Series 23, Number 23 April 2003

Community Environment

and Women’s Health

Outcomes: Contextual Data
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reproduced or copied without permission; citation as to source, however, is

appreciated.

Suggested citation

Mosher WD, Deang LP, Bramlett MD. Community environment and women’s

health outcomes: Contextual data. National Center for Health Statistics. Vital

Health Stat 23(23). 2003.

Library of Congress Cataloging-in-Publication Data

Community enviroment and women’s health outcomes: Contextual data.

p. cm.— (Vital and health statistics. Series 23, Data from the national

survey of family growth ; no. 23) (DHHS publication ; no. (PHS) 2003-1999)

ISBN 0-8406-0588-9

1. Women—United States—Social conditions—Statistics. 2 Women—Health

and hygiene—United States—Statistics. 3. Family life survey—United States.

4. Statistics, Vital—United States. I. National Center for Health Statistics (U.S.)

II. Series. III. Series: DHHS publication ; no. (PHS) 2003-1999

HQ1421.C654 2003

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Printed on acid-free paper.
Series 23, Number 23

Community Environment and

Women’s Health Outcomes:

Contextual Data

Data From the National Survey of

Family Growth

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

Hyattsville, Maryland

April 2003

DHHS Publication No. (PHS) 2003-1999
National Center for Health Statistics

Edward J. Sondik, Ph.D., Director

Jack R. Anderson, Deputy Director

Jack R. Anderson, Acting Associate Director for

International Statistics

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

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

and Methodology

Diane M. Makuc, Dr.P.H., Acting Associate Director for

Analysis, Epidemiology, and Health Promotion

P. Douglas Williams, Acting Associate Director for Data

Standards, Program Development, and Extramural Programs

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

and Legislation

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

Vital and Health Statistics Systems

Douglas L. Zinn, Acting Associate Director for

Management and Operations

Charles J. Rothwell, Associate Director for Information

Technology and Services

Division of Vital Statistics

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

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

Branch

Nicholas F. Pace, Systems Programming and Statistical

Resources Branch
Contents

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

Highlights ............................................................................................... 1

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

What Are Contextual Data? ................................................................................. 3

Previous Research on Community Characteristics ............................................................... 4

Interpretations of Community-Level Effects .................................................................... 5

The Multilevel Statistical Models Used in This Report ........................................................... 6

Summary .............................................................................................. 6

Hierarchical Linear Models ............................................................................... 6

The Intra-Class Correlation ............................................................................... 8

Units Used for the Community-Level Analysis ............................................................... 8

Community-Level Indicators .............................................................................. 9

Results ................................................................................................. 9

Childlessness .......................................................................................... 9

Unwanted Births ........................................................................................ 12

Number of Sexual Partners ............................................................................... 14

Marital Status .......................................................................................... 14

Use of Contraception and Sterilization ...................................................................... 15

Breast-Feeding ......................................................................................... 17

Vaginal Douching ....................................................................................... 19

Cigarette Smoking ...................................................................................... 20

Discussion ............................................................................................... 21

Analyzing Contextual Data Through the NCHS Data Research Center .............................................. 21

References ............................................................................................... 22

Appendixes

I. Definitions of Terms ................................................................................ 46

II. Subject Index for the NSFG Contextual Data File ........................................................ 48

III. Standard Errors for the Cross-Tabulations in This Report ................................................... 55

IV. Sample SAS Programs to Fit Multilevel Models .......................................................... 68

V. Alternative Procedures for Mixed Modeling ............................................................. 71

Figures

1. Percent of women 20–34 years of age who have never had a birth, by race and Hispanic origin and community

median family income in 1990: United States, 1995 ...................................................... 10

2. Percent of women 20–34 years of age who have never had a birth, by race and Hispanic origin and community

poverty rate in 1990: United States, 1995 ............................................................... 11

3. Percent of women 18–39 years of age who have had no births but expect to have children in the future, by race and

Hispanic origin and community median family income in 1990: United States, 1995 ............................ 12

iii
4. Percent of births in 1991–95 reported as unwanted by the mother, by race and Hispanic origin and community

poverty rate in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

5. Percent of births in 1991–95 reported as unwanted by the mother, by race and Hispanic origin and percent of employed

adults in professional or managerial jobs in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

6. Percent of unmarried women 15–44 years of age with 3 or more male sexual partners in the last 12 months,

by race and Hispanic origin and community median family income in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . 14

7. Percent of women 20–44 years of age who are currently married, by race and Hispanic origin and community male

unemployment rate in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

8. Percent of women 30–44 years of age who are currently married, by race and Hispanic origin and community

median family income in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

9. Percent of contraceptors 20–44 years of age using female sterilization, by race and Hispanic origin and community

poverty rate in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

10. Percent of babies born between January 1990 and September 1994 who were breast-fed at all, by race and Hispanic

origin and community median family income in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

11. Percent of babies born between January 1990 and September 1994 who were breast-fed for 12 weeks or more, by

race and Hispanic origin and community median family income in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . 19

12. Percent of women 15–44 years of age who douche regularly, by race and Hispanic origin and community median family

income in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

13. Percent of women 15–44 years of age who currently smoke cigarettes, by race and Hispanic origin and community

median family income in 1990: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Text Tables

A. Estimated Intra-class correlations for selected outcome variables ............................................ 8

B. Pearson correlation coefficients for the correlations between pairs of contextual or community-level variables ....... 9

Detailed Tables

1. Number of women 20–34 years of age by race and Hispanic origin, and percent who have never had a birth as of

the date of interview (percent childless), by selected community characteristics: United States, 1995 . . . . . . . . . . . . . . . 24

2. Number of women 18–39 years of age by race and Hispanic origin, and percent who are childless but expect to have

one or more children in the future, by selected community characteristics: United States, 1995 . . . . . . . . . . . . . . . . . . . . 25

3. Multilevel statistical model predicting whether women are childless (have never had a birth), for women 20–34 years

of age: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4. Number of births in the last 5 years to women 15–44 years of age, by race and Hispanic origin, and percent of births

that were unwanted, by selected community characteristics: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5. Multilevel statistical model predicting whether births in the last 5 years were unwanted: United States, 1995 . . . . . . . . 28

6. Number of unmarried women 15–44 years of age (who have ever had intercourse), by race and Hispanic origin,

and percent with 3 or more male sexual partners in the last 12 months, by selected community characteristics:

United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

7. Number of women 20–44 years of age by race and Hispanic origin, and percent who are currently married, by

selected community characteristics: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

8. Number of women 30–44 years of age by race and Hispanic origin, and percent who are currently married, by

selected community characteristics: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

9. Number of women 20–44 years of age who were using 1 or more methods of contraception, by race and

Hispanic origin, and percent (of contraceptors) who were using female sterilization, by selected community

characteristics: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

10. Multilevel statistical model predicting use of female sterilization, for women 20–44 years of age who were using a

contraceptive method: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

11. Number of women 20–44 years of age who were using 1 or more methods of contraception by race and Hispanic

origin, and percent who were using the pill, by selected community characteristics: United States, 1995 . . . . . . . . . . . . 34

12. Multilevel statistical model predicting use of the pill, for women 20–44 years of age who were using a contraceptive

method: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

13. Number of women 20–44 years of age who were using 1 or more methods of contraception, by race and Hispanic

origin, and percent whose partners were using the condom, by selected community characteristics: United States,

1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

14. Multilevel statistical model predicting use of the condom, for women 20–44 years of age who were using a

contraceptive method: United States, 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

iv
15. Number of babies born between January 1990 and September 1994, by race and Hispanic origin, and percent who were

breast-fed at all, by selected community characteristics: United States, 1995 ................................... 38

16. Multilevel statistical model predicting whether a baby (born between January 1990 and September 1994) was

breast-fed at all: United States, 1995 ................................................................... 39

17. Number of babies born between January 1990 and September 1994, by race and Hispanic origin, and percent who were

breast-fed for 12 weeks or more, by selected community characteristics: United States, 1995 ..................... 40

18. Multilevel statistical model predicting whether a baby (born between January 1990 and September 1994) was

breast-fed at least 12 weeks: United States, 1995 ......................................................... 41

19. Number of women 15–44 years of age by race and Hispanic origin, and percent who douche regularly, by selected

community characteristics: United States, 1995 .......................................................... 42

20. Multilevel statistical model predicting whether or not a woman douches regularly: United States, 1995 ............. 43

21. Number of women 15–44 years of age by race and Hispanic origin, and percent who currently smoke cigarettes, by

selected community characteristics: United States, 1995 ................................................... 44

22. Multilevel statistical model predicting current cigarette smoking: United States, 1995 ............................ 45

Appendix Text Table

A1. Multilevel mixed model predicting parity for women 20–34 years of age: United States, 1995..................... 72

Appendix Tables

I. SUDAAN standard errors for the percent of women 20–34 years of age who have never had a birth (for the estimates

in table 1) ......................................................................................... 55

II. SUDAAN standard errors for the percent of women 18–39 years of age who are childless but expect 1 or more

children (for the estimates in table 2) ................................................................... 56

III. SUDAAN standard errors for the percent of births in the last 5 years to women 15–44 years of age that were

unwanted (for the estimates in table 4) ................................................................. 57

IV. SUDAAN standard errors for the percent of unmarried women 15–44 years of age (who have ever had intercourse)

with three or more sexual partners in the last 12 months (for the estimates in table 6) ........................... 58

V. SUDAAN standard errors for the percent of women 20–44 years of age who are currently married (for the estimates

in table 7) ......................................................................................... 59

VI. SUDAAN standard errors for the percent of women 30–44 years of age who are currently married (for the estimates

in table 8) ......................................................................................... 60

VII. SUDAAN standard errors for the percent of women 20–44 years of age using 1 or more methods of contraception

who were using female sterilization (for the estimates in table 9) ............................................ 61

VIII. SUDAAN standard errors for the percent of women 20–44 years of age using 1 or more methods of contraception

who were using the pill (for the estimates in table 11) ..................................................... 62

IX. SUDAAN standard errors for the percent of women 20–44 years of age using 1 or more methods of contraception

whose partners were using the condom (for the estimates in table 13) ........................................ 63

X. SUDAAN standard errors for the percent of babies born between January 1990 and September 1994 who were

breast-fed at all (for the estimates in table 15) ........................................................... 64

XI. SUDAAN standard errors for the percent of babies born between January 1990 and September 1994 who were

breast-fed for 3 months or more (for the estimates in table 17) .............................................. 65

XII. SUDAAN standard errors for the percent of women 15–44 years of age who douche regularly (for the estimates in

table 19) .......................................................................................... 66

XIII. SUDAAN standard errors for the percent of women 15–44 years of age who currently smoke cigarettes (for the

estimates in table 21) ................................................................................ 67

v
Objectives

This report presents some illustrative

data and analyses from the Contextual

Data File for the 1995 National Survey

of Family Growth (NSFG). Data are

shown by the woman’s race and

Hispanic origin, and selected

characteristics of the community in

which she lived.

Methods

Cycle 5 of the NSFG was based on

in-person interviews with a national

sample of 10,847 women 15–44 years

of age in the United States in 1995.

The interview included questions on the

woman’s births, marriages,

contraceptive use, and characteristics

such as her race and education.

Measures of the characteristics of the

woman’s neighborhood were added to

the interview data.

Results

This report shows that several

simple measures of the social and

economic status (SES) and resources

of the woman’s community of residence

are closely associated with outcomes

such as delayed childbearing,

unwanted births, current marital status,

the use of male or female contraceptive

sterilization, breast-feeding, vaginal

douching, and cigarette smoking.

Conclusions

It is well-documented that the

outcomes studied in this report are

closely associated with individual

characteristics such as age, race,

education, and household income. But

this report shows that these outcomes

are also related to characteristics of the

communities in which the individuals

live. Researchers are encouraged to

use the NSFG Contextual Data File to

study these relationships further.

Keywords: fertility c contraception c

sterilization c breast-feeding c

marriage c contextual data c

multilevel models.

Community Environment and

Women’s Health Outcomes:

Contextual Data

by William D. Mosher, Ph.D., Division of Vital Statistics, National

Center for Health Statistics; Lionel P. Deang, Ph.D., Battelle; and

Matthew D. Bramlett, Ph.D., Division of Health Interview Statistics,

National Center for Health Statistics

Highlights

This report presents data from the

National Survey of Family

Growth (NSFG) Cycle 5

Contextual Data File. The NSFG is a

nationally representative survey focused

on birth and pregnancy rates,

contraception, infertility, marriage and

divorce, and women’s health. The Cycle

5 survey was based on face-to-face

interviews with a national sample of

10,847 women 15–44 years of age in

1995 (1–12). The NSFG web page can

be found at: www.cdc.gov/nchs/

nsfg.htm.

The Contextual Data File includes a

number of characteristics of the

communities in which the sampled

women live. The characteristics of the

communities used in this report are all

drawn from the 1990 Census and are

measured at the county, census tract, or

block group level.

This report contains 22 tables

showing illustrative analyses from the

NSFG Contextual Data File on a wide

range of topics collected in the NSFG.

It is intended to:

+ Show, using cross-tabulations and

graphs, how several key behaviors

that vary by individual

characteristics also vary by

characteristics of the communities in

which the women live.

+ Show that some of these differences

in outcomes appear to be related to

characteristics of communities, even

after controlling for some major

individual-level predictors of those

behaviors.

+ Introduce statistical researchers to

the NSFG Contextual Data File and

show some illustrations of how it

can be used.

The analyses in this report, which

include both cross-tabulations and

Hierarchical Linear Models (HLM),

were all produced with the widely used

SAS computer software. Standard errors

for the cross-tabulations were calculated

using SUDAAN software. The analyses

shown here are not intended to be

The 1995 National Survey of Family Growth was jointly planned and funded primarily by the National

Center for Health Statistics (NCHS), the National Institute for Child Health and Human Development, and

the Office of Population Affairs, with additional support from the Children’s Bureau. Other agencies and

individuals also provided helpful advice and assistance. The authors gratefully acknowledge the helpful

review and comments of William R. Grady and Dr. John O.G. Billy of Battelle in Seattle, WA, and

reviews by Dr. Emily Wong of Family Health International, and by Dr. Glenn Deane of the State

University of New York at Albany. This report was edited by Gail V. Johnson, graphics produced by

Jarmila Ogburn, and typeset by Zung Le of the Publications Branch, Division of Data Services, NCHS.

Page 1
Page 2 [ Series 23, No. 23

definitive or exhaustive, but simply to

illustrate the point that some

characteristics of communities appear to

be strongly related to some of the

behaviors measured in the NSFG.

The following statements briefly

illustrate the kinds of findings shown in

this report (references and further details

are given in the later text of the report):

+ The proportion of women who are

childless at 20–34 years of age

increases markedly as the median

family income of the neighborhood

increases overall and for white,

black, and Hispanic women

(figure 1). Similarly, women living

in areas with low poverty rates are

substantially more likely to be

childless at age 20–34 than women

living in areas with high poverty

rates, and this is true for white,

black, and Hispanic women

(figure 2). A multilevel model

suggests that the prosperity of the

county of residence in 1990 is

directly related to the probability

that a woman is childless at ages

20–34 in 1995, even after

controlling for several individual

characteristics that are known to be

associated with childlessness.

+ Using a direct measure of delayed

childbearing—the percentage of

women 18–39 who currently have

no children but expect one or more

in the future—the same relationships

are seen: women in more prosperous

neighborhoods are more likely to

report that they are delaying

childbearing. Further, the differences

between white, black, and Hispanic

women in delayed childbearing tend

to disappear within neighborhoods

with similar income levels

(figure 3).

+ Women who lived in communities

with high poverty rates (figure 4)

and low proportions of the

workforce in professional or

managerial jobs in 1990 (figure 5)

reported more of their births in

1990–95 as unwanted.

+ The proportion of unmarried

women—especially unmarried white

women—who had had three or more

male sexual partners in the last 12

months was somewhat higher for

women who lived in areas with the

lowest income levels in 1990 than

for women in middle and high

income areas (figure 6).

+ The percent currently married in

1995 is lower in areas with higher

male unemployment rates in

1990—especially among black

women (figure 7). As the

community’s median family income

in 1990 rises, the proportion of

women 30–44 who are currently

married in 1995 increases markedly

(figure 8). This is a complex

relationship that deserves further

study (3).

+ As neighborhood poverty (measured

in 1990) increases, many more

contraceptive users chose female

sterilization as their method of

contraception in 1995 (figure 9).

Multilevel models suggest that

community characteristics are an

important part of the explanation of

variations in the use of female

sterilization and use of the male

condom. The community

characteristics used in this report,

however, are not strongly related to

use of oral contraceptive pills.

+ Breast-feeding has well-known

benefits for the mother and infant.

Breast-feeding is more commonly

experienced by babies born to

mothers in high-income than in

low-income areas, but there is still a

large difference in breast-feeding

between white and black mothers,

even within communities with

similar income levels (figure 10).

These patterns are clear and the

differences are large for white and

black women separately, and a

multilevel model suggests that

community characteristics in 1990

are part of the explanation of

patterns of breast-feeding in

1990–94, but there are also large

differences by individual

characteristics as well. Community

prosperity in 1990 is also closely

associated with breast-feeding for 12

weeks or more in 1990–94

(figure 11).

+ Vaginal douching is a known risk

factor for pelvic infection, tubal

pregnancy, infertility, and cervical

cancer. For white, black, and

Hispanic women, douching is

markedly more common in

low-income communities than in

high-income communities

(figure 12). A Hierarchical Linear

Model shows that community

socioeconomic status in 1990 is

related to douching in 1995,

independently of individual

characteristics.

+ White and black women who live in

low-income communities are more

likely to smoke cigarettes than

comparable women living in

high-income communities. For

Hispanic women, however, there is

no clear, significant pattern

(figure 13). Multilevel analysis

suggests, however, that the higher

the community’s educational level in

1990, the fewer women currently

smoke in 1995, even after

controlling for age, race/ethnicity,

and individual-level income.

In conclusion, these analyses

suggest that further studies of the NSFG

Contextual Data File may provide

further insights into fertility,

contraceptive use, sterilization, and such

aspects of women’s health as

breast-feeding, vaginal douching, and

smoking. The authors encourage

researchers to use the NSFG Contextual

Data File to study these and other topics

further.

Introduction

This report shows analyses of

results from the contextual data

file for the 1995 National Survey

of Family Growth (NSFG). Contextual

data are sometimes defined as

information on the ‘‘context,’’

community, or neighborhood, in which

the survey respondent lives. The

contextual measures used in this report

are measures of the characteristics of the

county, census tract, or block group in

which the respondent lived, derived

from the 1990 Census.
Series 23, No. 23 [ Page 3

The NSFG was conducted by the

Centers for Disease Control and

Prevention (CDC), National Center for

Health Statistics (NCHS), under a

contract with the Research Triangle

Institute, of Research Triangle Park, NC.

The NSFG is a multipurpose study

based on in-person (not telephone)

interviews, conducted in the homes of

the sampled women. The main function

of Cycle 5 of the NSFG was to collect

nationally representative data on factors

affecting birth and pregnancy rates,

family formation (including marriage,

cohabitation, and adoption), and

reproductive health, among a sample of

women of childbearing age.

The basic results of Cycle 5 of the

NSFG have been published previously

(1–12). A more complete list of reports

and articles based on the 1995 survey is

on the survey’s web page at:

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

This report looks at several aspects

of fertility, contraception, and women’s

and infants’ health. These aspects were

chosen to represent a wide range of

important outcome variables produced

by the survey, including many that are

of both theoretical and policy interest.

More background explaining the

importance of each outcome variable is

included in the ‘‘Results’’ section of this

report. The outcome variables included

in this report are:

+ The percent currently childless and

the percent temporarily childless

(tables 1–3)

+ The proportion of recent births that

were unwanted by the mother

(tables 4–5)

+ The percent of unmarried women

with three or more recent sexual

partners (table 6)

+ The percent currently married

(tables 7, 8)

+ The percent of those using

contraception who use female

sterilization, the pill, or the male

condom (tables 9–14)

+ The percent of babies who were

breast-fed at all and for 12 weeks or

more (tables 15–18)

+ The percent of women who douche

regularly (tables 19–20)—a

preventable risk factor for several

serious women’s health conditions

+ The percent of women who smoke

cigarettes (tables 21,22).

This report, however, shows that

these important behaviors are associated

with both individual demographic

characteristics and with the

characteristics of the communities in

which the women lived in 1990 (the

census date). Previous reports based on

the NSFG have encouraged readers to

consider the characteristics of the

community as possible explanations of

differences among demographic groups.

For example, in Series 23, No 19 it is

suggested that ‘‘. . .Differences among

white, black, and Hispanic women. . . in

the tables primarily reflect the lower

income and educational levels of black

and Hispanic women, their more limited

access to health care and health

insurance, the communities in which

they live, and other factors’’ (1).

