National Survey of Family Growth, cycle 6; sample design, weighting, imputation and variance estimation
Published Date:July 2006
Corporate Authors:National Center for Health Statistics (U.S.)
Family Life Surveys
Data Collection/Methods/United States
Family Characteristics/United States
Family Size/Statistical Methods
Fertility, Human/Statistical Methods
Reproductive Behavior/United States
Research Design/United States
Series:Vital and health statistics. Series 2, Data evaluation and methods research ; no. 142
DHHS publication ; no. (PHS) 2005-1342
Description:Objectives: Cycle 6 of the National Survey of Family Growth (NSFG) was conducted by the National Center for Health Statistics in 2002 and early 2003. This report describes how the sample was designed, shows response rates for various subgroups of men and women, describes how the sample weights were computed to make national estimates possible, shows how missing data were imputed for a limited set of key variables, and describes the proper ways to estimate sampling errors from the NSFG. The report includes both nontechnical summaries for readers who need only general information and more technical detail for readers who need an in-depth understanding of these topics.
Methods: The NSFG Cycle 6 was based on an independent, national probability sample of men and women 15-44 years of age. It was the first NSFG ever to include a national sample of men 15-44 as well as a sample of women. Fieldwork was carried out by the University of Michigan's Institute for Social Research (ISR) under a contract with NCHS. In-person, face-to-face interviews were conducted by professional female interviewers using laptop computers. In all, 12,571 women and men-7,643 females and 4,928 males-were interviewed, the largest NSFG ever done.
Results: Analysis of NSFG Cycle 6 data requires the use of sampling weights and estimation of sampling errors that accounts for the complex sample design and estimation features of the survey. Examples of how to use several available software packages that incorporate complex design features in estimation, such as SAS, SUDAAN, and STATA, are presented.
Supporting Files:No Additional Files
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