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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

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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    Date Created
    • 2012
    Resource Type
  • Text
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    Note
    • Partial requirement for: M.A., Arizona State University, 2012
      Note type
      thesis
    • Includes bibliographical references (p. 54-56)
      Note type
      bibliography
    • Field of study: Psychology

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    by Ingrid Carlson Wurpts

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