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E-ISSN : 2733-4538
Exploratory factor analysis (EFA) is a widely used analytical tool for development of psychological scales. Although guidelines for proper use of EFA have been proposed by many experts, special considerations for the item level factor analysis have been less emphasized. The current study highlighted that certain features of Likert-type items, such as low reliability and different levels of skewness, should be considered in EFA for scale development. The author suggested that a more than 5-point response scale is required for the common practice of EFA for the Likert-type scale development and, if not applicable, extraction of polychoric correlations is desirable, rather than Pearson correlations. Great emphasis has been placed on the use of parallel analysis and principle axis factoring or unweighted least squares method on polychoric correlations with oblique rotation. Higher item to factor ratio and larger sample size in comparison with scale level factor analysis are also emphasized. An EFA on the 10 items of the Rosenberg Self-Esteem Scale was illustrated with the proposed practices using the R statistical program.
이순묵 (1995). 요인분석 I. 서울: 학지사
Allen, M. J., Yen, W. M. (1979). Introduction to measurement theory. Long Grove, IL: Waveland Press.
Briggs, N. E., & MacCallum, R. C. (2003). Recovery of week common factors by maximum likelihood and ordinary least squares estimation. Multivariate Behavioral Research, 38(1), 25-56.
Buja, A., Eyuboglu, N. (1992). Remarks on parallel analysis. Multivariate Behavioral Research, 27(4), 509-540.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S.(2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.).Mahwah, NJ: Lawrence Erlbaum.
DeVellis, R. F. (2003). Scale development: Theory and applications (2nd ed.). Thousand Oaks, CA:Sage.
Dolan, C. V. (1994). Factor analysis of variables with 2, 3, 5, and 7 response categories: A comparison of categorical variable estimators using simulated data. British Journal of Mathematical and Statistical Psychology, 47, 309-326.
Embretson, S. E. & Reise, S. P. (2000). Item response theory for psychologist. Mahwah, NJ:Lawrence Erlbaum.
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(3), 272-299.
Floyd, J. F., Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7(3), 286-299.
Forero, C. G., Maydeu-Olivares, A., & Gallardo-Pujol, D. (2009). Factor analysis with ordinal indicators: A Monte Carlo study comparing DWLS and ULS estimation. Structural Equation Modeling, 16, 625-641.
Furr, R. M., & Bacharach, V. R. (2014). Psychometrics: An Introduction. Los Angeles: Sage Publications.
Gorsuch, R. L. (1983). Factor analysis (2nd ed..)Hillsdale , NJ: Lawrence Erlbaum.
Gorsuch, R. L. (1997). Exploratory factor analysis:Its role in item Analysis. Journal of Personality Assessment, 68(3), 532-560.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). London: Prentice Hall.
Harman, H. H. (1976). Modern factor analysis (3rd ed.). Chicago, IL: Chicago University Press.
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185.
Humphreys, L. G., Ilgen, D. R. (1969). Note on a criterion for the number of common factors. Educational and Psychological Measurement, 29, 571-578. doi:10.1007/BF02289447.
Johnson, R. A., Wichern, D. W. (2002). Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice Hall.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.
Lattin, J., Carroll, J. D., & Green, P. E. (2002). Analyzing multivariate data. New York:Cengage Learning
Loehlin, J. C. (2004). Latent variable models (4th ed.). Mahwah, NJ: Lawrence Erlbaum.
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1). 84-99.
Maydeu-Olivares, A. & Brown, A. (2010). Item response modeling of paired comparison and ranking data. Multivariate Behavioral Research, 45, 935-974.
McDonald, R. P. (1985). Factor analysis and related methods, Hillsdale, NJ: Lawrence Erlbaum.
Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York:McGraw-Hill.
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, Instrumentation, and Computers, 32, 396-402.
Olsson U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44(4), 443-460.
Owens, T. J. (1994). Two dimensions of self-esteem: Reciprocal effects of positive self worth and negative self-esteem on adolescent problems. American Sociological Review, 59, 391-407.
Reise, S. P., Waller, N. G., & Comrey, A. L.(2000). Factor analysis and scale revision. Psychological Assessment, 12, 287-297.
Rhemtulla, M, Brosseau-Liard, P. E., & Savalei, V.(2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354-373.
Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.
Stark, S., Chernyshenko, O. S., & Drasgow, F.(2005). An IRT approach to constructing and scoring pairwise preference items involving stimuli on different dimensions: The multi-unidimentional pairwise-preference Model. Applied Psychological Measurement, 29(3). 184-203.
Supple, A. J., Su, J., Plunkett, S. W., Peterson, G. W., & Bush, K. R. (2012) Factor structure of the Rosenberg Self-Esteem Scale. Journal of Cross-Cultural Psychology, 44(5), 748-764.
Tabachnick, B. G., Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA:Allyn & Bacon.
Timmerman, M. E., Loranzo-Seva U. (2011). Dimensionality assessment of ordered polytomous items with parallel analysis. Psychological Methods, 16(2), 209-220.
Wirth, R. J. & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12(1), 58-79.
Worthington, R. L., Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices, The Counseling Psychologist, 34(6), 806-838.
Zwick, W. R., Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99(3), 432-442.