Covariate inclusion in factor mixture modeling: Evaluating one-step and three-step approaches under model misspecification and overfitting-Reference-Cited by-同舟云学术

Covariate inclusion in factor mixture modeling: Evaluating one-step and three-step approaches under model misspecification and overfitting

Published:2022-09-12 Issue: Volume: Page:
ISSN:1554-3528
Container-title:Behavior Research Methods
language:en
Short-container-title:Behav Res

Author:

Wang Yan^ORCID,Cao Chunhua^ORCID,Kim Eunsook^ORCID

Publisher

Springer Science and Business Media LLC

Subject

General Psychology,Psychology (miscellaneous),Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Experimental and Cognitive Psychology

Link

https://link.springer.com/content/pdf/10.3758/s13428-022-01964-8.pdf

Reference43 articles.

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3. Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. https://doi.org/10.1080/10705511.2014.915181

4. Bakk, Z., Tekle, F. B., & Vermunt, J. K. (2013). Estimating the association between latent class membership and external variables using bias adjusted three-step approaches. Sociological Methodology, 43(1), 272–311. https://doi.org/10.1177/0081175012470644

5. Bauer, D. J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 42(4), 757–786. https://doi.org/10.1080/00273170701710338

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