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Testing the Limits of Latent Class Analysis


Abstract The purpose of this study was to examine under which conditions "good" data characteristics can compensate for "poor" characteristics in Latent Class Analysis (LCA), as well as to set forth guidelines regarding the minimum sample size and ideal number and quality of indicators. In particular, we studied to which extent including a larger number of high quality indicators can compensate for a small sample size in LCA. The results suggest that in general, larger sample size, more indicators, higher quality of indicators, and a larger covariate effect correspond to more converged and proper replications, as well as fewer boundary estimates and less parameter bias. Based on the results, it is not recommended to use LCA with ... (more)
Created Date 2012
Contributor Wurpts, Ingrid Carlson (Author) / Geiser, Christian (Advisor) / Aiken, Leona (Advisor) / West, Stephen (Committee member) / Arizona State University (Publisher)
Subject Psychology / Statistics / covariates / indicators / latent class analysis / parameter bias / simulation study
Type Masters Thesis
Extent 108 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.A. Psychology 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis