Department

University of Tennessee at Chattanooga. Dept. of Psychology

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

One of the most important decisions to make when performing an exploratory factor analysis regards the number of factors to retain. Parallel analysis is considered to be the best course of action in these circumstances as it consistently outperforms other factor extraction methods (Zwick & Velicer, 1986). Even so, parallel analysis requires further research and refinement to improve its accuracy. Characteristics such as factor loadings, correlations between factors, and number of variables per factor all have been shown to adversely impact the effectiveness of parallel analysis as a means of identifying the number of factors. Critically, even the choice of criteria on which to evaluate factors such as the eigenvalue at the 50th or 95th percentile can have deleterious effects on the number of factors extracted. One area of parallel analysis yet to be researched is the magnitude of the difference between the actual eigenvalue and the random data-based eigenvalue. Currently, even if the margin between the actual eigenvalue and the random data-based eigenvalue is nominal, the factor is considered to be meaningful. As such, it may behoove researchers to enforce a higher standard, such as a greater margin between the two eigenvalues than just an absolute difference. Accordingly, the purpose of this study will be to evaluate the efficacy of a 10 percent margin criterion as compared to an absolute margin. These margins will specifically be evaluated in conjunction with the 50th, 90th, 95th, and 99th percentile eigenvalue criteria on a population correlation matrix which engenders underextraction. Previous research (Matsumoto & Brown, 2017) explored the same conditions on a population correlation matrix designed to cause overextraction. They found that the most stringent standard (99th percentile eigenvalue plus 10 percent margin) was the most accurate. For the present study however, we hypothesize that the most accurate results will be obtained from a standard less stringent than the 99th percentile eigenvalue plus 10 percent margin. This research has important implications for the scientific and practical application of psychometrics.

Date

October 2017

Subject

Industrial and organizational psychology

Document Type

posters

Language

English

Rights

Under copyright.

License

http://creativecommons.org/licenses/by-nc-nd/3.0/

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Oct 28th, 10:00 AM Oct 28th, 10:55 AM

Investigating the accuracy of parallel analysis in underextraction conditions: A monte carlo study

One of the most important decisions to make when performing an exploratory factor analysis regards the number of factors to retain. Parallel analysis is considered to be the best course of action in these circumstances as it consistently outperforms other factor extraction methods (Zwick & Velicer, 1986). Even so, parallel analysis requires further research and refinement to improve its accuracy. Characteristics such as factor loadings, correlations between factors, and number of variables per factor all have been shown to adversely impact the effectiveness of parallel analysis as a means of identifying the number of factors. Critically, even the choice of criteria on which to evaluate factors such as the eigenvalue at the 50th or 95th percentile can have deleterious effects on the number of factors extracted. One area of parallel analysis yet to be researched is the magnitude of the difference between the actual eigenvalue and the random data-based eigenvalue. Currently, even if the margin between the actual eigenvalue and the random data-based eigenvalue is nominal, the factor is considered to be meaningful. As such, it may behoove researchers to enforce a higher standard, such as a greater margin between the two eigenvalues than just an absolute difference. Accordingly, the purpose of this study will be to evaluate the efficacy of a 10 percent margin criterion as compared to an absolute margin. These margins will specifically be evaluated in conjunction with the 50th, 90th, 95th, and 99th percentile eigenvalue criteria on a population correlation matrix which engenders underextraction. Previous research (Matsumoto & Brown, 2017) explored the same conditions on a population correlation matrix designed to cause overextraction. They found that the most stringent standard (99th percentile eigenvalue plus 10 percent margin) was the most accurate. For the present study however, we hypothesize that the most accurate results will be obtained from a standard less stringent than the 99th percentile eigenvalue plus 10 percent margin. This research has important implications for the scientific and practical application of psychometrics.