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Cited by 430Psychometric Methodologies and TestingBayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Detecting Mixtures From Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics

Henson, James M., Reise, Steven P., Kim, Kevin H.·2007·Structural Equation Modeling: A Multidisciplinary Journal
DOI 10.1080/10705510709336744
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Abstract

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).

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Research works printed but not linked: 5

  1. Cutler A., 1993, Proceedings of the first U. S.-Japan conference on the Frontiers of Statistical Modeling, 149
  2. Hays W. L., 1994, Statistics.
  3. Leroux B. G., 1992, AnnalsofStatistics, 20, 1350
  4. Mardia K. V., 1974, Sankhya, 36, 115
  5. Muthén L. K., 2001, Mplus user's guide,

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Metadata

Authors
Henson, J. M., Reise, S. P., & Kim, K. H.
Affiliations
  • Old Dominion University (US)
  • University of California, Los Angeles (US)
  • University of Pittsburgh (US)
ORCID
Venue
Structural Equation Modeling: A Multidisciplinary Journal
Publisher
Informa UK Limited
Published
2007-05
Volume
14
Issue
2
Pages
202-226
ISSN
  • 1070-5511 (ISSN-L)
  • 1532-8007
DOI
10.1080/10705510709336744
OpenAlex
W1985321835
Cited by
429
References
45
Citations by year
2026: 13 · 2025: 23 · 2024: 24 · 2023: 21 · 2022: 29 · 2021: 34 · 2020: 43 · 2019: 26 · 2018: 23 · 2017: 24 · 2016: 30 · 2015: 30 · 2014: 18 · 2013: 28 · 2012: 18
FWCI
15.26
Type
journal article
Language
en
Open access
closed
Locations
Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
model fit statistics, number of components, sample size, Akaike information criterion, Bayesian information criterion, entropy (information theory)

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