TY - JOUR
T1 - Missing data analysis
T2 - Making it work in the real world
AU - Graham, John W.
PY - 2009/1
Y1 - 2009/1
N2 - This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normalmodel 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.
AB - This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normalmodel 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.
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U2 - 10.1146/annurev.psych.58.110405.085530
DO - 10.1146/annurev.psych.58.110405.085530
M3 - Review article
C2 - 18652544
AN - SCOPUS:60549085055
SN - 0066-4308
VL - 60
SP - 549
EP - 576
JO - Annual Review of Psychology
JF - Annual Review of Psychology
ER -