This report is presented to

encourage readers to consider ways in

which differences by individual-level

characteristics (such as age, race, and

Hispanic origin; education; and income)

may be related, in part, to the

community environments in which the

survey respondents live; and to show

statistical researchers some examples of

how the NSFG Contextual Data File can

be used. Appendix I defines some of the

technical or specialized terms used in

this report.

What Are Contextual

Data?

For this report, ‘‘contextual’’ data

are simply measures of the

community or neighborhood

‘‘context,’’ or environment, in which a

person lives. The NSFG Contextual

Data File (described further in Appendix

II) includes information on the

characteristics of the State, county,

census tract, or block group in which

the woman lived, at three dates: in 1995

(the date of the NSFG interview); in

1993; and in April of 1990 (when the

census was conducted). Examples of

characteristics that are included in the

NSFG Contextual Data File include:

+ The unemployment rate in the area

+ The percent receiving public

assistance

+ The average value of public

assistance

+ The median rent or the median

value of homes in the area

+ The percent with incomes below the

poverty level

+ Indicators of State spending on

various categories of programs.

When community-level

characteristics such as these are used in

a regression model, along with

individual characteristics (such as the

woman’s age, race, Hispanic origin, and

how much education she has

completed), researchers call these

multilevel, contextual, or Hierarchical

Linear Models (13). In this report, they

will be referred to as multilevel models

or Hierarchical Linear Models.

This report contains 22 detailed

tables, including 13 tables that show

cross-tabulations and 9 tables that show

the results of multivariate analyses with

multilevel models. The contextual, or

community-level, variables used in this

report are:

1. Median family income at the census

date in 1990

2. Percentage of households with

income below the poverty level in

1990

3. Percentage of adults who were

college graduates in 1990

4. Male unemployment rate in 1990

5. Percentage employed in professional

or managerial occupations in 1990.

The variables are measured in 1990,

at the census date, and the outcome

variables typically refer to behavior or

characteristics that are ongoing at or just

before the date of interview in 1995.

This is done so that the measures of the

community-level variables are

temporally prior to the outcome

variables being measured in this report.

Certainly, other indicators of

community characteristics could be

used, but the five characteristics of

communities used here were chosen for

three principal reasons:
Page 4 [ Series 23, No. 23

1. Preliminary analyses showed that

they are related to the outcomes

shown in this report;

2. They represent some of the most

important dimensions of community

characteristics used in the existing

research literature; and

3. The variables measure factors such

as unemployment, poverty, and

education, which can be affected by

public policies.

For the cross-tabulations in this

report, we classified the contextual, or

community-level variables into three

categories. For these community-level

variables, the ‘‘low’’ category represents

the lowest third of the distribution, the

‘‘middle’’ category represents the middle

third, and the ‘‘high’’ category shows

the highest third. In some tables where

there were too few Hispanic respondents

in the sample to show reliable statistics,

we included Hispanic women in the

total but did not show them

separately.

This classification scheme

represents a compromise between the

need to present reliable statistics based

on large sample sizes, and the desire to

look at the full range of the distribution

by community characteristics. This

report does not show any crosstabulation in which the total number of

cases in a row is less than 250, and

most rows have many times more than

that. The standard errors of the

proportions shown in the crosstabulation tables were calculated using

SUDAAN, and are shown in Appendix

III, tables I–XIII. Differences discussed

in the text are significant at the 0.05

level unless they are qualified as

‘‘small’’ or ‘‘not significant.’’

The multivariate analyses are

run only on selected tables and

outcomes. These outcomes have been

chosen to represent a wide range of

topics, but it was not possible to

perform a multilevel multivariate

analysis of every cross-tabulation shown

in this report.

Previous Research on

Community

Characteristics

This section reviews a few selected

previous studies of community

effects, or contextual effects, on

fertility, contraception, and sexual

activity in the United States. It does not

include the large number of studies of

contextual effects outside the United

States (14,15) or the large literature on

community effects on educational and

child development outcomes, which

have been reviewed exhaustively

elsewhere (16–18).

The studies reviewed below, and

others like them, suggest that the

characteristics of communities measured

in the NSFG Cycle 5 Contextual Data

File might have significant effects on

some important outcome variables that

are also measured in the NSFG.

1. Hogan and Kitagawa (19) studied

the fertility of about 1,000 black

adolescent females 13–19 years

of age in Chicago, Illinois, who

were interviewed in 1979. Using

an index of neighborhood quality

composed of variables like those

used in this report, they found

that the probability of a premarital pregnancy before age 20

varied from 52 percent of black

teens in low-status neighborhoods

to 25 percent in high-status

neighborhoods.

2. In another study based on the

same survey in Chicago, Hogan,

Astone, and Kitagawa (20) found

that, after adjusting for other

variables, 36 percent of black teen

females in high-status neighborhoods, and only 20 percent in

low-status neighborhoods, used a

method of contraception at first

intercourse.

3. Billy, Brewster, and Grady (21)

used a contextual data file based

on the 1982 NSFG to study the

effects of various community and

individual factors on whether

adolescent females had ever had

intercourse. Even after controlling

for individual characteristics,

several community variables were

found to affect the probability of

premarital intercourse and the

effects were somewhat different

for white and black teens.

4. Brewster (22) used the same

contextual data file with the 1982

NSFG to study the large

difference between black and

white adolescent females in the

risk of premarital intercourse. She

found significant communitylevel effects that suggested that

‘‘the race difference in the risk of

first intercourse reflects race

differences in access to economic

resources and exposure to

successful adult role models.’’

5. Brewster (23) also used a

contextual data file with the 1982

NSFG to study sexual activity

and contraceptive use among

black teen females and concluded

that ‘‘neighborhood socioeconomic status, female

employment, and marital

dissolution rates’’ were related to

premarital intercourse and use of

contraception.

6. Billy and Moore (24) used the

1982 NSFG Contextual Data File

to investigate the effects of both

individual and community-level

variables on both the risk of a

birth among married couples

(marital fertility) and the risk of a

birth among unmarried women.

Using hazard-rate models with

time-dependent covariates, they

found that census characteristics

such as area marriage rates and

the characteristics of the local

labor force affected fertility, but

different variables (characteristics

of communities) affected marital

and nonmarital births.

7. Grady, Klepinger, and Billy (25),

also using 1982 NSFG

Contextual Data File, found that a

number of community-level

variables, including the number

of family planning clinics, were

associated with the effectiveness

of contraceptive use. The
Series 23, No. 23 [ Page 5

relationships remained after

controlling for the characteristics

of individuals.

8. Mosher and McNally (26) used

the 1988 NSFG and a few

contextual variables measured in

‘‘minor civil divisions.’’ (Minor

civil divisions are smaller than

counties, but larger than census

tracts.) Use of contraception at

first intercourse is a major

determinant of teenage pregnancy.

They found that, after controlling

for several individual characteristics, several community

characteristics affected the

probability that women used a

method of contraception at first

premarital intercourse.

9., 10. More recently, South and

Baumer (27,28) published two

studies using longitudinal data

from the National Survey of

Children, a sample of several

hundred children interviewed in

1976, 1981, and 1987. South and

Baumer found that black teens

were more likely to have

premarital births than white teens,

and that about two-thirds of the

difference was due to the fact that

black teens tend to live in

lower-SES neighborhoods than

white teens. They also found

some support for the theory that

some of the neighborhood effect

is related to the attitudes and

behaviors of peers.

11. South and Crowder (29) used

data from the Panel Study of

Income Dynamics and census

data to study the impact of

community characteristics on

premarital childbearing and on

the timing of first marriage, using

discrete-time event-history

models. They found that growing

up in a low-SES community

(‘‘neighborhood disadvantage’’)

has a significant nonlinear effect

on black women’s chances of

marrying before their first birth.

Among white women,

neighborhood disadvantage is

related to both marriage and to

the risk of a premarital birth.

Interpretations of

Community-Level

Effects

What explains findings like

these? Several explanations of

community effects have been

offered. For example, sociologist

William Julius Wilson (30,31) suggested

that concentrated ghetto poverty had

far-reaching effects on the residents of

those areas by removing job

opportunities in manufacturing and other

industries that could be performed by

those with limited education, and by

leaving behind only the poor, so that

many children and youth living in poor

neighborhoods had very few

economically successful adult to serve

as role models for them. Mayer and

Jencks (16) suggested several

mechanisms by which growing up in a

poor, or affluent, neighborhood could

affect the residents.

In addition, educational researchers

were attempting to quantify the effects

of characteristics of schools versus the

characteristics of students, on student

performance (32,33).

Recently, Leventhal and BrooksGunn (17) reviewed a large amount of

research that was done in the 1990s on

the effects of neighborhoods on the

behavior and achievement of children

and adolescents, including a major

two-volume study of Neighborhood

Poverty coordinated by the Social

Science Research Council (18).

Leventhal and Brooks-Gunn (17),

reviewing existing studies by others and

their own factor analytic studies,

suggested several dimensions of

neighborhoods that appear to have broad

effects on the behavior of their

residents, including: high Socioeconomic Status (SES), low SES, male

unemployment, and family composition.

In this report, high SES is measured

by the percentage of adults who are

college graduates and by the percent of

employed workers who are in

professional or managerial occupations;

low SES is measured by the percentage

of persons with incomes below the

poverty level. Male unemployment is

measured directly by the male

unemployment rate in the census tract.

Others have suggested that measures of

community characteristics should

include both average values and

extremes (34). This report uses both,

including median family income as an

average value, and indicators of low

SES such as the percent below poverty

and high SES such as the percent who

are college graduates.

How do these characteristics of

communities—high and low SES,

male unemployment, and family

composition—affect the behavior of

their residents? Researchers have

suggested (17) that communities may

affect individual behavior in at least

three important ways:

1. Institutional resources—these are

community assets that residents and

their children can use to enhance

their own educational or economic

success (for example, schools, child

care facilities, libraries, parks,

hospitals and health clinics, the

quality of housing, or job

opportunities).

2. Relationships—particularly the

quality and effectiveness of

relationships within families

(between adults as well as between

adults and children).

3. Norms/collective efficacy—which

means the extent to which the

community’s residents watch,

monitor, and supervise the use of

‘‘violence and harmful substances,’’

and other behavior, and either

correct such behavior or let it

spread.

Sampson (35) calls these factors ‘‘a

working trust among residents, and a

shared willingness of residents to

intervene in support of local social

order.’’ Sampson concluded that

‘‘evidence from social science research

can be capitalized on to design and

evaluate neighborhood-based prevention

strategies for dealing with health- and

violence-related outcomes and for

building community capacity’’ (35).

Additional research will be required

to arrive at satisfactory explanations for

all the community-level differences
Page 6 [ Series 23, No. 23

found in this report and whether it

makes sense to explain them in terms

of ‘‘institutional resources,’’

‘‘relationships,’’ ‘‘norms/collective

efficacy,’’ some combination of these, or

some new way of understanding

community resources. This report is

intended to suggest starting points for

further research using NSFG and other

NCHS contextual data. The final section

of the text of this report describes how

researchers can use the Cycle 5 (1995)

NSFG Contextual Data File in the

NCHS Research Data Center.

The Multilevel

Statistical Models

Used in This Report

Summary

This summary will give the general

reader a brief, relatively nontechnical

review of the statistical models used in

this report. The next section, called

‘‘Hierarchical Linear Models,’’ describes

the techniques further and in somewhat

more technical language.

One common feature of the studies

presented previously is an interest in

using both individual-level and

community-level independent variables

to explain individual-level outcomes.

While the studies cited above employed

several different statistical techniques,

they clearly illustrate a growing

recognition that neighborhood or

community context can influence

individual behavior. These studies also

show an effort to measure the effects of

community context in a rigorous

statistical manner. De Leeuw, writing in

Raudenbush and Bryk’s text (36),

defines the issue in the following way:

‘‘in the social sciences, data are often

hierarchical. . .: We have variables

describing individuals, but the

individuals also are grouped into larger

units, each unit consisting of a number

of individuals. We also have variables

describing those higher order units.’’

This idea is illustrated with the four

examples below. Each example has

1,000 level-1 units (individuals), which

are grouped into 50 level-2 units. The

examples are:

1. 1,000 students (level 1) grouped into

50 schools (level 2)

2. 1,000 adults (level 1) grouped into

50 counties (level 2)

3. 1,000 employees (level 1) working

for 50 companies (level 2)

4. 1,000 suspects (level 1) tried by 50

judges (level 2) (37).

In each case, a Hierarchical Linear

Model may be used to test the idea that

characteristics of the schools, the

county, the company, or the judge may

have effects on the outcome that are

independent of the effects of the

individuals (level-1 units).

One assumption that can be tested

with these models is whether level-1

units (individuals) within the same

group (or level-2 unit) are more similar

to each other than individuals in

different level-2 units. If that is true,

then special statistical models are

needed. According to Raudenbush and

Bryk (36), these models are described

by different names in different

disciplines: Hierarchical Linear Models,

Mixed Models, Random-Effects Models,

or Random-Coefficient Models (36–41).

Using these techniques with data

files like the NSFG Contextual Data

File allows the researcher to study

whether or not counties differ in an

individual-level outcome variable. If

there is no variation across counties in

these outcomes, then it is not necessary

to measure community characteristics

and it is not necessary to adapt research

and policy to that fact.

When counties do differ

significantly, tests can be done to see if

specific county characteristics predict

the individual-level outcomes (for

example, whether the woman smokes

cigarettes). If the counties differ in these

outcomes, then the models allow us to

test whether it is the county-level

independent variables, or the individuallevel independent variables, or both, that

affect smoking. The outcomes we will

study include whether or not women:

+ Delay childbearing until after their

teenage years (tables 1–3)

+ Have unwanted births (tables 4, 5)

+ Choose different contraceptive

methods (tables 9–14)

+ Breast-feed their babies (tables 15–

18)

+ Douche (tables 19, 20), and

+ Smoke cigarettes (tables 21, 22)

This report contains 22 tables,

including 13 cross-tabulations and 9

Hierarchical Linear Models. The

cross-tabulations do not prove that there

are community-level ‘‘effects’’—only

that there are wide variations in these

individual-level behaviors between

communities with different

characteristics that deserve further study.

The next step is taken with some of

these topics and a Hierarchical Linear

Model (HLM) is constructed.

Hierarchical Linear Models can be

thought of as regression models that are

done at two levels: the individual level

and the group (or county) level.

Essentially, they allow the researcher to

make statements about the statistical

‘‘effects’’ of community-level variables

on an outcome, controlling for

individual-level variables, and

statements about the effects of

individual-level variables on an

outcome, controlling for the effects of a

community-level variable. For example,

they allow us to say that if a county had

a higher median income in 1990, then

women living in that county had an

increased chance of being childless in

1995, controlling for the age, race, and

individual-level income of those women

(table 3).

Hierarchical Linear

Models

This section is intended for

professional statistical researchers or

others who want a more precise

understanding of Hierarchical Linear

Models. It assumes a basic knowledge

of the concepts and terminology of

ordinary least-squares (OLS) regression

and logistic regression techniques. This

description, like the previous section,

draws primarily on those in Raudenbush

and Bryk (36), Snijders and Bosker (37),

and Kreft and de Leeuw (38). Readers

may also wish to review Appendix I,

which gives definitions of some of the

technical terms used in this report;
Series 23, No. 23 [ Page 7

Appendix IV, which shows some

examples of GLIMMIX models; and

Appendix V, which shows the SAS

procedure called PROC MIXED (used

for continuous outcome variables).

PROC MIXED and GLIMMIX were

chosen over other specialized HLM

software for this report because they are

SAS procedures (42–45) and therefore

can be used with the NCHS Research

Data Center’s Remote Access Procedure.

Readers who do not need further

discussion of these statistical techniques

may wish to skim this section, or skip to

the ‘‘Results’’ section.

In some data sets, including the

NSFG Contextual Data File, the

individuals are clustered in second-level

units—such as students who are in the

same school, women who live in the

same neighborhood or county, or people

who live in the same State. Such data

are sometimes said to have a ‘‘nested

structure.’’ This nested structure may

mean that the assumptions of OLS

regression—including uncorrelated

errors and constant variance—do not

hold. Thus, a technique is needed to see

if there is a clustering effect, and if

there is, to use that fact explicitly in the

multivariate model. HLM allows us to

do this by using an estimation procedure

that explicitly models this clustering;

correlated errors due to clustering are no

longer left in the error term as they

would be in a single-level OLS

regression model. If a single-level model

were used on clustered data, it could

produce misleading results, in some

cases identifying individual

characteristics (for example, race) as a

determinant instead of a communitylevel characteristic (for example, median

family income or the unemployment

rate). According to Snijders and

Bosker (37), HLM may be thought of as

a regression model that has an error

term, or residual, for the individual level

and another error term for the group

level. HLM estimates a model in which

the value of the outcome variable

depends on both the group’s

characteristics and the individual’s

characteristics, and an error term at both

the group level and the individual level.

In this report, the social context is

modeled explicitly by specifying

two-level hierarchical models (36–45).

These models are sometimes called

Random-Intercept Models because the

intercept (e.g., the overall probability of

being childless at age 20–34) may vary

as a function of a known communitylevel variable (included in the model)

and other random variables (not

included in the model) (37). These

models may be more realistic than

models positing that county

characteristics have either no effect, or a

constant effect on the probability of

childlessness.

The Hierarchical Linear Models in

this report begin with a ‘‘Null’’

Model—a model with no explanatory

variable specified. The Null Model tests

whether counties differ significantly on

a given outcome—breast-feeding,

smoking, or childlessness. It therefore

measures the county-level variation in

the absence of control variables. If

results from the Null Model indicate

significant county variation, the next

step is to introduce explanatory

variables into the model at the

individual and/or county levels. As in

the Null Model, this new Random

Intercept Model allows for random

variability in the outcome variable

across counties. The only difference

between the two models is that the Null

Model has no explanatory variables,

while the random intercept model has at

least one explanatory variable. (It is

possible to study more complex effects

of community characteristics. For

example, a Random Coefficient Model

would allow the researcher to study

whether there is variation across

counties in the relationship between an

individual characteristic, age, and being

childless. The effect of age on

childlessness may be stronger in

high-income counties than in

low-income counties, for example.

Random Coefficient Models, however,

are more difficult to analyze and to

describe in understandable terms

however, so they are not included in this

report.)

Modeling variability between

groups on a given outcome variable

(childlessness) is not a new statistical

procedure. Analysis of Covariance

(ANCOVA) is an alternative method for

multivariate modeling, which estimates

fixed effects for contextual variables.

Estimating mixed models (as done in

this report), however, has some

advantages over Analysis of Covariance.

First, previous findings and the

cross-tabulations shown in this report,

show that these outcomes are likely to

vary across counties. Random

coefficient models such as the Random

Intercept Models estimated here are

therefore more realistic than the other

methods (fixed effects models), which

assume a constant level for the

outcomes. Second, the large number of

counties in the NSFG dataset prevents

the use of ANCOVA: the number of

counties in the NSFG data is much too

large for the efficient use of ANCOVA.

Third, the Random Coefficient Model

estimated here allows for testing the

effects of group-level variables, in this

case, a county. In a fixed effect model,

outcome variability between the groups

is assumed to have all been explained

away by the explanatory variables. If

there is no between-group variability

left, then there is no need for

group-level variables (36–38).

No claim is made here that the

multivariate techniques used in this

report are the only correct or the only

appropriate techniques to analyze

contextual (community-level) data. In

addition, even if multilevel or HLM is

the most appropriate kind of technique,

the models shown in this report are

illustrative, and should not be

considered the only possible ones. It is

always possible to try using different

sets of community variables, different

sets of individual-level variables, and

different sets of sample persons to

analyze.

This report does show researchers

some illustrations of how the NSFG

Contextual Data File can be analyzed,

using SAS PROC MIXED and

GLIMMIX. Using these SAS

procedures, it is possible to use the

NCHS Research Data Center to do

multilevel modeling from the

researcher’s own location, using the

Remote Access Procedure. In addition to

SAS PROC MIXED and GLIMMIX,

however, there is a growing collection

of software available to estimate

multilevel models. For example,

Raudenbush and Bryk’s software is

called HLM, for Hierarchical Linear
Page 8 [ Series 23, No. 23

Models. HLM is described further at:

http://ssicentral.com. Other software

available to implement these models

includes MLwiN, MIXOR, VARCL, and

a Bayesian software program called

WinBUGS (35). Some of these are

available on the internet; for

example, WinBUGS is available at:

www.mrc-bsu.cam.ac.uk/bugs.

Kreft and de Leeuw have also

presented a software program, called

MLN, which is described further at:

http://multilevel.ioe.ac.uk/index.html.

Kreft and de Leeuw (38)

recommend fitting models with just a

few independent, or explanatory,

variables. The techniques used for fitting

multilevel models—MaximumLikelihood Estimation and Bayesian

estimation—are useful for many

situations, but they also use considerable

computer memory. Kreft and de

Leeuw (38) caution that if the model has

too many explanatory variables that are

closely related to each other, the

equations will sometimes not be solved

at all (that is, the models will not

‘‘converge’’). Thus, Snijders and

Bosker (37) and Kreft and de

Leeuw (38) recommend using available

substantive knowledge and preliminary

analyses to choose a short list of

explanatory variables to include in a

multilevel model. We followed this

advice in order to keep the number of

explanatory variables in each model as

small as possible. We also kept the

number of cases in each table quite

large, to obtain the maximum possible

stability in the estimates. Researchers

doing more specialized analyses may

wish to select more targeted subsamples

for analysis.

The Intra-Class

in some ways—more similar to each

other than to people far away (38). The

intra-class correlation ‘‘. . .can also be

called a measure of group homogeneity.

More formally, with data having a

two-level. . . structure, it is defined as

the proportion of variance in the

outcome variable that is between the

second-level units’’—in this report, the

counties—as opposed to variance that is

between individuals in the sample.

When the intra-class correlation is

high, then a high proportion of the

variation (in the outcome) is between

the level-2 groups (counties). In this

situation, a level-2 explanatory variable

(median family income or the

unemployment rate) may be introduced

into the model when the researcher

wishes to explain the second-level

variance (36–38).

Table A shows the estimated

intra-class correlations for the selected

outcome variables in this report. They

indicate a degree of similarity between

women living in the same county from

low (7 percent) to high (44 percent). The

intra-class correlation for childlessness

is 14.9 or about 15 percent, a significant

but moderate level of intra-class

correlation. Similar levels are found for

use of female sterilization (18.6 percent)

and smoking (14.7 percent). The

intra-class correlation for unwanted

births is higher, at about 29 percent. For

vaginal douching, it is about 33 percent.

For breast-feeding, the intra-class

correlation is nearly 44 percent.

The estimated models in this report

have dependent variables that are

dichotomous. As a result, the

assumptions of normality and constant

variance in the individual-level residual

(error) term do not hold. The application

of GLIMMIX takes care of the

normality assumption but it does not

result in having a constant variance. In

dichotomous variables, the mean already

determines the variance, that is, the

variance is the product of the probability

of success times the probability of

failure (V=p(1–p)). Put simply, counties

have different within-county variances.

It is however permissible to interpret

first-level residual variance as the

‘‘average residual variance’’ in the

population of counties. With this

interpretation, the intra-class correlation

as computed for continuous variables

can be applied to dichotomous

variables (37).

Units Used for the

Community-Level Analysis

The analyses in this report use two

‘‘levels’’ of data: individual-level data

on women, such as her age, race, or

educational attainment (sometimes

called ‘‘level 1’’); and ‘‘communitylevel’’ data (sometimes called ‘‘level

2’’). For the cross-tabulations in this

report—such as tables 1 and 2—block

groups were used as the ‘‘community’’

or ‘‘level 2’’ units. Block groups are

more homogenous than counties or

States, and measure the immediate

neighborhood environment. (The male

unemployment rate was measured at the

census tract level, because census tracts

are larger than block groups and they

yielded more stable unemployment

rates.)

The Hierarchical Linear Models

used in this report compute average

regression coefficients for the

Table A. Estimated Intra-class correlations for selected outcome variables

Correlation

An early step in the Hierarchical

Linear Modeling process is to compute

the intra-class correlation. In a

traditional Ordinary Least-Squares

regression analysis of survey data, the

individuals in the sample would be

assumed to be sampled independently.

But it is common for people who live

near each other and share common

experiences to be similar to each other

(1)/(1)+(2)

(1) (2) Intra-class

Between-group Within-group correlation

Model variance variance (percent)

Childless (table 3) ........................ 0.169 0.969 14.9

Unwanted birth (table 5) .................... 0.352 0.859 29.1

Using female sterilization (table 10) ............. 0.220 0.964 18.6

Using the pill (table 12) .................... 0.078 0.974 7.4

Using the condom (table 14) ................. 0.208 0.935 18.2

Breast-fed at all (table 16) ................... 0.721 0.930 43.7

Breast-fed 12 weeks or more (table 18) .......... 0.633 0.916 40.9

Douches regularly (table 20) ................. 0.465 0.951 32.8

Smoking (table 22) ....................... 0.166 0.965 14.7

NOTE: The Intra-class correlations are derived from the estimated Null Models in the multilevel analyses in this report.
Series 23, No. 23 [ Page 9

individuals in each level-2 unit (in this

case the county), so they require a

minimum sample size within the level-2

units. Snijders and Bosker (37) caution

that when there are very few cases in

too many of the level-2 groups, the

coefficients for the contextual variables

are likely to be unstable, and possibly

biased. Preliminary analysis suggested

that there would not be enough cases to

obtain stable estimates if block groups

or census tracts were used as level-2

units. So it was decided that counties

would be used in the multivariate

analysis. There are more than enough

cases to compute reliable level-2

coefficients for nearly all counties in

Cycle 5. As a precaution, a few counties

with less than five sample cases were

excluded from the multivariate models

only. (Such cases were, of course,

included in the cross-tabulations because

only three categories of each ‘‘level 2’’

variable in the cross-tabulations were

used.) The samples in other surveys, and

in the 1982 NSFG, were much more

clustered, however, so it was possible to

use census tracts in their analyses of the

1982 NSFG data (21–24).

The potential disadvantage of using

counties is that their heterogeneity

(compared with block groups or census

tracts) may understate the effects of the

community-level factors. Thus, the

estimates of county-level effects in the

multilevel models in this report may

represent a conservative estimate of the

community-level associations in these

data.

Community-Level

Indicators

All of the community-level

characteristics used in this report were

derived from the 1990 census. They

were chosen after extensive preliminary

analysis and a review of the literature

cited in this report. Many of these

measures are correlated with each other.

Table B shows the correlations of the

measures with each other for all women

15–44 years of age. In the top left of the

table, the correlation between the

median family income (MEDFINC) and

the percent below poverty

(BELOWPOV) of counties in the NSFG

Table B. Pearson correlation coefficients for the correlations between pairs of contextual or

community-level variables

Variables MEDFINC BELOWPOV URTMALE ABGRAD

MEDFINC1 ............................ . . . . . . . . . . . .

BELOWPOV2 .......................... –.75 . . . . . . . . .

URTMALE3 ............................ –.49 .74 . . . . . .

ABGRAD4 ............................. .77 –.53 –.58 . . .

MANAGER5 ........................... .74 –.40 –.23 .94

. . . Category not applicable.

1MEDFINC=Median family income in the county in 1990.

2BELOWPOV=Percent below poverty in the county in 1990.

3URTMALE=Male unemployment rate in the county in 1990.

4ABGRAD=Percent of adults in the county who were college graduates in 1990.

5MANAGER=Percent of employed who were professionals and managers in 1990.

NOTE: Each of the variables used in the tables in this report is measured for the woman’s county of residence in April 1990, at the

time the census was taken.

sample was –.75 (p<0.0001); the

correlation between the male

unemployment rate (URTMALE) and

the percent below poverty

(BELOWPOV) was +.74. The

correlation between the percent of adults

25 and older with a college degree

(ABGRAD) and the county’s median

family income (MEDFINC) was +.77.

The proportion of adults who were

college graduates (ABGRAD) and the

percent who were professionals and

managers (MANAGER) were correlated

at +.94, nearly a perfect correlation.

The county-level variables may be

viewed as socioeconomic status (SES)

indicators. These include median

income, percent below poverty,

unemployment rate, percent with a

college degree, and the percent of

workers who are professionals or

managers. These correlations are large

enough that several of these variables

cannot be entered into the same model

at once. Preliminary analyses (not

shown here) showed that the problem of

multicollinearity was significant with

these indicators. Multicollinearity leads

to unstable estimates with large

sampling errors when more than one of

the socioeconomic status indicators is

entered in a model. When this situation

arises, some researchers create an index

of neighborhood characteristics; for

example, Hogan and Kitagawa called

their index ‘‘neighborhood quality,’’

while South, Kryder, and Baumer called

their index ‘‘neighborhood

disadvantage’’ (27–29). This can be an

elegant procedure, but we have chosen

not to do this because it increases the

complexity of the analysis, and increases

the complexity of the interpretation.

Instead, substantive concerns were used

to choose the five community-level

variables shown throughout this report.

Of the five community-level variables,

we retained the one or two variables in

each equation that improved the fit of

the model the most. The high

correlations among the community-level

variables means, however, that similar

results are obtained with most of the

other variables.

For convenience in writing, in the

text of this report, non-Hispanic white

women will be referred to as white and

non-Hispanic black women will be

referred to as black. The full labels are

used in the tables.

Results

Childlessness

Table 1 shows the percent of

women 20–34 years of age who have

never had a birth, by race, Hispanic

origin, and a series of community-level

variables. The age range 20–34 was

chosen for this table because we want to

relate the environment in which women

have lived recently to whether or not

births have occurred recently. Birth rates

are highest in the United States at ages

20–34. Thus, most of the births to these

women who have had a birth will have

been only a few years before the
Page 10 [ Series 23, No. 23

Figure 1. Percent of women 20–34 years of age who have never had a birth, by race and Hispanic origin and community median family

income in 1990: United States, 1995

interview, and therefore near or after the

time the community factors were

measured in 1990.

Remaining childless until after age

20 often indicates that the woman has

successfully postponed pregnancy, and is

pursuing employment, education beyond

high school, or both. On the other hand,

births to younger women—especially

before age 20—often result from

unintended pregnancies (1,7,10).

Pregnancy rates before age 25 are much

higher for black and Hispanic women

than for white women. In fact, much of

the difference between the birth and

pregnancy rates of white women

compared with black and Hispanic

women is found at the younger ages (7).

The proportion childless in the

United States varies from lowest among

Hispanic women to highest among white

women (1). In table 1 of this report,

those results are found as well:

31 percent of Hispanic, 36 percent of

black, and 47 percent of white women

20–34 years had no births when

interviewed in 1995. In most cases in

the United States, having had no birth in

this age range represents deliberate and

successful delayed childbearing.

The pattern by the community

variables in table 1 is striking. For

example, in the data on the percent

childless by median family income of

the community, 30 percent of women

living in communities with the lowest

incomes were childless, compared with

66 percent in areas with the highest

incomes (figure 1). Among Hispanic

women, 25 percent in the lowest-income

areas were childless at the date of

interview, while 53 percent of Hispanics

in high-income areas were childless. The

same pattern held for white women:

33 percent childless in low-income areas

and 67 percent childless in high-income

areas. The pattern was very similar for

black women (figure 1). In addition, the

differences between white and black

women are smaller within high-income

areas (67 versus 72 percent) and

low-income areas (33 versus 29 percent)

than overall (36 versus 47 percent).

A similar picture is shown by the

percentage of households with belowpoverty incomes: in neighborhoods with

low poverty rates, 58 percent of white

women were childless compared with

36 percent in areas with a high percent

below poverty (figure 2). The pattern is

similar for each variable in table 1, and

the differences by the prosperity of the

neighborhood are large, for white, black,

and Hispanic women.

Table 2 shows the proportion of

women 18–39 years of age who have

not had a birth as of the date of

interview, but expect to have one or

more births eventually. In other words,

they are currently childless, but expect

to have children eventually. This is a

direct measure of delayed childbearing.

This measure might also be called the

‘‘temporarily childless’’ or those

‘‘postponing motherhood.’’ About

34 percent of women ages 18–39 were

temporarily childless in 1995. The

proportion was slightly lower among

black women (25 percent) and Hispanic

women (28 percent) than among white

women (35 percent).

But the percent temporarily

childless was strongly associated with

every contextual measure in table 2. For

example, 26 percent of those living in

low-income communities in 1990 were

temporarily childless in 1995 compared

with 46 percent of those living in

high-income communities (figure 3).

This pattern was consistently observed

for Hispanic, white, and black women

separately. Of those living in areas with

low poverty rates, 40 percent were

temporarily childless compared with

28 percent of those living in areas with

high rates of poverty. Again, the pattern

is quite similar for Hispanic, white, and

black women separately. These findings

are consistent with the hypothesis that
Series 23, No. 23 [ Page 11

Figure 2. Percent of women 20–34 years of age who have never had a birth, by race and Hispanic origin and community poverty rate in

1990: United States, 1995

the economic prosperity of the results, in the second panel of + If the woman’s family income is

community one lives in is strongly table 3, show the variance of the higher, the probability of

associated with delayed childbearing. To intercept (that is, the proportion of childlessness is higher.

obtain more rigorous evidence on this the total variance accounted for by + If she is Hispanic or black, the

point, however, we conducted the the county-level variability). The probability of childlessness is

analysis shown in table 3. variance of the intercept term lower.

Table 3 shows results from a indicates that the counties vary + If the woman is older than 24,

multilevel model, estimated using the significantly (p<0.0001) in the the probability of childlessness

statistical procedure GLIMMIX as percent childless. is lower.

discussed in previous sections. Here the

dependent variable=1if a woman

20–34 years of age is childless and 0 if

she is not. The community-level

variables are measured at the county

level. The results of table 3 are perhaps

best described by indicating the steps

that were used to produce the model

shown there. The steps were suggested

by Bryk and Raudenbush (36,39):

The Null Model shows that the

intra-class correlation (table A)

accounts for about 15 percent of the

total variance in childlessness. This

means that some counties have

higher rates of childlessness than

other counties and there is a

substantial portion of the total

variance that is explainable by

county variation in median family

Again, it is not possible to enter

dozens of variables into the model

because the models become unstable

and cannot be solved—that is, they

do not ‘‘converge.’’ In this case, we

enter income (in three categories,

with ‘‘under $20,000’’ as the

reference category), race/ethnicity

(with non-Hispanic white as the

reference category), and age, with

1. The first step is to look at the income. 20–24 years as the reference

results of the Null Model in the

first two panels of table 3. The Null

Model shows that there is, in fact,

significant variation in the

intercepts across counties. It is, in

effect, a test to see if there is

significant variation between the

groups. If there is no significant

variation in the Null Model, we do

not need to go further; we can

estimate an individual-level model

without contextual variables. The

2. The next step is to rerun the ‘‘Null’’

Model in step 1 and add some

individual-level (‘‘level 1’’)

characteristics that are well-known

strong correlates of current

childlessness. The results show that

the effects of each variable are

significant (except for non-Hispanic

‘‘other’’ race, which has a small

sample size). The model shows the

following relationships, controlling

for other variables in the model:

category, and one county-level

variable.

3. With 15 percent of the total variance

that is explainable by group (i.e.,

county) variation, the communitylevel variable is added to the model

(as described in the section

‘‘Community-Level Indicators’’).

This model is a restricted one in that

only the Level 2 intercept (mean) is

allowed to vary from county to

county; the relationship between the
Page 12 [ Series 23, No. 23

Figure 3. Percent of women 18–39 years of age who have had no births but expect to have children in the future, by race and Hispanic

origin, and community median family income in 1990: United States, 1995

outcome variable, being childless at

age 20–34 years, and any of the

Level 1 predictors (age, race, etc.) is

constrained to be only a fixed effect.

The model thus predicts the

probability of being childless, for a

given combination of values for the

individual-level variables, using the

mean for a county plus a random

(varying) component. In other

words, some counties have a larger

(or smaller) effect on the level of

childlessness when their value for a

given community-level variable is

above (or below) the mean level for

the counties.

The overall importance of the

community-level variables is seen in

their ability to explain variability in

childlessness. As noted previously,

this works through each county’s

value for a given county-level

variable (holding other predictors

constant). The total effect of

clustering can be obtained and

computed from the variance figures

presented under the ‘‘Random

Effects’’ panel for both the Null

Model and the Random Intercept

Model. A comparison of the two

Random Effects (the variances)

indicates the extent to which the

community characteristics affect

childlessness. As the results show,

the community-level variable median

family income (MEDFINC) reduced

much of the explainable betweengroup variance (from .169 to .079).

The z-test presented in the bottom

panel of table 3 shows that after

including the fixed effects of median

family income in the model as well

as the random effect of the counties’

median family income on the

intercept, there still remains

statistically significant variation

(p≤0.0162) across counties in the

proportion childless at 20–34 years

of age.

4. The Random Intercept Model

presented in table 3 significantly

improved the fit of the model. A

comparison of the deviance

statistic—a measure of the statistical

‘‘lack of fit’’ of the model—shows a

deviance in the Null Model of 5,523,

declining to 4,840 in the Random

Intercept Model, a difference of 683

(12 percent smaller), a substantial

and significant decline.

In sum, this two-level model shows

that the median family income of the

county has a significant effect on the

probability of childlessness in the

county: childlessness is more likely in

communities with higher median family

income. After controlling for the effects

of the median family income of the

county, race, age, and individual

household income also have significant

effects on the probability that an

individual woman will be currently

childless. It is particularly noteworthy

that county-level median family income

has an effect on childlessness that is

independent of the woman’s household

income.

It is logically possible to go two

steps further in each model: first, to

model the effects of contextual variables

on the estimated slopes for individuallevel variables; and second, to test for

‘‘cross-level interactions’’ (for example,

to see if the effect of an individual

woman’s race or income interacts with

county-level median family income).

Because both are complex and beyond

the scope of this report, neither have

been done here. However, those who are

interested in answering these questions

for one of the outcome variables in this

report are encouraged to pursue them.

Unwanted Births

Table 4 shows the proportion of

births (in the 5 years before the survey)

that were reported as ‘‘unwanted’’ by
Series 23, No. 23 [ Page 13

the mother at the time they were

conceived. ‘‘Unwanted’’ means that,

when she became pregnant, she did not

want to have that many births in her

lifetime. For example, if she wanted to

have only two births in her life, and had

a pregnancy that resulted in a third

birth, that third birth is classified as

unwanted. The same is true if she

wanted three children, but had a fourth:

the fourth would be classified as

unwanted at the time it was

conceived (1). (Births that were

unwanted at conception do not

necessarily become unwanted children.

Even if a pregnancy is unwanted at the

time of conception, the child’s parents

may later cherish the child born as a

result of that pregnancy (46).)

The overall proportion of recent

births that were unwanted was about

9 percent. About 7 percent of births to

white women, 10 percent of births to

Hispanic women, and 19 percent of

births to black women, were unwanted.

For all races, and for Hispanic, white,

and black women separately, the

proportion of births that were unwanted

is higher in areas with the lowest

median incomes, the highest poverty

rates (figure 4), the highest male

unemployment rates, the fewest college

graduates, and the fewest professional

and managerial workers (figure 5).

For example, the proportion of

births unwanted was 4 percent for white

women in areas with the lowest poverty

rates and 10 percent for white women in

areas with the highest poverty rates

(figure 4). For Hispanic women, the

comparable figures were 8 and

14 percent unwanted. For black women,

the comparable figures were 14 percent

unwanted in areas with the lowest

poverty rates and 21 percent in areas

with the highest poverty rates (figure 4).

Using the proportion of adults in

the community employed in professional

or managerial jobs in 1990 (figure 5),

the findings were quite similar. In areas

with few professional workers,

13 percent of births were unwanted; in

areas with more professional workers,

5 percent were unwanted.

Table 5 shows a multilevel model in

which the dependent, or outcome

variable is whether a birth was

unwanted by the mother. The contextual

variable used in this case is the male

unemployment rate in the county. As in

table 3, the first two panels indicate

whether there is significant variation by

county, and if so, how much variance

Figure 4. Percent of births in 1991–95 reported as unwanted by the mother, by race and Hispanic origin and community poverty rate in

1990: United States, 1995

Figure 5. Percent of births in 1991–95 reported as unwanted by the mother, by race and Hispanic origin and percent of employed adults in

professional or managerial jobs in 1990: United States, 1995
Page 14 [ Series 23, No. 23

there is between counties in unwanted

births.

The county-level (intercept)

variance is .35, which shows that there

is significant and substantial variation

across counties in the proportion of

births that were unwanted. The

intra-class correlation (shown in table A)

shows that this county-level variation

accounts for about 29 percent of the

total variance in unwanted births.

In the third panel of table 5, the

‘‘Random Intercept’’ model, the male

unemployment rate in a county has a

significant effect on the proportion of

births that were unwanted; unwanted

births are more likely in communities

with higher male unemployment. A

conventional individual-level regression

model would miss that county-level

variation, perhaps including it in the

error term or attributing that variation to

individual-level variables such as the

mother’s race. Controlling for the

county-level unemployment rate and

looking at the individual-level variables,

the probability of an unwanted birth

increases significantly if the woman is

Hispanic or black, or is 40 years of age

or older.

In the fourth panel of table 5, under

the ‘‘Random Effects,’’ the estimate of

the intercept variance indicates that

there is still significant variation across

counties in unwanted births, but it is

substantially smaller (.2412 versus

.3521) than it was before the male

unemployment rate was controlled. This

suggests that other community-level

variables not included in this model may

be associated with some of the

remaining variation in unwanted births.

Number of Sexual Partners 

Table 6 shows the number of

unmarried women 15–44 years of age

who have had sexual intercourse at

some time in their lives, and the percent

who had three or more male sexual

partners in the last 12 months, by race,

origin, and community characteristics.

Those with multiple partners within a

short period of time are more likely to

acquire and spread sexually transmitted

diseases, possibly including HIV/AIDS.

Overall, 17 percent of unmarried women

(who had had sexual intercourse at some

time) had three or more partners in the

last 12 months, including 16 percent of

white women and 24 percent of black

women. Table 6 shows that overall, for

all races combined, the proportion with

three or more recent partners is higher

in communities that have the lowest

median incomes (figure 6), the highest

levels of poverty, and the highest male

unemployment rates, than in areas with

the opposite characteristics.

Looking at the results by the

percent of adults in the community who

were college graduates, the proportion

with three or more partners in the last

12 months was around 14–17 percent in

most areas, but in areas with the

least-educated populations it was

21 percent (table 6). This suggests that

unmarried women living in communities

with lower incomes and less education

are somewhat more likely to have

multiple recent partners, but the pattern

is not dramatic or pronounced.

Within categories of the contextual

variables, it appears that black

unmarried women are more likely to

have three or more partners in the last

12 months than white or Hispanic

unmarried women. For example, in

areas with low median incomes,

18–19 percent of white and Hispanic

women, and 27 percent of black

unmarried women, had three or more

partners in the last 12 months. In areas

with high poverty rates, 17–19 percent

of white and Hispanic women and

25 percent of black unmarried women

had three or more partners.

Marital Status 

Both scholarly works and policy

discussions have speculated on the

effects of the economic and social

environment on marriage and the

family (3,30,31). To illustrate how

marital status is related to the

community environment, tables 7 and 8

show the percent of women 20–44 years

of age who are currently married, by

race and the community-level variables.

Table 7 shows women 20–44 years of

age and table 8 is limited to women

30–44 years of age.

Figure 6. Percent of unmarried women 15–44 years of age with 3 or more male sexual partners in the last 12 months, by race and Hispanic

origin and community median family income in 1990: United States, 1995
Series 23, No. 23 [ Page 15

Figure 7. Percent of women 20–44 years of age who are currently married, by race and Hispanic origin and community male unemployment

rate in 1990: United States, 1995

The percent currently married for

women 20–44 years of age in 1995 was

about 57 percent overall, about

63 percent for white women and only

30 percent for black women (table 7).

Among black women, the percent who

are currently married (in 1995) increases

as the area’s income in 1990 increases.

For example, in areas with the lowest

median income (under $20,000 in 1990),

25 percent of black women were

currently married in 1995; in areas with

the highest median income in 1990,

42 percent of black women were

currently married in 1995. Among white

women, differences were much smaller

and generally not significant (table 7).

Only 29 percent of black women in

the lowest-income communities were

married compared with 57 percent of

black women in the highest-income

communities. The difference by

community income level was much

smaller for white women compared with

16 percentage points for black women

(table 7 and figure 7).

At age 30–44, about 67 percent of

all women were currently married in

1995, including 73 percent of white

women and only 38 percent of black

women (table 8). In figure 8 and table 8,

a similar result is found when the

percent of women 30–44 years of age

who are currently married is shown by

the median family income in the

community. The percent married ranges

from 65 to 78 percent among white

women (a difference of 13 percent) and

29 to 57 percent among black women (a

difference of 28 percent).

Use of Contraception and 

Sterilization 

Table 9 shows the percent of

contraceptive method users

(‘‘contraceptors’’) 20–44 years of age

who were using female sterilization by

race, Hispanic origin, and by the

community-level variables; table 10

shows a multilevel model predicting use

of female sterilization; table 11 shows

the percent of contraceptors 20–44 years

of age using the pill; table 12 shows a

multilevel model predicting use of the

pill; table 13 shows the percent of

contraceptors 20–44 years of age using

condoms, and table 14 shows a

multilevel model predicting use of the

condom. Female sterilization is the

leading method of contraception in the

United States; in 1995, about 30 percent

of contraceptors were using it, including

44 percent of black contraceptors,

40 percent of Hispanic contraceptors,

and 26 percent of non-Hispanic white

contraceptors. These differences among

these three groups have been

well-documented (1,4,8). It is also

known that use of female sterilization is

related to family income, female

education, the woman’s age, and other

individual characteristics.

The differences in use of female

sterilization by the contextual (or

community) variables are large (table 9).

For example, by the poverty rate in the

community, the percent of white

contraceptors 20–44 years of age using

female sterilization varies from

21 percent in areas with the lowest

poverty rates to 34 percent in areas with

the highest poverty rates. Among

Hispanic contraceptors 20–44 years of

age, 29 percent of those in areas with

the lowest poverty rates and 47 percent

in areas with the most poverty, were

relying on female sterilization for

contraception. Among black

contraceptors, this range was from 34 to

44 percent (table 9 and figure 9). Within

communities with similar poverty rates,

black and Hispanic women had higher

proportions using female sterilization

than white women (figure 9).

Table 10 shows a multilevel model

in which the dependent variable is use

of female sterilization as a contraceptive

method:
Page 16 [ Series 23, No. 23

Figure 8. Percent of women 30–44 years of age who are currently married, by race and Hispanic origin and community median family

income in 1990: United States, 1995

1. The second panel, the Random

Effects Null Model, shows that there

is significant variation between

counties in the proportion using

female sterilization (p<0.0001).

Table A shows that this betweencounty variation accounts for about

19 percent of the variance.

2. The third panel in table 10 is the

Random Intercept Model. The level

2 variable shows that as median

family income in the county

(MEDFINC) rises, the county’s

mean proportion of contraceptors

using female sterilization declines,

significantly and very substantially

(p<0.0001)

3. The level 1 variables show that,

controlling for the county’s mean

level of female sterilization, the

probability that a woman will use

female sterilization as her

contraceptive method increases

significantly as her age increases; it

increases significantly if she is either

Hispanic or black; and it decreases

significantly as her family income

increases. It is interesting to note

that both individual-level family

income and community-level median

family income are independently

associated with the probability of

using female sterilization.

Table 11 shows the proportion of

contraceptive users 20–44 years of age

who were using the pill, by community

characteristics. The differences by

community characteristics are much

smaller for pill use than for female

sterilization and not always consistent

across categories of communities.

Table 12 shows a multilevel model

in which the dependent variable is use

of the pill as a contraceptive method:

1. The second panel, the Random

Effects Null Model, shows that there

is significant variation between

counties in the proportion using the

pill (p<0.0058). Table A shows,

however, that this between-county

variation accounts for only about

7 percent of the total variance. Of all

the values in table A, this is the

smallest percent of total variance

that is accounted for by variance

between counties, suggesting that the

effect of the community environment

is smaller for pill use than for other

variables examined in this report.

2. The third panel in table 12 is the

Random Intercept Model. The level

2 variable shows that the median

family income in the county

(MEDFINC) has no significant

effect on pill use. The Null Model

showed that there is significant

variation between counties in pill

use, but median family income is

not the best characteristic of

communities to use to try to explain

the effect of community. (In the

interest of providing comparisons

between the effects of community on

the choice of different methods of

contraception, we estimate the same

model for female sterilization, pill

use, and condom use, but a model

with a different contextual variable

might be more appropriate for

studying pill use.)

The level 1 variables show that,

controlling for the county’s mean level

of pill use, the probability that a woman

will use the pill as her contraceptive
Series 23, No. 23 [ Page 17

Figure 9. Percent of contraceptors 20–44 years of age using female sterilization, by race and Hispanic origin and community poverty rate in

1990: United States, 1995

method increases significantly if her

family income is greater than $20,000

and decreases significantly as her age

increases or if she is not white.

Table 13 shows the proportion of

contraceptive users 20–44 years of age

who were using the condom, by

community characteristics. The

measurement of condom use includes

use of the condom along with other

methods such as the pill. Women are

more likely to have their partners use

the condom if the woman lives in

communities with higher median family

income, more college-educated adults,

and more of the local labor force in

managerial or professional jobs. The

differences by community context are

much larger among Hispanic women

than non-Hispanic women (table 13).

Table 14 shows a multilevel model

in which the dependent variable is use

of the condom:

1. The second panel, the Random

Effects Null Model, shows that there

is significant variation between

counties in the proportion using the

condom (p<0.0001). Table A shows

that this between-county variation

accounts for about 18 percent of the

total variance.

2. The third panel in table 14 is the

Random Intercept Model. The level

2 variable shows that as the median

family income in the county

(MEDFINC) increases, condom use

in the county increases.

The level 1 variables show that,

controlling for the county’s mean level

of condom use, the probability that a

woman will use the condom as her

contraceptive method increases

significantly if her family income is

greater than $50,000 and decreases

significantly as her age increases. There

is no significant difference among white,

black, or Hispanic women but women of

other races are more likely to use the

condom.

Comparing the models for female

sterilization, pill use and condom use

reveals that higher median family

income in the community is associated

with a lower proportion using female

sterilization and a higher proportion

using the condom, but is not related to

pill use. Hispanic or black women are

less likely to use the pill but are more

likely to use female sterilization than

white women (compared with white

women, women of other races are less

likely to use the pill and more likely to

use the condom but do not differ from

white women in the choice of female

sterilization). Older women are less

likely to use the pill or the condom and

more likely to use female sterilization

than younger women, and women with

higher family income are more likely to

use the pill or the condom and less

likely to use female sterilization

(tables 10, 12, and 14).

The multilevel model for female

sterilization was applied to the other

methods of contraception in order to

provide this kind of comparison, but it

may be more appropriate to model each

contraceptive method independently. For

example, in the pill use model, median

family income was not significant and

may not have been the most appropriate

measure of context to include. The pill

use and condom models should be

viewed as preliminary and needing

further study.

Breast-Feeding

Research suggests that breastfeeding confers a number of health

benefits for babies and for their mothers.

Benefits for babies include short-term

immunity from diarrhea, respiratory and

ear infections, a lessened risk of asthma,

and others. The benefits for the mother

include reductions in postpartum

bleeding, earlier return to pre-pregnancy

weight, and reduced risks of breast

cancer and osteoporosis (47).
Page 18 [ Series 23, No. 23

The NSFG data have shown that,

since the 1970s, white and Hispanic

mothers have been more likely to

breast-feed their babies than black

mothers; that college-educated mothers

are more likely to breast-feed than

less-educated mothers, and that mothers

in the Western United States are more

likely to breast-feed than those in other

regions (47). One recent study of NSFG

data suggests that education and income

are strongly related to breast-feeding,

and concluded that the lower levels of

breast-feeding among black mothers

were just as important a predictor of the

higher level of infant mortality among

black babies as their lower levels of

timely prenatal care (1,12).

Table 15 shows the proportion of

babies born between 1990 and 1994

who were breast-fed at all, by race,

Hispanic origin, and community

characteristics. Looking at the data in

table 15 by median family income,

about 41 percent of babies in

low-income areas and 73 percent of

babies in high-income areas, were

breast-fed in 1990–94. Among babies

with black mothers, this range was from

19 percent in low-income areas to

48 percent in high-income areas. Among

babies with white mothers, 55 to

72 percent were breast-fed (table 15 and

figure 10). Within communities in the

same income categories, the proportion

breast-fed was lower for babies with

black mothers.

Table 16 shows a two-level model

with breast-feeding as the dependent

variable, and the percent of employed

people working in professional or

managerial jobs as the contextual

variable. The second panel of table 16,

showing the Null Model Random

Effects, shows that there is highly

significant variation between counties in

the proportion of recently born babies

who were breast-fed (z-value = 6.52,

p<0.0001). Table A shows that an

unusually large proportion of the

variance, 44 percent, can be explained

by variation across counties. This

suggests that ignoring the county-level

variation in these data could lead to

misleading conclusions.

The fixed effects Random Intercept

Model, the third panel of table 16,

shows that as the county’s proportion of

employed people who are professionals

or managers (MANAGER) increases, so

does the proportion of babies in the

county who are breast-fed. The level 1

(individual) variables show that,

controlling for the county mean

proportion breast-fed, the probability of

breast-feeding is lower if the baby’s

mother has a lower income, is younger

than age 25, is black, or did not

graduate from high school. After these

variables are introduced into the model,

the fourth panel, ‘‘Random Effects,’’

shows that the remaining betweencounty variance has been reduced by

about a third (.49 versus .72).

Table 17 shows the proportions of

babies born between January 1990 and

September 1994 who were breast-fed for

12 weeks or more. This longer term

breast-feeding increases the chances that

the benefits of breast-feeding for the

mother and child, described above,

would be significant. The proportion of

babies who were breast-fed for 12

weeks or more is closely associated with

measures of community socioeconomic

status. For example, the percent

breast-fed 12 weeks or more is higher in

communities with higher median income

(table 17 and figure 11), a lower poverty

rate (percent of households with

incomes below the poverty level), a

higher percentage of college graduates, a

lower male unemployment rate, and a

higher percentage with professional or

managerial jobs.

Figure 10. Percent of babies born between January 1990 and September 1994 who were breast-fed at all, by race and Hispanic origin and

community median family income in 1990: United States, 1995
Series 23, No. 23 [ Page 19

Figure 11. Percent of babies born between January 1990 and September 1994 who were breast-fed for 12 weeks or more, by race and

Hispanic origin and community median family income in 1990: United States, 1995

Thus, regardless of which measure (individual) variables show that, proportion of professional and

of community characteristics is used, controlling for the county mean managerial workers. Figure 12 shows

longer term breast-feeding (like proportion breast-fed at least 12 weeks, that these differences are very large in

breast-feeding at all) occurs most the probability of breast-feeding at least each racial/ethnic group—white, black,

frequently in communities with higher 12 weeks is lower if: the baby’s mother and Hispanic.

socioeconomic resources. This pattern is younger than age 25, or the baby’s Table 20 shows the results of a

appears to hold for white and black mother is black. Unlike the model for multilevel analysis of the data on

women for most of the contextual breast-feeding at all, the effects of vaginal douching. In table A, the

measures. The data appear to suggest, individual-level income and education intra-class correlation is 33 percent,

then, that the correlations between are not statistically significant in the suggesting that any attempt to explain

community characteristics and model of breast-feeding at least 12 douching practices using individual

breast-feeding apply to both short- and weeks. factors alone is likely to provide an

long-term breast-feeding. incomplete explanation.

Table 18 repeats the multilevel

breast-feeding model, this time using

Vaginal Douching In the third panel in table 20, the

results of the Fixed Effects Random

breast-feeding at least 12 weeks as the Vaginal douching has been Intercept Model show that the overall

dependent variable. The second panel of associated in epidemiological studies proportion douching in a county

table 18, showing the Random Effects with infertility, pelvic inflammatory declines as the proportion of employed

Null Model, shows that there is disease, and cervical cancer (48–53). In persons who are professionals and

significant variation between counties in the 1988 NSFG, 37 percent of women managers (MANAGER) increases. This

the proportion of recently born babies 15–44 reported that they used douching measure is highly correlated with the

who were breast-fed. (z-value = 5.79, regularly (53). By 1995, that proportion educational level of the county (+.94 in

p<0.0001). Table A shows that had declined to 27 percent, but douching table B). Professionals and managers are

41 percent of the variance is explained was still more common among black, highly educated workers, and previous

by variation across counties. Hispanic, and less-educated women than studies (1,53) have found that douching

The Random Intercept Model for among other women. (1). declines sharply as a woman’s

Fixed Effects, the third panel of In 1995, douching was also most individual level of education increases.

table 18, shows that as the county’s prevalent in neighborhoods with the The proportion of households with

proportion of employed people who are lowest median incomes (table 19 and income below the poverty line

professionals or managers (MANAGER) figure 12), and in neighborhoods with (BELOWPOV) is also a significant

increases, so does the proportion of the highest poverty rates, the lowest predictor of douching in a county:

babies in the county who are breast-fed educational levels, the highest male douching is more likely in communities

at least 12 weeks. The level 1 unemployment rates, and the lowest with more poverty.
Page 20 [ Series 23, No. 23

Within counties, the level 1

variables in the third panel of table 20

show that douching is significantly more

common among: older women than

younger women; those with less than a

high school education; and Hispanic

women, black women, and women of

other races (than white women). The

fourth panel of table 20, Random

Effects, shows that variation between

counties has been reduced by more than

one-half (from .4651 to .1967)

compared with the Null Model.

Cigarette Smoking

The report Healthy People

2010 (47), which lists health objectives

for the United States in the decade

2000–2010, singles out reducing

smoking as a ‘‘leading health indicator,’’

saying that, ‘‘Cigarette smoking is the

single most preventable cause of disease

and death in the United States. Smoking

results in more deaths each year in the

United States than AIDS, alcohol,

cocaine, heroin, homicide, suicide,

motor vehicle crashes, and fires—

combined’’ (47).

The report, summarizing many

other studies, notes that ‘‘Cigarette

smoking causes heart disease, several

kinds of cancer, and chronic lung

disease. Cigarette smoking also

contributes to cancer of the pancreas,

kidney, and cervix. Smoking during

pregnancy causes spontaneous abortions,

low birth weight, and sudden infant

death syndrome’’ (47).

Data previously published from the

1995 NSFG showed that less-educated

women and low-income women were

more likely to smoke than women with

high levels of education or income (1).

It also showed that white women were

more likely to smoke than black or

Hispanic women.

Table 21 and figure 13 show the

percent of women 15–44 who reported

that they were smoking at the date of

interview in 1995. In communities with

the lowest median income, 28 percent of

women smoked compared with

18 percent in areas with the highest

median income. For white women,

35 percent in the lowest income areas

and 19 percent in the highest-income

areas were current smokers. For black

women, 27 percent of women in the

lowest-income areas and 16 percent in

the highest-income areas, smoked

(figure 13).

Table 22 shows a multilevel model

with current smoking as the outcome (or

dependent) variable. The first panel, the

Fixed Effects Null Model, shows that

there is significant (p<0.0001) variation

by county in the proportion currently

smoking. Table A shows that about

15 percent of the variance is between

counties, and the remainder within

counties.

In the third panel of table 22 is the

Fixed Effects Random Intercept Model.

As the level 2 (county-level) variable,

the percent of adults with a college

education (ABGRAD) increases, the

proportion smoking decreases.

Controlling for the county-level

proportion college-educated, the

probability that an individual woman

smokes: decreases as individual

household income rises (p<0.0001); is

higher for women of other races than

for whites; and is higher in the older

ages than in the younger ages.

The fourth panel of table 22,

Random Effects, shows that the variance

in smoking has been reduced (from

.1664 to .0981) by more than one-third

by the variables in the model, indicating

the substantial influence of the

community’s education level on the

smoking practices of women 15–44

years of age.

Figure 12. Percent of women 15–44 years of age who douche regularly, by race and Hispanic origin and community median family income

in 1990: United States, 1995
Series 23, No. 23 [ Page 21

Figure 13. Percent of women 15–44 years of age who currently smoke cigarettes, by race and Hispanic origin and community median family

income in 1990: United States, 1995

Discussion

This report has described findings

on several topics related to

childbearing, pregnancy, and the

health of women and infants, by race,

Hispanic origin, and community

characteristics.

As stated in the introduction, the

intent of this report has not been to

produce the definitive, causal,

theory-driven study of this large and

growing area of research. The goal has

been much more modest—namely, to

investigate whether factors related to the

community environment appear to be

associated with family formation,

contraception, and reproductive health.

It has been found that indicators of

prosperity and economic opportunity in

the community—such as median

income, poverty rates, and the

proportion of adults who are college

graduates—are closely associated with

childlessness, the use of female

sterilization, breast-feeding, vaginal

douching, and cigarette smoking.

These variations often apply to

white, black, and Hispanic women

separately. Our findings suggest that

variations by race/ethnicity or other

individual characteristics may be

associated in part with variations in

exposure to prosperity, economic

opportunity, and the community

resources and facilities that accompany

prosperity.

It is possible for researchers to

apply to the NCHS Research Data

Center (RDC) in order to use the

National Survey of Family Growth

Contextual Data File. It is also possible

to use the RDC either in person at

NCHS (using any software), or via a

Remote Access Procedure (using SAS).

Because most researchers prefer to use

the Remote Access Procedure, which

requires SAS, this report used a SAS

procedure for the cross-tabulations and

all the multivariate analyses. The

authors hope that this report will

encourage researchers to pursue further

studies of these important issues.

Analyzing Contextual

Data Through the

NCHS Research Data

Center

This section describes how

researchers can use contextual

data files such as the NSFG

Contextual Data File through the NCHS

RDC. The RDC allows researchers to

use contextual and other data files under

conditions that protect the

confidentiality of survey respondents.

NCHS is required to protect the

confidentiality of NCHS survey

respondents. Any data file that could

allow the identification of a small

geographic area could increase the risk

of compromising the confidentiality of

those respondents, and cannot be

released publicly. The NCHS RDC was

established to provide a way for

researchers to use contextual and other

confidential files while still protecting

respondent confidentiality. The

procedures for submitting an application

to use the RDC are described

further on the RDC web page at:

www.cdc.gov/nchs/r&d/rdc.htm.

After a researcher’s proposal to use

the RDC has been reviewed and

approved, there are three basic ways to

use the RDC. All require the user to pay

a fee to help defray some of the costs of

maintaining the RDC. First, the RDC

staff can do the data analysis for the

researcher. This procedure is the most

costly of the three. Second, the

researcher can come to NCHS and

perform the research in the RDC. Third,

researchers can use the Remote Access

Procedure, in which programs are

submitted electronically from the

researcher’s office; visiting NCHS is not

necessary.
Page 22 [ Series 23, No. 23

The Remote Access Procedure uses

SAS, the Statistical Analysis System

(42–45), because it is widely available,

widely used, and contains a large

number of statistical procedures and

techniques. Further information about

SAS software is available in References

42–45 and at: www.sas.com/support.

Most users prefer to use the Remote

Access Procedure because it does not

require the researcher, or research team,

to travel to NCHS. This procedure also

allows the researcher to conduct the

research over an extended period of

time, instead of having to do everything

in a few consecutive days. Users of the

Remote Access Procedure can use any

SAS PROC, except those that could be

used to identify an individual

respondent. For example, PROCs such

as ANOVA (Analysis of Variance), REG

(Regression) and LOGISTIC (Logistic

Regression) may be used, but

procedures that list individual cases,

such as FIRSTOBS, FIRST., LAST., and

LIST cannot be used remotely. These

prohibited PROC’s may be used when

the researcher is present in the Data

Center itself—so that researchers who

need to use them can do so, without

violating the confidentiality of

respondents.

When a researcher submits a SAS

program under the Remote Access

Procedure, the SAS program is screened

to prevent the use of procedures that

could cause a breach of confidentiality

by listing the contents of individual

cases. If a prohibited procedure (such as

LIST or FIRSTOBS) is used, the

researcher receives an error message. If

no prohibited procedure is used, the

output is screened for confidentiality

and then returned to the researcher

electronically.

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Table 1. Number of women 20–34 years of age by race and Hispanic origin, and percent who have never had a birth as of the date of

interview (percent childless), by selected community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 29,800 43.8 29.9 43.6 66.4

Hispanic ............................ 3,613 30.7 25.0 33.8 53.1

Non-Hispanic white ...................... 20,627 47.3 33.2 45.6 67.1

Non-Hispanic black ...................... 4,130 35.7 29.4 39.7 71.5

Poverty rate

(percent below poverty)1

All races2 ............................. 29,800 43.8 57.5 44.9 33.4

Hispanic ............................ 3,613 30.7 47.8 30.0 29.2

Non-Hispanic white ...................... 20,627 47.3 57.9 47.1 36.3

Non-Hispanic black ...................... 4,130 35.7 54.4 42.5 30.8

Percent college graduates1

All races2 ............................. 29,800 43.8 32.1 40.5 61.0

Hispanic ............................ 3,613 30.7 25.4 34.1 47.0

Non-Hispanic white ...................... 20,627 47.3 35.6 44.4 62.4

Non-Hispanic black ...................... 4,130 35.7 29.5 34.2 58.1

Male unemployment rate3

All races2 ............................. 29,800 43.8 54.3 45.1 35.8

Hispanic ............................ 3,613 30.7 45.1 33.9 28.5

Non-Hispanic white ...................... 20,627 47.3 55.5 46.5 41.2

Non-Hispanic black ...................... 4,130 35.7 44.5 40.2 32.3

Percent in professional or managerial jobs1

All races2 ............................. 29,800 43.8 33.3 41.3 60.0

Hispanic ............................ 3,613 30.7 26.5 37.3 42.2

Non-Hispanic white ...................... 20,627 47.3 36.4 43.6 61.7

Non-Hispanic black ...................... 4,130 35.7 29.9 34.3 53.9

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 25

Table 2. Number of women 18–39 years of age by race and Hispanic origin, and percent who are childless but expect to have 1 or more

children in the future, by selected community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 44,519 33.7 25.7 33.0 46.4

Hispanic ............................ 5,142 27.9 24.0 31.7 45.4

Non-Hispanic white ...................... 31,297 35.2 27.9 33.2 45.8

Non-Hispanic black ...................... 6,107 29.2 24.5 32.8 48.2

Poverty rate (percent below poverty)1

All races2 ............................. 44,519 33.7 40.1 34.0 27.6

Hispanic ............................ 5,142 27.9 37.7 33.6 25.0

Non-Hispanic white ...................... 31,297 35.2 40.1 33.8 30.1

Non-Hispanic black ...................... 6,107 29.2 34.8 36.2 25.0

Percent college graduates1

All races2 ............................. 44,519 33.7 25.8 31.8 43.8

Hispanic ............................ 5,142 27.9 22.9 29.9 44.4

Non-Hispanic white ...................... 31,297 35.2 27.4 32.3 43.6

Non-Hispanic black ...................... 6,107 29.2 24.7 28.4 43.0

Male unemployment rate3

All races2 ............................. 44,519 33.7 38.2 34.9 28.8

Hispanic ............................ 5,142 27.9 36.6 29.3 27.3

Non-Hispanic white ...................... 31,297 35.2 38.5 35.1 31.3

Non-Hispanic black ...................... 6,107 29.2 32.8 35.7 25.9

Percent in professional or managerial jobs1

All races2 ............................. 44,519 33.7 25.9 33.3 42.8

Hispanic ............................ 5,142 27.9 23.7 33.4 39.8

Non-Hispanic white ...................... 31,297 35.2 26.8 34.0 43.0

Non-Hispanic black ...................... 6,107 29.2 24.6 31.4 36.9

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Table 3. Multilevel statistical model predicting whether women are childless (have never had a birth), for women 20–34 years of age:

United States, 1995

Parameter Estimate

Standard

error t value Pr (t)

Null Model

Fixed effects:

Intercept . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.5931 0.04057 –14.62 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.1690 0.04186 4.04 <0.0001

Residual . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.9694 0.02139 45.31 <0.0001

Deviance ............................. 5523.0036

Random Intercept Model

Estimate

Standard

error t value Pr (t)

Fixed effects:

Intercept . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.782 0.1837 –4.26 <0.0001

Level 2

MEDFINC1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.03145 0.004572 6.88 <0.0001

Level 1

Race and origin:

Hispanic . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.9777 0.1102 –8.88 <0.0001

Non-Hispanic other . . . . . . . . . . . . . . . . . . . . . . –0.2460 0.2158 –1.14 0.2543

Non-Hispanic black . . . . . . . . . . . . . . . . . . . . . . –0.6860 0.0896 –7.66 <0.0001

Income:

$50,000 or more . . . . . . . . . . . . . . . . . . . . . . . 0.9305 0.09921 9.38 <0.0001

$20,000-$49,000 . . . . . . . . . . . . . . . . . . . . . . . 0.4813 0.86770 5.55 <0.0001

Age:

25–29 years . . . . . . . . . . . . . . . . . . . . . . . . . . –1.1479 0.08713 –13.17 <0.0001

30–34 years . . . . . . . . . . . . . . . . . . . . . . . . . . –2.0093 0.08979 –22.38 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.07992 0.03737 2.14 0.0162

Residual . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.98560 0.02165 45.52 <0.0001

Deviance ............................. 4839.7098

1MEDFINC=Median family income in the county.

NOTE: Reference categories are non-Hispanic white, income under $20,000, and 20–24 years of age.

Page 26 [ Series 23, No. 23
Series 23, No. 23 [ Page 27

Table 4. Number of births in the last 5 years to women 15–44 years of age, by race and Hispanic origin, and percent of births that were

unwanted, by selected community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 19,573 9.1 15.7 8.6 3.5

Hispanic ............................ 2,994 10.4 14.6 8.8 6.5

Non-Hispanic white ...................... 12,883 6.7 10.7 7.2 3.2

Non-Hispanic black ...................... 2,800 19.1 22.4 18.9 *

Poverty rate (percent below poverty)1

All races2 ............................. 19,573 9.1 5.1 8.5 13.9

Hispanic ............................ 2,994 10.4 7.9 7.6 14.1

Non-Hispanic white ...................... 12,883 6.7 4.5 7.7 9.6

Non-Hispanic black ...................... 2,800 19.1 13.5 17.3 20.8

Percent college graduates1

All races2 ............................. 19,573 9.1 13.9 10.1 4.3

Hispanic ............................ 2,994 10.4 15.0 8.3 7.0

Non-Hispanic white ...................... 12,883 6.7 9.9 8.9 3.1

Non-Hispanic black ...................... 2,800 19.1 20.6 18.9 18.0

Male unemployment rate3

All races2 ............................. 19,573 9.1 6.2 7.5 14.0

Hispanic ............................ 2,994 10.4 3.1 11.1 13.7

Non-Hispanic white ...................... 12,883 6.7 4.7 6.4 11.0

Non-Hispanic black ...................... 2,800 19.1 26.5 10.4 20.2

Percent in professional or managerial jobs1

All races2 ............................. 19,573 9.1 12.8 10.2 4.9

Hispanic ............................ 2,994 10.4 14.4 9.9 5.3

Non-Hispanic white ...................... 12,883 6.7 8.5 8.9 3.8

Non-Hispanic black ...................... 2,800 19.1 21.7 18.3 14.4

*Figure does not meet standard of reliability or precision.

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Page 28 [ Series 23, No. 23

Table 5. Multilevel statistical model predicting whether births in the last 5 years were unwanted: United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ –2.147 0.06662 –32.23 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.3521 0.09490 3.71 <0.0001

Residual ............................ 0.8591 0.02199 39.07 <0.0001

Deviance ............................. 2110.7526

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ –2.9818 0.303 –9.84 <0.0001

Level 2

URTMALE1 ............................ 5.8079 2.6237 2.21 0.0274

Level 1

Race and origin:

Hispanic ............................ 0.54240 0.1620 3.35 0.0009

Non-Hispanic other ...................... –0.11960 0.4227 –2.28 0.7775

Non-Hispanic black ...................... 1.10090 0.1328 8.29 <0.0001

Age at birth:

20–24 years .......................... –0.12860 0.2619 –0.49 0.6234

25–29 years .......................... –0.13400 0.2560 –0.52 0.6008

30–34 years .......................... –0.06505 0.2589 –0.25 0.8016

35–39 years .......................... 0.19780 0.2720 0.73 0.4671

40–44 years .......................... 0.95360 0.3050 3.13 0.0018

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.2412 0.09135 2.64 0.0041

Residual ............................ 0.8719 0.02232 39.06 <0.0001

Deviance ............................. 2064.0281

1URTMALE=Male unemployment rate for the county.

NOTE: Reference categories (not shown) are non-Hispanic white and 15–19 years of age.
Series 23, No. 23 [ Page 29

Table 6. Number of unmarried women 15–44 years of age (who have ever had intercourse), by race and Hispanic origin, and percent with 3

or more male sexual partners in the last 12 months, by selected community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 23,941 17.4 22.3 15.7 14.3

Hispanic ............................ 2,709 15.6 17.5 19.8 13.5

Non-Hispanic white ...................... 15,034 15.6 18.8 14.4 13.5

Non-Hispanic black ...................... 5,393 23.6 27.2 19.7 23.3

Poverty rate (percent below poverty)1

All races2 ............................. 23,941 17.4 15.0 15.6 20.0

Hispanic ............................ 2,709 15.6 21.3 13.0 18.5

Non-Hispanic white ...................... 15,034 15.6 14.2 14.4 16.5

Non-Hispanic black ...................... 5,393 23.6 20.2 22.2 24.7

Percent college graduates1

All races2 ............................. 23,941 17.4 20.6 16.4 14.5

Hispanic ............................ 2,709 15.6 18.8 15.5 18.3

Non-Hispanic white ...................... 15,034 15.6 17.0 14.6 13.9

Non-Hispanic black ...................... 5,393 23.6 25.5 23.3 18.0

Male unemployment rate3

All races2 ............................. 23,941 17.4 15.5 15.9 20.0

Hispanic ............................ 2,709 15.6 18.4 16.1 18.3

Non-Hispanic white ...................... 15,034 15.6 14.0 14.9 16.7

Non-Hispanic black ...................... 5,393 23.6 23.1 25.3 23.7

Percent in professional or managerial jobs1

All races2 ............................. 23,941 17.4 19.8 17.4 14.1

Hispanic ............................ 2,709 15.6 17.9 17.9 16.8

Non-Hispanic white ...................... 15,034 15.6 15.6 16.4 13.1

Non-Hispanic black ...................... 5,393 23.6 25.6 21.9 19.4

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Page 30 [ Series 23, No. 23

Table 7. Number of women 20–44 years of age by race and Hispanic origin, and percent who are currently married, by selected community

characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 51,240 57.2 46.3 60.9 59.8

Non-Hispanic white ...................... 36,560 62.5 58.2 64.8 62.4

Non-Hispanic black ...................... 6,818 29.8 25.3 33.4 42.1

Poverty rate (percent below poverty)1

All races2 ............................. 51,240 57.2 62.9 61.0 48.0

Non-Hispanic white ...................... 36,560 62.5 65.2 64.0 58.7

Non-Hispanic black ...................... 6,818 29.8 43.3 35.1 25.4

Percent college graduates1

All races2 ............................. 51,240 57.2 53.3 59.2 59.0

Non-Hispanic white ...................... 36,560 62.5 63.2 64.8 61.7

Non-Hispanic black ...................... 6,818 29.8 27.5 30.1 35.4

Male unemployment rate3

All races2 ............................. 51,240 57.2 62.2 61.0 48.8

Non-Hispanic white ...................... 36,560 62.5 64.3 64.9 59.0

Non-Hispanic black ...................... 6,818 29.8 40.5 34.9 25.1

Percent in professional or managerial jobs1

All races2 ............................. 51,240 57.2 53.7 60.3 57.7

Non-Hispanic white ...................... 36,560 62.5 63.4 66.0 60.5

Non-Hispanic black ...................... 6,818 29.8 26.4 32.5 34.2

1In the Block Group.

2All races include non-Hispanic other races and Hispanic not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 31

Table 8. Number of women 30–44 years of age by race and Hispanic origin, and percent who are currently married, by selected community

characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 32,506 67.0 51.2 70.6 76.0

Non-Hispanic white ...................... 23,803 72.5 65.2 74.1 77.5

Non-Hispanic black ...................... 4,144 37.6 29.3 43.5 56.8

Poverty rate (percent below poverty)1

All races2 ............................. 32,506 67.0 76.5 70.9 53.9

Non-Hispanic white ...................... 23,803 72.5 78.4 73.5 65.0

Non-Hispanic black ...................... 4,144 37.6 56.5 47.0 31.0

Percent college graduates1

All races2 ............................. 32,506 67.0 61.2 68.4 72.2

Non-Hispanic white ...................... 23,803 72.5 71.2 73.7 75.1

Non-Hispanic black ...................... 4,144 37.6 35.3 37.4 45.9

Male unemployment rate3

All races2 ............................. 32,506 67.0 74.0 71.7 56.3

Non-Hispanic white ...................... 23,803 72.5 75.6 75.3 67.9

Non-Hispanic black ...................... 4,144 37.6 54.5 43.2 31.3

Percent in professional or managerial jobs1

All races2 ............................. 32,506 67.0 61.8 69.8 70.8

Non-Hispanic white ...................... 23,803 72.5 71.6 79.1 73.7

Non-Hispanic black ...................... 4,144 37.6 33.2 41.5 44.1

1In the Block Group.

2All races include non-Hispanic other races and Hispanic not shown separately.

3In the Census Tract.
Page 32 [ Series 23, No. 23

Table 9. Number of women 20–44 years of age who were using 1 or more methods of contraception, by race and Hispanic origin, and

percent (of contraceptors) who were using female sterilization, by selected community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 35,994 29.8 42.2 30.6 16.8

Hispanic ............................ 3,657 39.6 51.1 35.3 19.2

Non-Hispanic white ...................... 26,304 26.3 35.4 29.0 16.1

Non-Hispanic black ...................... 4,613 44.4 47.5 39.3 32.1

Poverty rate (percent below poverty)1

All races2 ............................. 35,994 29.8 21.6 29.8 38.9

Hispanic ............................ 3,657 39.6 28.6 32.2 46.8

Non-Hispanic white ...................... 26,304 26.3 20.7 27.9 33.7

Non-Hispanic black ...................... 4,613 44.4 33.9 45.9 44.2

Percent college graduates1

All races2 ............................. 35,994 29.8 41.9 29.6 19.1

Hispanic ............................ 3,657 39.6 48.8 37.6 22.0

Non-Hispanic white ...................... 26,304 26.3 37.6 27.6 17.7

Non-Hispanic black ...................... 4,613 44.4 49.8 36.1 34.2

Male unemployment rate3

All races2 ............................. 35,994 29.8 23.8 27.9 38.4

Hispanic ............................ 3,657 39.6 35.5 30.4 46.9

Non-Hispanic white ...................... 26,304 26.3 22.3 26.3 33.2

Non-Hispanic black ...................... 4,613 44.4 39.3 42.9 44.4

Percent in professional or managerial jobs1

All races2 ............................. 35,994 29.8 40.5 29.1 20.1

Hispanic ............................ 3,657 39.6 46.6 34.6 29.1

Non-Hispanic white ...................... 26,304 26.3 36.4 27.2 18.3

Non-Hispanic black ...................... 4,613 44.4 49.6 35.4 36.9

1In the Block Group. 2All races include non-Hispanic other races not shown separately. 3In the Census Tract.
Series 23, No. 23 [ Page 33

Table 10. Multilevel statistical model predicting use of female sterilization, for women 20–44 years of age who were using a contraceptive

method: United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ –0.6248 0.03932 –15.89 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.2200 0.04248 5.18 <0.0001

Residual ............................ 0.9643 0.01872 51.51 <0.0001

Deviance ............................. 6893.5952

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ –0.02999 0.1932 –0.16 0.8767

Level 2

MEDFINC1 ............................ –0.03287 0.004707 –6.98 <0.0001

Level 1

Race and origin:

Hispanic ............................ 0.7091 0.09877 7.18 <0.0001

Non-Hispanic other ...................... 0.0308 0.20460 0.15 0.8804

Non-Hispanic black ...................... 0.9707 0.08163 11.89 <0.0001

Income:

$50,000 or more ....................... –1.0980 0.09155 –11.99 <0.0001

$20,000–$49,000 ....................... –0.6026 0.08137 –7.41 <0.0001

Age:

20–24 years .......................... –1.6364 0.1706 –9.59 <0.0001

30–34 years .......................... 0.7797 0.1010 7.72 <0.0001

35–39 years .......................... 1.3336 0.1014 13.15 <0.0001

40–44 years .......................... 1.9601 0.1056 18.56 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.1829 0.04202 4.35 <0.0001

Residual ............................ 0.9329 0.01805 51.70 <0.0001

Deviance ............................. 5778.7347

1MEDFINC=Median family income.

NOTE: Reference categories are non-Hispanic white, income under $20,000, and 25–29 years of age.
Page 34 [ Series 23, No. 23

Table 11. Number of women 20–44 years of age who were using 1 or more methods of contraception by race and Hispanic origin, and

percent who were using the pill, by selected community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 35,994 25.7 22.8 24.0 29.7

Hispanic ............................ 3,657 22.0 16.9 23.2 23.0

Non-Hispanic white ...................... 26,304 27.1 26.1 24.0 31.2

Non-Hispanic black ...................... 4,613 22.9 21.8 27.0 23.8

Poverty rate (percent below poverty)1

All races2 ............................. 35,994 25.7 26.7 24.2 23.9

Hispanic ............................ 3,657 22.0 27.4 12.8 21.6

Non-Hispanic white ...................... 26,304 27.1 26.9 25.6 25.6

Non-Hispanic black ...................... 4,613 22.9 25.8 24.1 23.3

Percent college graduates1

All races2 ............................. 35,994 25.7 21.2 23.9 29.4

Hispanic ............................ 3,657 22.0 18.4 22.5 23.6

Non-Hispanic white ...................... 26,304 27.1 22.3 23.6 30.9

Non-Hispanic black ...................... 4,613 22.9 20.6 28.6 25.1

Male unemployment rate3

All races2 ............................. 35,994 25.7 26.0 25.1 23.6

Hispanic ............................ 3,657 22.0 20.0 23.7 19.4

Non-Hispanic white ...................... 26,304 27.1 27.0 25.5 25.4

Non-Hispanic black ...................... 4,613 22.9 20.6 29.8 22.7

Percent in professional or managerial jobs1

All races2 ............................. 35,994 25.7 21.5 23.6 29.7

Hispanic ............................ 3,657 22.0 18.4 24.9 21.3

Non-Hispanic white ...................... 26,304 27.1 22.9 22.7 31.5

Non-Hispanic black ...................... 4,613 22.9 21.2 28.6 23.3

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 35

Table 12. Multilevel statistical model predicting use of the pill, for women 20–44 years of age who were using a contraceptive method:

United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ –1.2132 0.03627 –33.45 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.0779 0.03089 2.52 0.0058

Residual ............................ 0.9741 0.01873 52.00 <0.0001

Deviance ............................. 6016.8361

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ –0.7389 0.1857 –3.98 <0.0001

Level 2

MEDFINC1 ............................ 0.004772 0.004478 1.07 0.2873

Level 1

Race and origin:

Hispanic ............................ –0.4438 0.10940 –4.06 <0.0001

Non-Hispanic other ...................... –0.7066 0.24470 –2.89 0.0041

Non-Hispanic black ...................... –0.3645 0.08887 –4.10 <0.0001

Income:

$50,000 or more ....................... 0.2597 0.09869 2.63 0.0087

$20,000–$49,000 ....................... 0.2676 0.08752 3.06 0.0023

Age:

20–24 years .......................... 0.4429 0.09947 4.45 <0.0001

30–34 years .......................... –0.4924 0.09170 –5.37 <0.0001

35–39 years .......................... –1.5353 0.10860 –14.14 <0.0001

40–44 years .......................... –2.4245 0.14480 –16.75 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.0715 0.03340 2.14 0.0161

Residual ............................ 0.9850 0.01892 52.06 <0.0001

Deviance ............................. 5270.5258

1MEDFINC=Median family income.

