FACTOR: A computer program to fit the exploratory factor analysis model

• Anderson, R. D., &Rubin, H. (1956). Statistical inference in factor analysis. InProceedings of the 3rd Berkeley Symposium of Mathematical Statistics and Probability (Vol. 5, pp. 111–150). Berkeley: University of California Press.

  Google Scholar 

• Bentler, P. M. (1977). Factor simplicity index and transformations.Psychometrika,42, 277–295.

  Article  Google Scholar 

• Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis.Multivariate Behavioral Research,36, 111–150.

  Article  Google Scholar 

• Browne, M. W., Cudeck, R., Tateneni, K., & Mels, G. (2004). CEFA: Comprehensive Exploratory Factor Analysis, Version 2.00 [Computer software and manual]. Available at http://quantrm2.psy .ohio-state.edu/browne/.

• Clarke, M. R. B. (1970). A rapidly convergent method for maximumlikelihood factor analysis.British Journal of Mathematical & Statistical Psychology,23, 43–52.

  Google Scholar 

• Clarkson, D. B., &Jennrich, R. I. (1988). Quartic rotation criteria and algorithms.Psychometrika,53, 251–259.

  Article  Google Scholar 

• Cureton, E. E., &Mulaik, S. A. (1975). The weighted varimax rotation and the promax rotation.Psychometrika,40, 183–195.

  Article  Google Scholar 

• Eysenck, S. B. G., Eysenck, H. J., &Barrett, P. T. (1985). A revised version of the Psychoticism scale.Personality & Individual Differences,6, 21–29.

  Article  Google Scholar 

• Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., &Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research.Psychological Methods,4, 272–299.

  Article  Google Scholar 

• Gorsuch, R. L. (1983).Factor analysis. Hillsdale, NJ: Erlbaum.

  Google Scholar 

• Hendrickson, A. E., &White, P. O. (1964). Promax: A quick method for rotation to oblique simple structure.British Journal of Statistical Psychology,17, 65–70.

  Google Scholar 

• Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis.Psychometrika,30, 179–185.

  Article  PubMed  Google Scholar 

• Jöreskog, K. G. (1977). Factor analysis by least-squares and maximumlikelihood methods. In K. Enslein, A. Ralston, & H. S. Wilf (Eds.),Statistical methods for digital computers (pp. 125–153). New York: Wiley.

  Google Scholar 

• Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis.Psychometrika,23, 187–200.

  Article  Google Scholar 

• Kiers, H. A. L. (1994). Simplimax: An oblique rotation to an optimal target with simple structure.Psychometrika,59, 567–579.

  Article  Google Scholar 

• Lorenzo-Seva, U. (1999). Promin: A method for oblique factor rotation.Multivariate Behavioral Research,34, 347–356.

  Article  Google Scholar 

• Lorenzo-Seva, U. (2000). The weighted oblimin rotation.Psychometrika,65, 301–318.

  Article  Google Scholar 

• Lorenzo-Seva, U. (2003). A factor simplicity index.Psychometrika,68, 49–60.

  Article  Google Scholar 

• MacCallum, R. (1983). A comparison of factor analysis programs in SPSS, BMDP, and SAS.Psychometrika,48, 223–231.

  Article  Google Scholar 

• McDonald, R. P. (1981). Constrained least squares estimators of oblique common factors.Psychometrika,46, 337–341.

  Article  Google Scholar 

• McKeon, J. J. (1968).Rotation for maximum association between factors and tests. Unpublished manuscript, George Washington University.

• Mislevy, R. J., &Bock, R. D. (1990).BILOG 3: Item analysis and test scoring with binary logistic models. Mooresville, IN: Scientific Software.

  Google Scholar 

• Mulaik, S. A. (1972).The foundations of factor analysis. New York: McGraw-Hill.

  Google Scholar 

• Shapiro, A., &ten Berge, J. M. F. (2002). Statistical inference of minimum rank factor analysis.Psychometrika,67, 79–94.

  Article  Google Scholar 

• ten Berge, J. M. F., &Hofstee, W. K. B. (1999). Coefficients alpha and reliabilities of unrotated and rotated components.Psychometrika,64, 83–90.

  Article  Google Scholar 

• ten Berge, J. M. F., &Kiers, H. A. L. (1991). A numerical approach to the exact and the approximate minimum rank of a covariance matrix.Psychometrika,56, 309–315.

  Article  Google Scholar 

• ten Berge, J. M. F., Krijnen, W., Wansbeek, T., &Shapiro, A. (1999). Some new results on correlation-preserving factor scores prediction methods.Linear Algebra & Its Applications,289, 311–318.

  Article  Google Scholar 

• ten Berge, J. M. F., &Nevels, K. (1977). A general solution to Mosier’s oblique Procrustes problem.Psychometrika,42, 593–600.

  Article  Google Scholar 

• Tucker, L. R. (1951).A method for synthesis of factor analysis studies (Personnel Research Section Report No. 984). Washington, DC: U.S. Department of the Army.

  Google Scholar 

• Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations.Psychometrika,41, 321–327.

  Article  Google Scholar 

• Yates, A. (1987).Multivariate exploratory data analysis: A perspective on exploratory factor analysis. Albany: State University of New York Press.

  Google Scholar 

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