中  華  心  理  學  刊
民92，45卷，4期，323-335

Li-Jen Weng（Department of Psychology, National Taiwan University）；Chun-Ting Lee（Department of Psychology, National Taiwan University）；Po-Ju Wu（Department of Psychology, National Taiwan University）

Abstract

Determining the number of factors is a critical step in factor analysis. Horn (1965) proposed the method of parallel analysis to use mean eigenvalues of random data correlation matrices for estimation of number of factors. Various regression equations were developed to simplify the estimation of mean eigenvalues of random data correlation matrices. The present research systematically evaluated the performance of four regression equations in estimating the eigenvalues of random data correlation matrices. The results indicated that the regression equation developed by Longman et al. (1989) performed the best, followed closely by Keeling (2000). Lautenschlager et al. (1989) came next, and Allen and Hubbard (1986) had the worst performance.

Keywords：parallel analysis, regression equations, eigenvalues, factor analysis, number of factors