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

PSC In The News

RSS Feed icon

Former trainee Herbert says residential squatters may be a good thing

Work by Couper, Farley et al. shows impact of racial composition on neighborhood choice

Thompson details killings and shaping of official narrative in 1971 Attica prison uprising

More News

Highlights

Michigan ranked #12 on Business Insider's list of 50 best American colleges

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

Next Brown Bag

PSC Brown Bags
will resume fall 2016

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.

Browse | Search : All Pubs | Next