR PSUs and 25

NSR PSUs. The Alaska and Hawaii

PSUs were also included in the

first-phase sample. None of the 11

additional Hispanic sample PSUs were

included. The first-phase sample was

thus drawn from 35 PSUs.

Within the eight SR PSUs,

two-thirds of the segments from each

domain and 17 of the 24 replicate line

samples in each segment were chosen.

In the remaining 27 one-quarter sample

PSUs, all segments and 17 of 24

replicate line samples were selected. The

first-phase sample contained 358

segments and 14,107 lines, adequate for

the size staff trained in the first phase.

The second-phase sample was

selected for release starting in June

2002, and included PSUs from the

national and Hispanic samples. A second

larger cohort of interviewers was trained

in June and September. An additional

425 segments were released across all

domains in the remaining 121–35=86

national and Hispanic sample PSUs. (No

additional sample was released in the

original quarter sample in Phase 2.) A

total of 14,443 lines were released in

June and 4,524 lines in September.

Thus, across the February, June, and

September training groups, a total of

33,074 lines were released across all

four domains in all 121 PSUs and 783

segments.

Sampling in Three

Phases

As the data collection process

matured during September

through January, preparations

began for a third-phase sample release.

This third phase was, as described

previously, a ‘‘double or two-phase

sample design’’ first defined by Hansen

and Hurwitz (8) as a tool to reduce the

nonresponse bias in survey statistics.

The elements of a ‘‘two-phase

design’’ (as Hansen and Hurwitz used

the term) are:

1. The design and implementation of a

survey design on a large sample

(labeled the ‘‘first phase’’)

2. The selection of a probability sample

of the nonrespondents to the

first-phase sample

3. The use of a different participation

protocol for the second phase

There are two impacts of a

two-phase design. First, if the

second-phase protocol is successful in

measuring 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. 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.

Multi-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 concentrates remaining

resources on increasing response rates.

In the NSFG Cycle 6 sample

design, a subsample of nonrespondents

was chosen as Phase 3 of the design.

Following the Phase 1 release, study

staff developed response propensity

models to predict the probability that a

given case would yield a completed

screening interview or a completed

NSFG interview (9).

Based on the propensity models,

the 783 sample segments of Phases 1

and 2 were stratified on two major

dimensions: the number of cases in the

segment that were not finalized and the

total expected propensities for active

cases in the segment based on the

propensity models (9, tables K and L).

The distribution of segments on each

dimension was divided into quartiles,

‘‘high,’’ ‘‘medium-high,’’ ‘‘mediumlow,’’ and ‘‘low.’’ Across the two

dimensions, the segments were thus

classified into 16 subgroups, as shown

in table C.

It was believed that the cell in

table C that would yield the largest

number of completed interviews in

Phase 3 was the upper-leftmost cell.

This cell contained 111 segments with

high total propensities and large

numbers of active cases. When visiting

these segments the interviewers could

visit several houses on the same trip,

reducing cost per unit.

The Phase 3 sample was a stratified

sample of segments, with all

nonrespondent cases in a selected

segment included in the sample. This

cluster sample design was chosen based

on cost model estimates computed

during Phases 1 and 2 that showed that

a large portion of the total cost of

completing a case arose from travel cost

to the sample segments.
783

Page 16 [ Series 2, No. 142

Table C. Number of Phases 1 and 2 segments by number of active incomplete cases in the segment at the end of Phase 2, and the

estimated total propensity of active cases to complete an interview on the next visit: National Survey of Family Growth, Cycle 6

Number of active incomplete cases at end of Phase 2 (Quartiles)

Estimated total propensity for active incomplete cases Medium- Medium­

(Quartiles) High high low Low Total

High (top 1⁄4 of propensities)................... 111 51 26 7 195

Medium-high (next highest 1⁄4) ................. 55 65 48 28 196

Medium-low (next highest 1⁄4) .................. 30 51 66 49 196

Low (smallest propensities) ................... 10 34 40 112 196

Total ................................. 206 201 180 196

Table D. Phase 3 segment sampling fractions, by number of active cases in the segment, and the estimated total propensity of active cases

in the segment to complete the interview on the next visit: National Survey of Family Growth, Cycle 6

Number of active incomplete cases at end of Phases 1 and 2

Estimated total propensity for active incomplete cases Medium- Medium­

(Quartiles) High high low Low

High (top 1⁄4 of propensities)................... 0.58 0.58 0.58 0.58

Medium-high (next highest 1⁄4) ................. 0.58 0.58 0.29 0.29

Medium-low (next highest 1⁄4) .................. 0.29 0.29 0.19 0.19

Low (smallest propensities) ................... 0.29 0.19 0.19 0.15

Sampling fractions across the strata

were chosen to increase the relative

numbers of segments that would contain

a higher yield of completed interviews.

The fractions varied by a factor of about

4.0 (table D) from the largest to

smallest. This unequal chance design

emphasized the cost efficiency of the

Phase 3 design to produce interviews,

not minimization of standard errors of

the resulting data set.

The variation in sampling fractions

over the 16 strata could add to an

increase in the variance of estimates.

Simulations prior to the selection of the

Phase 3 sample suggested an increase in

variance of approximately 20 percent.

The highest selection probabilities were

assigned to those segments with the

largest total sum of propensities across

cases in the segment, or the largest

numbers of active cases. The smallest

selection probabilities for the Phase 3

sample were assigned to segments with

few active cases that had low

propensities of being interviewed.

Under the theoretical perspective (10)

guiding the NSFG’s design and

fieldwork, study staff sought a Phase 3

interview recruitment protocol that was

distinctive from that used in Phases 1

and 2. Such a distinction is necessary

(but not a priori sufficient) to attract

sample persons who did not find the

Phases 1 and 2 protocol effective, and

thus 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 3 recruitment

protocol involved the following

components:

1. Use of the most productive

interviewers on staff

2. Increased use of proxy respondents

(such as neighbors) for the screening

interview

3. A prepaid $5 incentive (versus no

incentive) for cases that had not yet

completed the screening interview

4. A prepaid $40 incentive for the main

interview (versus a $40 incentive

provided after the informed consent

was signed)

5. A promised additional $40 incentive

for a completed main interview

Phase 3 was limited to a 1-month

period, following 11 months in the

first two phases. The results of Phases

1, 2, and 3 are described in the next

section.

Sample Characteristics

Response Rate Estimation

The target response rates for the

NSFG were 80 percent for females and

75 percent for males, traditional response

rates that do not reflect any of the unequal

probabilities of selection of the sample

design. The Cycle 6 design focused on 18

subclasses that were of key analytic

interest, defined by age, race and ethnicity,

and gender. Response rates for each of

these groups were monitored throughout

the data-collection period.

There are two complications for

computing response rate estimates in the

NSFG Cycle 6 survey:

1. The sample of housing units

required screening to determine

whether anyone 15–44 years of age

was a member of the household.

About 60 percent of the households

screened had one or more eligible

persons. However, sample units that

were not successfully screened have

unknown eligibility statuses.

2. A subsample of remaining

nonrespondents in Phases 1 and 2

was drawn to represent all

nonrespondents.
Series 2, No. 142 [ Page 17

The American Association for rate. However, the Phase 3 response rate n2

Public Opinion Research guidelines (11) is only applicable to the group of cases ∑
si2 for computation of response rates subsampled for Phase 3. The overall i=1

R2 = + n2 specifies a computational form for response rate incorporating the Phase 3

∑
[si2 + ei2 + l(ui2 response rates in such cases. Let the ( )]) response rates (which were based only

following indicator variables represent i=1 on a subsample of the remaining

the status of various outcomes for each n4

nonrespondents of Phases 1 and 2) is:

( ) ∑
 case:

Phases 1 and 2 screener cases (n1):

si1 = 1, if the case is an eligible

ii2

R1 + (1–R1) R2

The partially weighted response

rates computed in this fashion appear in

i=1

n4

∑
(ii2 + mi2)

household (containing one or

more persons 15–44 years of

age); 0, otherwise;

ei1 = 1, for eligible households with no

i=1

table E.

Here l in the denominator of the This response rate estimator is

response rate is an estimated proportion consistent with prior practice in the

of screener nonresponse cases of NSFG (as in Cycle 4, for example), screener; 0, otherwise;

ui1 = 1, for households of unknown unknown eligibility (that is, cases for but applied to the multiphase design

which the screener was not completed) of Cycle 6. The estimator reflects the eligibility; 0, otherwise.

that do contain one or more eligibles. sampling of a portion of the segments

Phase 3 screener cases (n2):

si2 = 1, if the case is an eligible

household in Phase 2; 0,

otherwise;

ei2 = 1, for eligible Phase 2 household

with no screener; 0, otherwise

ui2 = 1, for Phase 2 household of

unknown eligibility; 0, otherwise.

Phases 1 and 2 main interview cases (n3):

ii1 = 1, for completed interviews; 0,

otherwise;

mi1 = 1, for nonresponse to main

interview; 0, otherwise.

Phase 3 main interview cases (n4):

ii2 = 1, for completed interviews in

Phase 2, 0, otherwise;

mi2 = 1, for nonresponse to main

interview in Phase 2; 0,

otherwise.

The Phases 1–2 response rate not

reflecting household and person

selection weights is:

n1

( ) ∑
(si1+si2) i=1

R1 = + n1

∑
[(si1+si2)+(ei1+ei2) + l (ui1+ui2)] i=1

in the Phase 3 subsample, but does not

reflect the unequal probabilities of

selection applied to housing units and

persons used to oversample black,

Hispanic, and teenage respondents.

Another weighted response rate

estimate can, of course, be constructed

to address the oversampling of these

demographic groups. Such a fully

weighted response rate would estimate

the response that might have been

obtained if the entire target population

were, under the same protocol, selected

with no differential sampling at all.

Groups oversampled in Cycle 6 include

units in high density Hispanic and

African-American neighborhoods,

teenagers, and persons who live with

smaller numbers of other age-eligible

persons. A response rate estimate can be

constructed that reflects all the

selection weights associated with these

factors, in addition to the Phase 3

probability of selection. This latter

response rate is relevant to statistical

analysis in which the selection weights

are being used. It estimates the

selection had been employed in the

We have estimated l in two different

ways:

1. Using the proportion of completed

screening interviews that yielded

households containing one or more

eligibles; this is approximately

58 percent.

2. Using a statistical model reflecting

the amount of effort required to

contact the case and the tendency to

be reluctant to the screening

interview to predict whether the

household contains an eligible

person; this is approximately

64 percent.

It is standard practice to present

‘‘unweighted’’ response rates that do not

account for differential probabilities of

selection arising from oversampling

demographic groups. In the case of

double sampling designs, though, a

weighting to account for the differential

subsampling of the sample nonrespondents remaining after Phase 2

should be applied to obtain an estimated

rate that reflects the full survey design.

rate—one that accounts for the

Thus, an alternative to a completely percentage of eligible persons who

unweighted rate is a partially weighted would respond if equal probabilities of

subsampling of nonrespondents at the sample selection.

end of Phase 2 but does not also weight There are two weights needed for n3

( ) ∑
i to compensate for the varying selection computation of the fully weighted i1

i=1 probabilities for demographic group response rate. The first is the selection

oversampling within Phases 1 and 2. weight for the screener response rate, n3

∑
(ii1 + mi1) In other words, to estimate the which includes only the household i=1

The Phase 3 response rate not reflecting

household and person weights is:

response rate across the three phases, a selection probabilities. All cases have a

Phases 1 and 2 response rate must be weight that reflects the probability of

combined with the Phase 3 response selection of the household. For Phases 1
Page 18 [ Series 2, No. 142

and 2 cases, combine the multiple phases: 3. Lower response rates among

Hispanic persons outside of the 1 ws1i = Pr(Household selection) . n2 supplemental Hispanic PSUs (not

∑(wi1i

si1 + ws2isi2) shown in table F).

And for Phase 3 cases, Rw = i=1

(ws1isi1 + ws2isi2) + + n For comparisons to previous NSFG ( 2

1 ∑[(ws1i

ei1 + ws2iei2) +]) Cycles, the response rates that are ws2i = Pr(Household selection) + i=1 l(ws1i

ui1 + ws2iui2) unweighted except for the Phase 3

1 subsampling (table E) are most

Pr(Segment selection in Phase 3) .

For the main response rate, the

selection weights also incorporate

within-household selection probabilities.

The Phases 1 and 2 selection weight for

the main interview is the final product

of all constituent selection weights (see

the next section for detailed description

of the weights):

1 wm1i = Pr(Household selected) +

1

Pr(Person selected | Household selected)

The Phase 3 weight is:

1 wm2i = Pr(Household selected) +

1

Pr(Segment selected in Phase 3) +

1

Pr(Person selected | Household selected)

The final response rate can directly

n4

� (wm1i

ii1 + wm2iii2) i=n2+1

n4

∑ (wm1i

ii1 + wm2imi2 ( ) i=n2+1

)

These fully weighted response rates

are shown in table F. The fully weighted

rates are lower than the partially

weighted response rates in table E. This

decline is primarily due to:

1. Lower response rates in the

undersampled age groups (for

example, 65 percent for 25–44 year

olds using 0.64 eligibility for

screener noninterviews compared

with 73 percent for 15–19 year olds)

2. Lower response rates in the

undersampled other race and ethnicity

group (for example, 66 percent for

other using 0.64 eligibility for screener

noninterview compared with

73 percent for black persons)

appropriate. For consideration of

response rate differences that might be

applied in a fully weighted analysis of

the Cycle 6 data, response rates in

table F may be preferred.

The overall response rate at the end

of Phases 1 and 2 was approximately

64 percent, using the AAPOR definition

with an estimated eligibility rate among

the nonrespondent screener cases (but

not reflecting unequal probabilities of

selection across persons). The Phase 3

response rate was approximately

40 percent of the 36 percent of cases

remaining, which added about

14.4 percent to the 64 percent response

rate at the end of Phase 2. Adding the

14.4 to the 64 percent yielded a

combined response rate of between 78

and 79 percent, using the approved

AAPOR double sample computation,

not reflecting unequal probabilities

within phases (11). In that sense, the

Phase 3 sample was very effective in

Table E. Response rates, weighted only for subsampling in Phase 3, but not for other probabilities of selection, using two estimates (0.64

and 0.58) of the proportion of nonscreened households that had an eligible respondent: National Survey of Family Growth, Cycle 6

Overall Cycle 6 response rates, using .64 eligibility for screener noninterview

Female Male

Age and race Black Hispanic Other Total Black Hispanic Other Total Total

All ages ................... 81 82 77 79 82 78 75 77 78

15–19 years ............... 87 84 74 78 83 85 78 80 79

20–24 years ............... 82 82 79 80 80 80 79 79 80

25–44 years ............... 80 81 77 79 82 76 73 75 77

Black ..................... . . . . . . . . . . . . . . . . . . . . . . . . 82

Hispanic ................... . . . . . . . . . . . . . . . . . . . . . . . . 80

Other..................... . . . . . . . . . . . . . . . . . . . . . . . . 76

Overall Cycle 6 response rates, using .58 eligibility for screener noninterview

Female Male

Age and race Black Hispanic Other Total Black Hispanic Other Total Total

All ages ................... 82 83 78 80 83 79 76 78 79

15–19 years ............... 88 85 75 79 84 86 79 81 80

20–24 years ............... 82 83 80 81 81 81 80 80 81

25–44 years ............... 81 82 78 80 83 76 74 76 78

Black ..................... . . . . . . . . . . . . . . . . . . . . . . . . 82

Hispanic ................... . . . . . . . . . . . . . . . . . . . . . . . . 81

Other..................... . . . . . . . . . . . . . . . . . . . . . . . . 77

. . . Category not applicable.
Series 2, No. 142 [ Page 19

Table F. Fully weighted response rates, adjusted for all unequal probabilities of selection, using two estimates (0.64 and 0.58) of the

proportion of nonscreened households that had an eligible respondent: National Survey of Family Growth, Cycle 6

Overall Cycle 6 response rates, using .64 eligibility for screener noninterview

Female

Total

Age and race Black Hispanic Other female

All ages ................... 74 70 68 69

15–19 years ............... 78 77 69 72

20–24 years ............... 78 72 71 72

25–44 years ............... 72 68 66 68

Black ..................... . . . . . . . . . . . .

Hispanic ................... . . . . . . . . . . . .

Other..................... . . . . . . . . . . . .

Male

Total

Black Hispanic Other male Total

72 66 64 65 67

78 74 73 74 73

78 62 70 69 71

69 64 60 62 65

. . . . . . . . . . . . 73

. . . . . . . . . . . . 68

. . . . . . . . . . . . 66

Overall Cycle 6 response rates, using .58 eligibility for screener noninterview

Female

Total

Age and race Black Hispanic Other female

All ages ................... 75 71 68 70

15–19 years ............... 79 78 70 73

20–24 years ............... 79 73 72 73

25–44 years ............... 73 69 67 68

Black ..................... . . . . . . . . . . . .

Hispanic ................... . . . . . . . . . . . .

Other..................... . . . . . . . . . . . .

Male

Total

Black Hispanic Other male Total

66 65 66 73 68

75 74 75 79 74

63 70 70 79 71

65 61 63 70 66

. . . . . . . . . . . . 74

. . . . . . . . . . . . 69

. . . . . . . . . . . . 67

. . . Category not applicable.

increasing the overall response rate in

only one month.

Nonresponse Bias and

Phase 3 Sampling

The next question is whether the

Phase 3 sample and its increased

response rate produced any reduction in

nonresponse bias. Evidence regarding

the nonresponse bias characteristics of

the multiphase design is necessarily

indirect, because the characteristics of

the people who did not respond to the

screener are not known. The NSFG

attempted to control achieved interview

counts on 18 different subclasses

defined by age, gender, and race and

ethnicity groups. Examining the Phases

1 and 2 and overall response rates of the

18 subpopulations helps to assess

whether the response rates for subgroups

were approximately equal, or whether it

was necessary to use Phase 3 to attempt

to make them more similar.

Figure 6 presents the response rates

at the end of Phases 1 and 2 and the

overall response rates for the 18

subclasses. The coefficient of variation

of the response rates in Phases 1 and 2

is 7.6 percent of the mean response rate;

the same measure of variation for the

overall rates is 4.4 percent, a large

decrease in the variation in response

rates. This can be interpreted as an

indirect indication that the Phase 3

follow-up reduced nonresponse error

associated with age, gender, and race

and ethnicity differences.

Figure 7 shows that the variation is

quite systematic by age of the sample

persons. At the end of Phases 1 and 2,

teenagers had a 6 percentage points

higher response rate than the 25 through

44-year age group, with those 20–24

years in between these two. At the end

of Phase 3, the difference in response

rates among the three groups was just

3 percentage points.

If nonresponse bias were related to

survey effort and age, one would expect

a relationship between the overall

response rate and fertility experience

variables that are a function of age. That

is, as response rates increase and the

sample becomes older, sample estimates

of fertility experience variables (ever

had intercourse, ever had a child, etc.)

would be expected to change. Figure 8

presents a display used during data

collection to monitor the status of

fertility and other variables in the NSFG

(9). It shows the relationship between

the numbers of calls to sample addresses

and the estimated proportion of women

who reported ever having sexual

intercourse, for each call. The results in

figure 8 also include data from the

Phase 3 subsample interviews.

The horizontal line in figure 8 is the

estimated proportion of women who

reported ever having had sexual

intercourse from the Phase 3 subsample

cases. The lower, slightly increasing line

is the plot of the cumulative proportion

as the full sample is cumulated from

lower to higher numbers of calls. For

example, using the left axis for this

smooth cumulative line, the estimated

proportion for cases completed at the

second call is approximately 0.61, but

for cases with 10 or fewer calls, the

estimate has risen to nearly its final

level, around 0.67.

The irregular line above the

cumulative line is the estimated

proportion for cases obtained in Phase 3

with exactly the indicated number of

calls. Because the rates at each call in

Phase 3 are much higher than those for

the cumulative line across all phases

below, another axis has been added to

the right for this call-specific estimated
Page 20 [ Series 2, No. 142

Figure 6. Response rates for Phases 1 and 2 and overall for 18 subpopulations

Figure 7. Response rates for Phases 1 and 2 and overall (reflecting subsampling of

nonrespondents) by age group; National Survey of Family Growth Cycle 6, 2002

proportion for Phase 3. Those with

interviews obtained on the first call had

an estimated proportion near 0.95, while

those with 20 calls had an estimated

proportion near 0.70. Finally, near the

right y-axis appears a single point that is

the value of the final estimated rate,

which includes the Phases 1, 2, and 3

data.

The proportion of females who ever

had sex among Phase 3 respondents is

91 percent—higher than the estimate of

about 88 percent for the first-phase

respondents. This indicates that Phase 3

collected data for a different group of

females who were more likely to have

ever had sex than those interviewed in

Phases 1 and 2.

These findings are consistent with

findings of lower response rate variation

by age after Phase 3. Phases 1 and 2

ended with a smaller than expected

count of respondents older than 15–19

years of age. (It may be that the value

of the $40 incentive used in Phases 1

and 2 is greater for a teenager than for

an adult.) Phase 3 was more successful

in interviewing older respondents. The

effect on key statistics of this higher

response rate in Phase 3 is that

prevalence will be higher for those

variables associated with older age and

therefore with longer sexually active

lives. That is, as the number of adults in

the sample increases, due to higher

response rates for older persons in Phase

3, the prevalence of sexual behaviors in

the sample that cumulate over a lifetime

will increase. From these simple

analyses, there is evidence of reduced

nonresponse error associated with the

demographic characteristics because of

the Phase 3 design. Unfortunately, it is

not possible to provide a quantitative

assessment of the size of the reduction

for any given estimate.

Cost-Efficiency and

Response Rates in the

Phase 3 Sample

Table G presents the mean number

of calls (computed on a segment basis)

made on active cases in Phase 3. In the

1 month of Phase 3, an average of 4.08

calls were made on active screener

cases. More effort was expended per

case on segments with large numbers of

active cases than in segments with small

numbers (4.25 calls per case for high

number of incomplete active cases v.

2.96 for low). Ideally, the level of effort

would be more uniform across segments

with different numbers of active cases.

Such uniformity would produce more

equal response rates across the strata.

Table H shows the mean number of

calls on cases where main interviews

were sought in Phase 3. There is some

tendency for more effort in the segments

with low propensities (for example, 5.26

calls per active case in the lowpropensity group compared with 4.30 in

the high-propensity group), again

reflecting individual interviewer

decisions on how to obtain interviews.

Table J presents overall average

segment screener response rates (number

of interviews/number of eligibles) for

the Phase 3 cases. These segment

screener response rates are an average
Figure 8. Estimated proportion of females who have ever had sexual intercourse in Cycle 6

of the National Survey of Family Growth: Cumulative estimate, estimate by call number,

and estimate from Phase 3 interviewing

over the Phase 3 segments of the ratio

of interviews to sample cases within the

segments. The overall mean screener

response rate per segment was

51 percent, a rather remarkable rate

given the fact that the sample had been

worked for 12 months in Phases 1 and

2. The response rates range from 0 to

89 percent for the active cases for which

screeners were sampled.

Ideally, the response rates would be

constant over all 16 cells of the Phase 3

design. There is much greater stability

of these response rates at the margins,

reflecting greater number of segments

on which the means are based. Across

the four strata of number of incomplete

active cases, the response rates range

from 49 to 55 percent with no trend by

number of active cases. For the

propensity strata, the range is from 49 to

53 percent, again with no trend by

magnitude of propensities. This

distribution is desirable, given the value

of balancing representation of the Phase

3 sample over the 16 strata.

Table K presents the mean per

segment response rates for the main

interview cases. Across the 16 cells the

variation in response rates is from 0 to

65 percent. The marginal variation,

again, is much smaller. For strata

defined by active case count, the range

is from 42 to 54 percent, without a

trend across strata. For strata defined

by total propensities, the range is from

35 to 54 percent, with a trend toward

higher response rates in the segments

with higher propensities. This is

undesirable, but reflects the difficulty

of obtaining the main interviews after

a prior refusal.

