Transcription of Multiple Imputation of Multilevel Data
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Multiple Imputation of Multilevel Data
17310 Multiple Imputation of Multilevel DataStef van BuurenTNO Quality of Life, Department of Statistics, Leiden and University of Utrecht, The IntroductIonIn the early days of Multilevel analysis, Goldstein wrote: We shall require and assume that all the necessary data at each level are available (Goldstein, 1987). Despite the many conceptual and computational advances that have been made over the last two decennia, Goldstein s requirement is still dom-inant today. To illustrate this, consider how modern software for fitting Multilevel models deals with missing data . Dedicated packages like MLwiN (Rasbash, Steel, Browne, & Prosser, 2005) and HLM (Raudenbush, Bryk, & Congdon, 2008) remove all level-1 units with missing values on any level-1 variable.
Multiple Imputation of Multilevel Data • 175 which is a special case of the linear mixed model (Equation 10.1) with X j = Z j W j. Matrix W j has a special structure for the
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