101895: A Monte Carlo investigation of factors influencing latent class analysis: An application to eating disorder research

Volume 45 Issue 5

About the Course:

"A Monte Carlo investigation of factors influencing latent class analysis: An application to eating disorder research"

Latent class analysis (LCA) has frequently been used to identify qualitatively distinct phenotypes of disordered eating. However, little consideration has been given to methodological factors that may influence the accuracy of these results.

Journal/Publisher:

Wiley Interscience Journal, International Journal of Eating Disorders

Publication Date:

July 2012

Authors

Sonja A. Swanson, ScM; Katajun Lindenberg, Dipl-Psych; Stephanie Bauer, PhD; Ross D. Crosby, PhD

Recommended For:

This course is recommended for researchers (e.g., statisticians, principal investigators, etc.) who plan to conduct latent class analyses or who wish to better interpret previously published studies using latent class analysis.

Course Objectives:

  1. Interpret the role latent class analysis may have in exploring empirically-based classification systems.

  2. Evaluate the effects of the factors listed above (sample size, choice of information criterion, missing data, sample composition, class balance, number of indicators, and conditional independence violations) in the context of critically interpreting previously published latent class analyses.

  3. Explain the effects of the factors listed above (sample size, choice of information criterion, missing data, sample composition, class balance, number of indicators, and conditional independence violations) on the accuracy of a latent class analysis in the context of designing future studies.

Exam Questions

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