Series 2, No. 142 [ Page 21

Tables L and M display the segment

mean of the ratio of interviews to

number of calls, an indirect indicator of

the difficulty of obtaining an interview.

The larger the ratio, the less the effort

required to obtain interviews. It was

expected that the smallest ratios would

lie in the low-propensity strata.

Table L presents the results for the

screener interviewing efforts. Here, the

mean ratios range from 0.04 to 0.59

across the 16 cells. These correspond to

an interview every 1.7 calls (that is,

1/0.59) to an interview every 25 calls.

Looking at the margins of the table,

there is an unexpected tendency for the

segments with low propensities to have

higher rates of interviews per call: from

0.35 in the low-propensity stratum to

0.54 for the high. The segments with

low numbers of active cases also show

higher efficiencies: from 0.42 for low

number of incomplete active cases to

0.54 for high.

Table M shows interview per call

rate for the main interview cases. Here,

the efficiencies range from an interview

every four calls (1/0.25 in the low

propensity and low number of

incomplete active cases) to zero. The

next highest value is 1 in every 11 calls

(1/0.09 in several cells). The higher

efficiencies are found in the highpropensity strata (0.16 and 0.17 in the

high and medium-high strata compared

with 0.13 and 0.14 in the medium-low

and low strata), as expected.

In the segments with fewer active

cases, higher efficiencies were also

observed (0.20 in the low number of cases

stratum). This finding indicates that the

survey climate in strata with fewer active

cases was easier. There would be a

tendency to find in segments with fewer

active cases a better climate since the

Table G. Mean number of calls per active screener case by number of active cases and total propensity strata in Phase 3: National Survey

of Family Growth, Cycle 6

Number of incomplete active cases at end of Phase 2 (Quartiles)

Estimated total propensity to respond for Medium- Mediumincomplete active cases (Quartiles) High high low Low Total

High (top 1⁄4 of propensities)................... 4.04 4.30 3.03 1.00 3.96

Medium-high (next highest 1⁄4) ................. 4.49 4.46 3.69 3.92 4.33

Medium-low (next highest 1⁄4) .................. 4.00 4.37 3.91 1.92 3.68

Low (smallest propensities) ................... 7.06 4.22 3.00 5.11 5.02

Total ................................. 4.25 4.38 3.48 2.96 4.08

NOTE: Average over segments of the ratio of calls to segment sampled units not yet screened to number of cases screened during the third phase.
4.44

Page 22 [ Series 2, No. 142

Table H. Mean number of calls per active main interview case by number of active cases and estimated total propensity of cases in the

segment to respond on the next visit: Phase 3 of the National Survey of Family Growth, Cycle 6

Number of incomplete active cases at end of Phase 2 (Quartiles)

Medium- MediumTotal propensity for incomplete active cases (Quartiles) High high low Low Total

High (top 1⁄4 of propensities)................... 4.40 4.37 3.62 4.72 4.30

Medium-high (next highest 1⁄4) ................. 4.53 4.82 4.13 3.81 4.53

Medium-low (next highest 1⁄4) .................. 4.74 5.21 3.52 3.26 4.30

Low (smallest propensities) ................... 5.44 5.11 7.11 4.00 5.26

Total ................................. 4.50 4.74 4.07 3.79

NOTE: Average over segments of the ratio of calls to sampled respondents to number of main survey respondents interviewed during the third phase.

Table J. Mean screener response rates for segments in Phase 3 by number of active cases and total propensity strata: National Survey of

Family Growth, Cycle 6

Number of incomplete active cases at end of Phase 2 (Quartiles)

Medium- MediumTotal propensity for incomplete active cases (Quartiles) High high low Low Total

High (top 1⁄4 of propensities)................... 0.52 0.42 0.59 0.00 0.50

Medium-high (next highest 1⁄4) ................. 0.51 0.51 0.53 0.83 0.53

Medium-low (next highest 1⁄4) .................. 0.56 0.48 0.41 0.54 0.49

Low (smallest propensities) ................... 0.28 0.89 0.50 0.33 0.52

Total ................................. 0.51 0.49 0.52 0.55

0.0 Quantity more than zero but less than 0.05.

NOTE: Average over segments of the ratio of completed screeners in third phase to number of cases not yet screened at beginning of the third phase.

lower number of incomplete active cases

itself indicates that Phases 1 and 2 effort

was able to complete more cases.

These results indicate that the Phase

3 stratification was more highly

effective for the main than for the

screener interviews. In retrospect, the

effect of blending the screener and main

response propensity models to form the

Phase 3 strata led to this result. Most of

the Phase 3 cases were main interview

nonresponse, a set of cases where there

was a stronger relationship between

predictors and propensity to respond to

the main interview. Using the

‘‘blended’’ model forced a better

prediction for main than for screener

interviews.

Responsive Design

In previous cycles of the NSFG, as

in many other surveys, the survey

managers attempted to keep track of

interviewer effort and field costs using a

paper and pencil system to record each

interviewer’s hours worked and other

expenses. One problem with these

systems was that it was often 3–6 weeks

before their results were available. As a

result, data on survey costs and effort

lagged far behind data on completed

cases and response rates, and there was

little or no information that was specific

enough to provide advice to interviewers

that was based on detailed knowledge of

their work. As a result, the NSFG

contract required that the contractor

have a system that could provide data

on field costs and response rates that

were more timely and thus more useful

for controlling costs and managing

fieldwork.

The ISR system for monitoring

fieldwork, called SurveyTrak, required

the interviewers to record simple

characteristics of neighborhoods (sample

segments), sampled households, and

statements made by respondents to the

interviewers during attempts to contact

them for an interview. The contractor

collected and analyzed these data during

fieldwork, to provide data to allocate

interviewer labor during the study. This

information was used throughout

fieldwork: in Phase 1, it was used to

make estimates of interviewer

productivity and survey costs; in Phase

2, it was used to help survey managers

to allocate direct interviewer labor; and

in Phase 3, it was used to sort the

remaining (nonrespondent) cases to

decide where to put interviewer labor to

get the most cost-efficient return. This

approach to survey management is

called ‘‘Responsive Design.’’

A variety of statistical models were

used to evaluate the progress of field

work and to decide where to allocate

interviewer effort in the final stages of

interviewing:

1. A logistic regression model

predicting the probability of a

completed main interview, using as

predictor variables the number of

hours interviewers spent traveling,

hours doing administrative tasks,

hours making screener visits, and

hours making main interview

visits.

2. A linear regression model predicting

interviewer hours needed to obtain a

completed main interview, separately

for each of the 18 demographic

subgroups, using such predictors as

total number of calls made to a case,

an indicator for whether a contact

0.51
Series 2, No. 142 [ Page 23

Table K. Mean main interview response rates for segments during Phase 3 by number of active cases at the beginning of Phase 3, and the

total propensity of cases in the segment to respond on the next visit: National Survey of Family Growth, Cycle 6

Number of incomplete active cases at end of Phase 2 (Quartiles)

Medium- MediumTotal propensity for incomplete active cases (Quartiles) High high low Low Total

High (top 1⁄4 of propensities)................... 0.54 0.50 0.64 0.50 0.54

Medium-high (next highest 1⁄4) ................. 0.56 0.57 0.51 0.31 0.53

Medium-low (next highest 1⁄4) .................. 0.51 0.35 0.35 0.57 0.43

Low (smallest propensities) ................... 0.33 0.00 0.65 0.33 0.35

Total ................................. 0.54 0.49 0.53 0.42 0.51

0.0 Quantity more than zero but less than 0.05.

NOTE: Average over segments of the ratio of main interviews in third phase to number of cases eligible for the main interview in the third phase.

Table L. Mean ratio of number of screener interviews to number of total calls on screener cases by number of active cases in the segment,

and by the estimated total propensity of cases in the segment to respond on the next interviewer visit: National Survey of Family Growth,

Cycle 6

Number of incomplete active cases at end of Phase 2 (Quartiles)

Medium- MediumTotal propensity for incomplete active cases (Quartiles) High high low Low Total

High (top 1⁄4 of propensities)................... 0.15 0.14 0.29 0.00 0.16

Medium-high (next highest 1⁄4) ................. 0.13 0.18 0.20 0.59 0.18

Medium-low (next highest 1⁄4) .................. 0.13 0.14 0.11 0.37 0.17

Low (smallest propensities) ................... 0.04 0.47 0.50 0.04 0.24

Total ................................. 0.14 0.17 0.22 0.34 0.18

0.0 Quantity more than zero but less than 0.05.

NOTE: Average over segments of the ratio of completed screeners in third phase to total number of screener calls during the third phase.

had been made, and other

operational variables.

3. Using the logistic and linear models

in Phase 1 and Phase 2, a model to

forecast the final number of

interviews under different allocations

of staff.

These models were used in

conjunction with statistical tracking

(9) to provide more efficient data

collection.

In addition, simple statistics were

tracked to identify key lifecycle changes

in the data collection. For example,

figure 9 shows the cumulative ratio of

total interviewer hours worked to total

main interviews collected. It has the

pattern common to surveys involving

screening interviews. The first weeks of

Phase 1, especially in April 2002, based

on the quarter-sample, were devoted

mainly to identifying eligible sample

persons and relatively few main

interviews were obtained. This produced

a higher average number of hours of

labor per interview. Then there was a

decline and a rapid increase toward the

end of Phase 1 (the third week of June),

when interviewers were mostly making

repeated visits on those who were

difficult to contact or those reluctant to

be interviewed.

In the beginning of July 2002, new

sample areas were opened, and new

interviewers began to work in them, so

that all 121 PSUs were being worked.

The number of hours per interview rose

again as the new cases were contacted

and screened. Hours per interview

gradually declined and reached a plateau

of about 10.9 hours per interview in the

fall months, and then began increasing

again as the interviewers completed the

easy-to-contact and easy-to-interview

cases and focused on the more difficult

Table M. Mean ratios of number of main interviews to number of total calls on screener cases by number of active cases and total

propensity strata: National Survey of Family Growth, Cycle 6

Number of incomplete active cases at end of Phase 2 (Quartiles)

Medium- MediumTotal propensity for incomplete active cases (Quartiles) High high low Low Total

High (top 1⁄4 of propensities)................... 0.16 0.14 0.22 0.10 0.16

Medium-high (next highest 1⁄4) ................. 0.16 0.15 0.22 0.19 0.17

Medium-low (next highest 1⁄4) .................. 0.11 0.09 0.11 0.23 0.13

Low (smallest propensities) ................... 0.13 0.00 0.11 0.25 0.14

Total ................................. 0.16 0.13 0.18 0.20

0.0 Quantity more than zero but less than 0.05.

NOTE: Average over segments of the ratio of completed main interviews in third phase to total number of main interview calls during the third phase.

0.16
Page 24 [ Series 2, No. 142

cases. The February 1, 2003, Phase 3

introduction prevented the rapid increase

in hours per interview seen in Phase 1

by implementing more efficient

protocols on a subsample of the

remaining nonrespondent cases.

Sample Size

The use of a responsive design for

Cycle 6 effectively managed the survey

to achieve the maximum number of

completed interviews while remaining

within the fixed budget constraints of

the survey. The responsive design

approach continually examined expected

and achieved eligibility and response

rates throughout the data collection

period. As differences were observed,

sample release was altered to obtain the

largest total number of completed main

interviews, and a minimum number of

completed interviews with 15–19-yearolds and black and Hispanic persons. A

comparison of initial target sample size

to the final achieved results is less

meaningful in Cycle 6 under a

responsive design.

Table N presents the number of

completed interviews by 18 subclasses

formed by age group, gender, and race

and ethnicity group. (The race and

ethnicity grouping is that used in the

sample design: black, Hispanic, and

other, where ‘‘other’’ includes

non-Hispanic white, Asian, and

American Indian respondents; see

‘‘race’’ in Appendix I, the ‘‘Definition of

Terms’’). Sample size is an often used

indicator of the reliability of estimates

obtained from a survey. In Cycle 6, the

sample size was only 145 for black

males 20–24 years of age. Teen sample

sizes for both genders and all race and

ethnicity groups are larger, ranging from

205 for black males 15–19 years to 681

for (white and) other race males 15–19

years. The most reliable estimates will

be obtained for (white and) other race

females 25–44 years of age with a

sample size of 2,995, followed by

(white and) other race males 25–44

years with a sample size of 1,616.

In a stratified multistage sample

survey like the NSFG Cycle 6, sample

size is not the only feature of the

sample that affects the reliability of

estimates. Because the sample is

clustered, and because the estimates

must be weighted, the estimated levels

of precision can be expected to be

lower when these factors are

accounted for. The user of the

microdata must be aware that standard

statistical estimation software does not

properly account for these factors in

estimation of reliability

(that is, variances). Procedures are

given later in this report on how to

use statistical estimation software that

will properly account for the

clustering, stratification, and weighting

employed in the NSFG Cycle 6

sample.

Figure 9. Cumulative ratio of interviewer hours to number of main interviews completed by day of the data collection period: National

Survey of Family Growth Cycle 6, 2002
Series 2, No. 142 [ Page 25

Table N. Number of completed interviews by age group, sex, and race or ethnicity: National Survey of Family Growth, Cycle 6, 2002

Race or ethnicity

Sex and age Total Black Hispanic Other

Both sexes

Total .....................

15–19 years ...............

20–24 years ...............

25–44 years ...............

12,571

2,271

2,301

7,999

2,460

447

363

1,650

2,712

466

508

1,738

7,399

1,358

1,430

4,611

Female

Total .....................

15–19 years ...............

20–24 years ...............

25–44 years ...............

7,643

1,150

1,363

5,130

1,530

242

218

1,070

1,589

231

293

1,065

4,524

677

852

2,995

Male

Total .....................

15–19 years ...............

20–24 years ...............

25–44 years ...............

4,928

1,121

938

2,869

930

205

145

580

1,123

235

215

673

2,875

681

578

1,616

Weighting Procedures

Summary

NSFG Cycle 6 is designed to

estimate the number of men and women

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, not only for the total

population but also for age groups, such

as teenagers 15–19 years of age, men

ages 20–24 years, or Hispanic women

ages 25–44 years.

The NSFG deliberately oversampled

specified subgroups of the population. It

is also subject to nonresponse and

noncoverage error. Sampling weights

have been assigned to every person in

the public-use data file. These weights

must be used to compensate for unequal

probabilities of selection, and for

underrepresentation of some subgroups

due to nonresponse or noncoverage.

These 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 was constructed to account for

various deliberate over- and

undersampling features of the Cycle 6

sample design. These weights were

combined into a single ‘‘base weight’’

to represent the probabilities of

selection.

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 housing unit listing process,

interviewer observations collected at all

sample households from all contacts

with the household, household call

records, household composition, and a

few additional household and selected

person characteristics (for models on

eligible persons). The inverse of the

predicted probabilities were used as

adjustment factors applied to the base

weights.

Third, nonresponse adjusted base

weights were cumulated within each of

36 groups—6 age categories, 2

categories of gender, and 3 race and

ethnicity subgroups. The cumulated

weights were compared with

independent Census Bureau population

projection estimates for each cell, plus

counts of the military personnel in each

cell who were living off-base obtained

from the U.S. Department of Defense.

Poststratification weights were computed

for each cell as the ratio of Census plus

Defense counts to the cumulated sample

weights in each cell. The poststratification

factors were used to adjust the individual

nonresponse adjusted base weights.

Fourth, the poststratified weights

were inspected to detect extremely large

or small weight values. No weight

values were deemed to be extremely

small. A total of 49 large weight values

were ‘‘trimmed’’ to reduce their impact

on weighted estimates. The poststratification factors for the respective

cells of each trimmed weight were

recomputed after trimming. A more

detailed account of the weighting

procedures follows.

Inverse Probability

Selection Weighting

The NSFG Cycle 6 sample

management system preloaded required

information to calculate the probability

of selection of each sample case. The

inverse of the probability of selection of

each case is the base weight adjusting

for unequal chances of selection.

There are several components to the

probability of selection and the base

weight. Each case was selected through

four stages of sampling: primary,

segment, housing unit, and person

within selected household. The primary

stage of selection involved two

overlapping samples of primary

sampling units: a national sample and a

Hispanic sample. In addition, the Phase

3 sample selected a subset of cases for

intensive followup for the last month of

fieldwork.
Page 26 [ Series 2, No. 142

Primary Sampling Units

In the national sample, the land area

of the United States, including the

District of Columbia, was divided into

3,141 counties and county equivalent

units (see sample design description for

details). These units, in turn, were

combined to form a set of 2,402 primary

sampling units (PSUs), which were then

grouped into 110 strata (including

Alaska and Hawaii) for the first-stage

selection of the NSFG Cycle 6 national

sample and 11 additional strata for the

first-stage sample of the Hispanic

sample. The combination of the national

and Hispanic samples yielded 121

NSFG Cycle 6 PSUs.

The probability of selection of each

PSU in each sample was computed,

accounting for the overlap in selections,

at the time of selection and stored for

later use. All 121 PSUs were

subsampled in NSFG Cycle 6.

Segment Selection

Within each sample PSU, the land

area was further divided into blocks, as

defined by the 2000 census. The blocks

were divided into four domains within

each PSU:

1. Nonminority

2. More than 10 percent black

population

3. More than 10 percent Hispanic

population

4. More than 10 percent black and

more than 10 percent Hispanic

population

Blocks were selected within each

domain with probabilities proportionate

to the estimated number of households

in the area in the year 2000.

To improve data collection

efficiency by reducing travel costs,

census blocks falling below a minimum

number of estimated households were

linked with other geographically

contiguous blocks. In urban areas, the

minimum size was 75 estimated

households, while in rural areas the

minimum was 50. The final units

consisted of entire census blocks or set

of linked census blocks and are referred

to as segments. There were a total of

1,414 segments selected across the 4

domains and the 121 PSUs.

In all PSUs, segments in domains 3

and 4 were chosen with 2.1 times higher

probabilities of selection than those in

domain 1. Those in domain 2 were

chosen with a 1.8 times higher rate.

Probabilities of selection were

proportionate to the estimated number of

households in the segment. The

probability of selection of each block

was computed at the time of selection

and stored for later use.

Housing Unit Selection

Not all sample segments were

subsequently selected for the NSFG

sample. A larger number of segments

were selected initially to have available

a sufficient number of housing units in

each segment to achieve, if needed,

higher target sample size in the

responsive sample design.

Segments were subsampled in each

of the three phases of the sample

release. Across Phases 1 and 2 a total of

783 segments were selected for the

NSFG Cycle 6. Probabilities of selection

for the Phases 1 and 2 segment

sampling were computed for each

segment and stored for later use.

Each segment was subsequently

visited by an ISR interviewer trained to

list all housing units in the segment.

Sample listings were returned to Ann

Arbor, where they were keyed and

stored in a file containing ‘‘sample

lines,’’ an entry for a listed housing unit.

Sample lines were selected to

achieve a large initial target sample size

of more than 72,000 lines. Each sample

line (listed housing unit) was assigned

to 1 of 24 replicate subsamples within

each segment. Phases 1 and 2 sampling

used replicate samples to achieve target

sample sizes for each phase of selection

to be released for interviewers hired

from the PSU. The subsampling

probabilities of selection for housing

units were recorded in internal

memoranda to be used later in weight

computation.

Person Selection

Once sample lines (housing units)

were selected, they were released through

the ISR sample management system to

interviewers in each PSU. Interviewers

visited sample housing units and

determined if any eligible persons resided

there. If eligible persons did reside in the

housing unit, the interviewers completed a

household roster where they recorded the

age, gender, and race and ethnicity of each

eligible person.

In the interview, a random selection

was made of one eligible person per

household. The chances of selection

within the household were varied to

increase the chances of selection of

teens (ages 15–19), women, and black

and Hispanic persons. The household

roster and chances of selection were

recorded in the household interview.

Nonresponse Sampling

A final component of sample

selection was introduced in Phase 3

sampling in the final month of data

collection. A sample was chosen for

more intensive data collection effort. At

the last month of data collection, most

of the 783 segments selected for the

sample had sample lines that were not

completed. Segments containing sample

lines that had not reached a final

disposition 1 month before the end of

data collection were divided into 16

strata on the basis of the number of

active cases remaining and predicted

probabilities of obtaining a completed

interview cumulated across all

remaining cases in the segment.

A Phase 3 selection of segments

from each of the 16 groups was made,

with higher chances of selection

assigned to those segments with higher

expected numbers of completed

interviews and higher numbers of cases

remaining. The chances of selection

were thus varied for the selection of

second-phase segments released for data

collection in the last month of the

survey period. These probabilities of

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
(including segment replicate), sample

line and sample line replicate,

within-household selection, and Phase

3 subsampling of replicates.

Let Mhα denote the size measure of

the αth PSU in PSU stratum h. For

national sample PSUs, the measure of

size was the number of occupied

households in the PSU in 1990, while

for the Hispanic sample, the measure of

size was the 2000 Hispanic population.

Let Mhαβ denote the size measure

for the βth segment in the (hα)

th PSU.

The size measure for each segment was

an estimated number of households

obtained by dividing the census

population for the blocks in the segment

by the average number of persons per

household obtained from the March

1999 CPS. Also, let cdhα denote the

desired number of sample lines for the

dth domain in the (hα)

th PSU.

Let πp denote the probability of

selection for the national sample quarter

sample PSU replicate, πs,hαβ denote the

sample replicate selection probability for

the (hαβ)

th segment (which can take on

values of 1/3, 2/3, or 1, depending on

the subsampling rate employed in the

PSU and domain), and let πhu,hαβ denote

the replicate selection probability for the

housing unit or sample line in the

(hαβ)

th sample segment. Finally, let

π2,hαβ denote the Phase 3 selection

probability for the (hαβ)

th sample

segment.

The probability of selection of the

(hαβγ)

th eligible person (where γ 

denotes the γ

th sample person in the

(hαβ)

th segment) can be computed as

Mhα Mhαβ �h��� = • πp,hα • • πs,hαβ • ( ah ) ( bhα ) ∑Mhα ∑Mhαβ

α=1 β=1

cdhα �2,hαβ • (M ) • πhu,hαβ hαβ

The base weight compensating

for unequal chances of selection for the

(hαβγ)

th eligible person is the inverse

of this probability of selection,

whαβγ = w1i = π–1

hαβγ.

The base weights for the 12,571

completed interviews are summarized in

table O for each of the target 18

subclasses. Large values of these

Series 2, No. 142 [ Page 27

weights were not trimmed in the

weighting process.

Because of the large number of

components, the base weights have

substantial variation within subclasses.

For example, the base weight values

for female other race respondents ages

25–44 range from 215 to 137,124. The

largest contributing factor to this very

wide range is the subsampling within

households described previously and

summarized in figures 4 and 5. In

order to achieve a large number of

completed interviews with teens 15–19

years of age, a substantial oversample

within households was necessary. The

oversampling of teens led to a

substantial undersampling of 25–44

year olds and much larger weights for

the 25–44 year olds in households

where teens lived. At the same time,

25–44 year olds living in households

without teens had much higher

chances of selection and had much

lower weights.

