Exploratory Factor Analysis With Small Sample Sizes

2009/04/14 by J. C. F. de Winter*, D. Dodou*, Dimitra Dodou +2 · 1,154 citations
Chemistry · Computer Science · Decision Sciences · Mathematics · Psychology · #Advanced Statistical Modeling Techniques #Chemistry #Econometrics #Exploratory factor analysis #Factor analysis #Materials science #Mathematics #Mental Health Research Topics #Psychology #Psychometric Methodologies and Testing #Psychometrics #Sample (material) #Sample size determination #Statistics #Yield (engineering)

paper · doi:10.1080/00273170902794206

published in Multivariate Behavioral Research 44(2), 147-181 (Taylor & Francis)

openalex publication_date 2009/04/14 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/04

Abstract

Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes (N), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for N below 50. Simulations were carried out to estimate the minimum required N for different levels of loadings (λ), number of factors (f), and number of variables (p) and to examine the extent to which a small N solution can sustain the presence of small distortions such as interfactor correlations, model error, secondary loadings, unequal loadings, and unequal p/f. Factor recovery was assessed in terms of pattern congruence coefficients, factor score correlations, Heywood cases, and the gap size between eigenvalues. A subsampling study was also conducted on a psychological dataset of individuals who filled in a Big Five Inventory via the Internet. Results showed that when data are well conditioned (i.e., high λ, low f, high p), EFA can yield reliable results for N well below 50, even in the presence of small distortions. Such conditions may be uncommon but should certainly not be ruled out in behavioral research data. ∗ These authors contributed equally to this work.

Citations

• Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis

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