리커트 척도 개발을 위한 탐색적 요인분석의 사용
Best Practices in Exploratory Factor Analysis for the Development of the Likert-type Scale
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초록
탐색적 요인분석은 심리척도의 개발 과정에서 가장 널리 사용되는 분석 방법이다. 탐색적 요인분석의 올바른 사용에 대한 여러 지침이 있지만 문항 단위의 요인분석에서 특별히 고려되어야 할 사항들은 상대적으로 덜 강조되어 왔다. 본 연구는 척도를 구성하는 문항이 갖는 일반적인 특성들, 예를 들어 문항의 낮은 신뢰도, 리커트 반응 양식의 문항이 보이는 서로 다른 왜도 등이 이들 문항들에 대한 탐색적 요인분석에서 어떻게 고려되어야 하는지를 강조하였다. 문항 단위의 요인분석에서는 검사 점수 단위의 요인분석에 비해 더 큰 표본크기와 요인당 더 많은 문항수가 필요하다는 것을 강조하였다. 피어슨 상관계수 대신 다분상관계수를 사용할 것을 제안하였고 주축요인법과 최소제곱법, 평행분석, 사각회전 등을 사용할 것 또한 강조하였다. 이와 같은 절차를 적용하기 어려울 때는 응답 선택지를 6점 이상으로 할 것을 제안하였다. 마지막으로 대학생들에게서 수집한 로젠버그 자존감 척도 자료에 대해 제안된 고려사항들을 반영한 탐색적 요인분석 과정을 R 프로그램을 사용하여 예시하였다.
Abstract
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.
참고문헌 (39)
- 이순묵, "요인분석 I" (1995), 학지사
- Allen, M. J., "Introduction to measurement theory" (1979), Waveland Press
- Briggs, N. E., "Recovery of week common factors by maximum likelihood and ordinary least squares estimation", Multivariate Behavioral Research, 38(1), pp.25~56 (2003)
- Buja, A., "Remarks on parallel analysis", Multivariate Behavioral Research, 27(4), pp.509~540 (1992)
- correlation analysis for the behavioral sciences, "Applied multiple regression", pp.2003, Cohen, J.
- DeVellis, R. F., "Scale development: Theory and applications" (2003), Sage
- Dolan, C. V., "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, pp.309~326 (1994)
- Embretson, S. E., "Item response theory for psychologist" (2000), Lawrence Erlbaum
- Fabrigar, L. R., "Evaluating the use of exploratory factor analysis in psychological research", Psychological Methods, 4(3), pp.272~299 (1999)
- Floyd, J. F., "Factor analysis in the development and refinement of clinical assessment instruments", Psychological Assessment, 7(3), pp.286~299 (1995)
- Forero, C. G., "Factor analysis with ordinal indicators : A Monte Carlo study comparing DWLS and ULS estimation", Structural Equation Modeling, 16, pp.625~641 (2009)
- Furr, R. M., "Psychometrics : An Introduction" (2014), Sage Publications
- Gorsuch, R. L., "Factor analysis" (1983), Lawrence Erlbaum
- Gorsuch, R. L., "Exploratory factor analysis : Its role in item Analysis", Journal of Personality Assessment, 68(3), pp.532~560 (1997)
- Hair, J. F., "Multivariate data analysis" (2010), Prentice Hall
- Harman, H. H., "Modern factor analysis" (1976), Chicago University Press
- Horn, J. L., "A rationale and test for the number of factors in factor analysis", Psychometrika, 30, pp.179~185 (1965)
- Humphreys, L. G., "Note on a criterion for the number of common factors", Educational and Psychological Measurement, 29, pp.571~578 (1969), Springer Nature[DOI]
- Johnson, R. A., "Applied multivariate statistical analysis" (2002), Prentice Hall
- Kline, R. B., "Principles and practice of structural equation modeling" (2011), Guilford Press
- Lattin, J., "Analyzing multivariate data" (2002), Cengage Learning
- Loehlin, J. C., "Latent variable models" (2004), Lawrence Erlbaum
- MacCallum, R. C., "Sample size in factor analysis", Psychological Methods, 4(1), pp.84~99 (1999)
- Maydeu-Olivares, A., "Item response modeling of paired comparison and ranking data", Multivariate Behavioral Research, 45, pp.935~974 (2010)
- McDonald, R. P., "Factor analysis and related methods" (1985), Lawrence Erlbaum
- Nunnally, J. C., "Psychometric theory" (1994), McGraw-Hill
- O’Connor, B. P., "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, pp.396~402 (2000)
- Olsson U., "Maximum likelihood estimation of the polychoric correlation coefficient", Psychometrika, 44(4), pp.443~460 (1979)
- Owens, T. J., "Two dimensions of self-esteem : Reciprocal effects of positive self worth and negative self-esteem on adolescent problems", American Sociological Review, 59, pp.391~407 (1994)
- Reise, S. P., "Factor analysis and scale revision", Psychological Assessment, 12, pp.287~297 (2000)
- Rhemtulla, M, "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), pp.354~373 (2012)
- Rosenberg, M., "Society and the adolescent self-image" (1965), Princeton University Press
- Stark, S., "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), pp.184~203 (2005)
- Supple, A. J., "Factor structure of the Rosenberg Self-Esteem Scale", Journal of Cross-Cultural Psychology, 44(5), pp.748~764 (2012)
- Tabachnick, B. G., "Using multivariate statistics" (2007), Allyn & Bacon
- Timmerman, M. E., "Dimensionality assessment of ordered polytomous items with parallel analysis", Psychological Methods, 16(2), pp.209~220 (2011)
- Wirth, R. J., "Item factor analysis : Current approaches and future directions", Psychological Methods, 12(1), pp.58~79 (2007)
- Worthington, R. L., "Scale development research : A content analysis and recommendations for best practices", The Counseling Psychologist, 34(6), pp.806~838 (2006)
- Zwick, W. R., "Comparison of five rules for determining the number of components to retain", Psychological Bulletin, 99(3), pp.432~442 (1986)