title: Exploratory factor analysis for small samples.
abstract: Traditionally, two distinct approaches have been employed for exploratory factor analysis: maximum likelihood factor analysis and principal component analysis. A third alternative, called regularized exploratory factor analysis, was introduced recently in the psychometric literature. Small sample size is an important issue that has received considerable discussion in the factor analysis literature. However, little is known about the differential performance of these three approaches to exploratory factor analysis in a small sample size scenario. A simulation study and an empirical example demonstrate that regularized exploratory factor analysis may be recommended over the two traditional approaches, particularly when sample sizes are small (below 50) and the sample covariance matrix is near singular.
authors: Ulsan National Institute of Science and Technology, School of Technology Management, Ulsan, Korea. sjung@unist.ac.kr
authors: Sunho
authors: S
authors: Jung
authors: Soonmook
authors: S
authors: Lee
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005163:Factor Analysis, Statistical; D015233:Models, Statistical; D009010:Monte Carlo Method; D012106:Research; D018401:Sample Size