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Sensitivity Analysis of Longitudinal Measurement Non-Invariance: A Second-Order Latent Growth Model Approach with Ordered-Categorical Indicators


Abstract Researchers who conduct longitudinal studies are inherently interested in studying individual and population changes over time (e.g., mathematics achievement, subjective well-being). To answer such research questions, models of change (e.g., growth models) make the assumption of longitudinal measurement invariance. In many applied situations, key constructs are measured by a collection of ordered-categorical indicators (e.g., Likert scale items). To evaluate longitudinal measurement invariance with ordered-categorical indicators, a set of hierarchical models can be sequentially tested and compared. If the statistical tests of measurement invariance fail to be supported for one of the models, it is useful to have a method with which to gauge... (more)
Created Date 2016
Contributor Liu, Yu (Author) / West, Stephen G (Advisor) / Tein, Jenn-Yun (Advisor) / Green, Samuel (Committee member) / Grimm, Kevin J (Committee member) / Arizona State University (Publisher)
Subject Quantitative psychology / effect size / longitudinal measurement invariance / ordered-categorical data / second-order latent growth model / sensitivity analysis
Type Doctoral Dissertation
Extent 185 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Psychology 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis