A Methodological Review of Exploratory Factor Analysis in Sexuality Research: Used Practices, Best Practices, and Data Analysis Resources

References

• Bandalos, D. L. (2006). The role of simulation in structural equation modeling. In G. R. Hancock & R. Mueller (Eds.), Structural equation modeling: A second course (pp. 385–426). Greenwich, CT: Information Age.
   Google Scholar
• Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.
   Google Scholar
• Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111–150. doi:10.1207/S15327906MBR3601_05
   Web of Science ®Google Scholar
• Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245–276. doi:10.1207/s15327906mbr0102_10
   PubMed Web of Science ®Google Scholar
• Cattell, R. B. (1978). The scientific use of factor analysis. New York, NY: Plenum.
   Google Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.
   Google Scholar
• Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis. Hillsdale, NJ: Erlbaum.
   Google Scholar
• Conway, J. M., & Huffcutt, A. I. (2003). A review and evaluation of exploratory factor analysis practices in organizational research. Organizational Research Methods, 6, 147–168. doi:10.1177/1094428103251541
   Web of Science ®Google Scholar
• Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10, 1–8.
   Google Scholar
• Cudeck, R., & O’Dell, L. L. (1994). Applications of standard error estimates in unrestricted factor analysis: Significance tests for factor loadings and correlations. Psychological Bulletin, 115, 475–487. doi:10.1037/0033-2909.115.3.475
   PubMed Web of Science ®Google Scholar
• Everitt, B. S. (1975). Multivariate analysis: The need for data and other problems. British Journal of Psychiatry, 126, 237–240. doi:10.1192/bjp.126.3.237
   PubMed Web of Science ®Google Scholar
• Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory factor analysis. New York, NY: Oxford University Press.
   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. doi:10.1037/1082-989X.4.3.272
   Web of Science ®Google Scholar
• Fisher, T. D., Davis, C. M., Yarber, W. L., & Davis, S. L. (2011). Handbook of sexuality-related measures (3rd ed.). New York, NY: Routledge.
   Google Scholar
• Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7, 286–299. doi:10.1037/1040-3590.7.3.286
   Web of Science ®Google Scholar
• Ford, J. K., MacCallum, R. C., & Tait, M. (1986). The application of exploratory factor analysis in applied psychology: A critical review and analysis. Personnel Psychology, 39, 291–314. doi:10.1111/j.1744-6570.1986.tb00583.x
   Web of Science ®Google Scholar
• Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Erlbaum.
   Google Scholar
• Guttman, L. (1954). Some necessary conditions for common-factor analysis. Psychometrika, 19, 149–161. doi:10.1007/BF02289162
   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. doi:10.1111/j.2044-8317.1964.tb00244.x
   Web of Science ®Google Scholar
• Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66, 393–416. doi:10.1177/0013164405282485
   Web of Science ®Google Scholar
• Hetzel, R. D. (1996). A primer on factor analysis with comments on patterns of practice and reporting. In B. Thompson (Ed.), Advances in social science methodology (Vol. 4, pp. 175–206). Greenwich, CT: JAI.
   Google Scholar
• Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185. doi:10.1007/BF02289447
   PubMed Web of Science ®Google Scholar
• Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417–441. doi:10.1037/h0071325
   Google Scholar
• Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55. doi:10.1080/10705519909540118
   Web of Science ®Google Scholar
• Jennrich, R. I., & Sampson, P. F. (1966). Rotation for simple loadings. Psychometrika, 31, 313–323. doi:10.1007/BF02289465
   PubMed Web of Science ®Google Scholar
• Jöreskog, K. G. (1977). Factor analysis by least-squares and maximum-likelihood methods. In K. Enslein, A. Ralston, & H. S. Wilf (Eds.), Statistical methods for digital computers (pp. 125–153). New York, NY: Wiley.
   Google Scholar
• Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200. doi:10.1007/BF02289233
   Web of Science ®Google Scholar
• Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151. doi:10.1177/001316446002000116
   Web of Science ®Google Scholar
• Kaiser, H. F. (1970). A second generation Little Jiffy. Psychometrika, 35, 401–415. doi:10.1007/BF02291817
   Web of Science ®Google Scholar
• Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, Y: Guilford Press.
   Google Scholar
• Lawley, D. N. (1940). The estimation of factor loadings by the method of maximum likelihood. Proceedings of the Royal Society of Edinburgh, 60A, 64–82.
   Google Scholar
• MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4, 84–99. doi:10.1037/1082-989X.4.1.84
   Web of Science ®Google Scholar
• O’Connor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, and Computers, 32, 396–402. doi:10.3758/BF03200807
   PubMedGoogle Scholar
• Osbourne, J. W. (2015). What is rotating in exploratory factor analysis? Practical Assessment, Research, and Evaluation, 20(2), 1–7.
   Google Scholar
• Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2, 13–43. doi:10.1207/S15328031US0201_02
   Google Scholar
• Snook, S. C., & Gorsuch, R. L. (1989). Component analysis versus common factor analysis: A Monte Carlo study. Psychological Bulletin, 106, 148–154. doi:10.1037/0033-2909.106.1.148
   Web of Science ®Google Scholar
• Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). Upper Saddle River, NJ: Pearson.
   Google Scholar
• Thurstone, L. L. (1933). The theory of multiple factors. Chicago, IL: University of Chicago Bookstore.
   Google Scholar
• Thurstone, L. L. (1947). Multiple factor analysis. Chicago, IL: University of Chicago Press.
   Google Scholar
• Tucker, L. R., Koopman, R. F., & Linn, R. L. (1969). Evaluation of factor analytic research procedures by means of simulated correlation matrices. Psychometrika, 34, 421–459. doi:10.1007/BF02290601
   Web of Science ®Google Scholar
• Widaman, K. F. (1993). Common factor analysis versus principal component analysis: Differential bias in representing model parameters? Multivariate Behavioral Research, 28, 263–311. doi:10.1207/s15327906mbr2803_1
   PubMed Web of Science ®Google Scholar
• Yates, A. (1987). Multivariate exploratory data analysis: A perspective on exploratory factor analysis. Albany: State University of New York Press.
   Google Scholar