Missing Data Analysis: Making It Work in the Real World
Abstract
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...
Paper Details
Title
Missing Data Analysis: Making It Work in the Real World
Published Date
Jan 1, 2009
Journal
Volume
60
Issue
1
Pages
549 - 576
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