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Multiple Imputation for Two-Level Hierarchical Models with Categorical Variables and Missing at Random Data


Abstract Accurate data analysis and interpretation of results may be influenced by many potential factors. The factors of interest in the current work are the chosen analysis model(s), the presence of missing data, and the type(s) of data collected. If analysis models are used which a) do not accurately capture the structure of relationships in the data such as clustered/hierarchical data, b) do not allow or control for missing values present in the data, or c) do not accurately compensate for different data types such as categorical data, then the assumptions associated with the model have not been met and the results of the analysis may be inaccurate. In the presence of clustered/nested data, hierarchical linear modeling or multilevel modeling (ML... (more)
Created Date 2016
Contributor Kunze, Katie Lynn (Author) / Levy, Roy (Advisor) / Enders, Craig K (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Subject Quantitative psychology / Statistics / Educational tests & measurements / Bayesian Estimation / Categorical Data Analysis / Missing at Random Data / Missing Data Theory / Multilevel Modeling / Multiple Imputation
Type Doctoral Dissertation
Extent 145 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Educational Psychology 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis