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Multilevel multiple imputation: An examination of competing methods


Abstract Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is... (more)
Created Date 2015
Contributor Mistler, Stephen Andrew (Author) / Enders, Craig K (Advisor) / Aiken, Leona (Committee member) / Levy, Roy (Committee member) / West, Stephen G (Committee member) / Arizona State University (Publisher)
Subject Statistics / Psychology / Hierarchical / Missing Data / Multilevel Modeling / Multiple Imputation
Type Doctoral Dissertation
Extent 204 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Psychology 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis