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.
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- Partial requirement for: M.A., Arizona State University, 2012Note typethesis
- Includes bibliographical references (p. 54-56)Note typebibliography
- Field of study: Psychology