NOTE: Reference categories (not shown) are non-Hispanic white, income under $20,000, and 25–29 years of age.
Page 36 [ Series 23, No. 23

Table 13. Number of women 20–44 years of age who were using 1 or more methods of contraception, by race and Hispanic origin, and

percent whose partners were using the condom, by selected community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 35,994 21.7 17.1 20.6 25.7

Hispanic ............................ 3,657 19.5 17.2 20.4 34.6

Non-Hispanic white ...................... 26,304 21.0 14.5 19.6 23.9

Non-Hispanic black ...................... 4,613 22.3 21.2 23.0 25.0

Poverty rate (percent below poverty)1

All races2 ............................. 35,994 21.7 23.4 21.0 19.2

Hispanic ............................ 3,657 19.5 25.6 26.8 15.9

Non-Hispanic white ...................... 26,304 21.0 22.4 19.9 18.3

Non-Hispanic black ...................... 4,613 22.3 23.4 21.8 22.0

Percent college graduates1

All races2 ............................. 35,994 21.7 17.4 21.3 24.8

Hispanic ............................ 3,657 19.5 14.4 21.9 32.5

Non-Hispanic white ...................... 26,304 21.0 17.3 20.0 23.1

Non-Hispanic black ...................... 4,613 22.3 19.3 25.1 26.5

Male unemployment rate3

All races2 ............................. 35,994 21.7 22.0 20.8 21.0

Hispanic ............................ 3,657 19.5 25.3 23.7 16.7

Non-Hispanic white ...................... 26,304 21.0 21.1 19.5 21.2

Non-Hispanic black ...................... 4,613 22.3 23.3 19.0 23.0

Percent in professional or managerial jobs1

All races2 ............................. 35,994 21.7 18.2 21.9 23.8

Hispanic ............................ 3,657 19.5 17.4 22.6 24.8

Non-Hispanic white ...................... 26,304 21.0 17.5 21.0 22.4

Non-Hispanic black ...................... 4,613 22.3 19.4 24.9 25.7

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 37

Table 14. Multilevel statistical model predicting use of the condom, for women 20–44 years of age who were using a contraceptive method:

United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ –1.5692 0.04423 –35.48 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.2077 0.04542 4.57 <0.0001

Residual ............................ 0.9347 0.01797 52.01 <0.0001

Deviance ............................. 5,117.1743

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ –2.5518 0.2038 –12.52 <0.0001

Level 2

MEDFINC1 ............................ 0.02971 0.004741 6.27 <0.0001

Level 1

Race and origin:

Hispanic ............................ 0.06916 0.1086 0.64 0.5244

Non-Hispanic other ...................... 0.69140 0.1862 3.71 0.0002

Non-Hispanic black ...................... –0.07363 0.0932 –0.79 0.4299

Income:

$50,000 or more ....................... 0.2578 0.10060 2.56 0.0106

$20,000–$49,000 ....................... 0.1174 0.09293 1.26 0.2068

Age:

20–24 years .......................... 0.3103 0.1126 2.76 0.0059

30–34 years .......................... –0.2918 0.1056 –2.76 0.0058

35–39 years .......................... –0.3781 0.1073 –3.52 0.0004

40–44 years .......................... –0.7849 0.1187 –6.61 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.1373 0.03967 3.46 0.0003

Residual ............................ 0.9486 0.01820 52.11 <0.0001

Deviance ............................. 5,035.4048

1MEDFINC=Median family income.

NOTE: Reference categories (not shown) are non-Hispanic white, income under $20,000, and 25–29 years of age.
Page 38 [ Series 23, No. 23

Table 15. Number of babies born between January 1990 and September 1994, by race and Hispanic origin, and percent who were breast-fed

at all, by selected community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and births in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 18,415 56.3 41.2 59.5 72.6

Non-Hispanic white ...................... 12,069 60.6 55.2 61.4 72.1

Non-Hispanic black ...................... 2,640 25.0 18.5 29.4 47.8

Poverty rate (percent below poverty)1

All races2 ............................. 18,415 56.3 66.1 64.3 44.0

Non-Hispanic white ...................... 12,069 60.6 67.1 63.3 55.5

Non-Hispanic black ...................... 2,640 25.0 34.1 42.3 20.2

Percent college graduates1

All races2 ............................. 18,415 56.3 42.7 58.1 72.0

Non-Hispanic white ...................... 12,069 60.6 51.3 60.1 73.1

Non-Hispanic black ...................... 2,640 25.0 20.1 26.5 46.3

Male unemployment rate3

All races2 ............................. 18,415 56.3 65.5 60.1 46.8

Non-Hispanic white ...................... 12,069 60.6 66.8 62.3 56.1

Non-Hispanic black ...................... 2,640 25.0 30.2 27.8 23.7

Percent in professional or managerial jobs1

All races2 ............................. 18,415 56.3 47.4 55.6 70.2

Non-Hispanic white ...................... 12,069 60.6 57.1 57.6 72.3

Non-Hispanic black ...................... 2,640 25.0 18.8 28.8 40.0

1In the Block Group.

2All races include non-Hispanic other races and Hispanic not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 39

Table 16. Multilevel statistical model predicting whether a baby (born between January 1990 and September 1994) was breast-fed at all:

United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ 0.03002 0.06281 0.48 0.633

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.7257 0.11130 6.52 <0.0001

Residual ............................ 0.9298 0.02517 36.95 <0.0001

Deviance ............................. 3665.9559

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ –1.6539 0.2971 –5.57 <0.0001

Level 2

MANAGER1 ........................... 4.9453 1.065 4.64 <0.0001

Level 1

Race and origin:

Hispanic ............................ 0.05183 0.1320 0.39 0.6946

Non-Hispanic other ...................... 0.01928 0.3056 0.06 0.9497

Non-Hispanic black ...................... –1.22870 0.1241 –9.90 <0.0001

Income:

$50,000 or more ....................... 0.3834 0.1358 2.82 0.0048

$20,000–$49,000 ....................... 0.4785 0.1068 4.48 <0.0001

Age at birth:

25–44 years .......................... 0.3409 0.1054 3.23 0.0012

Education:

Below high school ...................... –0.4395 0.1147 –3.83 0.0001

Some college ......................... 0.5858 0.5812 1.01 0.3136

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.4923 0.09375 5.25 <0.0001

Residual ............................ 0.9227 0.02489 37.06 <0.0001

Deviance ............................. 2648.1493

1MANAGER=Percent of employed who are professionals or managers.

NOTE: Reference categories are non-Hispanic white, income under $20,000, 15–24 years of age, and high school graduate.
Page 40 [ Series 23, No. 23

Table 17. Number of babies born between January 1990 and September 1994, by race and Hispanic origin, and percent who were breast-fed

for 12 weeks or more, by selected community characteristics: United States, 1995

Contextual (community)

characteristic

Number of

Race, origin, and births in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 18,415 37.7 24.8 39.7 55.7

Non-Hispanic white ...................... 12,069 40.8 32.8 41.1 56.9

Non-Hispanic black ...................... 2,640 15.8 9.6 19.7 29.2

Poverty rate (percent below poverty)1

All races2 ............................. 18,415 37.7 46.9 43.9 28.2

Non-Hispanic white ...................... 12,069 40.8 48.1 44.0 35.3

Non-Hispanic black ...................... 2,640 15.8 18.3 24.9 12.7

Percent college graduates1

All races2 ............................. 18,415 37.7 26.1 39.0 53.1

Non-Hispanic white ...................... 12,069 40.8 29.9 41.3 54.8

Non-Hispanic black ...................... 2,640 15.8 13.2 13.8 28.7

Male unemployment rate3

All races2 ............................. 18,415 37.7 45.2 40.7 30.9

Non-Hispanic white ...................... 12,069 40.8 46.5 42.7 37.6

Non-Hispanic black ...................... 2,640 15.8 18.8 15.1 14.6

Percent in professional or managerial jobs1

All races2 ............................. 18,415 37.7 30.3 35.9 52.3

Non-Hispanic white ...................... 12,069 40.8 36.7 36.9 55.0

Non-Hispanic black ...................... 2,640 15.8 11.1 19.8 21.4

1In the Block Group.

2All races include Non-Hispanic other races and Hispanic not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 41

Table 18. Multilevel statistical model predicting whether a baby (born between January 1990 and September 1994) was breast-fed at least 12

weeks: United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ –0.7153 0.06179 –11.58 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.6326 0.10930 5.79 <0.0001

Residual ............................ 0.9158 0.02482 36.89 <0.0001

Deviance ............................. 3495.1898

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ –2.5667 0.3045 –8.43 <0.0001

Level 2

MANAGER1 ........................... 4.5203 1.0494 4.31 <0.0001

Level 1

Race and origin:

Hispanic ............................ 0.03275 0.1329 0.25 0.8054

Non-Hispanic other ...................... 0.34850 0.2999 1.16 0.2454

Non-Hispanic black ...................... –1.11110 0.1408 –7.89 <0.0001

Income:

$50,000 or more ....................... 0.03082 0.1445 0.21 0.8311

$20,000–$49,000 ....................... 0.19850 0.1152 1.72 0.0849

Age at birth:

25–44 years .......................... 0.7417 0.1213 6.12 <0.0001

Education:

Below high school ...................... –0.2336 0.1245 –1.88 0.0609

Some college ......................... 0.6494 0.5320 1.22 0.2224

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.3405 0.09281 3.67 0.0001

Residual ............................ 0.9173 0.02797 32.79 <0.0001

Deviance ............................. 2422.2884

1MANAGER=Percent of employed who are professionals or managers.

NOTE: Reference categories are non-Hispanic white, income under $20,000, 15–24 years of age, and high school graduate.
Page 42 [ Series 23, No. 23

Table 19. Number of women 15–44 years of age by race and Hispanic origin, and percent who douche regularly, by selected community

characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 60,004 26.9 42.6 24.0 14.1

Hispanic ............................ 6,675 33.4 39.9 30.8 23.3

Non-Hispanic white ...................... 42,428 20.8 31.2 19.4 12.7

Non-Hispanic black ...................... 8,165 55.4 61.2 52.6 31.8

Poverty rate (percent below poverty)1

All races2 ............................. 60,004 26.9 17.3 23.1 38.5

Hispanic ............................ 6,675 33.4 26.1 27.9 39.3

Non-Hispanic white ...................... 42,428 20.8 15.0 19.4 27.2

Non-Hispanic black ...................... 8,165 55.4 47.5 49.2 59.6

Percent college graduates1

All races2 ............................. 60,004 26.9 39.1 25.0 15.7

Hispanic ............................ 6,675 33.4 39.2 34.0 21.8

Non-Hispanic white ...................... 42,428 20.8 29.2 19.7 13.1

Non-Hispanic black ...................... 8,165 55.4 60.9 52.0 44.6

Male unemployment rate3

All races2 ............................. 60,004 26.9 18.1 23.5 37.2

Hispanic ............................ 6,675 33.4 22.4 34.0 37.1

Non-Hispanic white ...................... 42,428 20.8 15.0 20.1 26.3

Non-Hispanic black ...................... 8,165 55.4 49.5 50.9 58.8

Percent in professional or managerial jobs1

All races2 ............................. 60,004 26.9 36.8 24.6 17.5

Hispanic ............................ 6,675 33.4 37.5 32.6 26.7

Non-Hispanic white ...................... 42,428 20.8 27.9 19.0 14.0

Non-Hispanic black ...................... 8,165 55.4 59.4 53.0 49.9

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 43

Table 20. Multilevel statistical model predicting whether or not a woman douches regularly: United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ –0.8388 –0.04362 –19.23 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.4651 0.05766 8.07 <0.0001

Residual ............................ 0.9510 0.01439 66.06 <0.0001

Deviance ............................. 10483.8021

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ –1.2612 0.2189 –5.76 <.0001

Level 2

MANAGER1 ........................... –2.3379 0.6752 –3.46 0.0005

BELOWPOV2 .......................... 2.1514 0.7227 2.98 0.0029

Level 1

Race and origin:

Hispanic ............................ 0.6021 0.08381 7.18 <.0001

Non-Hispanic other ...................... 0.4417 0.16940 2.61 0.0091

Non-Hispanic black ...................... 1.5069 0.06807 22.14 <.0001

Age:

20–24 years .......................... 0.8542 0.08685 9.84 <0.0001

30–34 years .......................... 0.7525 0.07956 9.46 <0.0001

35–39 years .......................... 0.6472 0.08060 8.03 <0.0001

40–44 years .......................... 0.5497 0.08396 6.55 <0.0001

Education:

Below high school ...................... 0.1795 0.06348 2.83 0.0047

Some college ......................... –0.4635 0.34720 –1.34 0.1819

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.1967 0.03905 5.04 <0.0001

Residual ............................ 0.9583 0.01659 57.75 <0.0001

Deviance ............................. 7932.0509

1MANAGER=Percent of employed who are professionals or managers.

2BELOWPOV=Percent below poverty in the county.

NOTE: Reference categories are non-Hispanic white, 15–24 years of age, and high school graduate.
Page 44 [ Series 23, No. 23

Table 21. Number of women 15–44 years of age by race and Hispanic origin, and percent who currently smoke cigarettes, by selected

community characteristics: United States, 1995

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 60,201 26.1 28.4 26.2 18.3

Hispanic ............................ 6,702 15.8 16.5 14.8 21.5

Non-Hispanic white ...................... 42,522 28.9 35.1 28.9 18.6

Non-Hispanic black ...................... 8,210 23.1 26.8 19.7 15.8

Poverty rate (percent below poverty)1

All races2 ............................. 60,201 26.1 20.7 26.8 27.9

Hispanic ............................ 6,702 15.8 18.2 14.1 16.4

Non-Hispanic white ...................... 42,522 28.9 21.2 29.5 34.0

Non-Hispanic black ...................... 8,210 23.1 16.3 21.3 25.3

Percent college graduates1

All races2 ............................. 60,201 26.1 30.2 26.0 19.4

Hispanic ............................ 6,702 15.8 15.1 16.0 18.9

Non-Hispanic white ...................... 42,522 28.9 36.6 28.7 20.1

Non-Hispanic black ...................... 8,210 23.1 26.5 21.5 14.7

Male unemployment rate3

All races2 ............................. 60,201 26.1 22.4 26.0 27.2

Hispanic ............................ 6,702 15.8 20.2 16.5 14.9

Non-Hispanic white ...................... 42,522 28.9 23.4 27.8 33.1

Non-Hispanic black ...................... 8,210 23.1 17.3 20.6 25.4

Percent in professional or managerial jobs1

All races2 ............................. 60,201 26.1 29.4 26.7 19.3

Hispanic ............................ 6,702 15.8 15.3 15.7 19.3

Non-Hispanic white ...................... 42,522 28.9 35.8 28.7 20.2

Non-Hispanic black ...................... 8,210 23.1 24.9 24.6 15.4

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 45

Table 22. Multilevel statistical model predicting current cigarette smoking: United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ –1.0102 0.03344 –30.21 <0.0001

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.1664 0.02962 5.62 <0.0001

Residual ............................ 0.9646 0.01449 66.56 <0.0001

Deviance ............................. 10016.0143

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ –0.8902 0.205 –4.34 <0.0001

Level 2

ABGRAD1 ............................. –1.2173 0.3573 –3.41 0.0007

Level 1

Race and origin:

Hispanic ............................ –0.2178 0.1854 –1.17 0.2402

Non-Hispanic other ...................... 0.6464 0.1700 3.80 0.0001

Non-Hispanic black ...................... 0.2540 0.1754 1.45 0.1476

Income:

$50,000 or more ....................... –0.9199 0.07102 –12.95 <0.0001

$20,000–$49,000 ....................... –0.4463 0.06101 –7.32 <0.0001

Age:

15–19 years .......................... –0.4652 0.10540 –4.41 <0.0001

25–29 years .......................... 0.1887 0.09339 2.02 0.0434

30–34 years .......................... 0.3269 0.08753 3.74 0.0002

35–39 years .......................... 0.4679 0.08712 5.37 <0.0001

40–44 years .......................... 0.2751 0.09081 3.03 0.0025

Variance

Standard

error Z value Pr (Z)

Random effects:

Intercept ............................ 0.0981 0.02483 3.95 <0.0001

Residual ............................ 0.9846 0.01479 66.59 <0.0001

Deviance ............................. 9765.3773

1ABGRAD=Percent of persons with college education.

NOTE: Reference categories are non-Hispanic white, income under $20,000, and 20–24 years of age.
Page 46 [ Series 23, No. 23

Appendix I

Definitions of Terms

NOTE: Further details defining the

statistical terms in this report may be

found in the text of this report and in

references by Raudenbush and

Bryk (36), Snijders and Bosker (37), and

Kreft and de Leeuw (38).

Block group—A geographic unit

defined by the U.S. Census Bureau,

which contains an average of about

1,100 people.

Census tract—A geographic unit

defined by the U.S. Census Bureau,

which defines a neighborhood and

contains an average of about 3,000–

4,000 people.

Community—In this report, a general

term used to refer to the local area in

which a survey respondent lives. In the

cross-tabulations in this report,

‘‘community’’ is measured by the block

group. The unemployment rate is

calculated for census tracts. In the

multivariate models, all communitylevel variables are measured at the

county level for the statistical reasons

explained in the text.

Contextual data—In this report, data

that measure characteristics of the

neighborhood or community

environment, the ‘‘context,’’ in which a

person lives. Examples of contextual

data include the median family income,

the percent with incomes below the

poverty level, the percent of adults with

college degrees, the male unemployment

rate, and the percent of employed

persons who are professionals and

managers within a geographical area

(for example, block group, census tract,

or county). Additional examples are

given in the text.

Contextual models—For this report, the

term ‘‘contextual models’’ refers to

regression-based models that contain

both individual-level variables

(characteristics of individuals) and

characteristics of communities or other

higher-level units.

Fixed effect versus random effect—In a

two-level Random Intercept Model, two

sets of coefficients (intercepts and/or

slopes) are estimated. In this report, one

set of coefficients is estimated at the

individual level (or Level 1) and another

set at the county level (Level 2). A

Level 1 coefficient is treated as a

random distribution of coefficient values

derived from separate regressions of the

outcome (dependent) variable for each

county. (For example, the percent of

women 20–34 who are childless,

measured at the county level.)

Regressing each Level 1 random

coefficient using all the counties as

observations produces the county-level

coefficients.

The county-level regression of a

Level 1 random coefficient produces

two parts: the first part is an average

value (for a given variable) that is

estimated over all individuals in the

sample regardless of the group (County)

in which they live. The second part

represents a county’s deviation from the

overall average.

The first part, the estimated overall

average, is not group-dependent and is

therefore constant for all individuals in

the sample. This is the fixed effect. The

second part is group-dependent, that is,

it depends on the group’s value on the

variable and therefore may vary across

counties. This estimated variance for

this second part represents the random

effect. When there is no random effect

for a given variable, only the fixed

effect needs to be estimated in a

multilevel model (38).

Intra-class correlation—Intra-class

correlations for the data in this report

are shown in text table A. According to

Kreft and de Leeuw (38), the intra-class

correlation is a measure of the

proportion of the total variance that is

between groups—in this report, the

proportion that is between counties. It is

a measure of the extent to which the

individuals in the sample that are in the

same county are more similar to each

other than the individuals across

counties. If the intra-class correlation is

zero, then the community-level variables

do not need to be used, and only

individual-level variables need to be

used in the analysis. In this report,

however, most of the variables have

significant intra-class correlations, and

therefore have been analyzed with

hierarchical linear (or multilevel)

models.

Multilevel model—A statistical model in

which variables are measured at more

than one level. In this report, those

levels are the individual woman

(Level 1) and her community (Level 2,

county) (38).