This variation in base weights due

to the over- and undersampling within

households has the potential to increase

the variance of estimates. A summary

Table O. Mean, minimum, and maximum untrimmed base weight, and potential increase in variance due to weighting (1+L), by age group,

sex, and race or ethnicity: National Survey of Family Growth, Cycle 6

Mean Minimum Maximum Increase in

Sex, race or ethnicity, and age weight weight weight variance (1+L)

Male

Black:

15–19 ................... 4,556.55 1167.49 31,868.62 1.6853

20–24 ................... 4,936.14 405.35 51,268.90 2.8206

25–44 ................... 4,676.89 366.60 69,461.98 2.6211

Hispanic:

15–19 ................... 4,944.55 1167.57 23,432.59 1.4150

20–24 ................... 4,857.30 435.19 31,967.17 1.7063

25–44 ................... 5,224.50 1167.44 37,341.97 1.7702

Other:

15–19 ................... 4,754.88 1167.44 20,649.67 1.3072

20–24 ................... 5,915.56 1109.84 29,392.20 1.5592

25–44 ................... 7,309.55 215.26 137,124.02 2.6746

Female

Black:

15–19 ................... 3,803.41 766.42 13,893.39 1.3681

20–24 ................... 3,537.10 766.42 35,394.72 1.8366

25–44 ................... 3,126.23 366.60 21,375.42 1.5419

Hispanic:

15–19 ................... 4,488.10 1167.57 18,681.57 1.3745

20–24 ................... 3,772.34 1167.44 12,118.88 1.2802

25–44 ................... 3,559.23 366.60 22,426.69 1.4794

Other:

15–19 ................... 4,696.32 205.10 18,183.37 1.2703

20–24 ................... 4,478.61 478.56 47,029.78 1.4232

25–44 ................... 4,631.78 64.58 99,707.83 1.7192
�

�

�

�

Page 28 [ Series 2, No. 142

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 cell. The factor 1+L

the nonresponding housing unit. The

main interview nonresponse occurs any

time after the conclusion of screening,

when a sample person has been selected.

The main interview nonresponse has

additional information about household

is a measure of the relative variance of composition and race and ethnicity of

the weights themselves, and is computed the selected person, obtained during

is a vector of logistic regression

coefficients. The coefficient values can

be estimated through standard maximum

likelihood methods, and used to predict

the logit

^

si = (1+exp(Xi

' β ^

))–1

which, in turn, can be used to obtain a

for the total sample as household screening. predicted probability of successful

Nonresponse adjustment for NSFG screening is 12,571 ( 12,571 ∑ w1

2

i

Cycle 6 was implemented under an √ i=1 ) assumption of missing at random

1+L = ^ 12,571 (MAR). That is, within subgroups of ^ exp(–λsi)

( )

2 = sample units (housing units in the . ∑
 si w ^ 1i (1+exp(–λsi)) i=1 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 can be

computed as the inverse of an estimated

response rate. This sample based weight

represents an adjustment that, under the

MAR assumption, may substitute 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

developing estimated probabilities of

selection under MAR. One is to divide

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. A more general form

This factor is an approximation that

indicates the relative effect of weighting

across the cells. It is not an exact factor

that can be attributed to each case in a

cell.

Nonetheless, there are substantial

potential increases in variance because

of weighting across all cells. The largest

values of the factor 1+L are for females,

particularly black persons 20–24 and

25–44 years of age and other race and

ethnicity ages 25–44 years. These

largest increases can be directly

attributed to the oversampling of

15–19-year-olds within households.

The full effects of these increases

are reduced later in the weighting

process through trimming of the largest

final weight values. Further, sampling

variance estimates presented in the last

section of this report do not show large

design effects that would in part be

Similarly, let Ri denote a 0–1

indicator for main interview response

for the ith 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 were 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 ith selected eligible person and γ 

is a vector of logistic regression

coefficients. Standard maximum

likelihood methods yield

^ ' = (1+exp (Zi γ|))–1 mi .

of the weighting class analysis is to The predicted probability of successful generated by the increase in variance

estimate probabilities of response screening due to weighting. Thus, the increases in

through some form of a logit or variance shown in table O, and in ^

subsequent tables in this section, should

exp (–λmi) propensity model, as in a logistic ^ = . mi ^ regression. Separate models for (1+exp (–λmi)) not be interpreted as actual direct effects

on sampling variance estimates obtained

by NSFG Cycle 6 analysts who properly

screening and main interview

nonresponse were developed. That is, a model was developed for

Let Si denote a zero-one indicator screening, and a second model was account for the sample design in

analysis.

Nonresponse Adjustment

Nonresponse in NSFG Cycle 6

occurred at both screening to identify

for whether a sample address was developed for the main interview given

successfully screened to determine successful screening. The overall

whether eligible persons lived in the response propensity is then estimated for

household, where 1 denotes successful each housing unit based on the product

screening. (Si is not defined for sample of two predicted probabilities,

addresses that were not occupied.) The |πri = |πsi × |πmi. This predicted probability

screening logistic regression model for is computed for all selected eligible sample eligible persons in sample

households and at the main interview all occupied sample addresses is persons.

among selected eligible persons. At the ' A nonresponse weight was then

screener interview, nonresponse occurs

�si = Pr (Si=1|Xi) = (1+exp (–Xi β))–1

computed as the inverse of the predicted

at the time of initial contact when where Xi is a vector of predictor values probability, but only for completed

limited information is available about for the ith occupied housing unit and β interview cases. In particular,
Series 2, No. 142 [ Page 29

wri = π|ri

–1 if Ri = 1 and Si = 1, and

wri = 0 otherwise.

Unlike NSFG Cycle 5, which had

the National Health Interview Survey

data set for each sample person to use

as potential predictors in response

propensity models, Cycle 6 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 3 sample selection (9). Those

preliminary models employed a number

of predictors:

1. Counts and rates for the segment

from which the housing unit was

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 who listed the segment.

3. Respondent behavior recorded by the

interviewer at each contact with

models were re-examined for the

screening and main interview response

propensity models, again using stepwise

logistic regression to identify a set

significantly associated with response.

The screener and main interview

stepwise models yielded different sets

of predictors, despite considerable

overlap in predictors between the

models. There are also theoretical

considerations for choice of predictors

between the two models. The

development of each model will be

described separately.

Screener Response Propensity

Model

The predictors in the screener

propensity model and their estimated

coefficients are shown in table P. Not

only did all of these variables have

significant association with response to

screening, but also these variables

represent a theoretically important set of

predictors of response propensity. For

example, the survey methodology

literature (10,12) demonstrates that

certain behaviors recorded in contact

observations are highly correlated with

response propensity. Four ‘‘ever ...’’

variables stand out among the

predictors:

1. everresist: during any contact with

the household, any respondent

refused to participate in the survey

2. everquest: during contact with the

household, any respondent asked a

question about the content or

objectives of the survey

3. evercontact: during any contact

with the household, any respondent

was contacted and provided

information about the household

4. everdelay: during any contact with

the households, any respondent

made an appointment for an

interview at another time

Table P. Logistic Regression Model Predicting Response to the Screener, with 18 predictors

(Phases 1–3): National Survey of Family Growth, Cycle 6

Standard

Parameter Estimate error Pr > ChiSq

Intercept................... 0.7092 0.09020 <.0001

Everresist1 ................. –2.7902 0.05150 <.0001

Everquest2 ................. 0.9388 0.04420 <.0001

Release 13 ................. 1.3875 0.07780 <.0001

Release 24 ................. 1.0110 0.07470 <.0001

Release 35 ................. 0.8408 0.08300 <.0001

Evercontact6 ................ 0.1998 0.05370 0.0002

Callstocontact7 ............... –0.0526 0.00631 <.0001

Everdelay8 ................. –0.3625 0.04760 <.0001

Physimped9................. –0.3383 0.05460 <.0001

Uninhab10 .................. 0.2613 0.04820 <.0001

Bilingual11 .................. 0.2944 0.05440 <.0001

RaceID12 .................. 0.0265 0.01120 0.0178

Lspanish13 ................. 0.2887 0.05150 <.0001

Tenured14 .................. 0.7675 0.04330 <.0001

Urban15 ................... –0.1777 0.04510 <.0001

Manyunits16................. –0.3801 0.04630 <.0001

Evepct17................... 0.0157 0.00151 <.0001

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 were 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 were examined in stepwise

logistic regressions for the screening

indicator variable. The same predictor

variables plus main interview contact

observations, household composition,

and other variables available from the

screening interview were examined in

stepwise logistic regressions for the

main interview response indicator Ri

.

The predictors examined in the

Phase 3 sample selection propensity

1Everresist: during any contact with the household, any respondent refused to participate in the survey.

2Everquest: during contact with the household, any respondent asked a question about the content or objectives of the survey.

3Release 1: indicator that sample line was released during Phase 1.

4Release 2: indicator that sample line was released during Phase 2.

5Release 3: indicator that sample line was released during Phase 3.

6Evercontact: during any contact with the household, any respondent was contacted and provided information about the household.

7Callstocontact: indicates the number of calls until contact was made.

8Everdelay: during any contact with the households, any respondent made an appointment for interview at another time.

9Physimped: indicator of whether or not there was a physical impediment to accessing the housing unit.

10Uninhab: during screening the housing unit was unoccupied.

11Bilingual: indicator that housing unit is bilingual.

12RaceID: during field listing of the segment, the listing staff identified the predominant race of households in the segment as black.

13Spanish: during field listing, the listing staff reported that there were Spanish speaking households in the segment.

14Tenured: during field listing, the listing staff reported that the household was a single-family dwelling.

15Urban: indicator that housing unit was in an urban area.

16Manyunits: indicator that housing unit was a multi-unit structure.

17Evepct: percentage of calls made to the household during evening hours after 6 PM local time.
Page 30 [ Series 2, No. 142

All ‘‘ever’’ variables were derived

from contact information. The signs of

the coefficients for each of the ‘‘ever’’

variables in table P are consistent with

the theoretical and empirical literature.

In particular, resistance, delays, and

failure to contact persons in the housing

unit during a visit reduced response

propensity, while informant questions

(everquest) asked during contact

increased response propensity.

Other variables in the model had

substantial associations with screener

response as well:

1. When the sample line was released

(during Phase 1 in March (release1),

Phase 2 in June (release2), or Phase

2 in September (release3)) indicates

that later release yielded lower

response propensity.

2. Physical impediments to access

(physimped), multiunit structures

(manyunits), and urban location

(urban) were associated with lower

response rates.

3. More calls to the housing unit to

complete the screening interview

were associated with lower response

propensity (callstocontact).

4. Housing units in bilingual

(bilingual), Spanish speaking

(lspanish), and nonblack (raceID)

segments, and owner occupied

housing units (tenured) had higher

response propensities.

5. Higher percentages of evening calls

(evepct) were associated with

increased response propensity.

Predicted probabilities of response

ranged from near 0.05 to 0.98. This

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 of predicted values.

This method effectively imposes a

constraint on the underlying propensity

model. The predicted probabilities were

grouped by deciles of the predicted

probabilities. All completed screener

cases in the lowest predicted probability

decile were assigned the value of the

median of the predicted probabilities in

the decile. The remaining 90 percent of

the values were not changed. This

response propensity revision reduced the

variation to a range from 0.39 to 0.98.

The main interview nonresponse weight

was 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 was

more complicated than the screener

propensity model because a larger

number of predictors were available, and

a broader theoretical framework could

be used for weight development. The

stepwise logistic regression indicated

that age, gender, and race and ethnicity

were not strong predictors of response

propensity for the main interview model.

However, all are known to be strong

predictors of the values of 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

mean approach. If a group of sample

persons who have the same or similar

propensities have the same value of a

substantive measure, then nonresponse is

independent of the substantive measure.

Models that predict substantive

measures could 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 mean approach is

difficult to implement in practice

because it is seldom the case that

available predictors of substantive

measures are present. However, in

NSFG Cycle 6, age, gender, and race

and ethnicity are known for all selected

sample persons, and these are important

predictors of substantive measures.

Thus, while the response propensity

model stepwise regression screening did

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 mean models can be used for

the main interview nonresponse

adjustment.

Three different types of models

were examined for main interview

propensity. In the first, all statistically

significant predictors in the stepwise

regression were included, as well as age,

gender, and race and ethnicity

indicators. The second model included

the same predictors, but added

interactions between age and gender and

the ‘‘Ever...’’ variables from the

contact observation data that were

highly associated again with response.

A third model employed a reduced

set of only 6 predictors in each of 18

subclasses defined by age, gender, and

race and ethnicity. These 18 separate

models were based on samples that

ranged in size from approximately 200

for black male teens to more than 4,000

for white and other race females 25–44

years of age. These sizes were too small

for nine of the subclasses where

quasi-complete separation prevented

estimation of some model coefficients.

As a result, the 18 subclasses were

collapsed into 9 subgroups, each with

substantially larger sample sizes, from

approximately 1,200 to more than 4,000:

1. Male 15–19 years

2. Female 15–19 years

3. Male 20–24 years

4. Female 20–24 years
Series 2, No. 142 [ Page 31

5. Male black or Hispanic 25–44

years

6. Male white and other race 25–44

years

7. Female black 25–44

8. Female Hispanic 25–44

9. Female white and other race 25–44

For each subclass model, the same

six predictors are used in each subgroup

model:

1. ever resisted interview

2. ever asked questions

3. ever delayed interview

4. ever failed to contact person in

housing unit

5. screening interview completed in

Spanish

6. single-person household (table Q)

These six predictors have the

strongest association with main

interview response propensity in the

stepwise logistic regression selection.

The three models each showed similar

levels of change in the logarithm of the

likelihood from the ‘‘null model’’

(containing only an intercept as the

predictor to the model). Examination of

the properties of the estimators and

predicted values across the three models

revealed no particular features of one

model that would favor one over the

other. A choice was made to use the

nine subgroup model to generate

predicted response propensities. It

combined most completely the strongest

response propensity predictors with the

most important predictors available for a

predictive mean approach.

The estimated coefficients for this

model are shown in table Q. For the

most part, the six predictors are

associated with higher response

propensity in each of the nine subclasses

in the same way. More resistance

(everresist), delayed interviewing

(everdelay), and Spanish language of

interview (SCR_LANG) are associated

with lower response propensity.

Questions (everquest) or contact at each

call (evercontact) and single-person

households (SCR_SINGLEHH) are

associated with higher response

propensities, although single-person

households are in two subgroups only

marginally significant. Estimated

coefficients do vary across subclasses;

the hypothesis of subgroup coefficient

equality was not examined.

As for the screener interview

propensity model, the main interview

response propensity weights showed

substantial variation, based on predicted

probabilities that ranged from 0.05 to

0.98. The variation was reduced by the

same technique applied to the screener

response propensity model probabilities:

cases in the lowest decile of predicted

probabilities were assigned the value of

the median of the predicted probabilities

in the decile. The remaining 90 percent

of the values were not changed.

The main interview nonresponse

weight wmi was computed as the inverse

of the predicted probability or the

inverse of the median of the lowest

decile.

Combined Nonresponse Weight

As a final step in the construction

of the nonresponse adjustment weight,

the screener nonresponse weight wsi was

multiplied by the main interview

nonresponse adjustment to obtain the

final nonresponse adjusted weight,

w2i = wsi × wmi. No trimming of this

final weight value was needed.

Table R summarizes the distribution

of the combined nonresponse weights

within each of 18 subclasses of

substantive importance. The range of

nonresponse weights is limited, and the

potential increase in variance due to

weighting is much smaller than for the

base weight. When these adjustments

are applied to the base weights to yield

the nonresponse adjusted base weights,

w3i = w2i × w1i

, the distributions

observed in table O for the base weight

were changed only slightly.

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 Cycle 6 data could be

benchmarked in a calibration approach.

A calibration to external population

counts by age, gender, and race and

ethnicity was implemented.

Poststratification adjusts the nonresponse

adjusted weight w3i to the civilian

noninstitutionalized population

projections for June 2002 provided by

the Census Bureau for 36 age, gender,

and race ethnicity cells (see table S).

(The Census Bureau projections shown

in table S will not agree with published

projections on the census Web site since

the published projections undergo

revision regularly, and change over

time.) The poststratification weight w4i

is the ratio of the combined Census and

Defense counts to the sum of the

nonresponse adjusted weight w3i in each

cell. A preliminary final weight

w5i = w4i × w3i was computed for each

of the 12,571 completed interview cases.

Weight Trimming

The distribution of the preliminary

final weight was examined in each of

the primary 18 subclasses as well as by

groups formed by age, gender, and race

and ethnicity, and two-way combinations

of these variables. Potential increases in

variance greater than 2.0 were observed

in 4 of the 18 subclasses. After careful

investigation, it was recognized that

considerable reduction of the variability

in the weights could be achieved by an

arbitrary reduction of a few extremely

large weight values. These kinds of ad

hoc weight-trimming procedures are

used on occasion in many surveys, but

there is little theoretical justification for

the procedure. Still, some reduction in

variance was deemed worthwhile if it

could be achieved through limited

changes to individual weight values. A

weight-trimming procedure was thus

followed that changed weight values one

at a time to achieve the largest reduction

in variance and stopping the procedure

with as few values as possible being

changed.

Individual weight values and their

components (that is, w1i

, w2i, w3i, w4i)

were listed for these cells and

components for preliminary final weight

values greater than two times the mean

weight value for the cell inspected. The

nonresponse adjusted base weight for

selected cases was trimmed to the next

largest nonresponse adjusted base

weight value and the poststratification

weights recomputed following trimming.

The potential increase in variance was
Page 32 [ Series 2, No. 142

recomputed. Weighted estimates for

eight variables monitored during data

collection were also computed and

compared with the weighted estimates

obtained with the preliminary final

weights. The relative change in 1+L was

compared with the relative change in the

weighted estimates for the cell where

trimming occurred. As long as the

change in 1+L was greater than the

relative change in the square of the

weighted estimate, trimming was

accepted as satisfactory. Trimming

stopped in one cell when the relative

change in weighted estimates became

too large compared with the change in

1+L. A total of 49 weight values were

trimmed in two rounds of trimming

following this process.

Final Weight Summary

Table T summarizes the distribution

of the final trimmed weights for each of

the 18 subclasses of interest. The female

other race 25–44 year cell had the

largest potential increase in variance due

to weighting. Given the large sample

size in the cell, and that substantial

additional trimming would have been

required to reduce the 1+L factor

further, this somewhat larger 1+L factor

was accepted.

There is large variation in final

weights for the NSFG Cycle 6 shown in

table T, and several extremely large

weights. The variation and large weight

values may be due to any one of nine

weighting components in the final

weight: six adjustments for unequal

probabilities in the base weight, two

adjustments for nonresponse propensity,

and a final adjustment for

poststratification. The largest

contributing factor to the variation in

and largest final weights is the

adjustment for within-household

selection probabilities and the

substantial oversampling of 15–19-yearolds. Nonresponse adjustment and

poststratification contribute little to the

final weight variation.

Some of the variability has been

trimmed, reducing the variation

substantially. However, trimming was

only done if the reduction of a large

weight value substantially decreased

the potential increase in variance (that

Table Q. Nine logistic regression models predicting response to the main interview (Phases

1–3): National Survey of Family Growth, Cycle 6

Standard

Parameter Estimate error Pr > ChiSq

Male

15–19 years (n=1,330):

Intercept.................. –12.0367 626.1000 0.9847

Everresist ................. –3.5183 0.2451 <.0001

Everquest................. 0.7093 0.2100 0.0007

Everdelay ................. –0.7803 0.2177 0.0003

Evercontact................ 15.3092 626.1000 0.9805

SCR_LANG ............... –0.8422 0.2943 0.0042

SCR_SINGLEHH ............ 0.4381 0.8270 0.5963

20–24 years (n =1,169):

Intercept.................. –12.3111 520.3000 0.9811

Everresist ................. –3.0400 0.2502 <.0001

Everquest................. 0.8035 0.1864 <.0001

Everdelay ................. –1.1714 0.1940 <.0001

Evercontact................ 15.6769 520.3000 0.9760

SCR_LANG ............... –1.2964 0.2316 <.0001

SCR_SINGLEHH ............ –0.1679 0.2332 0.4716

25–44 years:

Black and Hispanic (n =1,594):

Intercept ................ –13.3214 577.0000 0.9816

Everresist ............... –2.4920 0.1994 <.0001

Everquest ............... 1.1283 0.1593 <.0001

Everdelay ............... –1.5796 0.1595 <.0001

Evercontact .............. 15.9141 577.0000 0.9780

SCR_LANG .............. –0.7933 0.1530 <.0001

SCR_SINGLEHH ........... 0.3105 0.1841 0.0917

Other (n=2,224):

Intercept ................ –1.5086 1.0967 0.3344

Everresist ............... –3.6956 0.1670 <.0001

Everquest ............... 1.0356 0.1414 <.0001

Everdelay ............... –1.0319 0.1422 <.0001

Evercontact .............. 4.8097 1.0316 <.0001

SCR_LANG .............. –1.8972 0.3786 <.0001

SCR_SINGLEHH ........... 0.0280 0.1481 0.8499

Female

15–19 years (n =1,391):

Intercept.................. –12.4894 767.8000 0.9870

Everresist ................. –3.6254 0.2364 <.0001

Everquest................. 0.5101 0.1997 0.0107

Everdelay ................. –1.0298 0.2089 <.0001

Evercontact................ 15.8693 767.8000 0.9835

SCR_LANG ............... –0.7978 0.3124 0.0106

SCR_SINGLEHH ............ –0.6299 0.5844 0.2811

20–24 years (n =1,672):

Intercept.................. –14.1156 584.7000 0.9807

Everresist ................. –3.3594 0.2282 <.0001

Everquest................. 0.9440 0.1773 <.0001

Everdelay ................. –1.2181 0.1868 <.0001

Evercontact................ 17.2140 584.7000 0.9765

SCR_LANG ............... –0.9392 0.2155 <.0001

SCR_SINGLEHH ............ 0.2310 0.2850 0.4176

25–44 years:

Black (n =1,291):

Intercept ................ –12.2802 644.9000 0.9848

Everresist ............... –3.4732 0.2449 <.0001

Everquest ............... 0.6225 0.2071 0.0027

Everdelay ............... –1.0455 0.2094 <.0001

Evercontact .............. 16.7438 644.9000 0.9793

SCR_LANG .............. –2.0470 1.0116 0.0430

SCR_SINGLEHH ........... 0.0104 0.2719 0.9694

See footnotes at end of table.
Series 2, No. 142 [ Page 33

Table Q. Nine logistic regression models predicting response to the main interview (Phases Estimating Equation for 1–3): National Survey of Family Growth, Cycle 6—Con.

Totals Standard

Parameter Estimate error Pr > ChiSq The NSFG Cycle 6 estimator of the

Female—Continued number of men or women with a

25–44 years: particular characteristic can be computed

Hispanic (n =1,282): as

Intercept ................ –13.1382 575.7000 0.9818

Everresist ............... –2.7555 0.2220 <.0001 N

^ ∑
w5i I = i Everquest ............... 0.5946 0.1736 0.0006 i

Everdelay ............... –1.2686 0.1806 <.0001

Evercontact .............. 16.5567 575.7000 0.9771

SCR_LANG .............. –0.9882 0.1613 <.0001

SCR_SINGLEHH ........... –0.2504 0.3387 0.4597

Other (n=3,862):

Intercept ................ –0.8169 1.0619 0.4417

Everresist ............... –3.6215 0.1329 <.0001

Everquest ............... 0.8117 0.1152 <.0001

Everdelay ............... –1.1168 0.1157 <.0001

Evercontact .............. 4.6207 1.0507 <.0001

SCR_LANG .............. –1.5114 0.1739 <.0001

SCR_SINGLEHH ........... –0.2867 0.1568 0.0675

1Everresist: during any contact with the household, any respondent refused to participate in the survey.

where w5i is the final trimmed weight

for the ith 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

2Everquest: during contact with the household, any respondent asked a question about the content or objectives of the survey. ^

Y ∑
w5i Ii y = i 3Everdelay: during any contact with the households, any respondent made an appointment for interview at another time. i 4Evercontact: during any contact with the household, any respondent was contacted and provided information about the household.

5SCR_LANG: language of interview as indicated on screener interview. where yi is the value of the characteristic 6SCR_SINGLEHH: single eligible person in household, recorded on screener interview.

is, a substantial reduction in the 1+L substantially to the potential increase

factor). Large weight values remain as weight values that have already

but they do not contribute as been trimmed.

