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Direct linkXia, Yan; Green, Samuel B.; Xu, Yuning; Thompson, Marilyn S. – Educational and Psychological Measurement, 2019
Past research suggests revised parallel analysis (R-PA) tends to yield relatively accurate results in determining the number of factors in exploratory factor analysis. R-PA can be interpreted as a series of hypothesis tests. At each step in the series, a null hypothesis is tested that an additional factor accounts for zero common variance among…
Descriptors: Effect Size, Factor Analysis, Hypothesis Testing, Psychometrics
Green, Samuel; Xu, Yuning; Thompson, Marilyn S. – Educational and Psychological Measurement, 2018
Parallel analysis (PA) assesses the number of factors in exploratory factor analysis. Traditionally PA compares the eigenvalues for a sample correlation matrix with the eigenvalues for correlation matrices for 100 comparison datasets generated such that the variables are independent, but this approach uses the wrong reference distribution. The…
Descriptors: Factor Analysis, Accuracy, Statistical Distributions, Comparative Analysis
Green, Samuel B.; Redell, Nickalus; Thompson, Marilyn S.; Levy, Roy – Educational and Psychological Measurement, 2016
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…
Descriptors: Accuracy, Factor Analysis, Hypothesis Testing, Monte Carlo Methods
Green, Samuel B.; Thompson, Marilyn S.; Levy, Roy; Lo, Wen-Juo – Educational and Psychological Measurement, 2015
Traditional parallel analysis (T-PA) estimates the number of factors by sequentially comparing sample eigenvalues with eigenvalues for randomly generated data. Revised parallel analysis (R-PA) sequentially compares the "k"th eigenvalue for sample data to the "k"th eigenvalue for generated data sets, conditioned on"k"-…
Descriptors: Factor Analysis, Error of Measurement, Accuracy, Hypothesis Testing
Green, Samuel B.; Levy, Roy; Thompson, Marilyn S.; Lu, Min; Lo, Wen-Juo – Educational and Psychological Measurement, 2012
A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to…
Descriptors: Monte Carlo Methods, Factor Structure, Data Analysis, Psychometrics
Crawford, Aaron V.; Green, Samuel B.; Levy, Roy; Lo, Wen-Juo; Scott, Lietta; Svetina, Dubravka; Thompson, Marilyn S. – Educational and Psychological Measurement, 2010
Population and sample simulation approaches were used to compare the performance of parallel analysis using principal component analysis (PA-PCA) and parallel analysis using principal axis factoring (PA-PAF) to identify the number of underlying factors. Additionally, the accuracies of the mean eigenvalue and the 95th percentile eigenvalue criteria…
Descriptors: Factor Analysis, Statistical Analysis, Comparative Analysis
