Exploratory factor analysis (EFA) has emerged in the field of animal behavior as a useful tool for determining
and assessing latent behavioral constructs. Because the small sample size problem often occurs in
this field, a traditional approach, unweighted least squares, has been considered the most feasible choice
for EFA. Two new approaches were recently introduced in the statistical literature as viable alternatives
to EFA when sample size is small: regularized exploratory factor analysis and generalized exploratory
factor analysis. A simulation study is conducted to evaluate the relative performance of these three
approaches in terms of factor recovery under various experimental conditions of sample size, degree
of overdetermination, and level of communality. In this study, overdetermination and sample size are
the meaningful conditions in differentiating the performance of the three approaches in factor recovery.