Table R. Mean, minimum, and maximum combined nonresponse adjustments, and potential

increase in variance due to weighting, by age group, sex, and race or ethnicity: National

Survey of Family Growth Cycle

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

Sex, race or ethnicity, and Mean Minimum Maximum Increase in

age group adjustment adjustment adjustment variance (1+L)

Female p =

∑
w5i Ii

i

∑
w5i

Black: i

15–19 ................... 1.2005 1.0517 2.5520 1.0368 Alternatively, the mean for a variable

20–24 ................... 1.2592 1.0652 2.0933 1.0340

yi can be computed as the ratio estimator 25–44 ................... 1.3135 1.0519 2.5520 1.0488

Hispanic:

15–19 ................... 1.2334 1.0485 2.5520 1.0369 ∑
w5i Ii yi

i 20–24 ................... 1.3410 1.0676 2.5520 1.0462 y =

∑
w5i Ii 25–44 ................... 1.3593 1.0570 2.5520 1.0485

Other:

15–19 ................... 1.2066 1.0514 2.5520 1.0315

20–24 ................... 1.2767 1.0556 2.5520 1.0394

25–44 ................... 1.2908 1.0662 2.5520 1.0414

Male

Black:

15–19 ................... 1.2235 1.0614 2.1015 1.0347

20–24 ................... 1.2412 1.0546 2.5520 1.0327

25–44 ................... 1.2553 1.0555 2.5520 1.0425

Hispanic:

15–19 ................... 1.2564 1.0560 2.1015 1.0384

20–24 ................... 1.3033 1.0519 2.5520 1.0386

25–44 ................... 1.3458 1.0574 2.5520 1.0512

Other:

15–19 ................... 1.2152 1.0541 2.5520 1.0335

20–24 ................... 1.2465 1.0543 2.5520 1.0361

25–44 ................... 1.2526 1.0527 2.5520 1.0394

i

Sampling variances for these

estimators must be computed taking the

stratified multistage sample design into

account. As mentioned before, standard

statistical software does not properly

account for these design features in

estimation. Variance estimation

procedures are described in a later

section and use of software that

accounts for weighting, stratification,

and clustering in estimation of variances

is illustrated.
Page 34 [ Series 2, No. 142

Imputation Procedures Table S. Poststratification adjustment factors for 36 groups by age, sex, and race or

ethnicity: National Survey of Family Growth, Cycle 6

Sum of

NSFG

Backgr Census Cycle 6 Post- ound on Imputation population nonresponse stratification

Sex, race or ethnicity, and plus DMDC adjusted adjustment for Missing Values

age group population weights factor

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

replacement of missing values with

predicted values. The latter, imputation,

will make analyses more consistent and

may reduce bias due to item

nonresponse (depending on the

predictive power of the imputation

procedure).

However, in a survey as complex as

NSFG Cycle 6 with thousands of

variables, imputation may be required

for a large number of variables. When,

as is typically the case, only a few

values are missing for the overwhelming

majority of variables, imputation can be

an expensive process. Each variable to

be imputed requires detailed

specification of how predicted values to

be assigned to replace missing values

will be estimated or obtained.

As in previous NSFG cycles,

imputation was used only for a subset of

variables, those to be used in the

primary tabulations and analyses to be

released by NCHS. There were three

files for which variables required

imputation: female, male, and

pregnancy. Each had somewhat different

sets of variables. An extensive set of

recoded variables was chosen for

imputation. These recoded variables

were usually combinations of two or

more questions from the questionnaires,

and they were judged to be necessary

for the production of NCHS reports

Female ................... 61,560,728 40,393,425 . . .

Black ................... 8,586,724 6,287,567 . . .

15–19 ................. 1,498,790 1,125,827 1.3313

20–24 ................. 1,457,657 960,712 1.5173

25–29 ................. 1,302,135 965,773 1.3483

30–34 ................. 1,392,656 1,071,172 1.3001

35–39 ................. 1,455,780 951,197 1.5305

40–44 ................. 1,479,706 1,212,886 1.2200

Hispanic ................. 9,106,748 7,870,993 . . .

15–19 ................. 1,520,555 1,301,782 1.1681

20–24 ................. 1,632,281 1,453,466 1.1230

25–29 ................. 1,654,328 1,525,397 1.0845

30–34 ................. 1,594,616 1,256,541 1.2691

35–39 ................. 1,447,511 1,338,191 1.0817

40–44 ................. 1,257,457 995,616 1.2630

Other race ................ 43,867,256 26,234,865 . . .

15–19 ................. 6,814,699 3,874,401 1.7589

20–24 ................. 6,749,458 4,798,243 1.4067

25–29 ................. 6,293,017 3,741,572 1.6819

30–34 ................. 7,285,198 4,198,948 1.7350

35–39 ................. 7,949,871 4,356,281 1.8249

40–44 ................. 8,775,013 5,265,420 1.6665

Male ..................... 61,146,887 35,440,295 . . .

Black ................... 7,277,915 5,178,239 . . .

15–19 ................. 1,473,555 1,081,183 1.3629

20–24 ................. 1,250,646 785,146 1.5929

25–29 ................. 1,047,040 915,933 1.1431

30–34 ................. 1,122,586 859,530 1.3060

35–39 ................. 1,171,917 841,770 1.3922

40–44 ................. 1,212,171 694,677 1.7449

Hispanic ................. 10,188,079 7,614,180 . . .

15–19 ................. 1,627,708 1,426,891 1.1407

20–24 ................. 1,951,833 1,380,156 1.4142

25–29 ................. 1,935,434 1,485,032 1.3033

30–34 ................. 1,789,473 1,147,133 1.5600

35–39 ................. 1,571,123 1,185,771 1.3250

40–44 ................. 1,312,508 989,197 1.3268

Other race ................ 43,680,893 22,647,876 . . .

15–19 ................. 7,106,686 3,933,169 1.8069

20–24 ................. 6,680,687 4,349,801 1.5359

25–29 ................. 6,243,607 3,298,239 1.8930

30–34 ................. 7,225,525 3,734,755 1.9347

35–39 ................. 7,813,845 3,662,293 2.1336

40–44 ................. 8,610,543 3,669,619 2.3464

. . . Category not applicable.

NOTE: DMDC is Defense Manpower Data Center and NSFG is National Survey of Family Growth.

from the NSFG. A list of these recoded indicator variables is available in the

variables is provided in the female, male, and pregnancy files that

documentation. There were a total of designate which cases received imputation

356 variables identified for imputation for each variable. These ‘‘imputation

across the three data files: 253 in the flags’’ may be used by the analyst to drop

female file, 78 in the male file, and 25 imputed values from an analysis or to

in the pregnancy file. These variables replace the imputed values in the data set

and the type and number of cases with values predicted from a model or

imputed are shown in the tables in procedure specified by the user.

Appendix II of this report. For all There is a considerable range of

variables with imputed values, a set of methods used to impute for item
missing values in survey practice (13).

In some instances, values can be

replaced through a logical derivation

based on the relationship among

variables. In most cases, 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 (13).

Imputation in NSFG Cycle 5—

Previous NSFG cycles have used a

combination of logical, hot deck, and

regression imputation procedures. For

example, most imputation in Cycle 5 used

a sequential hot deck procedure (5). The

Cycle 5 hot deck imputations were based

on a single-variable sequential procedure

for each variable. For each variable to be

imputed, data were sorted by one or more

‘‘predictor’’ variables (many sort variables

were used for more than one variable).

The sort variables were chosen from

among those observed to be most highly

correlated with the variable to be imputed.

The data were then grouped into

imputation classes (sometimes referred

to as matrices), and the sequential

imputation begun. In the sort order, if a

case had a nonmissing value for the

variable being imputed, the value was

stored as the ‘‘hot’’ value. If a case was

missing for the variable being imputed,

the ‘‘hot’’ value was substituted, or

imputed, for the missing value.

Variables were imputed in a

selected order to allow some variables

to be used as sorting or classing

variables which themselves had missing

values. Thus, the sorting and classing

variables had to be imputed before

being used in the imputation of another

variable.

The hot deck imputation procedure

used in Cycles 3–5 imputed one variable

at a time and used only a limited set of

sort or class variables as predictors. In

addition, hot deck imputation was

completed for some variables before

others, and the imputed values for some

variables were used in sorting or

classifying other subsequently imputed

variables. Thus, there was an order of

imputation imposed that could limit the

range of possible values that were

imputed for some variables. It is now

known, however, that these features may

have an undesirable effect on the results

obtained from imputed data. For

example, single-variable (as opposed to

multivariable) imputations may disrupt

or attenuate an association structure,

reducing correlations among variables in

the imputed data.

Imputation in Cycle 6—In Cycle 6,

most imputation was performed using a

regression procedure. The procedure was

programmed to impute all variables

within a data file simultaneously, using

a large number of potential predictors

for each imputed variable. The

procedure employed a generalized linear

regression model, providing for the

imputation of interval, dichotomous,

ordinal, nominal, and count scales for

the variable being predicted.

A small methodological

investigation compared Cycle 5

sequential hot deck imputed values for

a selected set of variables to

sequential regression imputations for

those variables. Findings indicated that

the sequential regression imputation

procedure generated imputed values

with similar distributions to those

obtained by the hot deck and did so

while preserving covariance structure.

The sequential regression procedure is

available in publicly available

software from the ISR Web site at the

University of Michigan Web site:

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

IVEware was used to generate most

of the imputed values for Cycle 6.

Logical imputation was also used, where

appropriate, sometimes for the same

variables receiving sequential regression

imputations.

Sequential Regression

Imputation

Regression imputation was

implemented using a sequential

regression method described in

Raghunathan et al. (14). 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

Table T. Mean, minimum, and maximum final (trimmed) weight, and potential increase in

variance due to weighting, by age group, sex, and race or ethnicity: National Survey of

Family Growth, Cycle 6

Sex, race or ethnicity, and age

Mean

weight

Minimum

weight

Maximum

weight

Increase in

variance (1+L)

Female

Black:

15–19 years . . . . . . . . . . . . . . . 6,193.45 1,143.98 25,581.37 1.4089

20–24 years . . . . . . . . . . . . . . . 6,686.64 1,238.52 63,141.59 1.8866

25–44 years . . . . . . . . . . . . . . . 5,261.97 579.5 33,290.92 1.5971

Hispanic:

15–19 years . . . . . . . . . . . . . . . 6,582.73 1,460.02 40,128.20 1.4732

20–24 years . . . . . . . . . . . . . . . 5,570.79 1,407.22 28,985.12 1.3738

25–44 years . . . . . . . . . . . . . . . 5,590.57 610.2 41,698.26 1.5477

Other:

15–19 years . . . . . . . . . . . . . . . 10,066.00 411.45 49,916.15 1.3274

20–24 years . . . . . . . . . . . . . . . 7,922.17 727.85 101,214.07 1.5886

25–44 years . . . . . . . . . . . . . . . 10,117.77 118.66 261,879.95 1.8732

Male

Black:

15–19 years . . . . . . . . . . . . . . . 7,188.02 1,690.83 26,374.42 1.5231

20–24 years . . . . . . . . . . . . . . . 8,625.23 733.93 34,284.43 1.7284

25–44 years . . . . . . . . . . . . . . . 7,851.04 494.85 48,561.20 1.9309

Hispanic:

15–19 years . . . . . . . . . . . . . . . 6,926.19 1,413.10 29,573.44 1.4179

20–24 years . . . . . . . . . . . . . . . 9,078.21 662.56 59,698.43 1.6788

25–44 years . . . . . . . . . . . . . . . 9,819.59 1,643.42 61,190.55 1.8014

Other:

15–19 years . . . . . . . . . . . . . . . 10,435.89 2,251.27 67,365.59 1.4039

20–24 years . . . . . . . . . . . . . . . 11,558.58 1,878.62 68,059.86 1.5941

25–44 years . . . . . . . . . . . . . . . 18,498.43 746.43 109,064.17 2.0273

Series 2, No. 142 [ Page 35
alternative scales for each variable to be

imputed, including linear, dichotomous,

polytomous, and count.

In the latter three scales, predicted

values can be obtained that are outside

the range of acceptable values. Under

logistic regression, predicted proportions

between zero and one are generated.

These values are 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 less than 0.4, the imputed value of

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

value of ‘‘0’’ 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 NSFG Cycle 6, where the p

variables do not require imputation (all

values were observed, or logically

imputed prior to regression imputation).

The q variables may be ordered from

largest to smallest number of cases

requiring imputation, although this

ordering is not necessary and was not

employed in Cycle 6 imputation.

The imputation requires 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.

Cycle 2, 3, 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. (14).

Variables may have restrictions to

limit the set of cases to be imputed. For

example, persons who have never had

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 Cycle 6,

a major part of the work of imputation

was in specifying appropriate bounds for

imputed values because of the extensive

interrelations in the data.

The sequential regression

imputation method is implemented in

the IVEware software system

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

Only a single run of this software was

used to impute missing values for all

recode variables in each data set

simultaneously.

Multiple Imputation Using

Sequential Regression

The IVEware system also can

perform multiple imputation, a method

that allows users to obtain valid

estimates of precision, test statistics,

and statistical significance for imputed

data. This feature was not used with

the NSFG Cycle 6 imputations, but

may be used by NSFG users in

Cycle 6 or in the future, so a brief

description of multiple imputation is

included.

Instead of a single imputation, the

imputation process is repeated, say m=5

times. The multiple imputation thus

yields not one but m=5 data sets with

valid values repeated and imputed

values that differ among data sets.

Variation among data sets can be

combined with variation within data sets

to account for the effect of imputation

on variances.

Suppose, for example, that |β(l)

represents a vector of estimated

regression coefficients obtained from

one of the five imputed data sets, say

from the l-th multiple imputation. Let

V(l) denote the covariance matrix for

the coefficients for the l-th imputed

data set.

The multiply imputed estimate of

the regression coefficients is obtained

as



^ = ∑

l=1

m

β

^(l) / m,

the average of the m estimated

regression model coefficients. The

covariance of the regression coefficients

is obtained as

V

^ = (∑

l=1

m

V

^(l)

/ m) + (

m+1

m ) β

^

, where

β

^ = ∑

l=1

m

(β

^(l) – β^) (β^(l) – β^)t

/ (m–1).

These estimation procedures are

implemented in a series of analytic

procedures (including linear and logistic

regression) within IVEware.

The NSFG Cycle 6 data were

obtained from a weighted stratified

multistage sample of eligible persons. It

is most appropriate if the estimates V(l)

are obtained from a procedure that

accounts properly for these factors.

IVEware implements multiple

imputation estimation using an estimate

V(l) obtained from a jackknife

replication procedure employing NSFG

strata and PSU variables and weighted

estimates.

Page 36 [ Series 2, No. 142
Series 2, No. 142 [ Page 37

Regression Imputation in

NSFG Cycle 6

Tables I (female), II (male), and III

(pregnancy) in Appendix II show the

variables imputed in Cycle 6, the

number of regression imputed values,

and the number of logically imputed

values. Extended names and definitions

for each variable are shown in the

public-use documentation.

In the female file, there are 7,643

records. The largest number of

imputed values for any variable

occurred for POVERTY (family

income as a percent of the poverty

level) and TOTINCR (total family

income). Both received 630 imputed

values, 7.6 percent of all values for

these variables. All of these were from

the sequential regression imputation.

Other variables had higher percentages

imputed but these were typically for

variables that had very few

occurrences in the data set. For

example, AGEPREG13, the age at the

13th mentioned pregnancy, has four

values, one of which is imputed. In

most variables, the amount of

imputation is only a few cases.

Other variables had logical as well

as regression imputed values assigned.

For example, AGEBABY1 (the

woman’s age when her first baby was

born) in the female file had 31 logical

and 20 regression imputed values. The

logical imputations were assigned on the

basis of the date of birth of the mother

and the child. Regression imputations

were assigned when those dates or other

information needed to logically assign a

value were missing.

In the male and pregnancy files,

similar results are observed. For

example, among the 4,928 male records,

POVERTY and TOTINCR have the

largest number of imputed values, 413,

or 8 percent. In the pregnancy file,

FMAROUT5 (formal marital status at

pregnancy outcome) had the largest

number of imputed values (all from

regression imputation), with 8.2 percent

of the 13,240 values imputed.

Variance Estimation

Background

Since NSFG Cycle 6 estimates are

based on a sample rather than a

complete enumeration of the eligible

population, they are subject to 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 bias. It 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 Cycle 6, 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

(15) for a review of variance estimation

techniques.

In this section, the focus is on the

estimation of sampling variance for

NSFG Cycle 6 estimates 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 in Cycle 6: stratification of PSUs,

selection of PSUs (cluster sample

selection), weights which adjust for

unequal probabilities of selection and

other design features, and the presence

of imputed values.

The proper treatment of imputed

values in variance estimation can be

done through multiple imputation.

However, because of the size of the data

files, multiply imputed values have not

been released for the NSFG Cycle 6. A

future publication may explore the

extent to which sampling variances are

altered by imputed values. Such an

investigation is beyond the scope of this

report.

The remainder of this section

describes how variance estimates may

be obtained from Cycle 6 that properly

account for the remaining three features:

stratification, clustering, and weighting.

The discussion considers the effect of

these features on estimated sampling

variances, the availability of software

that can estimate variances taking these

features into account, the creation of

codes that can be used for variance

estimation, and illustrations of

computation of sampling variances from

the Cycle 6 data using four different

software systems.

In these illustrations, the magnitude

of the effects of the complex design, the

stratification, clustering, and weighting,

are presented. Usually, the sampling

variances computed taking the design

into account are larger than those

obtained using methods that do not

account for these features. This increase

is the reason that analysts must consider

carefully how to compute estimates of

variance from the NSFG.

If software is used that does not

account for the complex design,

sampling variances will often be

underestimated. The consequence will

be test statistics that generate smaller

probability test values than is actually

correct and confidence intervals that are

narrower than they should be. Both of

these types of errors can lead to

incorrect inferences drawn from the

data. For example, smaller probability

test levels might lead an analyst to

reject a null hypothesis incorrectly.

For this reason, all users of NSFG

Cycle 6 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 Cycle 6

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.
Page 38 [ Series 2, No. 142

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 (16). 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 the NSFG Cycle 6 data, the

Taylor series method can be adapted

to the paired sampling error computing

units (SECUs) created to represent the

stratification and clustering in the

sample. Paired differences 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 (17) and Deming (18).

They proposed and encouraged the use

of interpenetrating or replicating

sampling methods, which 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

1 T ^ � = T ∑ θt

t=1

can be computed simply as

1 T ^ var (θ) = T(T–1)∑(θt – θ)2. t=1

The idea of replicated sampling

was extended to survey data by

McCarthy (19), who proposed selecting

replicate samples from a larger sample

by employing in the replicate sampling

procedures, selection methods that were

identical to those used in the original

sample selection. For example, in the

NSFG Cycle 6, replicate half-samples

would be drawn by selecting a single

SECU from each stratum, and taking all

cases within a SECU, to comprise the

replicate sample. Alternatively, a

‘‘jackknife’’ replication procedure

‘‘drops one’’ SECU in a stratum, and

retains all other SECUs and their cases

in the sample. In the NSFG Cycle 6, 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

underrepresentation of the stratum.

With half-sample 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

~ 1 T ^ ~ 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) = ∑ (θ ^

t – ~

θ)2. t=1

See Rust (20) for a more detailed

review of these procedures.

Variance Estimation

Software

These variance estimation

procedures are all implemented in a

number of computer software packages

that are commercially available, or

available for free download over the

Internet. In earlier NSFG cycles, it was

often complex and difficult to obtain

and use variance estimation software,

but now there are several software

packages that allow users to estimate

variances for means, proportions,

regression coefficients, logistic

regression coefficients, and other

statistics. These statistical software

packages implement the Taylor series

approximation, repeated replication

methods, or both approaches for a

complex sample survey like the NSFG

Cycle 6.

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. For

example, WesVar is a stand-alone

package that implements repeated

replication procedures and requires data

be converted to the WesVar format for

estimation. Fortunately, WesVar is an

easy to use window-driven system that

can read data directly from a number of

standard statistical software systems,

such as SPSS. Other stand-alone

systems require more time-consuming

conversion of data to their particular

format and are based on command input

rather than a ‘‘point and click menu’’

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 or STATA, and then compute

estimates and test statistics within those

systems that take complex design

features into account. For example, SAS

version 9 has, at the time of this

writing, four procedures that properly

account for complex features such as

stratification, clustering, and weighting.
Series 2, No. 142 [ Page 39

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. SUDAAN

is a stand-alone system that comes in a

SAS compatible version, which allows

SUDAAN commands to be embedded in

SAS programs as though they were part

of the SAS language. SUDAAN

implements a wider range of statistical

procedures for complex sample survey

data than are currently available in SAS

version 9. Users with SAS data sets can

thus analyze data using either SAS or

SUDAAN, although they will find more

analysis procedures available in

SUDAAN. Finally, SPSS has recently

announced the introduction of variance

estimation procedures as an optional

module. As of this writing, the SPSS

optional module has not been tested on

NSFG Cycle 6 data, nor have examples

been developed for users to illustrate

implementation on Cycle 6 data.

NSFG Cycle 6 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 Web

site maintained by the Survey Research

Methods Section of the American

Statistical Association, currently located

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

survey-soft/survey-soft.html. All NSFG

Cycle 6 users are strongly encouraged to

visit this Web site and become more

familiar with the features of these

software systems.

Empirical comparisons of these

software packages have shown that they

provide 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.

That is, all four systems yield nearly

identical estimates.

The choice among these systems

can thus be based on practical

considerations of convenience, cost of

software acquisition and maintenance,

and current data format.

For example, a user with data

already in SAS format, or working in an

environment where SAS is widely used,

would find SAS and SUDAAN to be

most convenient to use. SUDAAN can

compute variances for some types of

analyses that SAS cannot, but SUDAAN

must be purchased and the license must

be renewed each year. A free software

system that operates within SAS is also

available, IVEware. It can be

downloaded from a Web site at the

University of Michigan, together with

documentation for installation and use:

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

IVEware has as wide a range of

variance estimation procedures as

SUDAAN.

Users with data in SPSS format will

find WesVar a convenient package to

use since WesVar converts SPSS data

into its own internal format through a

few simple windows. Alternatively,

SPSS data may be converted to SAS,

STATA, or another format through any

number of file conversion systems, and

then used by another complex sample

survey package such as SUDAAN.

Users with data in STATA format

can apply the many ‘‘svy’’ versions of

STATA commands to the data directly.

The STATA system does require

specification of stratification, cluster,

and weight variables through a design

statement. The ‘‘svy’’ commands are

completely integrated into the STATA

command language.

Application to the NSFG

Sampling Error Computing

Units

The NSFG sample has 121 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 were arranged into a set of

SECUs that could be grouped into pairs

for variance estimation purposes.

The 28 self-representing (SR) PSUs

were 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 were numbered in sample

selection order with each sample domain

as 1, 2, 3, 4, 1, 2, 3, 4, 1, 2 ... Segments

with the same number were grouped

together to form a pseudo-PSU

representing the entire MSA. The first

and third such ‘‘combined units’’ were

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 were divided into two

SECUs, which are in turn grouped into

a single pair as a pseudo-stratum. This

SR PSU process generated a total of 38

stratum pairs of SECUs.

For the remaining 93 non-selfrepresenting (NSR) PSUs (including the

11 unique Hispanic sample PSUs), the

strata were inspected to identify pairs or

other groupings that were as alike as

possible. Two NSR PSUs were linked to

form one pseudo-NSR PSU for purposes

of variance estimation, yielding a total

of 92 NSR pseudo PSUs. Criteria for

deciding similarity included MSA status

and geographic location. A total of 46

pairs of PSUs were created.

In combination with the SR PSU

SECUs, there are thus a total of 84 pairs

of SECUs or 168 SECUs. Two

variables, SEST and SECU, are in the

Cycle 6 data set to identify these

groupings. The codes for these values

have been randomly ordered to mask the

identity of the units.

Appendix III contains tables

showing the distribution of all 12,571

female and male cases and the 13,593

pregnancy records in the pregnancy file.

