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(2000), “Multiple Imputation for Missing Data: A Cautionary Tale,” Sociological Methods and Research, 28, 301–309.Īllison, P. D. Descriptive Statistics EM Algorithm for Data with Missing Values Statistical Assumptions for Multiple Imputation Missing Data Patterns Imputation Methods Monotone Methods for Data Sets with Monotone Missing Patterns Monotone and FCS Regression Methods Monotone and FCS Predictive Mean Matching Methods Monotone and FCS Discriminant Function Methods Monotone and FCS Logistic Regression Methods Monotone Propensity Score Method FCS Methods for Data Sets with Arbitrary Missing Patterns Checking Convergence in FCS Methods MCMC Method for Arbitrary Missing Multivariate Normal Data Producing Monotone Missingness with the MCMC Method MCMC Method Specifications Checking Convergence in MCMC Input Data Sets Output Data Sets Combining Inferences from Multiply Imputed Data Sets Multiple Imputation Efficiency Imputer’s Model Versus Analyst’s Model Parameter Simulation versus Multiple Imputation Sensitivity Analysis for the MAR Assumption Multiple Imputation with Pattern-Mixture Models Specifying Sets of Observations for Imputation in Pattern-Mixture Models Adjusting Imputed Values in Pattern-Mixture Models Summary of Issues in Multiple Imputation ODS Table Names ODS GraphicsĪllison, P. D.
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