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Abstract Details

Activity Number: 583
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #305587
Title: Multiple Imputation for Hierarchical Data Sets
Author(s): Joerg Drechsler*+ and Joe Sakshaug and Matthias Speidel
Companies: German Institute for Employment Research (IAB) and German Institute for Employment Research (IAB) and Ludwig-Maxilians-Universität München
Address: Regensburger Str. 104 , Nuremberg , , Germany
Keywords: fixed effects ; hierarchical ; multilevel ; multiple imputation ; random effects ; missing data

Although multiple imputation is widely accepted as the preferable approach to deal with item nonresponse in surveys, research on imputation involving multilevel databases is still limited. Nevertheless, it has been shown that a naïve imputation ignoring cluster effects leads to biased results for hierarchical data. This is especially true if the aim of the analysis is to estimate intra cluster correlations or other quantities that combine variables from different levels of hierarchy. Imputation strategies that account for the hierarchical data structures are typically based on fixed or random effects models. While the fixed effects approach is easy to implement it can still lead to biased results if the analysis of interest involves a random effects model. On the other hand, random-effects models are more difficult to fit and might be subject to misspecification, especially if the normality assumption of the random effects is violated. In this talk we evaluate different approaches for incorporating the hierarchical structure of the data in the imputation models.

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