2011/10/11 by Samuel B. Green, Roy Levy, Marilyn S. Thompson +2 · 1 voice
Computer Science · Decision Sciences · Agricultural and Biological Sciences · #Advanced Statistical Modeling Techniques #Psychometric Methodologies and Testing #Sensory Analysis and Statistical Methods
paper · doi:10.1177/0013164411422252
openalex publication_date 2011/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
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 compare revised and traditional parallel analysis approaches. Five dimensions are manipulated in the study: number of observations, number of factors, number of measured variables, size of the factor loadings, and degree of correlation between factors. Based on the results, the revised parallel analysis method, using principal axis factoring and the 95th percentile eigenvalue rule, offers promise.