A comparison of dimensionality assessment methods in factor analysis under second order, bifactorial, and oblique factor models [r-libre/4135]

Caron, Pier-Olivier (06 juin 2026). A comparison of dimensionality assessment methods in factor analysis under second order, bifactorial, and oblique factor models. Communication présentée au 87e congrès de la Société Canadienne de Psychologie (SCP), Montréal.

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Catégorie de document :	Communications à des congrès/colloques et conférences (non publiées)
Évaluation par un comité de lecture :	Oui
Étape de publication :	Non publié
Résumé :	Determining the number of factors to extract has remained a challenging issue in factor analysis. Several dimensionality assessment methods have been proposed over the past 125 years, and several promising recent ones deserved careful comparison, especially in hard, but realistic conditions. In this study, we compared the performance (accuracy, bias, root mean square error) of five recent and promising methods (Next Eigenvalue Sequential Test, Exploratory Graph Analysis , Empirical Kaiser Criterion, Sequential χ^2 test, and Out-of-Sample Prediction Error) as well as two versions of the more traditional parallel analysis under sophisticated factorial model, which are the second order(2ND), the bifactorial (BIF) and the oblique factors (OBL). The formers have been relatively neglected. A Monte Carlo simulation varying N (250, 500, 750, 1000), minimal loadings (.4, .5, .6) and three population factorial model (BIF, 2ND, OBL) with there defining parameters. Each of the 432 conditions is repeated 1000 times. Methods tended to under extract the correct numbers of factors in 2ND and BIF, because either the general factor was combined with the first specific factor or because the last factor was too small to be detected. The presentation discussed which methods is the best in which conditions and their corresponding pitfalls.
Déposant:	Caron, Pier-Olivier
Responsable :	Pier-Olivier Caron
Dépôt :	09 juin 2026 19:27
Dernière modification :	09 juin 2026 19:27

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