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Original paper

Multiple Imputation of Missing Data for Multilevel Models

Volume: 21, Issue: 1, Pages: 111 - 149
Published: May 15, 2017
Abstract
Multiple imputation (MI) is one of the principled methods for dealing with missing data. In addition, multilevel models have become a standard tool for analyzing the nested data structures that result when lower level units (e.g., employees) are nested within higher level collectives (e.g., work groups). When applying MI to multilevel data, it is important that the imputation model takes the multilevel structure into account. In the present...
Paper Details
Title
Multiple Imputation of Missing Data for Multilevel Models
Published Date
May 15, 2017
Volume
21
Issue
1
Pages
111 - 149
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