A Comparison of Regression Equations for Estimation of Eigenvalues of Random Data Correlation Matrices in Parallel Analysis
Journal
中華心理學刊
Journal Volume
45
Journal Issue
4
Pages
323-335
Date Issued
2003
Author(s)
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.
Type
journal article
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