Race and Hispanic origin—In this

report, race and Hispanic origin were

classified into four categories: Hispanic,

non-Hispanic white, non-Hispanic black,

and non-Hispanic others. Each woman

in the survey was asked: ‘‘Are you of

Hispanic or Spanish origin?’’ If she

answered ‘‘yes,’’ she was classified as

Hispanic. Women were also asked,

‘‘Which of the groups best describes

your racial background?’’ The response

list was: Alaskan Native or American

Indian, Asian or Pacific Islander, black,

or white. Small sample sizes preclude

showing data for American Indian,

Asian, or Pacific Islander women as

separate categories. Instead, these

groups are combined and shown as a

‘‘Non-Hispanic other’’ races category.

Random effect—See ‘‘Fixed effect

versus random effect.’’

Remote access procedure—The remote

access procedure is one way to obtain

access to the NSFG and other NCHS

Contextual Data files. After a proposal

is approved and access is granted, the

researcher can submit SAS programs to

the NCHS Research Data Center

electronically from his or her own

office. The SAS program is scanned for

confidentiality. If it uses procedures that

can list individual cases, the program is

rejected and the researcher receives an

error message. If the program does not

use prohibited procedures, the researcher

receives the output back electronically.

The procedure is described further in

‘‘Analyzing Contextual Data Through

the NCHS Research Data Center’’ in

this report.
Series 23, No. 23 [ Page 47

Research Data Center—An office

at the National Center for Health

Statistics that is set up to allow

researchers access to data that cannot be

released on public-use files. The

Research Data Center is described

further in the text of this report and at:

www.cdc.gov/nchs/r&d/rdc.htm.

Statistical Analysis System (SAS)—A

general-purpose computer software

program that allows a user to

manipulate, manage, and analyze data.

SAS contains a large number of

statistical procedures, called SAS

PROCs, which allow the user to perform

a wide range of statistical analyses.

Further information is found in the

references and at: www.sas.com/support.

For sample programs and further

information on the GLIMMIX procedure

used in this report, visit http://

ftp.sas.com/techsup/download/stat, and

scroll down to glmm800 for information

on GLIMMIX in SAS Version 8, and

glmm800e for examples of GLIMMIX

in Version 8. Examples for Versions 6

and 7 are also available in the same

location. Sample programs for many

SAS problems can be found at

http://support.sas.com/techsup/sample/

sample_library.html

SUDAAN—SUDAAN is an acronym

formed from the first two letters of each

word in the phrase ‘‘survey data

analysis.’’ SUDAAN was developed by

the Research Triangle Institute (RTI)

specifically to produce accurate

estimates of sampling errors for survey

data that are not from simple random

samples. While most software packages

are able to compute standard errors for

estimates based on simple random

samples, SUDAAN is able to account

for clustering and other effects of

complex multistage sample designs. The

percentages or other statistics (‘‘point

estimates’’) calculated in SAS and

SUDAAN are identical, but the standard

error estimates calculated by SUDAAN

are typically larger than those calculated

by SAS, because the standard errors

from clustered samples are typically

larger than from non-clustered samples

of the same size. As a result, estimates

based on SUDAAN are more

conservative because there is a lower

probability of type-1 error (rejecting the

null hypothesis when it is true, or

falsely detecting a significant difference

when there is no true difference). In this

report, SUDAAN was used to estimate

the standard errors shown in Appendix

III, tables I–XIII. Additional information

about SUDAAN software, manuals, and

training courses can be found at

www.rti.org/sudaan/.
Page 48 [ Series 23, No. 23

Appendix II

Subject Index for the NSFG Contextual Data File

The SUBJECT INDEX lists available measures by subject. Variable numbers associated with each measure are listed.

Measures may be listed multiple times under different subjects.
Series 23, No. 23 [ Page 49
Page 50 [ Series 23, No. 23
Series 23, No. 23 [ Page 51
Page 52 [ Series 23, No. 23
Series 23, No. 23 [ Page 53
Page 54 [ Series 23, No. 23
Series 23, No. 23 [ Page 55

Appendix III estimated using the software package

called SUDAAN, which takes into

To create a 95-percent confidence

interval for the estimate, multiply 29.9

account the complex sample design of plus or minus (1.96) (1.45), or 29.9,

Standard Errors for the

Cross-Tabulations in This

the National Survey of Family Growth

(NSFG).

Example of use of these tables: In

plus or minus 2.84, or 27.06 to 32.74.

In other words, the chances are 95%

that the true percentage in the

Report table 1 of this report, it is shown that

29.9 percent of women 20–34 years of

population is 27.06 to 32.74. One

simple but conservative way to compare

Tables I–XIII of this appendix show age who were living in low-income two percentages in the tables is to see if

standard errors for the cross-tabulations areas had never had a birth. Table I in their confidence intervals overlap: if

shown in this report. For example, Appendix III shows the standard errors they do overlap, the difference is

appendix table I shows standard errors for the data in table 1. interpreted as not statistically significant.

for table 1; appendix table II shows Appendix table I shows that the If the confidence intervals do not

standard errors for table 2; and appendix standard error of that 29.9 percent overlap, the difference is interpreted as

table XIII shows standard errors for estimate is 1.45 percent. statistically significant at the 5 percent

table 21. These standard errors were level.

Table I. SUDAAN standard errors for the percent of women 20–34 years of age who have never had a birth (for the estimates in table 1)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 29,800 0.94 1.45 1.43 2.03

Hispanic ............................ 3,613 1.48 2.51 3.08 6.13

Non-Hispanic white ...................... 20,627 1.15 2.81 1.63 2.26

Non-Hispanic black ...................... 4,130 1.65 2.95 3.18 7.49

Poverty rate (percent below poverty)1

All races2 ............................. 29,800 0.94 1.67 1.88 1.31

Hispanic ............................ 3,613 1.48 5.05 4.34 2.33

Non-Hispanic white ...................... 20,627 1.15 1.85 2.12 2.15

Non-Hispanic black ...................... 4,130 1.65 5.22 4.96 2.17

Percent college graduates1

All races2 ............................. 29,800 0.94 1.51 1.69 1.52

Hispanic ............................ 3,613 1.48 2.71 3.78 4.85

Non-Hispanic white ...................... 20,627 1.15 2.30 1.94 1.81

Non-Hispanic black ...................... 4,130 1.65 2.55 3.52 5.09

Male unemployment rate3

All races2 ............................. 29,800 0.94 1.66 1.73 1.53

Hispanic ............................ 3,613 1.48 5.25 3.50 2.11

Non-Hispanic white ...................... 20,627 1.15 1.91 1.94 2.26

Non-Hispanic black ...................... 4,130 1.65 5.56 3.97 2.28

Percent in professional or managerial jobs1

All races2 ............................. 29,800 0.94 1.55 1.56 1.63

Hispanic ............................ 3,613 1.48 2.54 3.17 5.55

Non-Hispanic white ...................... 20,627 1.15 2.30 2.00 1.90

Non-Hispanic black ...................... 4,130 1.65 2.73 3.29 5.41

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Page 56 [ Series 23, No. 23

Table II. SUDAAN standard errors for the percent of women 18–39 years of age who are childless but expect 1 or more children (for the

estimates in table 2)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 44,519 0.71 1.30 1.06 1.84

Hispanic ............................ 5,142 1.23 2.25 2.60 6.13

Non-Hispanic white ...................... 31,297 0.86 2.30 1.26 2.01

Non-Hispanic black ...................... 6,107 1.35 2.30 2.24 6.69

Poverty rate (percent below poverty)1

All races2 ............................. 44,519 0.71 1.32 1.37 1.14

Hispanic ............................ 5,142 1.23 4.69 3.79 1.62

Non-Hispanic white ...................... 31,297 0.86 1.44 1.50 1.81

Non-Hispanic black ...................... 6,107 1.35 3.85 3.51 1.77

Percent college graduates1

All races2 ............................. 44,519 0.71 1.22 1.17 1.39

Hispanic ............................ 5,142 1.23 2.25 3.46 4.63

Non-Hispanic white ...................... 31,297 0.86 1.77 1.35 1.57

Non-Hispanic black ...................... 6,107 1.35 2.15 2.75 3.99

Male unemployment rate3

All races2 ............................. 44,519 0.71 1.29 1.32 1.18

Hispanic ............................ 5,142 1.23 4.89 3.20 1.81

Non-Hispanic white ...................... 31,297 0.86 1.38 1.47 1.74

Non-Hispanic black ...................... 6,107 1.35 4.52 3.38 1.79

Percent in professional or managerial jobs1

All races2 ............................. 44,519 0.71 1.20 1.23 1.48

Hispanic ............................ 5,142 1.23 2.09 2.75 5.01

Non-Hispanic white ...................... 31,297 0.86 1.69 1.55 1.65

Non-Hispanic black ...................... 6,107 1.35 2.16 2.63 4.01

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 57

Table III. SUDAAN standard errors for the percent of births in the last 5 years to women 15–44 years of age that were unwanted (for the

estimates in table 4)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 19,573 0.49 1.57 0.92 0.92

Hispanic ............................ 2,994 1.34 3.23 2.75 4.06

Non-Hispanic white ...................... 12,883 0.60 2.24 1.11 1.13

Non-Hispanic black ...................... 2,800 1.73 2.89 3.65 4.69

Poverty rate (percent below poverty)1

All races2 ............................. 19,573 0.49 0.81 1.25 1.18

Hispanic ............................ 2,994 1.34 3.03 3.44 2.43

Non-Hispanic white ...................... 12,883 0.60 0.89 1.41 1.47

Non-Hispanic black ...................... 2,800 1.73 5.65 4.78 2.59

Percent college graduates1

All races2 ............................. 19,573 0.49 1.24 1.18 0.76

Hispanic ............................ 2,994 1.34 2.76 3.61 3.42

Non-Hispanic white ...................... 12,883 0.60 1.64 1.26 0.78

Non-Hispanic black ...................... 2,800 1.73 2.53 3.89 5.16

Male unemployment rate3

All races2 ............................. 19,573 0.49 1.05 1.03 1.17

Hispanic ............................ 2,994 1.34 1.89 3.08 2.52

Non-Hispanic white ...................... 12,883 0.60 1.00 1.19 1.66

Non-Hispanic black ...................... 2,800 1.73 5.80 3.05 2.74

Percent in professional or managerial jobs1

All races2 ............................. 19,573 0.49 1.19 1.23 0.97

Hispanic ............................ 2,994 1.34 2.49 2.97 2.96

Non-Hispanic white ...................... 12,883 0.60 1.54 1.41 0.95

Non-Hispanic black ...................... 2,800 1.73 2.93 3.38 5.06

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Page 58 [ Series 23, No. 23

Table IV. SUDAAN standard errors for the percent of unmarried women 15–44 years of age (who have ever had intercourse) with 3 or more

sexual partners in the last 12 months (for the estimates in table 6)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 23,941 0.68 1.68 0.97 1.79

Hispanic ............................ 2,709 1.68 3.75 3.23 5.68

Non-Hispanic white ...................... 15,034 0.86 2.69 1.25 1.85

Non-Hispanic black ...................... 5,393 1.41 2.61 2.37 7.05

Poverty rate (percent below poverty)1

All races2 ............................. 23,941 0.68 1.34 1.39 1.27

Hispanic ............................ 2,709 1.68 5.27 4.21 3.39

Non-Hispanic white ...................... 15,034 0.86 1.43 1.53 1.85

Non-Hispanic black ...................... 5,393 1.41 4.27 4.16 1.93

Percent college graduates1

All races2 ............................. 23,941 0.68 1.35 1.19 1.27

Hispanic ............................ 2,709 1.68 2.83 3.52 4.97

Non-Hispanic white ...................... 15,034 0.86 2.01 1.60 1.47

Non-Hispanic black ...................... 5,393 1.41 2.20 3.00 3.62

Male unemployment rate3

All races2 ............................. 23,941 0.68 1.41 1.12 1.23

Hispanic ............................ 2,709 1.68 5.56 3.39 2.67

Non-Hispanic white ...................... 15,034 0.86 1.57 1.51 1.92

Non-Hispanic black ...................... 5,393 1.41 4.14 3.93 1.72

Percent in professional or managerial jobs1

All races2 ............................. 23,941 0.68 1.25 1.10 1.21

Hispanic ............................ 2,709 1.68 2.43 4.21 4.95

Non-Hispanic white ...................... 15,034 0.86 1.98 1.57 1.35

Non-Hispanic black ...................... 5,393 1.41 2.13 2.50 3.89

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 59

Table V. SUDAAN standard errors for the percent of women 20–44 years of age who are currently married (for the estimates in table 7)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 51,240 0.63 1.59 0.95 1.57

Non-Hispanic white ...................... 36,560 0.70 2.51 1.08 1.68

Non-Hispanic black ...................... 6,818 1.14 1.82 2.34 5.85

Poverty rate (percent below poverty)1

All races2 ............................. 51,240 0.63 1.20 1.28 1.19

Non-Hispanic white ...................... 36,560 0.70 1.22 1.45 1.60

Non-Hispanic black ...................... 6,818 1.14 3.88 3.21 1.48

Percent college graduates1

All races2 ............................. 51,240 0.63 1.26 1.14 1.15

Non-Hispanic white ...................... 36,560 0.70 1.63 1.28 1.17

Non-Hispanic black ...................... 6,818 1.14 1.81 2.29 3.63

Male unemployment rate3

All races2 ............................. 51,240 0.63 1.18 1.19 1.10

Non-Hispanic white ...................... 36,560 0.70 1.22 1.26 1.56

Non-Hispanic black ...................... 6,818 1.14 3.90 2.92 1.52

Percent in professional or managerial jobs1

All races2 ............................. 51,240 0.63 1.22 1.22 1.32

Non-Hispanic white ...................... 36,560 0.70 1.58 1.39 1.32

Non-Hispanic black ...................... 6,818 1.14 1.81 2.63 3.97

1In the Block Group.

2All races include non-Hispanic other races and Hispanic, not shown separately.

3In the Census Tract.
Page 60 [ Series 23, No. 23

Table VI. SUDAAN standard errors for the percent of women 30–44 years of age who are currently married (for the estimates in table 8)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 32,506 0.62 2.08 0.99 1.38

Non-Hispanic white ...................... 23,803 0.71 3.19 1.11 1.41

Non-Hispanic black ...................... 4,144 1.54 2.79 2.93 6.24

Poverty rate (percent below poverty)1

All races2 ............................. 32,506 0.62 1.11 1.30 1.49

Non-Hispanic white ...................... 23,803 0.71 1.19 1.52 1.95

Non-Hispanic black ...................... 4,144 1.54 4.71 3.83 2.16

Percent college graduates1

All races2 ............................. 32,506 0.62 1.38 1.23 1.18

Non-Hispanic white ...................... 23,803 0.71 1.70 1.48 1.28

Non-Hispanic black ...................... 4,144 1.54 2.61 2.96 4.12

Male unemployment rate3

All races2 ............................. 32,506 0.62 1.19 1.24 1.33

Non-Hispanic white ...................... 23,803 0.71 1.30 1.34 1.85

Non-Hispanic black ...................... 4,144 1.54 3.93 3.88 2.22

Percent in professional or managerial jobs1

All races2 ............................. 32,506 0.62 1.45 1.23 1.25

Non-Hispanic white ...................... 23,803 0.71 1.71 1.36 1.33

Non-Hispanic black ...................... 4,144 1.54 2.54 3.41 4.28

1In the Block Group.

2All races include non-Hispanic other races and Hispanic, not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 61

Table VII. SUDAAN standard errors for the percent of women 20–44 years of age using 1 or more methods of contraception who were using

female sterilization (for the estimates in table 9)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 35,994 0.64 1.74 1.01 1.45

Hispanic ............................ 3,657 1.93 3.91 2.50 5.43

Non-Hispanic white ...................... 26,304 0.75 2.70 1.21 1.44

Non-Hispanic black ...................... 4,613 1.87 2.34 3.21 7.37

Poverty rate (percent below poverty)1

All races2 ............................. 35,994 0.64 1.05 1.24 1.16

Hispanic ............................ 3,657 1.93 4.76 3.63 2.72

Non-Hispanic white ...................... 26,304 0.75 1.13 1.44 1.73

Non-Hispanic black ...................... 4,613 1.87 5.17 4.44 1.91

Percent college graduates1

All races2 ............................. 35,994 0.64 1.32 1.17 1.15

Hispanic ............................ 3,657 1.93 2.99 3.96 3.76

Non-Hispanic white ...................... 26,304 0.75 1.77 1.33 1.19

Non-Hispanic black ...................... 4,613 1.87 2.35 3.19 4.42

Male unemployment rate3

All races2 ............................. 35,994 0.64 1.16 1.29 1.28

Hispanic ............................ 3,657 1.93 5.53 3.58 2.83

Non-Hispanic white ...................... 26,304 0.75 1.23 1.45 1.87

Non-Hispanic black ...................... 4,613 1.87 4.32 4.53 2.04

Percent in professional or managerial jobs1

All races2 ............................. 35,994 0.64 1.28 1.22 1.14

Hispanic ............................ 3,657 1.93 3.20 3.86 4.86

Non-Hispanic white ...................... 26,304 0.75 1.72 1.40 1.26

Non-Hispanic black ...................... 4,613 1.87 2.39 3.37 4.51

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Page 62 [ Series 23, No. 23

Table VIII. SUDAAN standard errors for the percent of women 20–44 years of age using 1 or more methods of contraception who were

using the pill (for the estimates in table 11)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 35,994 0.64 1.49 0.81 1.91

Hispanic ............................ 3,657 1.48 2.36 3.19 5.39

Non-Hispanic white ...................... 26,304 0.82 2.61 0.97 2.08

Non-Hispanic black ...................... 4,613 1.29 1.77 2.56 5.35

Poverty rate (percent below poverty)1

All races2 ............................. 35,994 0.64 1.17 1.22 1.29

Hispanic ............................ 3,657 1.48 4.05 2.99 2.29

Non-Hispanic white ...................... 26,304 0.82 1.29 1.45 1.90

Non-Hispanic black ...................... 4,613 1.29 4.59 3.57 1.68

Percent college graduates1

All races2 ............................. 35,994 0.64 1.00 1.10 1.28

Hispanic ............................ 3,657 1.48 2.16 2.66 4.66

Non-Hispanic white ...................... 26,304 0.82 1.54 1.27 1.45

Non-Hispanic black ...................... 4,613 1.29 1.61 2.74 3.67

Male unemployment rate3

All races2 ............................. 35,994 0.64 1.20 1.15 1.07

Hispanic ............................ 3,657 1.48 4.19 4.11 2.29

Non-Hispanic white ...................... 26,304 0.82 1.39 1.34 1.74

Non-Hispanic black ...................... 4,613 1.29 3.41 3.69 1.49

Percent in professional or managerial jobs1

All races2 ............................. 35,994 0.64 1.04 1.01 1.20

Hispanic ............................ 3,657 1.48 2.07 2.55 4.01

Non-Hispanic white ...................... 26,304 0.82 1.56 1.20 1.41

Non-Hispanic black ...................... 4,613 1.29 1.74 3.04 3.32

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 63

Table IX. SUDAAN standard errors for the percent of women 20–44 years of age using 1 or more methods of contraception whose partners

were using the condom (for the estimates in table 13)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 35,994 0.52 1.43 0.82 1.66

Hispanic ............................ 3,657 1.68 2.35 2.36 7.10

Non-Hispanic white ...................... 26,304 0.66 1.94 0.97 1.69

Non-Hispanic black ...................... 4,613 1.53 2.50 2.51 6.21

Poverty rate (percent below poverty)1

All races2 ............................. 35,994 0.52 1.10 1.20 1.05

Hispanic ............................ 3,657 1.68 5.05 4.22 2.06

Non-Hispanic white ...................... 26,304 0.66 1.17 1.32 1.41

Non-Hispanic black ...................... 4,613 1.53 4.48 3.52 1.93

Percent college graduates1

All races2 ............................. 35,994 0.52 1.05 1.00 1.16

Hispanic ............................ 3,657 1.68 1.72 3.74 5.17

Non-Hispanic white ...................... 26,304 0.66 1.37 1.30 1.26

Non-Hispanic black ...................... 4,613 1.53 2.03 3.16 3.90

Male unemployment rate3

All races2 ............................. 35,994 0.52 1.04 1.04 0.98

Hispanic ............................ 3,657 1.68 5.21 3.78 1.87

Non-Hispanic white ...................... 26,304 0.66 1.07 1.25 1.49

Non-Hispanic black ...................... 4,613 1.53 4.13 3.47 1.96

Percent in professional or managerial jobs1

All races2 ............................. 35,994 0.52 1.04 1.07 1.16

Hispanic ............................ 3,657 1.68 2.01 3.62 4.69

Non-Hispanic white ...................... 26,304 0.66 1.45 1.24 1.28

Non-Hispanic black ...................... 4,613 1.53 1.98 2.83 4.09

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Page 64 [ Series 23, No. 23

Table X. SUDAAN standard errors for the percent of babies born between January 1990 and September 1994 who were breast-fed at all (for

the estimates in table 15)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent breast-fed

Median income1

All races2 ............................. 18,415 1.04 2.20 1.63 2.72

Non-Hispanic white ...................... 12,069 1.40 4.29 1.97 3.11

Non-Hispanic black ...................... 2,640 1.97 3.12 3.59 13.26

Poverty rate (percent below poverty)1

All races2 ............................. 18,415 1.04 2.41 2.09 1.94

Non-Hispanic white ...................... 12,069 1.40 2.87 2.51 2.81

Non-Hispanic black ...................... 2,640 1.97 8.09 6.94 2.59

Percent college graduates1

All races2 ............................. 18,415 1.04 2.04 2.37 1.96

Non-Hispanic white ...................... 12,069 1.40 2.84 2.70 2.42

Non-Hispanic black ...................... 2,640 1.97 3.08 4.69 6.85

Male unemployment rate3

All races2 ............................. 18,415 1.04 2.25 2.11 1.99

Non-Hispanic white ...................... 12,069 1.40 2.57 2.44 2.92

Non-Hispanic black ...................... 2,640 1.97 5.96 5.85 2.70

Percent in professional or managerial jobs1

All races2 ............................. 18,415 1.04 1.89 2.30 2.14

Non-Hispanic white ...................... 12,069 1.40 2.82 2.78 2.34

Non-Hispanic black ...................... 2,640 1.97 2.53 5.17 6.34

1In the Block Group.

