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

PSC In The News

RSS Feed icon

Stephenson assessing in-home HIV testing and counseling for male couples

Thompson says mass incarceration causes collapse of Detroit neighborhoods

Liberal-conservative gap by education level growing in U.S.

Highlights

Maggie Levenstein named director of ISR's Inter-university Consortium for Political and Social Research

Arline Geronimus receives 2016 Harold R. Johnson Diversity Service Award

PSC spring 2016 newsletter: Kristin Seefeldt, Brady West, newly funded projects, ISR Runs for Bob, and more

AAUP reports on faculty compensation by category, affiliation, and academic rank

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