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150765 https://hdl.handle.net/2286/R.I.14788 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2012 vi, 101 p. : ill. (some col.) Masters Thesis Academic theses Text eng Wurpts, Ingrid Carlson Geiser, Christian Aiken, Leona West, Stephen Arizona State University Partial requirement for: M.A., Arizona State University, 2012 Includes bibliographical references (p. 54-56) Field of study: Psychology 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 sample sizes lower than N = 100, and to use many high quality indicators and at least one strong covariate when using sample sizes less than N = 500. Psychology Statistics covariates Indicators Latent Class Analysis parameter bias simulation study Latent structure analysis Social sciences--Mathematical models. Testing the limits of latent class analysis