Missing Data Analysis
Roderick J. A. Little
Abstract
Methods for handling missing data in clinical psychology studies are reviewed. Missing data are defined, and a taxonomy of main approaches to analysis is presented, including complete-case and available-case analysis, weighting, maximum likelihood, Bayes, single and multiple imputation, and augmented inverse probability weighting. Missingness mechanisms, which play a key role in the performance of alternative methods, are defined. Approaches to robust inference, and to inference when the mechanism is potentially missing not at random, are discussed.
Topics & Concepts
Missing dataImputation (statistics)InferenceWeightingComputer scienceInverse probability weightingData miningMachine learningStatisticsArtificial intelligenceData scienceInformation retrievalMathematicsEstimatorMedicineRadiologyStatistical Methods and Bayesian InferenceAdvanced Causal Inference TechniquesSurvey Methodology and Nonresponse