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Title: CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers
Authors: Bulteel, Kirsten
Wilderjans, Tom
Tuerlinckx, Francis
Ceulemans, Eva # ×
Issue Date: 2013
Publisher: Psychonomic Society
Series Title: Behavior Research Methods vol:45 issue:3 pages:782-791
Abstract: Mixture analysis is commonly used for clustering objects on the basis of multivariate data. When the data contain a large number of variables, regular mixture analysis
may become problematic, because a large number of parameters
need to be estimated for each cluster. To tackle this
problem, the mixtures-of-factor-analyzers (MFA) model was
proposed, which combines clustering with exploratory factor
analysis. MFA model selection is rather intricate, as both the number of clusters and the number of underlying factors have to be determined. To this end, the Akaike (AIC) and Bayesian(BIC) information criteria are often used. AIC and BIC try to identify a model that optimally balances model fit and model complexity. In this article, the CHull (Ceulemans & Kiers, 2006) method, which also balances model fit and complexity, is presented as an interesting alternative model selection strategy for MFA. In an extensive simulation study, the performances of AIC, BIC, and CHull were compared. AIC performs poorly and systematically selects overly complex models, whereas BIC performs slightly better than CHull when considering the best model only. However, when taking model selection uncertainty into account by looking at the first three models retained, CHull outperforms BIC. This especially holds in more complex, and thus more realistic, situations (e.g., more clusters, factors, noise in the data, and overlap among clusters).
ISSN: 1554-351X
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Quantitative Psychology and Individual Differences
Methodology of Educational Sciences
× corresponding author
# (joint) last author

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