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Activity Number: 138
Type: Contributed
Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #311822 View Presentation
Title: Different Ways of Dealing with Missingness in Hierarchical Data Sets
Author(s): Matthias Speidel*+ and Jörg Drechsler and Joseph Sakshaug
Companies: Institute for Employment Research and Institute of Employment Research and Institute for Employment Research
Keywords: hierarchical imputation ; multiple imputation ; linear mixed model
Abstract:

Missing values are a common problem in surveys and imputation potentially reduces the nonresponse bias. However, commonly used statistical software packages don't account for hierarchical data structures (e.g. students within schools) in their imputation routines. The consequences on post-imputation analyses are seldom considered.

In this talk I present a simulation study to evaluate the impact of different imputation methods on parameter estimates from a linear mixed model with random intercepts and random slopes. Four methods are compared: the available case analysis, an imputation model based on a simple Bayesian linear regression, a Bayesian linear imputation model including dummy variables to allow for cluster specific intercepts and slopes, and a mixed effects imputation model that is congenial to the analysis model.

The results show that the available case analysis has the expected nonresponse bias. The simple Bayesian linear regression imputation and the dummy variable imputation lead to biased variance estimates. The mixed effects imputation shows the best results.


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