By chance, one of the SECUs contained

no cases in the pregnancy file: SEST 42,

SECU 1. This anomaly would create

difficulty for users seeking to compute

sampling variances using some of the

statistical software systems illustrated

subsequently. Six of the 32 pregnancies

in SECU 2 in SEST 42 were from one

woman each. One of these was chosen

at random and its SECU code for the

pregnancy file changed to 1. Thus, there

is now one pregnancy record in SEST

42, SECU 1. This one record has a

different SECU code than originally

assigned. Its presence will not cause any

noticeable bias in estimated sampling

variances but it will allow the

computations to proceed.
Sampling Variance Estimation

Examples

To illustrate the convenience of

using these software systems for the

NSFG Cycle 6 data, nine example

analyses have been developed and

implemented on each of four software

systems. These illustrations are shown in

their entirety on the Internet at:

www.cdc.gov/nchs/about/major/nsfg/

nsfgvar.htm.

Selected examples are shown in

Appendixes IV through VII of this

report. The software systems used are

SAS version 9.1 (which uses Taylor

series approximation methods in several

survey procedures), SUDAAN 8.0.2

(which uses both Taylor series

approximation and repeated replication

methods), and STATA version 8.0

(which uses Taylor series approximation

methods). In the examples on the

Internet, examples with WesVar version

4.1 (which uses repeated replication

methods) are also shown.

The nine illustrations on the Internet

site have been chosen to provide users

with examples of how to compute

sampling variances for NSFG Cycle 6

for contingency tables, tables of mean

values, linear regression, logistic

regression, and tables of estimated total

counts. Some of the illustrations use a

subset of data, such as an age group.

Most of the variables used in the

illustrations are selected from the

extensive recoded variables available in

NSFG Cycle 6. These recoded variables

have no item missing values because the

item missing values have been replaced

with imputed values. Users can suppress

the use of imputed values through the

use of the imputation flags.

The illustration tables and analyses

have at most three to five variables,

reducing the complexity of recoding and

software set up. The four examples

shown in Appendixes IV, V, VI, and VII

of this report contain the programs and

output for:

1. Appendix IV: Female respondent

file. Percentage of all women using

the oral contraceptive pill, by 5 year

age groups 15–19, 20–24, 25–29,

30–34, 35–39, and 40–44.

2. Appendix V: Female respondent file.

Mean number of children ever born

(PARITY) per woman, for women

20–44, by race and Hispanic origin

and age.

3. Appendix VI: Female respondent file.

Multiple regression, regressing

number of children ever born on race

and ethnicity, age (single years), and

education, for women 20–44.

4. Appendix VII: Male and female

combined file. Logistic regression of

the probability of strongly agreeing

with the statement, ‘‘a young couple

should not live together unless they

are married’’ on age (single years),

gender, and race and ethnicity, and

education.

The NSFG Web site

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

contains the following additional

examples showing STATA, SAS,

SUDAAN, and WesVar output:

1. Male file. Percentage of all men

20–44 years of age who have ever

fathered one or more children, by

race.

2. Male and female combined file.

Percentage of all men and women

15–44 years of age, by gender and

age (15–24, 25–34, 35–44) who

strongly agree that ‘‘a young couple

should not live together unless they

are married.’’

3. Male and female combined file.

Percentage of males and females

15–19 who have ever had sexual

intercourse, by gender.

4. Pregnancy file. Percent distribution

of births that were wanted,

mistimed, or unwanted, by race and

Hispanic origin and age at

pregnancy outcome.

5. Female file. Estimates of the total

number of women using the oral

contraceptive pill, by 5-year age

groups.

Programs and a subset of the output

for the first illustration are presented

here. The problem is to estimate the

percent of women using the oral

contraceptive pill, and to estimate that

percentage for women in six 5-year age

groups. The female data file is used for

this analysis in all four packages, and in

all four software systems a crosstabulation procedure is used.

All four systems require some prior

recoding of the variables to be used in

the analysis. Since SAS version 9 and

SUDAAN use the same SAS data set

and recoding commands, the recodes are

given only in the SAS program. While

the SAS and STATA programs ‘‘use’’

the female data directly, the WesVar

program has three windows that

illustrate the conversion of an SPSS data

file to WesVar format.

We begin with the SAS version 9

program and output from the SAS

procedure SURVEYFREQ in figure 10.

The program has a short recode in a

DATA step to convert the continuous

age variable AGER to a recoded

variable with six age group categories

and to code oral contraceptive use from

CONSTAT1.

Figure 10. SAS 9.1 program code for illustration 1

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Series 2, No. 142 [ Page 41

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Figure 11. SAS 9.1 output from illustration 1 for estimation of the percent of women using the oral contraceptive pill in each of six 5-year

age groups, National Survey of Family Growth Cycle 6, 2002

This program defines a data set

called NSFG.EX1, which is derived

from the NSFG female file. Two new

variables are added to the female file in

NSFG.EX1: an age group variable that

defines 5-year age groups, and the

outcome variable ‘‘pill use’’ which is

equal to one if the value of CONSTAT1,

the current contraceptive status, is equal

to 6. PROC SURVEYFREQ is then

applied to NSFG.EX1, and incorporates

three survey design features in

estimation: the stratum variable is

identified as SEST, the cluster variable

as SECU, and the weight as

FINALWGT. The table specification

cross-classifies the age group variable

agerx by pill use, and requests that row

percentages be estimated and design

effect calculated for those row

percentages.

This program generated the output

presented in figure 11. The first panel is

standard PROC SURVEYFREQ output

that does not contain the estimated

percentages of interest. The estimated

row percentages and their standard

errors are given in the second panel.

The estimated percent using the oral

contraceptive pill is highest for women

20–24 years of age, 31.7826 percent,

with estimated standard error of 1.9966.

The SUDAAN version of the same

program to compute similar estimates

can be computed using a crosstabulation program as well, PROC

CROSSTAB (see figure 12). The DATA

step illustrated for PROC

SURVEYFREQ can be used for

SUDAAN as well. In SUDAAN, the

data must be sorted by stratum and by

sampling error computing unit within

stratum.

The PROC CROSSTAB command

includes several specifications:

1. The input data set, NSFG.EX1

2. A request for estimation of design

effects, DEFF

3. A design specification,

DESIGN = WR.

The appropriate specification for the

NSFG Cycle 6 data is DESIGN=WR,

with a replacement variance estimation
Page 42 [ Series 2, No. 142

SAS 9.1 Output cont. 

Figure 11—Continued

statement combines the two variables to

produce a cross-tabulation of age group

as rows, and contraceptive use as

columns.

SUDAAN has a wide variety of

printed estimates that can be produced.

The PRINT specification includes the

unweighted count of cases in each cell

(NSUM), the sum of weights for cases

in each cell (WSUM), the row

percentages (ROWPER), the standard

error of the row percentages (SEROW),

and the design effect of the row

percentages (DEFFROW). There are

numerous other print specifications that

could be applied, but these in SUDAAN

will print the same estimates obtained

above in PROC SURVEYFREQ.

The output from this SUDAAN run

is presented in figure 13. SUDAAN

includes the weighted and unweighted

counts in each cell and estimated row

percentages and standard errors that are

identical to those produced in SAS (the

standard errors differ only in the third

significant digit).

STATA uses a much more compact

code specification than SAS or

SUDAAN and the output is also

considerably more compact (see

figure 14). STATA has a set of ‘‘survey’’

commands that are preceded by the

abbreviation ‘‘svy,’’ which allow many

standard analyses to handle the complex

sample design features. In STATA, one

must specify the data set in a USE

statement before generating new

variables, and specify the survey design

features through a SVYSET statement

before presenting the requested SVY

command.

procedure within each stratum,

appropriate for the NSFG under the

assumptions of ultimate cluster

sampling.

The NEST statement is used in

SUDAAN to specify the stratification

and clustering in one statement. The

first variable in the next statement,

SEST, is treated as the stratum variable.

The second is treated as the cluster

within the stratum, SECU. As in PROC

SURVEYFREQ, the WEIGHT statement

specifies the weight variable

FINALWGT.

The PROC CROSSTAB

specification of the table is somewhat

more complicated than PROC

SURVEYFREQ. A specification is given

variable. The SUBGROUP statement

identifies the cross-tabulation variables,

while the LEVEL statement gives the

number of categories in each (6 for

AGERX and 2 for PILL). The TABLE

for each variable in the cross tabulation Figure 12. SUDAAN program code for illustration 1

of the number of categories in each
Figure 13. SUDAAN 8.0.2 output from illustration 1 for estimation of the percent of women

using the oral contraceptive pill in each of six 5-year age groups, National Survey of Family

Growth Cycle 6, 2002

The USE statement specifies that

the female data set (females.dta, in

STATA) be used. The svyset command

specifies the weight (FINALWGT),

strata (SEST), and cluster (SECU)

variables to be used in estimation. These

settings are saved for the current

session, but can be cleared by entering

the clear command, or running svyset

again with different settings. The

generate and replace statements create

the recodes of ‘‘agerx’’ and ‘‘pill.’’ The

syntax is somewhat different than SAS.

For example, for ‘‘agerx,’’ first a

variable ‘‘agerx’’ is generated with an

assigned value. Then the value is

replaced if the AGER is in an age range

other than 15–19. Finally, the svytab

command produces a cross-tabulation of

‘‘agerx’’ and ‘‘pill,’’ and adds

specifications for row percentages (row),

design effects for the row percentages

Series 2, No. 142 [ Page 43

(deff percent), and standard errors of the

estimated row percentages (se). Svytab

uses the design specifications given by

svyset.

The STATA output is shown in

figure 15. In addition to the percentages

and standard errors in the cross

tabulation, STATA produces several test

statistics for the hypothesis of no

difference in proportions across the

rows. The F-test takes into account the

complex sample design, while the

uncorrected chi-square test does not.

Also given is a generalized design effect

that can be used in adjusting chi-square

tests of independence for cross-tabulated

data.

The last program illustrated is

WesVar, a stand-alone menu-driven

program. WesVar uses a series of menus

to specify data preparation, recodes, and

estimates. The presentation of the

screens showing the various menus and

steps required to generate a WesVar

format data set, prepare variables for

analysis, and specify cross tabulations

are quite extensive. Only one screen

image is given in this report (see

figure 16). The full set of screens is

given at the NSFG Cycle 6 Web site,

www.cdc.gov/nchs/about/major/nsfg/

nsfgvar.htm, together with the output

from WesVar’s cross-tabulation of age

group by pill use.

The remaining illustrations in

Appendixes IV–VII, and on the Web

site, provide additional examples of the

use of these four packages for NSFG

Cycle 6 data analysis. They show

recode, table specification, analysis

options, and other features not shown in

this first illustration. NSFG Cycle 6

users not familiar with the use of this

software are urged to study these

examples to prepare programs for other

analyses of interest.

Users of the NSFG Cycle 6 may

follow a strategy that uses these

software systems only at a last stage of

estimation. Many users will compute

estimates and statistical tests using the

FINALWGT using standard statistical

software procedures in SPSS, SAS,

STATA, and other systems. When they

have produced the final, or near final

table or analysis, they then use one of

these packages to compute standard

errors and estimated values, taking into
Page 44 [ Series 2, No. 142

Figure 14. STATA 8.0 program code for illustration 1

Figure 15. STATA output from illustration 1 for estimation of the percent of women using

the oral contraceptive pill in each of six 5-year age groups, National Survey of Family

Growth Cycle 6, 2002

account the full complexity of the

sample design. Thus, they are able to

operate for nearly all of their analytical

work in a more familiar software

environment before turning to the use of

one of these packages designed to

handle the complex sample design

features such as weights, stratification,

and clustered sample selection.

Finally, Appendix VIII provides

estimates of design effects for variables

from the female, male, and pregnancy

files. Some NSFG Cycle 6 users may

find these summary design effects useful

in assessing the importance of using the

sampling estimate software illustrated

here, in the appendixes, and on the

NSFG Web site.

References

1. French DK. National Survey of Family

Growth, Cycle 1: Sample design,

estimation procedures, and variance

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

2. Grady WR. National Survey of Family

Growth, Cycle 2: Sample design,

estimation procedures, and variance

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

3. Bachrach CA, Horn MC, Mosher WD,

Shimizu I. National Survey of Family

Growth, Cycle 3: Sample design,

weighting, and variance estimation.

Vital Health Stat (2)98. 1985.

4. Judkins DR, Mosher WD, Botman S.

National Survey of Family Growth:

Design, estimation, and inference. Vital

Health Stat (2)109. 1991.

5. Potter FJ, Iannacchione VG, Mosher

WD, Mason RE, Kavee JD. Sample

design, sampling weights, imputation,

and variance estimation in the 1995

National Survey of Family Growth.

Vital Health Stat (2)124. 1998.

6. Kelly JE, Mosher WD, Duffer AD, Jr,

and Kinsey SH. Plan and operation of

the 1995 National Survey of Family

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

7. Kish L, Scott A. Retaining units after

changing strata and probabilities. J Am

Stat Assoc 66:461–70. 1971.

8. Hansen MH, Hurwitz WN. 1946. The

problem of nonresponse in sample

surveys. J Am Stat Assoc 41:517–29.

1946.

9. Groves RM, Benson G, Mosher WD,

Rosenbaum J, et al. Plan and operation

of Cycle 6 of the National Survey of

Family Growth. Vital Health Stat (1)42.

2005.
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Figure 16. Wesvar table specification window for illustration 1 for estimation of the percent of women using the oral contraceptive pill in

each of six 5-year age groups, National Survey of Family Growth Cycle 6, 2002

10. Groves RM, Couper MP. Nonresponse

in Household Surveys. New York: John

Wiley and Sons. 1998.

14.

Methodology 12: 1–16. 1986.

Raghunathan TE, Lepkowski JM, Van

Hoewyk J, Solenberger P. Sequential

18. Deming WE. On simplifications of

sampling design through replication

with equal probabilities and without

11. American Association for Public

Opinion Research. Standard

Regression Imputation for Survey Data.

Survey Methodology 27:85–96. 2001.

stages. J Am Stat Assoc 51:24–53.

1956.

Definitions: Final dispositions of case

codes and outcome rates for surveys.

15. Wolter KM. Introduction to variance

estimation. New York: Springer-Verlag.

19. McCarthy PJ. Replication. An approach

to the analysis of data from complex

Available at: http://www.aapor.org.

On-line edition 3.1. Revised 2005. 16.

1985.

Kish L, Frankel MR. Inference from

sample surveys. Vital Health Stat (2)14.

Washington, DC: U.S. Government

12. Groves RM, Dillman DA, Eltinge JL,

Little RJA, editors. Survey

complex samples. J Royal Statistical

Soc B 36:1–37. 1974. 20.

Printing Office. 1996.

Rust K. Variance estimation for

Nonresponse, New York: Wiley and

Sons. 2001.

17. Mahalanobis PC. ‘‘On large-scale

sample surveys,’’ Philosophical

complex estimators in sample surveys.

J Official Stat 1:381–97. 1985.

13. Kalton G, Kasprzyk D. The treatment

of missing survey data. Survey

transactions of the Royal Soc B

231:329–451. 1944.
Page 46 [ Series 2, No. 142

Appendix I

Definition of Terms

ACASI—Audio computer-assisted

self-interviewing, in which the survey

respondent uses a laptop computer to

complete the last part of the NSFG

questionnaire (Section K of the Male

Questionnaire and Section J of the

female questionnaire). The interviewer

asks the respondent to use earphones to

hear audio-recorded questions played

over the laptop computer sound system

through the earphones. The question text

is also displayed on the laptop monitor.

The respondent enters his or her

answers using the computer keyboard.

The respondent performed these steps

out of the sight of the interviewer to

increase the privacy of the answers.

Blaiset—A software system for

computer-assisted questionnaires.

Blaiset was developed by Statistics

Netherlands and is licensed in the

United States and Canada by Westat,

Inc. When appropriately programmed,

Blaiset can present the questions to the

interviewer, route the respondent to the

next appropriate question, record the

answers entered by the interviewer, and

check those answers for consistency

with previous answers. Blaiset was used

for Cycle 6 of the NSFG and an earlier

version was used in Cycle 5. Further

information is available at http://www.

westat.com/statistical_software/blaise/.

CAPI—Computer-assisted personal

interviewing, in which an interviewer

uses a laptop computer to conduct the

interview. The laptop displays the

questions for the interviewer to ask the

respondent and provides any necessary

instructions the interviewer may need.

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—The percentage of

sample households where an interviewer

talked with someone at the household at

the screener stage (the screener contact

rate); at the main interview stage, the

percentage of sample persons who met

with the interviewer on one or more visits

to the household by the interviewer (the

main interview contact rate).

Cooperation rate—The percentage

of sample households who were

contacted and granted a screener

interview (screener cooperation rate);

the percentage of sample persons

contacted who granted a main interview

(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

list on the sampling frame (a) some

households containing eligible persons

and (b) some eligible persons within

sample households.

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 sample (or two-phase

sample)—A sample of a survey sample

(hence the term, ‘‘double sample’’).

In NSFG Cycle 6, a sample of

nonrespondent cases was selected after

the completion of the first two phases of

data collection, at the end of January

2003. The sample of nonrespondents

was then released in Phase 3 for more

intensive followup and interviewing

efforts in February and early March

2003. The double sample was used in

the NSFG Cycle 6 in an attempt to raise

response rates and reduce nonresponse

bias.

Eligible household—A household

containing at least one person who was

eligible for the NSFG Cycle 6—that is,

males or females 15–44 years of age at

the date on which the screener was

completed in 2002, and living in the

household population of the United

States (all 50 States and the District of

Columbia). Whether a selected

household had an eligible person was

not known until the household screener

was conducted. If a household had two

or more persons 15–44 years of age, one

person was selected randomly.

Eligibility rate—The percentage of

sample cases that are members of the

target population. In NSFG, Cycle 6 the

eligibility rate is the percentage of

households that contain a person 15–44

years of age.

Epsem—Equal probability selection

method; 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 conducted the

fieldwork and data processing for Cycle

6 of the NSFG under a contract with

NCHS. ISR has several centers that

participated in the NSFG: the Survey

Research Center provided overall

coordination and was responsible for

data collection, weighting, and variance

estimation; the InterUniversity

Consortium on Political and Social

Research processed data and developed

documentation and web-based systems;

and the Population Studies Center

provided substantive expertise on

demography and family growth.

Item imputation—The process of

assigning answers to cases with missing

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

ascertained’’). In the NSFG, item

imputation was only performed on

approximately 350 ‘‘recoded variables,’’

or ‘‘recodes’’ (defined below, under

‘‘recodes’’). 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 was missing and a value of ‘‘high

school graduate’’ was assigned, her

education was imputed. Imputation was

done in two ways in Cycle 6 of the

NSFG: logical and regression

imputation. Logical imputation uses a

subject-matter expert to assign a value

based on the value of other variables for

the case with missing data. Regression

imputation uses a regression equation to

estimate a value for a case with missing

data. For most of the recoded variables

for which imputation was done in the

NSFG, less than 2 percent of the cases

received an imputed value.

Main interview—An interview

sought within sample households known

to contain a member of the target

population. If the screening interview

reveals that the household is eligible for

the survey, a main interview is

requested. In NSFG Cycle 6, if the

household had one or more members
Series 2, No. 142 [ Page 47

15–44 years of age, one such person

was selected at random for the main

interview request.

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 could

have been the count of occupied

housing units obtained in the 2000

Census of Population and Housing,

since sample selection within the PSU

would have selected housing units. In

NSFG Cycle 6, the counts of occupied

housing units from the year 2000

Census were not yet available at the

time of sample selection. Instead, an

approximate measure of size was

calculated for each PSU based on

population counts from the year 2000

Census divided by an estimated average

household size.

Measures of size were 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 and

females. Each person in the household

was assigned a measure of size between

0 and 1, where the measures were

predetermined values for each of 18

groups defined by age (3 groups),

gender (2 groups), and race or ethnicity

(3 groups). The measures of size were

cumulated across eligible persons, a

random number from zero to the sum of

the measures was generated, and an

individual was selected based on the

cumulated measures of size.

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, the National

Health Interview Survey (NHIS), the

National Health and Nutrition

Examination Survey (NHANES), the

National Health Care Survey, 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.

Paradata—Information collected

via computer software or interviewer

observations describing the sample unit,

interactions with sample household

members, or features of the interview

situation. NSFG Cycle 6 used

observations of characteristics of sample

housing units to reduce number of

callbacks, statements made by

respondents to diagnose their concerns,

call record data to model response

propensities, and 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

NSFG Cycle 6 there were three phases:

first, a quarter-sample in which interviewers were permitted to visit households

as many times as they thought was

necessary (March–May 2002); second, the

full sample with prespecified call rules

(May 2002–January 2003), and third, a

subsample of nonrespondents from Phases

1–2 offered higher incentives by a subset

of high-productivity interviewers

(February 2003).

Pretest—A survey done before a

main survey. It is usually smaller than

the main survey. Its purpose is to test

questionnaires, procedures, equipment,

and personnel before they are used in

the larger main survey.

Primary sampling unit (PSU)—The

first-stage selection unit in a multistage

area probability sample. In Cycle 6 of

the NSFG, PSUs are counties or groups

of counties in the United States; there

were 121 PSUs selected into the NSFG

sample selected from the more than

3,000 counties in the United States.

Secondary sampling units—called

segments and tertiary units—or selected

households are drawn from the primary

sampling units.

Race or ethnicity—Race or ethnicity

is used in this report as it was used to

select the NSFG Cycle 6 sample. Three

categories were used for purposes of

sample design: Hispanic, non-Hispanic

black, and other. Hispanic and

non-Hispanic black men and women

were selected at higher rates than others

in Cycle 6 of the NSFG to obtain

adequate numbers of Hispanic and black

persons to make reliable national

estimates for these groups. Thus, when

this report contains tables showing

‘‘race or ethnicity,’’ the three categories

are those used to design and select the

sample. In contrast, in reports that are

designed to present substantive results,

the ‘‘other’’ category is often split into

‘‘non-Hispanic white’’ and ‘‘nonHispanic other’’ categories, and those

who reported only one race are shown

separately from the small percentage

who reported more than one race.

Receptor case—In imputation, a

case with missing data that receives the

imputed value from another (donor)

case. If Case B has missing data and

Case B receives an imputed value from

Case A, then Case A is the donor case

and Case B is the receptor case.

Recodes or recoded variables—It

was not possible to edit or impute all of

the variables in the NSFG data file.

NSFG staff selected about 350 variables

from the NSFG data file that were to be

constructed, edited, and imputed. These

were called recodes or recoded

variables. Recodes were variables that

were likely to be used frequently by

NCHS and other data users. They were

edited for consistency, and missing

values were 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 were not edited

or imputed in this way.

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, such as the NSFG Cycle 6,

early replicates are used to measure key

cost and error features of a survey.

Response rate—The number of

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
Page 48 [ Series 2, No. 142

eligible persons (15–44 years of age).

Given that not all screeners were

completed, the number of eligible

persons is not known precisely, so the

number of eligible persons was

estimated.

Respondent—A person who

answers, or responds to, a survey. In

Cycle 6 of the NSFG, the ‘‘respondents’’

are the 12,571 men and women 15–44

years of age who completed the NSFG

interview.

Responsive design—Survey designs

that

+ Pre-identify a set of design features

potentially affecting costs and errors

of survey statistics

+ Identify a set of indicators of the

cost and error properties of those

features

+ Monitor those indicators in initial

phases of data collection

+ Alter the active features of the

survey in subsequent phases based

on cost/error tradeoff decision rules

+ Combine data from the separate

design phases into a single estimator

A responsive design approach was used

in Cycle 6 of the NSFG to maximize

response rates and reduce bias while

controlling costs.

Sample line—A housing unit listed

within a sample segment prior to the

data-collection phase of the NSFG,

which was subsequently sampled for

inclusion in the NSFG sample.

Sampling variance—The sampling

variance is a measure of the variation of

a statistic, such as a proportion or a

mean, that is due to having taken a

random sample instead of collecting

data from every person in the full

population. It measures the variation of

a statistic such as 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,

which may underestimate the sampling

variance. Estimating the sampling

variance requires special software, such as

those discussed in Appendixes IV, V, VI,

and VII.

