Detecting Mixtures From Structural Model Differences Using Latent Variable Mixture Modeling
A Comparison of Relative Model Fit Statistics
Bibliographic Data
| ID | 23345296 |
|---|---|
| Authors | James M Henson (0000-0003-3888-9864, Dominion University College), Steven P Reise (0000-0002-5408-6992, University of California, Los Angeles), Kevin H Kim (0000-0002-9505-4882, University of Pittsburgh) |
| Year | 2007 |
| Volume | 14 |
| Issue | 2 |
| Pages | 202-226 |
| Publication date | 2007-05-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705510709336744 |
| OpenAlex | W1985321835 |
| Language | EN |
| Citations received | 117 |
| References cited | 36 |
The accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) × 3 (exogenous latent mean difference) × 3 (endogenous latent mean difference) × 3 (correlation between factors) × 3 (mixture proportions) factorial design. In addition, the efficacy of several likelihood-based statistics (Akaike's Information Criterion [AIC], Bayesian Information Ctriterion [BIC], the sample-size adjusted BIC [ssBIC], the consistent AIC [CAIC], the Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test [aVLMR]), classification-based statistics (CLC [classification likelihood information criterion], ICL-BIC [integrated classification likelihood], normalized entropy criterion [NEC], entropy), and distributional statistics (multivariate skew and kurtosis test) were examined to determine which statistics best recover the correct number of components. Results indicate that the structural parameters were recovered, but the model fit statistics were not exceedingly accurate. The ssBIC statistic was the most accurate statistic, and the CLC, ICL-BIC, and aVLMR showed limited utility. However, none of these statistics were accurate for small samples (n = 500).
Akaike information criterion · Bayesian inference · Bayesian information criterion · Bayesian probability · Deviance information criterion · Kurtosis · Latent variable · Latent variable model · Likelihood-ratio test · Mixture model · Multivariate statistics · Sample size determination · Statistic · Statistics · Bayesian Methods and Mixture Models · Mathematics · Optimal Experimental Design Methods · Statistical Methods and Bayesian Inference
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| Unique citing works | 117 |
|---|---|
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| Citation span | 2009 - 2026 (18) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 109 |