Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Smock cited in story on how low marriage rates may exacerbate marriage-status economic inequality

Shapiro says Americans' seemingly volatile spending pattern linked to 'sensible cash management'

Work of Cigolle, Ofstedal et al. cited in Forbes story on frailty risk among the elderly

Highlights

Sarah Burgard and former PSC trainee Jennifer Ailshire win ASA award for paper

James Jackson to be appointed to NSF's National Science Board

ISR's program in Society, Population, and Environment (SPE) focuses on social change and social issues worldwide.

McEniry and Schoeni host Conference on Long-run Impacts of Early Life Events

Next Brown Bag


PSC Brown Bags will return in the fall

Multiple imputation of missing income data in the National Health Interview Survey

Publication Abstract

Schenker, N., Trivellore Raghunathan, P.L. Chiu, D.M. Makuc, G.Y. Zhang, and A.J. Cohen. 2006. "Multiple imputation of missing income data in the National Health Interview Survey." Journal of the American Statistical Association, 101(475): 924-933.

The National Health Interview Survey (NHIS) provides a rich source of data for studying relationships between income and health and for monitoring health and health care for persons at different income levels. However, the nonresponse rates are high for two key items, total family income in the previous calendar year and personal earnings from employment in the previous calendar year. To handle the missing data on family income and personal earnings in the NHIS, multiple imputation of these items, along with employment status and ratio of family income to the federal poverty threshold (derived from the imputed values of family income), has been performed for the survey years 1997-2004. (There are plans to continue this work for years beyond 2004 as well.) Files of the imputed values, as well as documentation, are available at the NHIS website (http://www.cdc.gov/nchs/nhis.htm). This article describes the approach used in the multiple-imputation project and evaluates the methods through analyses of the multiply imputed data. The analyses suggest that imputation corrects for biases that occur in estimates based on the data without imputation, and that multiple imputation results in gains in efficiency as well.

DOI:10.1198/016214505000001375 (Full Text)

Browse | Search : All Pubs | Next