Sampling weight—For a respondent

in the NSFG, the 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 or ethnicity category, then his

‘‘sampling weight’’ is 12,000. The

NSFG Cycle 6 sampling weight adjusts

for different sampling rates (of the age

and race or 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 (usually short)

set of questions, asked of one adult in a

household, with the chief goal of

determining whether the household is

eligible for the chosen target population

of the survey. In the NSFG Cycle 6, the

screening interview consisted of a

household roster collecting age, race,

and sex. Households having one or more

persons 15–44 years of age were

eligible for a main interview.

Self-representing area—A county or

group of counties selected with

certainty; a self-representing area forms

a primary sampling unit with population

counts large enough 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

non-self-representing areas. When PSUs in

a sampling frame are ‘‘selected with

certainty,’’ it means that they are

automatically included in the sample. For

Cycle 6 of the NSFG, 28 of the 121 PSUs

were selected with certainty and are

referred to as ‘‘self-representing’’ PSUs.

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 is not a simple random

sample. The NSFG sample was

stratified, selected in stages, and

employed unequal chances of selection

for the respondents, varying by age, race

or ethnicity, and sex. Such designs are

referred to as ‘‘complex’’ and require

special software to account completely

for all features of the 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 Cycle 6 of the 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

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 concentration of

black and Hispanic populations.

SurveyTrak—A software-based

sample administration system used in

Cycle 6 of the 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 in all other ways to keep track of

their job duties.

Target population—The population

to be described by estimates from the

survey. In NSFG Cycle 6 the target

population was the household population

of the United States. It omits persons in

prisons, hospitals, and other large

residences under central control. College

students living in dormitories were

interviewed but sampled through their

parents’ or guardians’ households. In

addition, military personnel living in

households off military bases were

included in the sample.

WEBDOC—An electronic

‘‘codebook’’ for Cycle 6 of the NSFG,

presenting data items, frequencies, code

categories, and other survey

documentation. The NSFG WEBDOC

can be found at http://www.cdc.gov/

nchs/about/major/nsfg/nsfgdoc.htm.

Weight—see ‘‘Sampling weight.’’
Series 2, No. 142 [ Page 49

Appendix II

Variables Imputed and Types of Imputation for Female, Male, and Pregnancy Files

Table I. Imputed case counts in the female file

Valid Regression Logical Percent

Recode variable N imputation imputation imputed

ABORTION ............................. 4,945 0 31 0.6

ADDEXP .............................. 7,643 30 73 1.3

ADVICE ............................... 569 7 0 1.2

AGEBABY1............................. 4,413 20 31 1.1

AGECON01 ............................ 5,033 42 0 0.8

AGECON02 ............................ 3,766 38 0 1.0

AGECON03 ............................ 2,334 28 0 1.2

AGECON04 ............................ 1,224 27 0 2.2

AGECON05 ............................ 613 24 0 3.8

AGECON06 ............................ 308 19 0 5.8

AGECON07 ............................ 158 17 0 9.7

AGECON08 ............................ 78 12 0 13.3

AGECON09 ............................ 38 5 0 11.6

AGECON10 ............................ 17 2 0 10.5

AGECON12 ............................ 5 1 0 16.7

AGECON13 ............................ 3 1 0 25.0

AGEDD1 .............................. 1,290 282 1 18.0

AGEDISS1 ............................. 1,550 502 0 24.5

AGEMOMB1 ............................ 7,643 51 0 0.7

AGEPRG01............................. 4,945 40 1 0.8

AGEPRG02............................. 3,662 36 2 1.0

AGEPRG03............................. 2,253 24 4 1.2

AGEPRG04............................. 1,186 21 5 2.1

AGEPRG05............................. 596 17 7 3.9

AGEPRG06............................. 300 13 6 6.0

AGEPRG07............................. 147 11 5 9.8

AGEPRG08............................. 76 7 5 13.6

AGEPRG09............................. 36 4 1 12.2

AGEPRG10............................. 17 0 2 10.5

AGEPRG12............................. 5 1 0 16.7

AGEPRG13............................. 3 1 0 25.0

ANYMSCHP ............................ 7,172 7 0 0.1

ANYPRGHP ............................ 7,172 7 0 0.1

B1PREMAR ............................ 4,413 31 31 1.4

BIRTHS5 .............................. 4,945 0 31 0.6

CEBOW ............................... 4,741 0 31 0.6

CEBOWC .............................. 4,741 0 31 0.6

COH1DUR ............................. 3,904 148 12 3.9

COHAB1 .............................. 3,904 103 12 2.9

COHOUT .............................. 3,904 103 57 3.9

COHSTAT.............................. 7,643 49 12 0.8

COMPREG ............................. 7,643 0 7 0.1

CON1MAR1 ............................ 5,531 66 0 1.2

CONDOMR ............................. 6,785 5 0 0.1

CONSTAT1 ............................. 7,643 55 0 0.7

CONSTAT2 ............................. 7,643 55 0 0.7

CONSTAT3 ............................. 7,643 55 0 0.7

CONSTAT4 ............................. 7,643 55 0 0.7

DATBABY1 ............................. 4,413 8 31 0.9

DATCON01 ............................. 5,033 28 1 0.6

DATCON02 ............................. 3,766 27 2 0.8

DATCON03 ............................. 2,334 18 4 0.9

DATCON04 ............................. 1,224 19 4 1.8

DATCON05 ............................. 613 18 5 3.6

DATCON06 ............................. 308 14 4 5.5

DATCON07 ............................. 158 13 4 9.7

DATCON08 ............................. 78 11 1 13.3

DATCON09 ............................. 38 4 1 11.6

DATCON10 ............................. 17 2 0 10.5

DATCON12 ............................. 5 1 0 16.7
Page 50 [ Series 2, No. 142

Table I. Imputed case counts in the female file—Con.

Valid Regression Logical Percent

Recode variable N imputation imputation imputed

DATCON13 ............................. 3 1 0 25.0

DATEND01 ............................. 4,945 27 1 0.6

DATEND02 ............................. 3,662 27 2 0.8

DATEND03 ............................. 2,253 18 4 1.0

DATEND04 ............................. 1,186 19 3 1.8

DATEND05 ............................. 596 19 4 3.7

DATEND06 ............................. 300 14 4 5.7

DATEND07 ............................. 147 13 3 9.8

DATEND08 ............................. 76 11 1 13.6

DATEND09 ............................. 36 4 1 12.2

DATEND10 ............................. 17 2 0 10.5

DATEND12 ............................. 5 1 0 16.7

DATEND13 ............................. 3 1 0 25.0

DATESEX1 ............................. 6,776 36 129 2.4

DATEUSE1 ............................. 6,631 101 0 1.5

DD1REMAR ............................ 1,290 283 3 18.1

EDUCAT .............................. 7,643 3 0 0.0

EDUCMOM ............................. 7,643 99 0 1.3

ENDOMET ............................. 569 7 0 1.2

EVHIVTST ............................. 7,643 53 0 0.7

FIBROIDS.............................. 569 7 0 1.2

FMAR1AGE ............................ 4,126 48 0 1.1

FMARNO .............................. 7,643 8 0 0.1

FMETHOD1 ............................ 6,764 18 0 0.3

FPREGMED ............................ 599 1 0 0.2

FPTIT12............................... 1,137 42 0 3.6

FPTITABO ............................. 96 3 0 3.0

FPTITBC .............................. 2,682 35 0 1.3

FPTITCBC ............................. 1,526 27 0 1.7

FPTITCEC ............................. 288 9 0 3.0

FPTITCHK ............................. 1,879 22 0 1.2

FPTITCST ............................. 372 6 0 1.6

FPTITEC .............................. 93 2 0 2.1

FPTITMED ............................. 1,540 54 0 3.4

FPTITPAP.............................. 4,968 43 0 0.9

FPTITPEL.............................. 4,517 39 0 0.9

FPTITPPR ............................. 495 7 0 1.4

FPTITPRE ............................. 1,648 26 0 1.6

FPTITPRN ............................. 601 2 0 0.3

FPTITSTD ............................. 1,092 14 0 1.3

FPTITSTE.............................. 157 14 0 8.2

FSEXPAGE............................. 6,785 60 0 0.9

HIEDUC ............................... 7,643 0 5 0.1

HISPANIC.............................. 7,643 3 0 0.0

INFERT ............................... 3,812 0 28 0.7

INFERTH .............................. 569 7 0 1.2

INFERTR .............................. 569 7 0 1.2

INFEVER .............................. 7,172 9 0 0.1

INSEM................................ 569 7 0 1.2

INSURANC ............................. 7,643 54 6 0.8

INTCTFAM ............................. 7,643 0 0 0.0

INTENT ............................... 7,643 2 67 0.9

INVITRO .............................. 569 7 0 1.2

LABORFOR ............................ 7,643 5 0 0.1

LBPREGS ............................. 4,945 0 31 0.6

LIFPRTNR ............................. 7,643 75 1 1.0

LIV1CHLD ............................. 4,413 0 31 0.7

LOSSNUM ............................. 4,945 0 31 0.6

LSEXDATE ............................. 6,785 45 31 1.1

LSEXRAGE............................. 6,785 92 0 1.3

MAR1BIR1 ............................. 5,213 38 31 1.3

MAR1CON1 ............................ 5,535 57 0 1.0

MAR1DISS ............................. 4,126 523 0 11.2

MARCON01 ............................ 5,033 176 0 3.4

MARCON02 ............................ 3,766 249 0 6.2

MARCON03 ............................ 2,334 207 0 8.1

MARCON04 ............................ 1,224 145 0 10.6

MARCON05 ............................ 613 100 0 14.0
Series 2, No. 142 [ Page 51

Table I. Imputed case counts in the female file—Con.

Valid Regression Logical Percent

Recode variable N imputation imputation imputed

MARCON06 ............................ 308 64 0 17.2

MARCON07 ............................ 158 40 0 20.2

MARCON08 ............................ 78 17 0 17.9

MARCON09 ............................ 38 7 0 15.6

MARCON10 ............................ 17 3 0 15.0

MARCON11 ............................ 8 1 0 11.1

MARDAT01 ............................. 4,126 37 0 0.9

MARDAT02 ............................. 690 4 0 0.6

MARDIS01 ............................. 1,550 509 0 24.7

MARDIS02 ............................. 258 135 0 34.4

MARDIS03 ............................. 46 29 0 38.7

MARDIS04 ............................. 9 6 0 40.0

MARDIS05 ............................. 2 2 0 50.0

MAREND01 ............................ 1,550 122 0 7.3

MAREND02 ............................ 258 37 0 12.5

MAREND03 ............................ 46 7 0 13.2

MAREND04 ............................ 9 1 0 10.0

MAROUT01 ............................ 4,945 224 0 4.3

MAROUT02 ............................ 3,662 273 0 6.9

MAROUT03 ............................ 2,252 213 0 8.6

MAROUT04 ............................ 1,186 150 0 11.2

MAROUT05 ............................ 596 102 0 14.6

MAROUT06 ............................ 300 65 0 17.8

MAROUT07 ............................ 147 38 0 20.5

MAROUT08 ............................ 76 17 0 18.3

MAROUT09 ............................ 36 6 0 14.3

MAROUT10 ............................ 17 3 0 15.0

MAROUT11............................. 8 1 0 11.1

METH12M1............................. 4,408 37 0 0.8

METH12M2............................. 626 37 0 5.6

METH12M3............................. 34 37 0 52.1

METH12M4............................. 6 37 0 86.0

METH3M1 ............................. 3,986 75 0 1.8

METH3M2 ............................. 531 75 0 12.4

METH3M3 ............................. 33 75 0 69.4

METH3M4 ............................. 6 75 0 92.6

MTHUSE12............................. 6,019 32 0 0.5

MTHUSE3 ............................. 5,426 79 0 1.4

NOSEX12.............................. 7,643 80 61 1.8

NUMP3MOS ............................ 6,785 231 0 3.3

OLDWP01 ............................. 5,033 16 0 0.3

OLDWP02 ............................. 3,766 12 0 0.3

OLDWP03 ............................. 2,334 14 0 0.6

OLDWP04 ............................. 1,224 10 0 0.8

OLDWP05 ............................. 613 11 0 1.8

OLDWP06 ............................. 308 6 0 1.9

OLDWP07 ............................. 158 6 0 3.7

OLDWP08 ............................. 78 4 0 4.9

OLDWR01 ............................. 5,033 58 0 1.1

OLDWR02 ............................. 3,766 44 0 1.2

OLDWR03 ............................. 2,334 37 0 1.6

OLDWR04 ............................. 1,224 20 0 1.6

OLDWR05 ............................. 613 18 0 2.9

OLDWR06 ............................. 308 5 0 1.6

OLDWR07 ............................. 158 6 0 3.7

OLDWR08 ............................. 78 3 0 3.7

OLDWR09 ............................. 38 1 0 2.6

OUTCOM01 ............................ 5,033 0 1 0.0

OUTCOM02 ............................ 3,766 0 2 0.1

OUTCOM03 ............................ 2,334 0 4 0.2

OUTCOM04 ............................ 1,224 0 4 0.3

OUTCOM05 ............................ 613 0 8 1.3

OUTCOM06 ............................ 308 0 6 1.9

OUTCOM07 ............................ 158 0 5 3.1

OUTCOM08 ............................ 78 0 5 6.0

OUTCOM09 ............................ 38 0 1 2.6

OUTCOM10 ............................ 17 0 2 10.5

OVULATE.............................. 569 7 0 1.2
Page 52 [ Series 2, No. 142

Table I. Imputed case counts in the female file—Con.

Valid Regression Logical Percent

Recode variable N imputation imputation imputed

PARAGE14 ............................. 7,643 7 0 0.1

PARITY ............................... 7,643 0 31 0.4

PARTS1YR ............................. 6,785 19 0 0.3

PIDTREAT ............................. 7,643 9 1 0.1

PILLR ................................ 7,643 7 0 0.1

POVERTY ............................. 7,643 630 0 7.6

PREGNUM ............................. 7,643 0 7 0.1

PUBASSIS ............................. 7,643 156 0 2.0

RELIGION ............................. 7,643 23 0 0.3

RMAROUT01 ........................... 4,945 219 0 4.2

RMAROUT02 ........................... 3,662 263 0 6.7

RMAROUT03 ........................... 2,253 196 0 8.0

RMAROUT04 ........................... 1,186 139 0 10.5

RMAROUT05 ........................... 596 90 0 13.1

RMAROUT06 ........................... 300 59 0 16.4

RMAROUT07 ........................... 147 31 0 17.4

RMAROUT08 ........................... 76 17 0 18.3

RMAROUT09 ........................... 36 6 0 14.3

RMAROUT10 ........................... 17 3 0 15.0

RMAROUT11............................ 8 1 0 11.1

SEX1AGE.............................. 6,776 72 2 1.1

SEX1FOR.............................. 6,776 188 0 2.7

SEX1MTHD1 ............................ 6,785 57 0 0.8

SEX1MTHD2 ............................ 3,328 54 0 1.6

SEX1MTHD3 ............................ 2,673 54 0 2.0

SEX1MTHD4 ............................ 2,645 54 0 2.0

SEX3MO .............................. 6,785 71 0 1.0

SEXEVER ............................. 7,643 63 0 0.8

SEXMAR .............................. 6,785 164 0 2.4

SEXONCE ............................. 6,785 2 0 0.0

SEXP3MO ............................. 6,785 63 0 0.9

SOURCEM1 ............................ 2,489 307 0 11.0

SOURCEM2 ............................ 299 53 0 15.1

URCEM3 .............................. 14 10 0 41.7

URCEM4 .............................. 0 10 0 100.0

TOTINCR .............................. 7,643 630 0 7.6

TUBES ............................... 569 7 0 1.2

VRY1STAG ............................. 6,785 30 2 0.5

VRY1STSX ............................. 6,785 30 102 1.9

WANTP01.............................. 5,033 21 0 0.4

WANTP02.............................. 3,766 18 0 0.5

WANTP03.............................. 2,334 21 0 0.9

WANTP04.............................. 1,224 11 0 0.9

WANTP05.............................. 613 15 0 2.4

WANTP06.............................. 308 8 0 2.5

WANTP07.............................. 158 8 0 4.8

WANTP08.............................. 78 6 0 7.1

WANTP5 .............................. 2,823 106 0 3.6

WANTRP01............................. 5,033 58 0 1.1

WANTRP02............................. 3,766 44 0 1.2

WANTRP03............................. 2,334 37 0 1.6

WANTRP04............................. 1,224 20 0 1.6

WANTRP05............................. 613 18 0 2.9

WANTRP06............................. 308 5 0 1.6

WANTRP07............................. 158 6 0 3.7

WANTRP08............................. 78 3 0 3.7

WANTRP09............................. 38 1 0 2.6
Series 2, No. 142 [ Page 53

Table II. Imputed case counts in the male file

Valid Regression Logical Percent

Recode variable N imputation imputation imputed

ABORTION ............................. 2,190 4 35 1.7

ADDEXP .............................. 4,928 17 0 0.3

AGEBABY1............................. 1,731 16 0 0.9

AGEMOMB1 ............................ 4,928 64 0 1.3

B1PREMAR ............................ 1,731 4 28 1.8

CEBOW ............................... 1,731 0 1 0.1

CEBOWC .............................. 1,731 0 1 0.1

CEBOWP .............................. 858 4 1 0.6

COH1DUR ............................. 1,971 297 1 13.1

COHAB1 .............................. 2,115 25 1 1.2

COHOUT .............................. 1,971 36 1 1.8

COHSTAT.............................. 4,928 26 1 0.5

COMPREG ............................. 4,928 1 23 0.5

DATBABY1 ............................. 1,731 13 0 0.7

EDUCAT .............................. 4,928 1 0 0.0

EDUCMOM ............................. 4,928 114 0 2.3

EVHIVTST ............................. 4,928 40 0 0.8

EVRNOPAT............................. 858 4 0 0.5

FMARNO .............................. 4,928 0 2 0.0

FSEXPAGE............................. 4,109 62 21 2.0

FSEXRLTN ............................. 4,109 84 0 2.0

HIEDUC ............................... 4,928 4 0 0.1

HISPANIC.............................. 4,928 3 0 0.1

INFEVER .............................. 4,109 18 0 0.4

INSURANC ............................. 4,928 80 13 1.9

INTENT ............................... 4,928 3 0 0.1

LABORFOR ............................ 4,928 18 0 0.4

LIFPRTNR ............................. 4,928 162 0 3.2

LOSSNUM ............................. 2,190 4 35 1.7

LSEXDATE ............................. 4,109 74 1 1.8

LSEXDATE2 ............................ 4,109 74 1 1.8

LSEXPAGE ............................. 4,109 46 33 1.9

LSEXRAGE............................. 4,109 86 0 2.1

LSEXRLTN ............................. 4,109 46 4 1.2

LSEXUSE1 ............................. 4,109 35 1 0.9

LSEXUSE2 ............................. 1,935 42 0 2.1

LSEXUSE3 ............................. 1,183 35 0 2.9

LSEXUSE4 ............................. 1,111 35 0 3.1

MARBABY1 ............................ 1,731 4 0 0.2

MARDAT01 ............................. 1,754 38 2 2.2

MARDIS01 ............................. 705 34 3 5.0

MAR1DISS ............................. 1,754 48 3 2.8

MAREND1 ............................. 703 20 1 2.9

METH12M1............................. 3,578 76 0 2.1

METH12M2............................. 1,693 80 0 4.5

METH12M3............................. 1,040 76 0 6.8

METH12M4............................. 980 76 0 7.2

METH3M1 ............................. 3,071 77 0 2.4

METH3M2 ............................. 1,462 81 0 5.2

METH3M3 ............................. 914 77 0 7.8

METH3M4 ............................. 866 77 0 8.2

NONLIVEB ............................. 4,928 1 3 0.1

NUMP3MOS ............................ 4,109 71 0 1.7

PARAGE14 ............................. 4,928 1 0 0.0

PARTS1YR ............................. 4,928 74 1 1.5

POVERTY ............................. 4,928 413 0 7.7

PREMARW1 ............................ 1,754 10 0 0.6

PUBASSIS ............................. 4,928 137 0 2.7

RELIGION ............................. 4,928 20 0 0.4

SEX12MO ............................. 4,109 1 69 1.7

SEX1MTHD1 ............................ 4,109 60 0 1.4

SEX1MTHD2 ............................ 1,928 59 0 3.0

SEX1MTHD3 ............................ 1,579 59 0 3.6

SEX1MTHD4 ............................ 1,556 59 0 3.7

SEX3MO .............................. 4,109 71 0 1.7

SEXONCE ............................. 4,109 1 0 0.0

SUPP12MO ............................ 629 5 0 0.8

TOTINCR .............................. 4,928 413 0 7.7
Page 54 [ Series 2, No. 142

Table II. Imputed case counts in the male file—Con.

Valid Regression Logical Percent

Recode variable N imputation imputation imputed

UNINTB5 .............................. 886 58 1 6.2

VRY1STAG ............................. 4,109 46 2 1.2

VRY1STSX ............................. 4,109 46 129 4.1

WANTB1 .............................. 528 50 0 8.7

WANTB2 .............................. 353 21 0 5.6

WANTB3 .............................. 183 17 0 8.5

WANTB4 .............................. 70 9 0 11.4

WANTB5 .............................. 30 5 0 14.3

WANTB6 .............................. 11 2 0 15.4

WANTB8 .............................. 1 1 0 50.0

Table III. Imputed case counts in the pregnancy file

Valid Regression Logical Percent

Recode variable N imputation imputation imputed

AGECON .............................. 13,593 216 0 1.6

AGEPREG ............................. 13,241 175 38 1.6

BFEEDWKS ............................ 7,998 22 38 0.8

BIRTHORD ............................. 9,148 0 38 0.4

DATECON ............................. 13,593 156 26 1.3

DATEND .............................. 13,241 156 23 1.4

FMARCON5 ............................ 13,593 1,009 0 7.4

FMAROUT5 ............................ 13,240 1,092 0 8.2

LBW1 ................................ 9,148 10 40 0.5

LEARNPRG ............................ 4,052 87 82 4.2

MATERNLV............................. 3,191 18 38 1.8

OLDWANTP ............................ 13,593 79 0 0.6

OLDWANTR ............................ 13,593 192 0 1.4

OUTCOME ............................. 13,593 0 38 0.3

PAYDELIV ............................. 3,210 3 38 1.3

PMARPREG ............................ 13,241 0 239 1.8

PNCAREWK ............................ 4,052 74 39 2.8

PRGLNGTH ............................ 13,593 62 0 0.5

RMAROUT6 ............................ 13,241 1,024 0 7.7

WANTPART ............................ 13,593 108 0 0.8

WANTRESP ............................ 13,593 192 0 1.4
Series 2, No. 142 [ Page 55

Appendix III

Distribution of Female, Male, and Pregnancy Records, by Stratum and Sampling Error

Computing Units in the National Survey of Family Growth, Cycle 6

Table IV. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the combined

female and male file

SECU

Stratum Statistic 1 2 Total

1 Weighted n 671,524 922,508 1,594,032

Sample n 63 75 138

Percent 42.13 57.87 100.00

2 Weighted n 3,019,568 692,583 3,712,151

Sample n 280 127 407

Percent 81.34 18.66 100.00

3 Weighted n 496,943 306,710 803,653

Sample n 47 25 72

Percent 61.84 38.16 100.00

4 Weighted n 466720 212291 679011

Sample n 39 28 67

Percent 68.74 31.26 100.00

5 Weighted n 489,087 520,163 1,009,250

Sample n 45 50 95

Percent 48.46 51.54 100.00

6 Weighted n 535,265 457,514 992,779

Sample n 32 27 59

Percent 53.92 46.08 100.00

7 Weighted n 462,993 443,405 906,398

Sample n 56 44 100

Percent 51.08 48.92 100.00

8 Weighted n 369,240 788,358 1,157,598

Sample n 26 71 97

Percent 31.90 68.10 100.00

9 Weighted n 531,194 525,753 1,056,947

Sample n 95 70 165

Percent 50.26 49.74 100.00

10 Weighted n 1,349,103 882,223 2,231,326

Sample n 131 93 224

Percent 60.46 39.54 100.00

11 Weighted n 742,704 623,236 1,365,940

Sample n 90 61 151

Percent 54.37 45.63 100.00

12 Weighted n 507,099 580,743 1,087,842

Sample n 41 46 87

Percent 46.62 53.38 100.00

13 Weighted n 227,079 318,688 545,767

Sample n 21 34 55

Percent 41.61 58.39 100.00

14 Weighted n 1,011,590 1,045,454 2,057,044

Sample n 88 79 167

Percent 49.18 50.82 100.00

15 Weighted n 831,398 776,095 1,607,493

Sample n 62 57 119

Percent 51.72 48.28 100.00

16 Weighted n 576,336 555,101 1,131,437

Sample n 59 57 116

Percent 50.94 49.06 100.00

17 Weighted n 1,287,458 1,108,151 2,395,609

Sample n 134 118 252

Percent 53.74 46.26 100.00

18 Weighted n 176,286 349,629 525,915

Sample n 25 43 68

Percent 33.52 66.48 100.00

19 Weighted n 426,169 554,926 981,095

Sample n 35 41 76

Percent 43.44 56.56 100.00

20 Weighted n 277,366 359,145 636,511

Sample n 28 33 61

Percent 43.58 56.42 100.00
Page 56 [ Series 2, No. 142

Table IV. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the combined

female and male file—Con.

