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    Methods for Handling Missing Non-Normal Data in Structural Equation Modeling

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    Jia_ku_0099D_14859_DATA_1.pdf (1.710Mb)
    Issue Date
    2016-08-31
    Author
    Jia, Fan
    Publisher
    University of Kansas
    Format
    111 pages
    Type
    Dissertation
    Degree Level
    Ph.D.
    Discipline
    Psychology
    Rights
    Copyright held by the author.
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    Abstract
    Methods for dealing with non-normal data have been broadly discussed in the structural equation modeling (SEM) literature. The issue of how to properly handle normal missing data has also received enough attention. However, much less research has been done to deal with the situation where non-normality and missingness coexist. Generally speaking, there are three classes of methods for dealing with missing non-normal data (continuous and ordinal) in SEM: a) robust procedures, b) Bayesian analysis, and c) multiple imputation. None of these methods, except for robust full information maximum likelihood (robust FIML), have been systematically evaluated in the SEM context with incomplete non-normal data. In this dissertation, I investigated and compared the performance of the three classes of methods under a broad range of conditions for the two types of missing non-normal data.
    URI
    http://hdl.handle.net/1808/22401
    Collections
    • Dissertations [4475]
    • Psychology Dissertations and Theses [459]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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