naa a22 4500 510776914 CHVBK 20180411083233.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.3758/s13428-012-0293-y doi (NATIONALLICENCE)springer-10.3758/s13428-012-0293-y CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers [Elektronische Daten] [Kirsten Bulteel, Tom Wilderjans, Francis Tuerlinckx, Eva Ceulemans] 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). Psychonomic Society, Inc., 2013 Mixture analysis nationallicence Model selection nationallicence AIC nationallicence BIC nationallicence CHull nationallicence Bulteel Kirsten KU Leuven, Leuven, Belgium aut Wilderjans Tom KU Leuven, Leuven, Belgium aut Tuerlinckx Francis KU Leuven, Leuven, Belgium aut Ceulemans Eva KU Leuven, Leuven, Belgium aut Behavior Research Methods Springer US; http://www.springer-ny.com 45/3(2013-09-01), 782-791 45:3<782 2013 45 13428 https://doi.org/10.3758/s13428-012-0293-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.3758/s13428-012-0293-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bulteel Kirsten KU Leuven, Leuven, Belgium aut NATIONALLICENCE 700 1- Wilderjans Tom KU Leuven, Leuven, Belgium aut NATIONALLICENCE 700 1- Tuerlinckx Francis KU Leuven, Leuven, Belgium aut NATIONALLICENCE 700 1- Ceulemans Eva KU Leuven, Leuven, Belgium aut NATIONALLICENCE 773 0- Behavior Research Methods Springer US; http://www.springer-ny.com 45/3(2013-09-01), 782-791 45:3<782 2013 45 13428 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer