Choosing the Optimal Number of Factors in Exploratory Factor Analysis: A Model Selection Perspective
- 1 January 2013
- journal article
- research article
- Published by Taylor & Francis Ltd in Multivariate Behavioral Research
- Vol. 48 (1), 28-56
- https://doi.org/10.1080/00273171.2012.710386
Abstract
A central problem in the application of exploratory factor analysis is deciding how many factors to retain (m). Although this is inherently a model selection problem, a model selection perspective is rarely adopted for this task. We suggest that Cudeck and Henly's (1991) framework can be applied to guide the selection process. Researchers must first identify the analytic goal: identifying the (approximately) correct m or identifying the most replicable m. Second, researchers must choose fit indices that are most congruent with their goal. Consistent with theory, a simulation study showed that different fit indices are best suited to different goals. Moreover, model selection with one goal in mind (e.g., identifying the approximately correct m) will not necessarily lead to the same number of factors as model selection with the other goal in mind (e.g., identifying the most replicable m). We recommend that researchers more thoroughly consider what they mean by “the right number of factors” before they choose fit indices.Keywords
This publication has 56 references indexed in Scilit:
- An Overview of Analytic Rotation in Exploratory Factor AnalysisMultivariate Behavioral Research, 2001
- Cross-Validation MethodsJournal of Mathematical Psychology, 2000
- Akaike's Information Criterion and Recent Developments in Information ComplexityJournal of Mathematical Psychology, 2000
- Assessing sources of error in structural equation models: The effects of sample size, reliability, and model misspecificationStructural Equation Modeling: A Multidisciplinary Journal, 1997
- Structural Equations with Latent VariablesPublished by Wiley ,1989
- Choice of structural model via parsimony: A rationale based on precision.Psychological Bulletin, 1989
- Factor analysis and AICPsychometrika, 1987
- An Expert Model Selection Approach to Determine the “Best” Pattern Structure in Factor Analysis ModelsPublished by Springer Science and Business Media LLC ,1987
- Multivariate Analysis with Latent Variables: Causal ModelingAnnual Review of Psychology, 1980
- Science and StatisticsJournal of the American Statistical Association, 1976