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A Primer on the Use of Modern Missing-Data Methods in Psychosomatic Medicine Research

From Arizona State University, Tempe, Arizona.

Address correspondence and reprint requests to Craig K. Enders, Arizona State University, Department of Psychology, P.O. Box 871104, Tempe, AZ 85287-1104. E-mail: cenders{at}asu.edu

This paper summarizes recent methodologic advances related to missing data and provides an overview of two "modern" analytic options, direct maximum likelihood (DML) estimation and multiple imputation (MI). The paper begins with an overview of missing data theory, as explicated by Rubin. Brief descriptions of traditional missing data techniques are given, and DML and MI are outlined in greater detail; special attention is given to an "inclusive" analytic strategy that incorporates auxiliary variables into the analytic model. The paper concludes with an illustrative analysis using an artificial quality of life data set. Computer code for all DML and MI analyses is provided, and the inclusion of auxiliary variables is illustrated.

Key Words: missing data • full information maximum likelihood • direct maximum likelihood • maximum likelihood • multiple imputation • attrition

Abbreviations: DML = direct maximum likelihood; MI = multiple imputation; ML = maximum likelihood; LW = listwise deletion; AMI = arithmetic mean imputation; SRI = stochastic regression imputation; DA = data augmentation; QOL = quality of life; MAR = missing at random; MCAR = missing completely at random; MNAR = missing not at random; LOCF = last observation carried forward.

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