Missing data analysis: making it work in the real world.
This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normal-model multiple imputation (MI) and maximum likelihood methods. Practical missing data analysis issues are discussed, most notably the inclusion of auxiliary variables for improving power and reducing bias. Solutions are given for missing data challenges such as handling longitudinal, categorical, and clustered data with normal-model MI; including interactions in the missing data model; and handling large numbers of variables. The discussion of attrition and nonignorable missingness emphasizes the need for longitudinal diagnostics and for reducing the uncertainty about the missing data mechanism under attrition. Strategies suggested for reducing attrition bias include using auxiliary variables, collecting follow-up data on a sample of those initially missing, and collecting data on intent to drop out. Suggestions are given for moving forward with research on missing data and attrition.
Graham
John W
JW
Department of Biobehavioral Health and the Prevention Research Center, The Pennsylvania State University, University Park, Pennsylvania 16802, USA. jgraham@psu.edu
D016000:Cluster Analysis; D003625:Data Collection / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D011594:Psychometrics / Q000706:statistics & numerical data*; D012107:Research Design / Q000706:statistics & numerical data