2All races include non-Hispanic other races and Hispanic not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 65

Table XI. SUDAAN standard errors for the percent of babies born between January 1990 and September 1994 who were breast-fed for 3

months or more (for the estimates in table 17)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 18,415 1.10 2.25 1.69 3.23

Non-Hispanic white ...................... 12,069 1.49 4.16 2.06 3.46

Non-Hispanic black ...................... 2,640 1.66 2.36 3.06 12.96

Poverty rate (percent below poverty)1

All races2 ............................. 18,415 1.10 2.44 2.14 2.02

Non-Hispanic white ...................... 12,069 1.49 2.85 2.51 2.66

Non-Hispanic black ...................... 2,640 1.66 5.96 5.28 2.10

Percent college graduates1

All races2 ............................. 18,415 1.10 2.00 2.39 2.42

Non-Hispanic white ...................... 12,069 1.49 3.07 2.70 2.86

Non-Hispanic black ...................... 2,640 1.66 2.37 3.21 6.58

Male unemployment rate3

All races2 ............................. 18,415 1.10 2.47 2.21 2.05

Non-Hispanic white ...................... 12,069 1.49 2.81 2.42 2.72

Non-Hispanic black ...................... 2,640 1.66 5.60 4.82 2.25

Percent in professional or managerial jobs1

All races2 ............................. 18,415 1.10 1.97 2.10 2.32

Non-Hispanic white ...................... 12,069 1.49 3.14 2.48 2.60

Non-Hispanic black ...................... 2,640 1.66 1.75 4.78 4.63

1In the Block Group.

2All races include Non-Hispanic other races and Hispanic not shown separately.

3In the Census Tract.
Page 66 [ Series 23, No. 23

Table XII. SUDAAN standard errors for the percent of women 15–44 years of age who douche regularly (for the estimates in table 19)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 60,004 0.63 1.82 0.73 1.19

Hispanic ............................ 6,675 1.53 4.17 2.24 4.10

Non-Hispanic white ...................... 42,428 0.69 2.34 0.84 1.26

Non-Hispanic black ...................... 8,165 1.47 2.77 2.33 5.35

Poverty rate (percent below poverty)1

All races2 ............................. 60,004 0.63 0.92 1.02 1.26

Hispanic ............................ 6,675 1.53 3.33 2.83 2.87

Non-Hispanic white ...................... 42,428 0.69 0.96 1.11 1.54

Non-Hispanic black ...................... 8,165 1.47 4.13 3.06 2.02

Percent college graduates1

All races2 ............................. 60,004 0.63 1.18 0.94 0.89

Hispanic ............................ 6,675 1.53 2.79 3.03 3.05

Non-Hispanic white ...................... 42,428 0.69 1.44 0.97 0.95

Non-Hispanic black ...................... 8,165 1.47 2.40 2.71 3.78

Male unemployment rate3

All races2 ............................. 60,004 0.63 0.96 0.91 1.17

Hispanic ............................ 6,675 1.53 3.93 2.85 2.65

Non-Hispanic white ...................... 42,428 0.69 0.95 0.99 1.46

Non-Hispanic black ...................... 8,165 1.47 3.74 2.87 1.80

Percent in professional or managerial jobs1

All races2 ............................. 60,004 0.63 1.26 1.01 1.00

Hispanic ............................ 6,675 1.53 2.60 2.73 2.77

Non-Hispanic white ...................... 42,428 0.69 1.51 1.08 1.03

Non-Hispanic black ...................... 8,165 1.47 2.35 2.57 4.03

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Series 23, No. 23 [ Page 67

Table XIII. SUDAAN standard errors for the percent of women 15–44 years of age who currently smoke cigarettes (for the estimates in

table 21)

Contextual (community) characteristic

Number of

Race, origin, and women in All

contextual variable thousands women Low Middle High

Percent

Median income1

All races2 ............................. 60,201 0.59 1.42 0.85 1.24

Hispanic ............................ 6,702 1.23 3.38 2.06 6.62

Non-Hispanic white ...................... 42,522 0.72 2.15 1.05 1.24

Non-Hispanic black ...................... 8,210 1.06 1.61 1.63 5.62

Poverty rate (percent below poverty)1

All races2 ............................. 60,201 0.59 0.98 0.96 1.03

Hispanic ............................ 6,702 1.23 4.16 2.16 2.51

Non-Hispanic white ...................... 42,522 0.72 1.03 1.12 1.47

Non-Hispanic black ...................... 8,210 1.06 3.16 2.33 1.34

Percent college graduates1

All races2 ............................. 60,201 0.59 1.11 1.05 0.86

Hispanic ............................ 6,702 1.23 2.46 2.44 3.49

Non-Hispanic white ...................... 42,522 0.72 1.52 1.25 0.96

Non-Hispanic black ...................... 8,210 1.06 1.53 2.39 2.71

Male unemployment rate3

All races2 ............................. 60,201 0.59 1.01 1.06 1.00

Hispanic ............................ 6,702 1.23 4.49 2.28 2.28

Non-Hispanic white ...................... 42,522 0.72 1.08 1.22 1.32

Non-Hispanic black ...................... 8,210 1.06 2.86 2.25 1.24

Percent in professional or managerial jobs1

All races2 ............................. 60,201 0.59 1.08 0.98 0.95

Hispanic ............................ 6,702 1.23 2.51 2.46 3.79

Non-Hispanic white ...................... 42,522 0.72 1.58 1.13 1.06

Non-Hispanic black ...................... 8,210 1.06 1.55 2.00 2.76

1In the Block Group.

2All races include non-Hispanic other races not shown separately.

3In the Census Tract.
Page 68 [ Series 23, No. 23

Appendix IV

Sample SAS Programs to

Fit Multilevel Models

This brief appendix cites some

reasons why SAS PROC MIXED and

SAS GLIMMIX were used in

this report, and shows some sample

programs for Multilevel Models for

Continuous Dependent Variables

(sample program 1, showing PROC

MIXED), Multilevel Models for

dichotomous dependent variables

(GLIMMIX, sample programs 2 and 3),

and an ordinary least- squares regression

model (PROC REG, sample program 4),

which may be an appropriate way

(36–38) to choose variables at the

individual level for use in the multilevel model. Further details and

example programs may be found at

www.sas.com/support and in the sources

of SAS information cited in this report

(42–45).

SAS PROC MIXED, first

introduced in 1992, is now a viable

alternative to special software for fitting

multilevel models. Its flexibility has

increased significantly since its

introduction with the addition of more

options for modifying certain aspects of

the procedure. It can be used to fit a

variety of multilevel models including

Hierarchical Linear Models (HLM) that

are now often used by behavioral

scientists and other statistical

researchers.

SAS PROC MIXED and some of

its variants (such as GLIMMIX) may be

a good choice for some researchers for

several reasons. First, SAS is already

widely used; many current SAS users

therefore do not have to learn and

acquire new software to use PROC

MIXED. Second, SAS has the ability to

do data management, data reduction,

and data analysis, all in the same

package. Third, by using SAS PROC

MIXED, researchers who want to use

multilevel modeling on data in the

NCHS Research Data Center can use it

with the Remote Access Procedure,

without having to travel to NCHS.

Researchers can use other software, such

as STATA, MLn, and HLM in the

Research Data Center, but using SAS

allows them to use the data remotely.

Very often, social and behavioral

researchers deal with outcome variables

that are not continuous. PROC MIXED,

however, is designed for continuous

outcome variables. SAS accommodates

the regression of outcome variables that

are nonnormal and noncontinuous (for

example, count data or dichotomous

variables) with a macro file called

GLIMMIX. This macro can be

downloaded at no cost from the SAS

Web site at: http://ftp.sas.com/techsup/

download/stat. The multilevel models

presented in this report were produced

using GLIMMIX.

To fit Hierarchical Linear Models, it

is very helpful to be thoroughly familiar

with the data first. Knowledge of the

subject matter, relevant theory, and

knowledge of the assumptions made in

multilevel modeling, will make the

modeling process go more smoothly

(36–38).

To run PROC MIXED, a single

equation is specified in SAS statements,

as shown in the examples below. Like

other SAS modeling procedures, PROC

MIXED uses a MODEL statement with

the dependent or outcome variable on

the left of the equal sign, and the

independent or predictor variables on

the right. PROC MIXED further allows

for defining in a RANDOM statement

which variables, including the intercept,

are specified to vary randomly. A

cross-level interaction can simply be

entered as a multiplicative term between

a Level 1 and a Level 2 variable.

To avoid confusion, it is good

practice to write down the equations at

each level (for example, Level 1 and

Level 2 in a two-level model), and then

by substitution derive a final single

equation (Combined Model). Clearly

documenting which of the parameter

estimates at Level 1 are declared as

random at Level 2 facilitates the

translation of the model into SAS

statements. It also lessens the chances

for error in entering the variables as the

researcher can easily visualize the terms

used in the final equation, particularly

specified interactions. An illustration of

a 2-level model with two predictor

variables at Level 1 (X1ij and X2ij) and

one at Level 2 (Zi

) is shown in Example

equations. The intercept and one of the

two coefficients estimated at Level 1 are

defined (presumably based on existing

theory or substantive knowledge) to be

random at Level 2. The contextual

variable (Zj

) affects both the intercept

and the slope of the first Level 1

predictor. The latter is expressed as a

cross-level interaction in the final

equation (Zj X1ij). Note that the fixed

part of the model is inside the curved

brackets and the random part in regular

brackets. The point for this illustration is

to guide the beginner in PROC MIXED

or GLIMMIX to correctly identify the

terms in the equation and then write the

proper syntax for the right input in SAS.

Sample program 1 is a PROC

MIXED model with parity (the number

of children a woman has had) as the

dependent variable (parity has a range

of 0 to about 12 in this sample). (PROC

MIXED is designed for continuous

dependent variables. While parity is,

strictly speaking, count data, for this

purpose it will be treated as continuous.)

A model is run in which the Level 2

variable is at the county level (CTY),

and individual-level income and race are

independent variables. The dependent

variable (DV) is a function of individual

level variable 1 (race/ethnicity),

individual level variable 2 (household

income), and a contextual variable; the

intercept and slope are declared to be

random.

Sample program 2 shows how a

similar program is run when the

dependent variable has been changed to

a dichotomous outcome (no births

versus one or more births) for women

20–34 years of age. This time, the

GLIMMIX procedure is used instead of

PROC MIXED because the outcome

variable is dichotomous. In this step, a

random analysis of variance (ANOVA)

model is run to compute the Null

Model.

In sample program 3, the random

intercept model (similar to the

‘‘RANDOM INTERCEPT’’ part of

table 3 in this report) is estimated for

women 20–34 years of age, where

‘‘NOBIRTH’’—that is, childlessness—is

the dichotomous dependent variable.

The Random intercept part of the model

shows that the dependent variable

NOBIRTH is a function of county-level
Series 23, No. 23 [ Page 69

median family income (MEDFINC), and

three individual-level variables: family

or household income, race (BLACK,

HISPANIC, WHITE), and age. The

intercept is allowed to be random.

Sample program 4 shows a SAS

program for ordinary least-squares

regression, using parity as a continuous

dependent variable. The predictor

variables used are high household

income (INCHI, more than $50,000),

medium income (INCMED, $20,000–

49,000), with income less than $20,000

as the reference category; 5-year age

groups with 20–24 as the reference

category; and variables indicating black

or Hispanic origin, with non-Hispanic

white as the reference category. In the

output (not shown here), all of these

variables have strong, significant effects.

But the results of table 3 suggest that

this PROC REG would be a

misspecified model, because it does not

control for the median family income of

the county. (County median family

income was shown in table 3 to be an

important predictor of the dichotomous

version of parity, ‘‘NOBIRTH.’’)

Example equations:

Level 1:

Yij = β0j + β1j X1ij + β2j X2ij + εij 

Level 2:

β0j = γ00 + γ01 Zj + μ0j 

β1j = γ10 + γ11 Zj + μ1j 

β2j = γ20

Combined model:

Yij = {γ00 + γ10 X1ij + γ20 X2ij + γ01 Zj 

+ γ11 Zj X1ij}+[μ0j + μ1j X1ij + εij]

Sample Program 1, Using Proc Mixed: Random Intercept

Libname IN ‘D:\My Documents\My SAS Files\V8\NSFG\SAS Data Sets’;

Libname library ‘D:\My Documents\My SAS Files\V8\NSFG\Formtcat’;

Data a;Set in.a;

/*Data manipulation or data management procedures can be done here as illustrated in the next

SAS statement */

Xlevel1=Leve1Var1*Context1;

Run;

******************************************************************** ;

Proc Mixed;

class CTY90IDS income race;

model DV=Lev1Var1 Lev1Var2 Context1 Xlevel1 /S ;

random intercept Xlevel1 / sub=Groups type=un G;

Run;

******************************************************************** ;

Sample Program 2: Using GLIMMIX: Null Model

Libname IN ‘D:\My Documents\My SAS Files\V8\NSFG\SAS Data Sets’;

Libname library ‘D:\My Documents\My SAS Files\V8\NSFG\Formtcat’;

%Include ‘D:\My Documents\My SAS Files\V8\NSFG\SASMacros\

glmxc1m1.sas’;

Run;

Data rcm1;Set in.cntyrcm1b;

If 20<=agap12dg<=34;

If NOBIRTH=2 then NOBIRTH=0;

Run;

******************************************************************** ;

%glmxc1m1;

Run;

******************************************************************** ;

Title ‘‘Random ANOVA’’;

%glimmix (data=RCM1,

stmts=%str(class CTY90IDS;

model NOBIRTH=/S ddfm=bw ;

random intercept / sub=CTY90IDS G;)

error=binomial);

Run;

Title;

Run;
Page 70 [ Series 23, No. 23

Sample Program 3: Using GLIMMIX: Random Intercept

Libname IN ‘D:\My Documents\My SAS Files\V8\NSFG\SAS Data Sets’;

Libname library ‘D:\My Documents\My SAS Files\V8\NSFG\Formtcat’;

%Include ‘D:\My Documents\My SAS Files\V8\NSFG\SASMacros\

glmxc1m1.sas’;

Run;

Data rcm1;Set in.cntyrcm1b;

If 20<=agap12dg<=34;

If NOBIRTH=2 then NOBIRTH=0;

MEDFINC=CST90257;

Run;

******************************************************************** ;

%glmxc1m1;

Run;

******************************************************************** ;

Title ‘‘Random Intercept’’;

%glimmix (data=RCM1,

stmts=%str(class CTY90IDS income race;

model NOBIRTH=income race a25_29 a30_34 medfinc/S ddfm=bw ;

random intercept / sub=CTY90IDS G;),

error=binomial);

Run;

Title;

Run;

Sample Program 4: PROC REG, ordinary regression with a continuous dependent

variable

proc reg;

model parity = inchi incmed age2529 age3034 black hisp;

weight post_wt;

run;
Series 23, No. 23 [ Page 71

Appendix V

Alternative Procedures for 

Mixed Modeling

The dependent variables examined

in this report are all dichotomous

variables, with values of 0 or 1

indicating the absence or presence of a

particular characteristic. The analyses of

these dependent variables were

performed using GLIMMIX, a macro

that adapts the SAS procedure PROC

MIXED for use with dichotomous

dependent variables. However, this is

not the only possible method of

examining these outcomes.

For example, the first GLIMMIX

model that was presented in table 3

analyzed the percent of women ages

20–34 who had had no children. This

measure of childlessness was coded as 0

if the woman had ever had any children

(absence of childlessness) and 1 if the

woman was childless. This dependent

variable could have been measured as

parity—the exact number of children

that the woman had ever had. The

measure of childlessness in this report

was dichotomized because the difference

between no children and any children

was the focus of the analysis, but a

similar analysis of the total number of

children is possible.

If the focus were on the number of

children ever born, the appropriate SAS

procedure would be PROC MIXED,

which is designed for mixed modeling

of continuous (or count) dependent

variables. The PROC MIXED SAS code

to model the number of children ever

born, using the same model as was

presented for childlessness in table 3, is

as follows:

PROC MIXED covtest; class

CTY90IDS;

model PARITY = INCOMHI INCOMMED

AGE20_29 AGE30_34 BLACK

HISPANIC

MEDFINC / solution;

random intercept / sub=CTY90IDS

type=un G;

run;

where:

CTY90IDS identifies the county in 1990

(the clustering variable); PARITY is the

continuous dependent variable

measuring the number of children ever

born; INCOMHI and INCOMMED are

dichotomous variables indicating high or

medium income; AGE25–29 and

AGE30–34 are dichotomous variables

indicating ages 25–29 and 30–34;

BLACK and HISPANIC are

dichotomous variables indicating black

or Hispanic race/ethnicity; MEDFINC is

a county-level variable indicating the

median family income in the county;

low-income, ages 20–24 and nonHispanic white and other race/ethnicity

are omitted as reference categories

(table 4). Non-Hispanic other women

did not differ significantly from

non-Hispanic white women, so there is

no need to include a separate indicator

variable for them.

The results of this model are

presented in table A1. The estimates for

the fixed-effects solution suggest that

the higher the median family income in

a county, the lower the average parity of

the women in the county. Within

counties, an individual woman’s parity

is predicted to be lower if her family

income is higher and her parity is

predicted to be higher if she is older,

Hispanic, or black. These results closely

agree with the findings based on the

GLIMMIX model of childlessness

presented in table 3: that childlessness is

more prevalent in higher-income

communities and that the probability of

childlessness is higher for women with

more family income, and lower for older

women, Hispanic women, and black

women.

The GLIMMIX macro and PROC

MIXED are not the only options for

multilevel modeling in SAS. A third

procedure, PROC NLMIXED, is

available for analysis of nonlinear

outcomes. This procedure is very

flexible, allowing any nonlinear

equation that the researcher specifies

with SAS programming statements. All

SAS modeling procedures are available

in the Research Data Center via the

Remote Access Procedure described

previously.

In addition, for researchers who

choose to use software other than SAS,

virtually any software program can be

used as long as the researcher is able to

travel to NCHS in person to conduct the

analysis. Advance notice is also

necessary so that the appropriate

software can be obtained and set up in

advance if it is not already supported by

Research Data Center staff.
Page 72 [ Series 23, No. 23

Table A1. Multilevel mixed model predicting parity for women 20–34 years of age: United States, 1995

Standard

Parameter Estimate error t value Pr (t)

Null Model

Fixed effects:

Intercept ............................ 1.1555 0.02559 45.15 <0.0001

Variance Standard

components error Z value Pr (Z)

Fixed effects:

Intercept ............................ 0.08955 0.01754 5.11 <0.0001

Residual ............................ 7.91180 0.1753 45.12 <0.0001

AIC ................................. 15088.1

Random Intercept Model

Standard

Estimate error t value Pr (t)

Fixed effects:

Intercept ............................ 1.2917 0.09195 14.05 <0.0001

Level 2

MEDFINC1 ............................ –0.01675 0.002279 –7.35 <0.0001

Level 1

Race and origin:

Hispanic ............................ 0.5468 0.05526 9.89 <0.0001

Non-Hispanic black ...................... 0.3612 0.05011 7.21 <0.0001

Income:

$50,000 or more ....................... –0.4881 0.0476 –10.25 <0.0001

$20,000-$49,000 ....................... –0.2845 0.04186 –6.80 <0.0001

Age:

25–29 years .......................... 0.5784 0.04336 13.34 <0.0001

30–34 years .......................... 1.1248 0.04158 27.05 <0.0001

Variance Standard

components error Z value Pr (Z)

Random effects:

Intercept ............................ 0.02896 0.01083 2.67 0.0037

Residual ............................ 6.58130 0.14600 45.09 <0.0001

AIC ................................. 14208.1

1MEDFINC=Median family income.

NOTE: Reference categories are non-Hispanic white and other, income under $20,000, and 20–24 years of age.
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