What is it about?

Factor mixture modeling (FMM) allows researchers to simultaneously model unobserved grouping of variables (known as latent factors) and unobserved grouping of individuals (known as latent classes). FMM provides a unified framework combining factor analysis and latent class/profile analysis. We conducted a systematic review of 76 FMM applications and examined how FMM has been utilized in the real-world research.

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Why is it important?

Based on the analysis of the current usage of FMM in applied studies and the identified gap between methodology and applications, this review informs applied researchers of optimal practices and potentials of FMM. Additionally, this review provides methodologists with directions for future research that can be used to facilitate and enhance applied research using FMM.

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This page is a summary of: A systematic review of and reflection on the applications of factor mixture modeling., Psychological Methods, December 2023, American Psychological Association (APA),
DOI: 10.1037/met0000630.
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