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Assessing Measurement Invariance and Latent Mean Differences with Bifactor Multidimensional Data in Structural Equation Modeling

Abstract Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation for single-group analyses, little research on this issue has been conducted for multiple-group analyses. This study explored the effects of mismatch in dimensionality between data and analysis models with multiple-group analyses at the population and sample levels. Datasets were generated using a bifactor model with different factor structures and were analyzed with bifactor and single-factor models to assess misspecification effects on assessments of MI and latent mean differences. As baseline models,... (more)
Created Date 2018
Contributor Xu, Yuning (Author) / Green, Samuel (Advisor) / Levy, Roy (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Subject Educational tests & measurements / Quantitative psychology / Statistics / bifactor models / dimensionality / latent means / measurement invariance / simulation / structural equation modeling
Type Doctoral Dissertation
Extent 99 pages
Language English
Note Doctoral Dissertation Educational Psychology 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis