Factor Mixture Models is a latent variable model that combines factor analysis and finite mixture modeling by assuming the population consists of unobserved subpopulations, each with distinct factor structures, thereby allowing for heterogeneity in both the mean and covariance structure of the latent factors across classes.
278
Publications
38K
Citations
771
Authors
410
Institutions
Publications per year
2017–2026
68
Variants
Fmm
Parents
Publications
278
Simultaneous Factor Analysis in Several Populations
Karl G. Jöreskog · Psychometrika · 1971 · 2K citations
1–5 of 278
Authors
771
- BM
12
h-index
- GH
7
h-index
- PD
6
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- NB
6
h-index
- EK
6
h-index
1–5 of 771
Institutions
410
University of California, Los Angeles
Los Angeles, United States
14 h-index
17 publications
25K citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
8 h-index
11 publications
766 citations
Florida State University
Tallahassee, United States
8 h-index
23 publications
924 citations
University of Guelph
Guelph, Canada
7 h-index
16 publications
1.4K citations
University of South Florida
Tampa, United States
7 h-index
18 publications
1.4K citations
University of Notre Dame
Notre Dame, United States
7 h-index
9 publications
3K citations
University of British Columbia
Vancouver, Canada
6 h-index
11 publications
737 citations
The University of Queensland
Brisbane, Australia
6 h-index
14 publications
2K citations
1–8 of 410
Venues
- Structural Equation Modeling A Multidisciplinary Journal
SEM · 18 publications · 13.5K citations
15
h-index
- Computational Statistics & Data Analysis
CSDA · 13 publications · 1.1K citations
13
h-index
- Multivariate Behavioral Research
MBR · 10 publications · 2K citations
10
h-index
- Psychometrika
9 publications · 3.2K citations
9
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- Psychological Assessment
7 publications · 270 citations
7
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1–5