Accuracy of Revised and Traditional Parallel Analyses for Assessing Dimensionality with Binary Data.

Parallel analysis (PA) is a useful empirical tool for assessing the number of factors in exploratory factor analysis. On conceptual and empirical grounds, we argue for a revision to PA that makes it more consistent with hypothesis testing. Using Monte Carlo methods, we evaluated the relative accuracy of the revised PA (R-PA) and traditional PA (T-PA) methods for factor analysis of tetrachoric correlations between items with binary responses.