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Sastry's 10-year study of New Orleans Katrina evacuees shows demographic differences between returning and nonreturning

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Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

David Lam is new director of Institute for Social Research

Elizabeth Bruch wins Robert Merton Prize for paper in analytic sociology

Next Brown Bag

Monday, Oct 12
Joe Grengs, Policy & Planning for Social Equity in Transportation

Trivellore Raghunathan photo

What Do We Do With Missing Data? Some Options for Analysis of Incomplete Data

Publication Abstract

Raghunathan, Trivellore. 2004. "What Do We Do With Missing Data? Some Options for Analysis of Incomplete Data." Annual Review of Public Health, 25:99-117.

Missing data are a pervasive problem in many public health investigations. The standard approach is to restrict the analysis to subjects with complete data on the variables involved in the analysis. Estimates from such analysis can be biased, especially if the subjects who are included in the analysis are systematically different from those who were excluded in terms of one or more key variables. Severity of bias in the estimates is illustrated through a simulation study in a logistic regression setting. This article reviews three approaches for analyzing incomplete data. The first approach involves weighting subjects who are included in the analysis to compensate for those who were excluded because of missing values. The second approach is based on multiple imputation where missing values are replaced by two or more plausible values. The final approach is based on constructing the likelihood based on the incomplete observed data. The same logistic regression example is used to illustrate the basic concepts and methodology. Some software packages for analyzing incomplete data are described.

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