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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 v... Full description

Journal Title: Annual review of psychology 2009, Vol.60 (1), p.549-576
Main Author: GRAHAM, John W
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: Palo Alto, CA: Annual Reviews
ID: ISSN: 0066-4308
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recordid: cdi_proquest_miscellaneous_66725670
title: Missing Data Analysis: Making It Work in the Real World
format: Article
creator:
  • GRAHAM, John W
subjects:
  • Analysis
  • Biological and medical sciences
  • Clinical psychology
  • Cluster Analysis
  • Data analysis
  • Data Collection - statistics & numerical data
  • Data Interpretation, Statistical
  • Fundamental and applied biological sciences. Psychology
  • Humans
  • Likelihood Functions
  • Longitudinal Studies
  • Maximum likelihood method
  • Medical diagnosis
  • Missing observations (Statistics)
  • Models, Statistical
  • Multiple imputation (Statistics)
  • Psychology. Psychoanalysis. Psychiatry
  • Psychology. Psychophysiology
  • Psychometrics - statistics & numerical data
  • Psychometrics. Statistics. Methodology
  • Research Design - statistics & numerical data
  • Selection bias
  • Statistics. Mathematics
  • Usage
ispartof: Annual review of psychology, 2009, Vol.60 (1), p.549-576
description: 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.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0066-4308
fulltext: fulltext
issn:
  • 0066-4308
  • 1545-2085
url: Link


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descriptionThis 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.
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subjectAnalysis ; Biological and medical sciences ; Clinical psychology ; Cluster Analysis ; Data analysis ; Data Collection - statistics & numerical data ; Data Interpretation, Statistical ; Fundamental and applied biological sciences. Psychology ; Humans ; Likelihood Functions ; Longitudinal Studies ; Maximum likelihood method ; Medical diagnosis ; Missing observations (Statistics) ; Models, Statistical ; Multiple imputation (Statistics) ; Psychology. Psychoanalysis. Psychiatry ; Psychology. Psychophysiology ; Psychometrics - statistics & numerical data ; Psychometrics. Statistics. Methodology ; Research Design - statistics & numerical data ; Selection bias ; Statistics. Mathematics ; Usage
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abstractThis 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.
copPalo Alto, CA
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pmid18652544
doi10.1146/annurev.psych.58.110405.085530