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Selection of Variables in Exploratory Factor Analysis: An Empirical Comparison of a Stepwise and Traditional Approach

Published online by Cambridge University Press:  01 January 2025

Kristine Y. Hogarty ,
Jeffrey D. Kromrey ,
John M. Ferron  and
Constance V. Hines
Kristine Y. Hogarty*
Affiliation:
University of South Florida
Jeffrey D. Kromrey
Affiliation:
University of South Florida
John M. Ferron
Affiliation:
University of South Florida
Constance V. Hines
Affiliation:
University of South Florida
*
Requests for reprints should be sent to Kristine Y. Hogarty, Department of Educational Measurement and Research, University of South Florida, 4202 E. Fowler Ave., EDU 162, Tampa, FL 33620. E-mail: hogarty@tempest.coedu.usf.edu
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Abstract

The purpose of this study was to investigate and compare the performance of a stepwise variable selection algorithm to traditional exploratory factor analysis. The Monte Carlo study included six factors in the design; the number of common factors; the number of variables explained by the common factors; the magnitude of factor loadings; the number of variables not explained by the common factors; the type of anomaly evidenced by the poorly explained variables; and sample size. The performance of the methods was evaluated in terms of selection and pattern accuracy, and bias and root mean squared error of the structure coefficients. Results indicate that the stepwise algorithm was generally ineffective at excluding anomalous variables from the factor model. The poor selection accuracy of the stepwise approach suggests that it should be avoided.

Keywords

Stepwise variable selectionexploratory factor analysisgoodness-of-fitvarimax rotationstatistical biasselection accuracypattern accuracy
Type
Theory And Methods
Information
Psychometrika , Volume 69 , Issue 4 , December 2004 , pp. 593 - 611
Copyright
Copyright © 2004 The Psychometric Society

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