SECU

Stratum Statistic 1 2 Total

21 Weighted n 741,249 367,624 1,108,873

Sample n 60 38 98

Percent 66.85 33.15 100.00

22 Weighted n 1,188,813 1,152,949 2,341,762

Sample n 88 87 175

Percent 50.77 49.23 100.00

23 Weighted n 696,409 867,498 1,563,907

Sample n 48 58 106

Percent 44.53 55.47 100.00

24 Weighted n 330,077 810,225 1,140,302

Sample n 24 63 87

Percent 28.95 71.05 100.00

25 Weighted n 1,089,561 1,891,240 2,980,801

Sample n 97 144 241

Percent 36.55 63.45 100.00

26 Weighted n 597,452 133,032 730,484

Sample n 29 11 40

Percent 81.79 18.21 100.00

27 Weighted n 606,471 709,396 1,315,867

Sample n 40 91 131

Percent 46.09 53.91 100.00

28 Weighted n 655,332 827,292 1,482,624

Sample n 114 153 267

Percent 44.20 55.80 100.00

29 Weighted n 561,894 350,925 912,819

Sample n 38 22 60

Percent 61.56 38.44 100.00

30 Weighted n 1,343,591 976,877 2,320,468

Sample n 99 77 176

Percent 57.90 42.10 100.00

31 Weighted n 969,967 969,267 1,939,234

Sample n 112 101 213

Percent 50.02 49.98 100.00

32 Weighted n 1,033,768 1,109,201 2,142,969

Sample n 57 76 133

Percent 48.24 51.76 100.00

33 Weighted n 667,545 639,038 1,306,583

Sample n 90 99 189

Percent 51.09 48.91 100.00

34 Weighted n 602,221 538,225 1,140,446

Sample n 46 44 90

Percent 52.81 47.19 100.00

35 Weighted n 916,183 787,162 1,703,345

Sample n 137 118 255

Percent 53.79 46.21 100.00

36 Weighted n 993,871 215,592 1,209,463

Sample n 67 24 91

Percent 82.17 17.83 100.00

37 Weighted n 872,294 1,043,667 1,915,961

Sample n 69 71 140

Percent 45.53 54.47 100.00

38 Weighted n 744,862 1,056,404 1,801,266

Sample n 91 68 159

Percent 41.35 58.65 100.00

39 Weighted n 575,007 435,975 1,010,982

Sample n 85 42 127

Percent 56.88 43.12 100.00

40 Weighted n 418,562 473,335 891,897

Sample n 43 43 86

Percent 46.93 53.07 100.00

41 Weighted n 492,765 577,401 1,070,166

Sample n 59 62 121

Percent 46.05 53.95 100.00

42 Weighted n 159,296 342,615 501,911

Sample n 12 24 36

Percent 31.74 68.26 100.00
Series 2, No. 142 [ Page 57

Table IV. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the combined

female and male file—Con.

SECU

Stratum Statistic 1 2 Total

43 Weighted n 583,233 1,107,579 1,690,812

Sample n 62 94 156

Percent 34.49 65.51 100.00

44 Weighted n 861,871 597,768 1,459,639

Sample n 149 106 255

Percent 59.05 40.95 100.00

45 Weighted n 990,736 762,649 1,753,385

Sample n 145 100 245

Percent 56.50 43.50 100.00

46 Weighted n 744,831 1,047,659 1,792,490

Sample n 113 111 224

Percent 41.55 58.45 100.00

47 Weighted n 320,256 642,505 962,761

Sample n 28 40 68

Percent 33.26 66.74 100.00

48 Weighted n 454,169 491,608 945,777

Sample n 57 39 96

Percent 48.02 51.98 100.00

49 Weighted n 532,638 480,304 1,012,942

Sample n 36 39 75

Percent 52.58 47.42 100.00

50 Weighted n 811,828 564,539 1,376,367

Sample n 76 52 128

Percent 58.98 41.02 100.00

51 Weighted n 962,558 437,675 1,400,233

Sample n 62 39 101

Percent 68.74 31.26 100.00

52 Weighted n 633,713 969,912 1,603,625

Sample n 66 86 152

Percent 39.52 60.48 100.00

53 Weighted n 764,603 855,097 1,619,700

Sample n 113 80 193

Percent 47.21 52.79 100.00

54 Weighted n 580,042 520,197 1,100,239

Sample n 88 63 151

Percent 52.72 47.28 100.00

55 Weighted n 667,986 873,011 1,540,997

Sample n 58 100 158

Percent 43.35 56.65 100.00

56 Weighted n 1,130,285 783,961 1,914,246

Sample n 133 102 235

Percent 59.05 40.95 100.00

57 Weighted n 1,576,855 1,199,631 2,776,486

Sample n 137 134 271

Percent 56.79 43.21 100.00

58 Weighted n 634,245 627,104 1,261,349

Sample n 69 72 141

Percent 50.28 49.72 100.00

59 Weighted n 813,248 702,374 1,515,622

Sample n 67 66 133

Percent 53.66 46.34 100.00

60 Weighted n 508,019 348,121 856,140

Sample n 41 24 65

Percent 59.34 40.66 100.00

61 Weighted n 1,097,764 1,108,886 2,206,650

Sample n 140 117 257

Percent 49.75 50.25 100.00

62 Weighted n 844,515 732,735 1,577,250

Sample n 77 78 155

Percent 53.54 46.46 100.00

63 Weighted n 1,293,743 1,282,051 2,575,794

Sample n 123 123 246

Percent 50.23 49.77 100.00

64 Weighted n 322,331 355,637 677,968

Sample n 27 31 58

Percent 47.54 52.46 100.00
Page 58 [ Series 2, No. 142

Table IV. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the combined

female and male file—Con.

SECU

Stratum Statistic 1 2 Total

65 Weighted n 1,314,201 1,285,580 2,599,781

Sample n 203 169 372

Percent 50.55 49.45 100.00

66 Weighted n 154,945 303,011 457,956

Sample n 18 22 40

Percent 33.83 66.17 100.00

67 Weighted n 751,028 590,712 1,341,740

Sample n 52 46 98

Percent 55.97 44.03 100.00

68 Weighted n 327,764 244,737 572,501

Sample n 26 21 47

Percent 57.25 42.75 100.00

69 Weighted n 849363 1,132,988 1,982,351

Sample n 166 178 344

Percent 42.85 57.15 100.00

70 Weighted n 658,538 691,408 1,349,946

Sample n 49 52 101

Percent 48.78 51.22 100.00

71 Weighted n 496,303 601,423 1,097,726

Sample n 72 66 138

Percent 45.21 54.79 100.00

72 Weighted n 896,616 703,720 1,600,336

Sample n 53 60 113

Percent 56.03 43.97 100.00

73 Weighted n 856,784 879,916 1,736,700

Sample n 74 52 126

Percent 49.33 50.67 100.00

74 Weighted n 1,128,429 1,358,227 2,486,656

Sample n 74 86 160

Percent 45.38 54.62 100.00

75 Weighted n 543,806 868,995 1,412,801

Sample n 83 131 214

Percent 38.49 61.51 100.00

76 Weighted n 307,946 381,518 689,464

Sample n 39 49 88

Percent 44.66 55.34 100.00

77 Weighted n 608,677 858,176 1,466,853

Sample n 89 106 195

Percent 41.50 58.50 100.00

78 Weighted n 2,547,999 1,959,068 4,507,067

Sample n 316 264 580

Percent 56.53 43.47 100.00

79 Weighted n 481,863 531,235 1,013,098

Sample n 49 38 87

Percent 47.56 52.44 100.00

80 Weighted n 253,655 303,417 557,072

Sample n 26 36 62

Percent 45.53 54.47 100.00

81 Weighted n 890,770 732,600 1,623,370

Sample n 76 85 161

Percent 54.87 45.13 100.00

82 Weighted n 669,916 904,813 1,574,729

Sample n 110 100 210

Percent 42.54 57.46 100.00

83 Weighted n 157,601 272,965 430,566

Sample n 16 27 43

Percent 36.60 63.40 100.00

84 Weighted n 736,693 839,935 1,576,628

Sample n 121 111 232

Percent 46.73 53.27 100.00

Total SECU ....... . . . . . . . . . 122,707,741

Total sample n ..... . . . . . . . . . 12,571

. . . Not applicable.

NOTE: n is number.
Series 2, No. 142 [ Page 59

Table V. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the pregnancy file,

NSFG Cycle 6

SECU

Stratum Statistic 1 2 Total

1 Weighted n 335,908 1,194,416 1,530,324

Sample n 48 93 141

Percent 21.95 78.05 100.00

2 Weighted n 2,155,897 838,522 2,994,419

Sample n 261 188 449

Percent 72.00 28.00 100.00

3 Weighted n 127,356 430,893 558,249

Sample n 15 32 47

Percent 22.81 77.19 100.00

4 Weighted n 264,482 201,787 466,269

Sample n 33 37 70

Percent 56.72 43.28 100.00

5 Weighted n 435,600 426,786 862,386

Sample n 46 45 91

Percent 50.51 49.49 100.00

6 Weighted n 541,596 480,327 1,021,923

Sample n 41 39 80

Percent 53.00 47.00 100.00

7 Weighted n 713,984 337,650 1,051,634

Sample n 74 40 114

Percent 67.89 32.11 100.00

8 Weighted n 207,088 287,085 494,173

Sample n 18 37 55

Percent 41.91 58.09 100.00

9 Weighted n 572,814 475,072 1,047,886

Sample n 138 55 193

Percent 54.66 45.34 100.00

10 Weighted n 965,427 769,685 1,735,112

Sample n 139 97 236

Percent 55.64 44.36 100.00

11 Weighted n 951,734 350,263 1,301,997

Sample n 141 80 221

Percent 73.10 26.90 100.00

12 Weighted n 476,750 511,929 988,679

Sample n 60 54 114

Percent 48.22 51.78 100.00

13 Weighted n 88,347 263,496 351,843

Sample n 11 36 47

Percent 25.11 74.89 100.00

14 Weighted n 910,814 1,084,807 1,995,621

Sample n 116 94 210

Percent 45.64 54.36 100.00

15 Weighted n 1,152,617 1,019,418 2,172,035

Sample n 90 96 186

Percent 53.07 46.93 100.00

16 Weighted n 436,044 546,520 982,564

Sample n 51 80 131

Percent 44.38 55.62 100.00

17 Weighted n 1,098,492 1,042,806 2,141,298

Sample n 146 119 265

Percent 51.30 48.70 100.00

18 Weighted n 202,321 213,388 415,709

Sample n 47 37 84

Percent 48.67 51.33 100.00

19 Weighted n 250,722 558,546 809,268

Sample n 23 54 77

Percent 30.98 69.02 100.00

20 Weighted n 438,486 360,849 799,335

Sample n 46 45 91

Percent 54.86 45.14 100.00

21 Weighted n 431,655 448,916 880,571

Sample n 43 53 96

Percent 49.02 50.98 100.00

22 Weighted n 1,352,741 922,132 2,274,873

Sample n 105 90 195

Percent 59.46 40.54 100.00
Page 60 [ Series 2, No. 142

Table V. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the pregnancy file,

NSFG Cycle 6—Con.

SECU

Stratum Statistic 1 2 Total

23 Weighted n 685,894 1,033,665 1,719,559

Sample n 56 45 101

Percent 39.89 60.11 100.00

24 Weighted n 325,124 674,948 1,000,072

Sample n 29 48 77

Percent 32.51 67.49 100.00

25 Weighted n 936,069 1,226,250 2,162,319

Sample n 113 123 236

Percent 43.29 56.71 100.00

26 Weighted n 1,991,560 174,332 2,165,892

Sample n 39 11 50

Percent 91.95 8.05 100.00

27 Weighted n 232,938 652,445 885,383

Sample n 23 92 115

Percent 26.31 73.69 100.00

28 Weighted n 1,095,420 598,117 1,693,537

Sample n 177 167 344

Percent 64.98 35.32 100.30

29 Weighted n 301,287 341,239 642,526

Sample n 28 27 55

Percent 46.89 53.11 100.00

30 Weighted n 1,034,597 1,152,505 2,187,102

Sample n 105 111 216

Percent 47.30 52.70 100.00

31 Weighted n 1,088,613 1,027,288 2,115,901

Sample n 137 109 246

Percent 51.45 48.55 100.00

32 Weighted n 1,391,667 810,294 2,201,961

Sample n 64 71 135

Percent 63.20 36.80 100.00

33 Weighted n 483,099 586,482 1,069,581

Sample n 93 73 166

Percent 45.17 54.83 100.00

34 Weighted n 487,132 495,178 982,310

Sample n 46 56 102

Percent 49.59 50.41 100.00

35 Weighted n 637,579 830,968 1,468,547

Sample n 125 124 249

Percent 43.42 56.58 100.00

36 Weighted n 782,453 302,001 1,084,454

Sample n 68 33 101

Percent 72.15 27.85 100.00

37 Weighted n 871,661 649,849 1,521,510

Sample n 69 60 129

Percent 57.29 42.71 100.00

38 Weighted n 958,089 700,673 1,658,762

Sample n 144 62 206

Percent 57.76 42.24 100.00

39 Weighted n 631,307 282,884 914,191

Sample n 119 44 163

Percent 69.06 30.94 100.00

40 Weighted n 138,455 708,259 846,714

Sample n 26 57 83

Percent 16.35 83.65 100.00

41 Weighted n 988,969 424,603 1,413,572

Sample n 86 56 142

Percent 69.96 30.04 100.00

42 Weighted n 7,396.40 396,795 404,191.40

Sample n 1 31 32

Percent 1.83 98.17 100.00

43 Weighted n 836,814 1,302,174 2,138,988

Sample n 99 105 204

Percent 39.12 60.88 100.00

44 Weighted n 879,516 299,255 1,178,771

Sample n 218 72 290

Percent 74.61 25.39 100.00
Series 2, No. 142 [ Page 61

Table V. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the pregnancy file,

NSFG Cycle 6—Con.

SECU

Stratum Statistic 1 2 Total

45 Weighted n 1,006,595 710,282 1,716,877

Sample n 184 110 294

Percent 58.63 41.37 100.00

46 Weighted n 682,761 1,250,469 1,933,230

Sample n 175 156 331

Percent 35.32 64.68 100.00

47 Weighted n 525,235 1,242,201 1,767,436

Sample n 45 64 109

Percent 29.72 70.28 100.00

48 Weighted n 581,398 519,302 1,100,700

Sample n 73 38 111

Percent 52.82 47.18 100.00

49 Weighted n 196,491 494,129 690,620

Sample n 24 41 65

Percent 28.45 71.55 100.00

50 Weighted n 840,332 395,948 1,236,280

Sample n 97 32 129

Percent 67.97 32.03 100.00

51 Weighted n 856,934 229,050 1,085,984

Sample n 59 31 90

Percent 78.91 21.09 100.00

52 Weighted n 537,689 910,421 1,448,110

Sample n 81 103 184

Percent 37.13 62.87 100.00

53 Weighted n 473,568 1,432,568 1,906,136

Sample n 89 144 233

Percent 24.84 75.16 100.00

54 Weighted n 643,536 932,819 1,576,355

Sample n 120 101 221

Percent 40.82 59.18 100.00

55 Weighted n 436,203 542,387 978,590

Sample n 50 102 152

Percent 44.57 55.43 100.00

56 Weighted n 848,719 823,553 1,672,272

Sample n 105 134 239

Percent 50.75 49.25 100.00

57 Weighted n 1,523,584 1,021,502 2,545,086

Sample n 147 171 318

Percent 59.86 40.14 100.00

58 Weighted n 813,488 477,789 1,291,277

Sample n 88 69 157

Percent 63.00 37.00 100.00

59 Weighted n 672,614 603,334 1,275,948

Sample n 62 64 126

Percent 52.71 47.29 100.00

60 Weighted n 235,072 385,377 620,449

Sample n 22 30 52

Percent 37.89 62.11 100.00

61 Weighted n 558,987 594,316 1,153,303

Sample n 89 98 187

Percent 48.47 51.53 100.00

62 Weighted n 1,060,201 808,049 1,868,250

Sample n 82 130 212

Percent 56.75 43.25 100.00

63 Weighted n 845,452 946,549 1,792,001

Sample n 79 129 208

Percent 47.18 52.82 100.00

64 Weighted n 167,157 434,743 601,900

Sample n 19 38 57

Percent 27.77 72.23 100.00

65 Weighted n 1,505,616 1,132,924 2,638,540

Sample n 274 196 470

Percent 57.06 42.94 100.00

66 Weighted n 199,483 162,752 362,235

Sample n 33 9 42

Percent 55.07 44.93 100.00
Page 62 [ Series 2, No. 142

Table V. Sum of weights and number of cases for Sampling Error Computing Units (SECU) by sampling error stratum for the pregnancy file,

NSFG Cycle 6—Con.

SECU

Stratum Statistic 1 2 Total

67 Weighted n 594,270 435,220 1,029,490

Sample n 50 51 101

Percent 57.72 42.28 100.00

68 Weighted n 196,953 131,151 328,104

Sample n 20 17 37

Percent 60.03 39.97 100.00

69 Weighted n 604,406 1,131,221 1,735,627

Sample n 107 204 311

Percent 34.82 65.18 100.00

70 Weighted n 352,133 603,591 955,724

Sample n 32 63 95

Percent 36.84 63.16 100.00

71 Weighted n 537,826 392,940 930,766

Sample n 114 41 155

Percent 57.78 42.22 100.00

72 Weighted n 652,920 762,037 1,414,957

Sample n 65 67 132

Percent 46.14 53.86 100.00

73 Weighted n 670,546 680,238 1,350,784

Sample n 50 53 103

Percent 49.64 50.36 100.00

74 Weighted n 1,079,797 1,207,360 2,287,157

Sample n 65 82 147

Percent 47.21 52.79 100.00

75 Weighted n 259,188 827,277 1,086,465

Sample n 65 129 194

Percent 23.86 76.14 100.00

76 Weighted n 186,294 228,138 414,432

Sample n 42 22 64

Percent 44.95 55.05 100.00

77 Weighted n 514,646 462,009 976,655

Sample n 72 109 181

Percent 52.69 47.31 100.00

78 Weighted n 1,804,700 2,071,197 38,75,897

Sample n 342 306 648

Percent 46.56 53.44 100.00

79 Weighted n 189,511 441,664 631,175

Sample n 38 29 67

Percent 30.03 69.97 100.00

80 Weighted n 226,986 222,110 449,096

Sample n 26 39 65

Percent 50.54 49.46 100.00

81 Weighted n 1,106,935 407,579 1,514,514

Sample n 107 71 178

Percent 73.09 26.91 100.00

82 Weighted n 473,905 540,277 1,014,182

Sample n 93 81 174

Percent 46.73 53.27 100.00

83 Weighted n 96,768 295,582 392,350

Sample n 16 41 57

Percent 24.66 75.34 100.00

84 Weighted n 479,588 953,415 1,433,003

Sample n 111 151 262

Percent 33.47 66.53 100.00

Total SECU ....... . . . . . . . . . 111,413,968

Total sample n ..... . . . . . . . . . 13,593

. . . Not applicable.

NOTE: n is number.
Series 2, No. 142 [ Page 63

Appendix IV

Variance Estimates Using

SAS, SUDAAN, and

STATA for a Percentage

Following are programs and output

for an analysis of the percentage of

females interviewed in NSFG Cycle 6

using the oral contraceptive pill during

the month of interview. A crosstabulation of the use of the oral

contraceptive pill by age (in six

categories: 15–19, 20–24, 25–29, 30–34,

and 40–44 years of age) was generated

by SAS 9.1, SUDAAN 8.0.2, STATA

8.0, and WesVar 4.1. The estimates

calculated are equivalent across

software. Standard errors vary slightly

across packages, and design effects vary

more substantially. (Output for WesVar

is shown only on the Web site to

conserve space in this printed report.)

SAS data files were converted to

STATA 8.0 and SPSS formats using

DBMS/COPY 8.0. Variables in upper

case are original NSFG Cycle 6

variables or recodes. Variables in lower

case represent variables that were

recoded as part of the variance

estimation program. Library and file

names are generic; the user will apply

names specific to his or her computing

environment. Formatting and library

options have been deleted since values

will vary across user organizations.

SAS 9.1

The DATA and SET steps create a

dataset for females that contains the

variables to be used in the analysis: age

categories (‘‘agerx’’) and use of

contraceptive pill (‘‘pill’’).

The PROC SURVEYFREQ

produces a cross-tabulation of

unweighted and weighted cell counts for

the variables (that is, ‘‘agerx’’ by ‘‘pill’’)

specified in the TABLE statement. The

WEIGHT statement identifies the weight

variable FINALWGT. PROC

SURVEYFREQ calculates standard

errors appropriate to the complex

sample design identified by the

STRATUM and CLUSTER statements.

The specification of ROW in the

TABLE statement limits the cell counts

and percentages to the row and DEFF

requests calculation of the design effects

for the row percentages.

Design effects are greater than 1.0

for all but one of the row proportions

due to clustering in the selection and an

increase in variance due to weighting.

The estimated proportions are equivalent

to the other software systems.

SUDAAN 8.0.2

A SAS-callable version of

SUDAAN 8.0.2 was used to calculate

the estimates for this example. The

DATA and SET steps used to create a

dataset and the variables needed for this

analysis (‘‘agerx’’ and ‘‘pill’’), are

identical to those used above in the SAS

9.1 program and are omitted.

The PROC CROSSTAB procedure

produces a frequency cross-tabulation of

unweighted and weighted cell counts for

the analysis variables (agerx by pill)

specified in the TABLE statement. The

DESIGN used in this computation is

specified as WR, with replacement. By

specifying the option DEFF in the

CROSSTAB statement, design effects

will be calculated. The NEST statement

specifies the strata (SEST) and cluster

(SECU_R) variables for calculating

standard errors appropriate to the

complex sample design. The WEIGHT

statement identifies FINALWGT for

estimating the weighted frequency. The

specification of NSUM, WSUM,

ROWPER, SEROW, and DEFFROW in

the PRINT statement limits printed

output to row percentages, standard

errors of row percentages, and design

effects for row percentages.

The estimated percentage of women

using a contraceptive pill is identical to

those calculated by SAS 9.1 in the six

age categories.

STATA 8.0

The use statement specifies the

dataset to be used. The svyset command

specifies the weight (FINALWGT),

strata (SEST), and cluster (SECU_R)

variables to be used by STATA 8.0 in

estimation. These settings are saved for

the current session, but can be cleared

by entering the clear command or

running svyset again with different

settings.

The generate and replace

statements create the recodes ‘‘agerx’’

and ‘‘pill.’’ The svytab command

produces a cross-tabulation of ‘‘agerx’’

and ‘‘pill’’ and provides estimates

appropriate to the complex sample

design identified by the svyset

command. The requested estimates and

output are limited by specifying row,

deff, and se after the svyta command.

Again, the estimated percentages of

women using a contraceptive pill in the

six age categories are identical to those

calculated by SAS 9.1 and SUDAAN

8.0.2.
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SAS 9.1 Output cont. 
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Series 2, No. 142 [ Page 67
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Appendix V

Variance Estimates Using

SAS, SUDAAN, and

STATA, for a Mean

Following are the programs and

output for an analysis of the mean

number of children born to women

20–44 years of age interviewed in Cycle

6 of the NSFG, by race and Hispanic

origin and age. Means were generated

by SAS 9.1, SUDAAN 8.0.2, STATA

8.0, and WesVar 4.1. The estimates

calculated are equivalent across

software. However, due to specific

methods used in calculations, standard

errors vary slightly across packages, and

design effects vary more substantially.

(Output for WesVar is shown only on

the Web site to conserve space in this

printed report.) SAS data files were

converted to STATA 8.0 and SPSS

formats (for use in WesVar 4.1) using

DBMS/COPY 8.0. Variables in upper

case are original NSFG Cycle 6

variables or recodes. Variables in lower

case represent variables that were

recoded as part of the variance

estimation program. Library and file

names are generic and it is assumed the

user will apply names specific to his or

her computing environment. Formatting

and library options have been deleted;

values will vary across user

organizations.

SAS 9.1

The DATA and SET steps create a

dataset that contains the variables for

females to be used in the analysis. The

PROC SURVEYMEANS produces a

table of weighted means for the variable

specified in the VAR statement. The

WEIGHT statement identifies the weight

variable (FINALWGT) to be used in

estimating the means. PROC

SURVEYMEANS calculates standard

errors appropriate to the complex

sample design specified in the

STRATUM and CLUSTER statements.

The DEFF option is not available with

PROC SURVEYMEANS.

The estimated weighted means are

equivalent to the other software systems.

SUDAAN 8.0.2

A SAS-callable version of

SUDAAN 8.0.2 was used to calculate

the estimates for this example. The

DATA and SET statements used to

create a dataset and the variables needed

for this analysis (‘‘agerx’’ and ‘‘group’’)

are identical to those used above in the

SAS 9.1 program and are omitted for

this program. The PROC DESCRIPT

procedure produces descriptive statistics

for the variable specified in the VAR

statement (PARITY). The DESIGN used

in this analysis is WR, with

replacement. By specifying DEFF in the

DESCRIPT statement, design effects

will be calculated. The NEST statement

specifies the strata (SEST) and cluster

(SECU_R) variables for calculating

standard errors appropriate to the

complex sample design. The WEIGHT

statement identifies FINALWGT for

estimating the weighted means.

SUBGROUP identifies subpopulations

for which estimates are requested.

The estimated mean number of

children born to women 20–44 years of

age by race and Hispanic origin and age

is identical to those calculated by SAS

9.1.

STATA 8.0

The use statement specifies the

dataset to be used. The svyset command

specifies the weight (FINALWGT),

strata (SEST), and cluster (SECU_R)

variables to be used by STATA 8.0 in

estimation. These settings are saved for

the current session, but can be cleared

by entering the clear command or

running svyset again with different

settings.

The generate and replace

statements create the recodes ‘‘agerx’’

and ‘‘group.’’ The svymean command

produces estimated weighted means for

each of the levels of the by variable

‘‘group.’’ The estimates provided are

appropriate to the complex sample

design identified by the svyset

command. Design effect calculations are

requested by entering deff after the

svymean command.

Again, the estimated mean number

of children born by race and Hispanic

origin and age is identical to those

calculated by SAS 9.1 and SUDAAN

8.0.2.
Series 2, No. 142 [ Page 69
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Series 2, No. 142 [ Page 71
Page 72 [ Series 2, No. 142
Series 2, No. 142 [ Page 73

Appendix VI

Variance Estimates Using

SAS, SUDAAN, and

STATA for a Regression

Following are the programs and

output for an analysis of the relationship

among the number of children born to

women 20–44 years of age interviewed

in Cycle 6 of the NSFG, race and

Hispanic origin, and education.

Coefficients were generated by SAS 9.1,

SUDAAN 8.0.2, STATA 8.0, and

WesVar 4.1. The estimates calculated are

equivalent across software. However, due

to specific methods used in calculations,

standard errors vary slightly across

packages, and design effects vary more

substantially. (Output for WesVar is shown

only on the web site to conserve space in

this printed report.)

SAS data files were converted to

STATA 8.0 and SPSS formats using

DBMS/COPY 8.0. Variables in upper

case are original NSFG Cycle 6

variables or recodes. Variables in lower

case represent variables that were

recoded as part of the variance

estimation program. Library and file

names are generic and it is assumed the

user will apply names specific to his or

her computing environment. Formatting

and library options have been deleted;

preferences will vary across user

organizations.

SAS 9.1

The DATA and SET steps create a

dataset which contains the variables for

females to be used in the analysis. The

PROC SURVEYREG estimates the

relationship between a continuous

variable (PARITY) and a set of

predictors (AGER, ‘‘hieducx’’, and

‘‘black’’) specified by the MODEL

statement. The WEIGHT statement

identifies the weight variable

(FINALWGT) to be used in estimating

the means. PROC SURVEYREG

calculates standard errors appropriate to

the complex sample design specified in

the STRATUM and CLUSTER

statements. The DEFF option requests

the calculation of design effects.

The estimated regression

coefficients are equivalent to the other

software systems.

SUDAAN 8.0.2

A SAS-callable version of

SUDAAN 8.0.2 was used to calculate

the estimates for this example. The

DATA and SET statements used to

create a dataset and the variables needed

for this analysis are identical to those

used above in the SAS 9.1 program and

are omitted for this program.

The PROC REGRESS estimates the

relationship between a continuous

variable (PARITY) and a set of

predictors (AGER, ‘‘hieducx’’, and

‘‘black’’) specified by the MODEL

statement. The DESIGN used in this
Page 74 [ Series 2, No. 142

analysis is WR, with replacement. By

specifying DEFT4 in the REGRESS

statement, design effects will be

calculated. The NEST statement

specifies the strata (SEST) and cluster

(SECU_R) variables for calculating

standard errors appropriate to the

complex sample design. The WEIGHT

statement identifies FINALWGT for

estimated the weighted means.

The estimated coefficients

calculated by SUDAAN 8.0.2 are

identical to those from SAS 9.1.

STATA 8.0

The use statement specifies the

dataset to be used. The svyset command

specifies the weight (FINALWGT),

strata (SEST), and cluster (SECU_R)

variables to be used by STATA 8.0 in

estimation. These settings are saved for

the current session, but can be cleared

by entering the clear command or

running svyset again with different

settings.

The generate and replace

statements create the recodes ‘‘hieducx’’

and ‘‘black.’’ The svyreg command

models the relationship between

PARITY and a set of predictors (AGER,

‘‘hieducx,’’ and ‘‘black’’). The estimates

provided are appropriate to the complex

sample design identified by the svyset

command. Design effect calculations are

requested by entering deff after the

svyreg command.

The estimated coefficients as

calculated by STATA 8.0 are identical to

those calculated by SAS 9.1 and

SUDAAN 8.0.2.
Series 2, No. 142 [ Page 75
Page 76 [ Series 2, No. 142

Appendix VII

Variance Estimates in SAS,

SUDAAN, and STATA for

a Logistic Regression

Following are the programs and

output for an analysis of the probability

of strongly agreeing that ‘‘a young

couple should not live together unless

they are married.’’ An indicator variable

of strong agreement was regressed on

age, gender, race and Hispanic origin,

and education. Regression coefficients

and odds ratios were generated by SAS

9.1, SUDAAN 8.0.2, STATA 8.0, and

WesVar 4.1. The estimates calculated are

equivalent across software. However,

due to specific methods used in

calculations, standard errors vary

slightly across packages, and design

effects vary more substantially. (Output

for WesVar is shown only on the Web

site to conserve space in this printed

report.)

SAS data files were converted to

STATA 8.0 and SPSS formats (for use

in WesVar 4.1) using DBMS/COPY 8.0.

Variables in upper case are original

NSFG Cycle 6 variables or recodes.

Variables in lower case represent

variables that were recoded as part of

the variance estimation program. Library

and file names are generic and it is

assumed the user will apply names

specific to his or her computing

environment. Formatting and library

options have been deleted; values will

vary across user organizations.

SAS 9.1

The DATA, SET, and MERGE steps

create a dataset that contains the

variables and recodes (‘‘okcohabx,’’

‘‘black,’’ and ‘‘hieducx’’) for males and

females to be used in the analysis. The

PROC SURVEYLOGISTIC models the

relationship between a dichotomous

variable (‘‘okcohabx’’) and a set of

predictors (AGER, ‘‘hieducx,’’ ‘‘black,’’

and FEMALE) specified in the MODEL

statement. The WEIGHT statement

identifies the weight variable

(FINALWGT) to be used in estimating

the model. PROC SURVEYLOGISTIC

calculates standard errors appropriate to

the complex sample design specified in

the STRATUM and CLUSTER

statements. The DEFF option, which

requests calculation of design effects, is

not available with PROC

SURVEYLOGISTIC.

The estimated coefficients and odds

ratios are equivalent to the other

software systems.

SUDAAN 8.0.2

A SAS-callable version of

SUDAAN 8.0.2 was used to calculate

the estimates for this example. The

DATA, SET, and MERGE statements

used to create a dataset and the

variables needed for this analysis are

identical to those used previously in the

SAS 9.1 program and are omitted for

this program.

The PROC RLOGIST estimates the

relationship between a dichotomous

variable (‘‘okcohabx’’) and a set of

predictors (AGER, ‘‘hieducx,’’ ‘‘black,’’

and FEMALE) specified in the MODEL

statement. The DESIGN used in this

analysis is WR, with replacement. By

specifying DEFT4 in the RLOGIST

statement, design effects will be

calculated. The NEST statement

specifies the strata (SEST) and cluster

(SECU) variables for calculating

standard errors appropriate to the

complex sample design. The WEIGHT

statement identifies FINALWGT for

estimation.

The estimated coefficients and odds

ratios calculated by SUDAAN 8.0.2 are

identical to those from SAS 9.1.
Series

2, No. 142[ Page 77

STATA8.0

The use statement specifies the

dataset to be used. The svyset command

specifies the weight (FINALWGT),

strata (SEST), and cluster (SECU)

variables to be used by STATA 8.0 in

estimation. These settings are saved for

the current session, but can be cleared

by entering the clear command or

running svyset again with different

settings.

The generate andreplace

statements create recodes:‘‘okcohabx,’’

‘‘hieducx,’’ and‘‘black.’’ The svylogit

the relationship betweena dichotomous

variable (‘‘okcohabx’’) anda set of

predictors (AGER,‘‘hieducx,’’‘‘black,’’

and FEMALE) specified in the svylogit

statement. The estimates provided are

appropriate to the complex sample

design identified by the svyset

command. Design effect calculations are

requested by entering deff after the

svylogit command.

The estimated coefficients as

calculated by STATA 8.0 are identical to

those calculated by SAS 9.1 and

SUDAAN 8.0.2.
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Series 2, No. 142 [ Page 79
Appendix VIII

Median Design Effect for

Female, Male, and

Pregnancy File Means and

Proportions

Median design effects for the

female respondent data, displayed in

table VI, were based on the proportion

of women:

1. Whose first menstrual period was

before age 13

2. Who had had at least one

completed pregnancy

3. Who had at least one live birth

4. Who were fecund

5. Whose current contraceptive

method was either the pill or a

male condom

6. Who had ever used the pill

7. Who had ever used a male condom

8. Whose first method of

contraception was either the pill or

a male condom

9. Who intended to have additional

children

10. Who strongly agreed with the

statement that ‘‘A young couple

should not live together unless they

are married’’

Median design effects for the

pregnancy data, displayed in table VII,

were based on the proportion of babies:

1. Who were not breast feed

2. Who were delivered vaginally

3. Whose delivery was paid through

personal income or private insurance

Three additional variables include

the proportion of pregnancies in which:

4. The length of the pregnancy

exceeded 38 weeks

5. The first baby was male

6. The outcome was a live birth

Median design effects for the male

respondent data, displayed in table VIII,

were based on the proportion of men:

1. Who were surgically sterile

2. Who had ever had sex

3. Who had ever biologically fathered a

child

4. Who had ever cohabited with a

woman (excluding wives)

5. Who had ever cohabited (including

premarital cohabitation)

The direct estimates of the sampling

variances were computed for each of

these outcomes using the ‘‘with

replacement’’ variance estimator (that is,

DESIGN = WR) in SUDAAN. The

parameter estimates and the direct

variance estimates were computed for

the female respondent data file (one

record for each of the 7,643 responding

women), for the pregnancy database

(one or more records for each of the

13,593 responding women with at least

one pregnancy), and for the male

respondent data file (one record for each

of the 4,928 responding men). For each

data file, the parameter estimates and

their sampling variances were computed

for all women, pregnancies, or men and

for each of the three race/ethnicity

categories (Hispanic, non-Hispanic

black, and other women). For females in

Cycle 6, the median design effect was

2.31, compared with 1.46 in Cycle 5,

1.57 in Cycle 4, and 3.00 in Cycle 3.

Table VI. Median design effects for 10 female respondent file variables, by race and

ethnicity and demographic characteristics: National Survey of Family Growth, Cycle 6

Characteristic

Race and ethnicity

Hispanic Black Other Total

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.52 1.55 2.31 2.31

Age

15–17 years . . . . . . . . . . . . . . . . . . . . . . . 1.37 1.22 1.33 1.39

18–19 years . . . . . . . . . . . . . . . . . . . . . . . 1.32 1.49 1.38 1.55

20–24 years. . . . . . . . . . . . . . . . . . . . . . . . 1.79 1.77 1.93 2.46

25–29 years. . . . . . . . . . . . . . . . . . . . . . . . 1.77 1.40 1.80 1.77

30–34 years. . . . . . . . . . . . . . . . . . . . . . . . 1.25 1.41 1.56 1.64

35–39 years . . . . . . . . . . . . . . . . . . . . . . . 1.28 1.46 1.59 1.69

40–44 years . . . . . . . . . . . . . . . . . . . . . . . 1.34 1.30 1.58 1.76

Marital status

Married . . . . . . . . . . . . . . . . . . . . . . . . . . 1.27 1.37 1.91 1.95

Widowed, divorced, or separated. . . . . . . . . . . 1.40 1.97 1.23 1.55

Never married . . . . . . . . . . . . . . . . . . . . . . 1.30 1.55 1.78 2.01

Education

Less than high school . . . . . . . . . . . . . . . . . 1.59 1.48 1.38 1.71

High school diploma. . . . . . . . . . . . . . . . . . . 1.27 1.59 1.52 1.82

Some college . . . . . . . . . . . . . . . . . . . . . . 1.45 2.05 2.45 2.53

College graduate. . . . . . . . . . . . . . . . . . . . . 1.63 1.74 1.95 1.95

Poverty level

0–100% . . . . . . . . . . . . . . . . . . . . . . . . . . 1.78 1.83 2.14 2.09

101–200%. . . . . . . . . . . . . . . . . . . . . . . . . 1.10 1.40 1.88 1.98

201–399% . . . . . . . . . . . . . . . . . . . . . . . . 1.76 1.36 1.96 2.08

400% or more . . . . . . . . . . . . . . . . . . . . . . 1.36 1.36 1.67 1.86

Metropolitan residence

Metropolitan. . . . . . . . . . . . . . . . . . . . . . . . 1.47 1.38 2.23 2.24

Nonmetropolitan . . . . . . . . . . . . . . . . . . . . . 1.59 1.43 2.26 2.33

Labor force status

Full-time work. . . . . . . . . . . . . . . . . . . . . . . 1.66 1.49 1.90 2.04

Part-time work . . . . . . . . . . . . . . . . . . . . . . 1.45 1.53 1.75 1.97

In school. . . . . . . . . . . . . . . . . . . . . . . . . . 1.58 1.63 1.32 1.50

Other . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.50 1.71 1.45 1.64

Page 80 [ Series 2, No. 142
Series 2, No. 142 [ Page 81

Table VII. Median design effects for six female pregnancy file variables, by race and ethnicity and demographic characteristics:

National Survey of Family Growth, Cycle 6

Race and ethnicity

Characteristic Hispanic Black Other Total

Total ................................. 2.81 2.75 3.68 3.91

Age at outcome1

15–17 years ............................ 1.77 1.84 1.47 1.84

18–19 years ............................ 1.41 1.43 1.79 1.98

20–24 years ............................ 2.02 2.24 2.62 2.87

25–29 years ............................ 1.94 1.93 1.97 2.29

30–34 years ............................ 1.74 1.97 1.87 2.06

35–39 years ............................ 1.55 1.67 1.91 2.28

40–44 years ............................ 1.20 1.05 2.18 2.41

Marital status at outcome

Married ............................... 2.16 2.17 2.93 3.15

Widowed, divorced, or separated ............... 2.08 2.86 2.26 2.42

Never married ........................... 3.05 2.99 2.96 3.72

Education2

Less than high school ...................... 2.85 2.84 2.20 3.71

High school diploma ....................... 2.39 1.96 3.52 3.75

Some college............................ 1.70 2.40 2.43 2.35

College graduate ......................... 1.66 3.39 2.19 2.56

Poverty level2

0–100% ............................... 2.82 2.55 3.92 3.55

101–200% ............................. 1.60 3.02 2.28 2.58

201–399% ............................. 2.03 2.21 2.57 2.58

400% or more ........................... 2.06 2.75 2.08 2.44

Metropolitan residence2

Metropolitan ............................ 2.88 2.71 3.33 3.89

Nonmetropolitan .......................... 2.20 1.27 3.40 2.89

Labor force status2

Full-time work ........................... 2.18 2.20 3.35 3.51

Part-time work ........................... 2.18 2.49 3.04 3.01

In school .............................. 0.39 2.04 1.23 1.63

Other................................. 2.07 3.16 2.74 3.36

1Status at the end of the pregnancy interval.

2Measured at time of interview.
Page 82 [ Series 2, No. 142

Table VIII. Median design effects for five male respondent file variables, by race and ethnicity and demographic characteristics:

National Survey of Family Growth, Cycle 6

Race and ethnicity

Characteristic Hispanic Black Other Total

Total ................................. 2.1 1.23 2.02 2.31

Age

15–17 years ............................ 0.87 0.83 2.29 2.16

18–19 years ............................ 1.36 0.94 1.57 1.52

20–24 years ............................ 1.84 1.45 1.30 1.77

25–29 years ............................ 1.70 1.41 1.91 1.87

30–34 years ............................ 1.33 2.15 1.26 1.31

35–39 years ............................ 2.37 1.83 1.83 2.32

40–44 years ............................ 1.79 1.14 1.65 1.81

Marital status

Married ............................... 2.35 1.04 1.55 2.13

Widowed, divorced, or separated ............... 1.67 1.40 1.92 2.17

Never married ........................... 1.56 1.44 2.08 2.38

Education

Less than high school ...................... 1.46 1.16 3.00 2.66

High school diploma ....................... 2.18 1.06 1.87 2.01

Some college............................ 1.43 1.56 2.09 2.34

College graduate ......................... 1.38 1.45 1.79 1.86

Poverty level

0–100% ............................... 1.78 1.22 2.08 1.97

101–200% ............................. 2.31 1.53 1.85 1.89

201–399% ............................. 1.66 1.70 1.88 2.11

400% or more ........................... 1.64 1.67 2.37 2.51

Metropolitan residence

Metropolitan ............................ 2.06 1.36 2.30 2.80

Nonmetropolitan .......................... 1.51 0.54 1.68 1.91

Labor force status

Full-time work ........................... 1.54 1.35 1.51 1.65

Part-time work ........................... 2.61 1.76 2.63 2.25

In school .............................. 0.54 0.75 2.24 2.31

Other ................................ 2.23 1.50 2.11 2.07
Vital and Health Statistics

series descriptions

SERIES 1. Programs and Collection Procedures—These reports

describe the data collection programs of the National Center

for Health Statistics. They include descriptions of the methods

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

material necessary for understanding the data.

SERIES 2. Data Evaluation and Methods Research—These reports

are studies of new statistical methods and include analytical

techniques, objective evaluations of reliability of collected

data, and contributions to statistical theory. These studies

also include experimental tests of new survey methods and

comparisons of U.S. methodology with those of other

countries.

SERIES 3. Analytical and Epidemiological Studies—These reports

present analytical or interpretive studies based on vital and

health statistics. These reports carry the analyses further than

the expository types of reports in the other series.

SERIES 4. Documents and Committee Reports—These are final

reports of major committees concerned with vital and health

statistics and documents such as recommended model vital

registration laws and revised birth and death certificates.

SERIES 5. International Vital and Health Statistics Reports—These

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

vital and health statistics with those of other countries or

present other international data of relevance to the health

statistics system of the United States.

SERIES 6. Cognition and Survey Measurement—These reports are

from the National Laboratory for Collaborative Research in

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instruments.

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

reports contain statistics on illness; unintentional injuries;

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

and a wide range of special current health topics covering

many aspects of health behaviors, health status, and health

care utilization. They are based on data collected in a

continuing national household interview survey.

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

National Health and Nutrition Examination Surveys, and

the Hispanic Health and Nutrition Examination Survey—

Data from direct examination, testing, and measurement on

representative samples of the civilian noninstitutionalized

population provide the basis for (1) medically defined total

prevalence of specific diseases or conditions in the United

States and the distributions of the population with respect to

physical, physiological, and psychological characteristics, and

(2) analyses of trends and relationships among various

measurements and between survey periods.

SERIES 12. Data From the Institutionalized Population Surveys—

Discontinued in 1975. Reports from these surveys are

included in Series 13.

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

reports contain statistics on health resources and the public’s

use of health care resources including ambulatory, hospital,

and long-term care services based on data collected directly

from health care providers and provider records.

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

Discontinued in 1990. Reports on the numbers, geographic

distribution, and characteristics of health resources are now

included in Series 13.

SERIES 15. Data From Special Surveys—These reports contain

statistics on health and health-related topics collected in

special surveys that are not part of the continuing data

systems of the National Center for Health Statistics.

SERIES 16. Compilations of Advance Data From Vital and Health

Statistics—Advance Data Reports provide early release of

information from the National Center for Health Statistics’

health and demographic surveys. They are compiled in the

order in which they are published. Some of these releases

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

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

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

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

demographic variables, and geographic and trend analyses

are included.

SERIES 21. Data on Natality, Marriage, and Divorce—These reports

contain statistics on natality, marriage, and divorce that are

not included in regular, annual, or monthly reports. Special

analyses by health and demographic variables and

geographic and trend analyses are included.

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

Discontinued in 1975. Reports from these sample surveys,

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

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

reports contain statistics on factors that affect birth rates,

including contraception, infertility, cohabitation, marriage,

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

family planning and infertility; and related maternal and infant

health topics. These statistics are based on national surveys

of women of childbearing age.

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

Divorce—These include advance reports of births, deaths,

marriages, and divorces based on final data from the National

Vital Statistics System that were published as National Vital

Statistics Reports (NVSR), formerly Monthly Vital Statistics

Report. These reports provide highlights and summaries of

detailed data subsequently published in Vital Statistics of the

United States. Other special reports published here provide

selected findings based on final data from the National Vital

Statistics System and may be followed by detailed reports in

Series 20 or 21.

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

in these series, contact:

Information Dissemination Staff

National Center for Health Statistics

Centers for Disease Control and Prevention

3311 Toledo Road, Room 5412

Hyattsville, MD 20782

1-866-441-NCHS (6247)

E-mail: nchsquery@cdc.gov

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

HEALTH & HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

3311 Toledo Road

Hyattsville, MD 20782

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