Exploratory factor analysis with small sample sizes

  • de Winter J
  • Dodou D
  • Wieringa P
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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 (œ), 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 unequalp/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 data set of individuals who filled in a Big Five Inventory via the Internet. Results showed that when data are well conditioned (i.e., highœ, lowf, highp), 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.

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  • J.C.F de Winter

  • D. Dodou

  • P.A. Wieringa

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