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체육학연구에서 확인적 요인분석 이용의 문제점과 올바른 적용

Issues and Concurrent Solutions for the Application of Confirmatory Factor Analysis in Physical Education and Exercise Science Research

이기봉|엄한주|박일혁
한국체육측정평가학회 (Korean Society For Measurement And Evaluation In Physical Education And Sports Science)|2014.04|한국체육측정평가학회지 (The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science)|제16권 제1호|pp. 1-22|DOI 10.21797/ksme.2014.16.1.001

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키워드

구조방정식모형확인적 요인분석confirmatory factor analysis탐색적 요인분석exploratory factor analysisstructural equation modeling공분산covariance잠재변인체육학 연구physical education and exercise science researchlatent variable

초록

본 논문은 tutorial 성격의 논문으로서 연구의 목적은 체육⋅스포츠 분야의 연구에서 흔히 사용되고 있는 요인분석법의 적용 시 나타나는 문제점들을 고찰하고 이들에 대한 바람직한 해결방안과 요인분석의 올바른 적용 방법을 제시하는 것이다. 스포츠심리학, 스포츠사회학, 스포츠경영학, 스포츠여가학 등에서 가장 빈번하게 사용되고 있는 자료 분석법인 요인분석은 잘못 적용되거나 부분적으로 문제점을 가진 경우가 많다. 연구자들이 요인분석을 자료 분석에 적용할 때 가장 흔히 겪는 문제점은 확인적 요인분석과 탐색적 요인분석의 선택, 자료의 비정규성, 측정척도 문제, 표본의 크기, 모형의 적합도 평가 시 적합도 지수 선택과 기준 등이다. 이 외에도 요인분석법 자체에 대한 이해 부족에서 오는 적용의 오류 등도 있다. 본 논문에서는 이와 같은 문제점들을 고찰, 정리하고 이에 대한 해결방안을 제시하였다. 요인분석법은 수학적, 통계적으로 매우 복잡한 방법으로 적용 시 많은 요소들을 고려해야 하며 올바른 이해를 바탕으로 한 올바른 적용이 필요하다.

Abstract

This is a tutorial paper on the appropriate understanding and usage of factor analysis model in applied physical education and exercise science research. This paper discusses several issues that one may encounter in using factor analysis model in an applied research. Discussed issues include selection between an exploratory factor analysis and a confirmatory factor analysis model, non-normality of data, analyzing ordinal scale data, sample size, model evaluation with goodness-of-fit indexes and its criteria. This paper discussed these issues based on literature review, small simulation examination and authors’ experience as statistical consultants and as reviewer of many research journals. Factor analysis model and analysis is a very complex technique which requires a high level knowledge and experience. Researchers need to understand these common issues and apply this statistical model with caution.

참고문헌 (49)

  1. 엄한주, "요인분석 모형의 이해와 적용: 주성분모형과 공통요인모형의 방법론적 비교", 한국체육측정평가학회지, 3, pp.101~130 (2001)
  2. 엄한주, "체육학연구에서 요인분석모형의 적용 및 오용에 관한 현황분석", 한국체육학회지, 47(2), pp.1~16 (2008), 한국체육학회
  3. Antonakis, J, "SEMNET discussion" (2001)
  4. Babakus, E., "The sensitivity of confirmatory maximum likelihood factor analysis to violations of measurement scale and distributional assumptions", Journal of Marketing Research, 37, pp.72~141 (1987)
  5. Bollen, K. A., "Structural equations with latent variables", pp.256~289 (1989), John Wiley & Sons
  6. Bollen, K. A., "Latent variables in psychology and the social sciences", Annual Review of Psychology, 53, pp.605~634 (2002)
  7. Bollen, K. A., "Pearson's r and coarsely categorized measures", American Sociological Review, 46, pp.232~239 (1981)
  8. Bollen, K. A., "Testing structural equation models" (1993), Sage
  9. Boomsma, A., "Systems under indirect observation: Causality, structure, prediction", pp.149~173 (1982), North-Holland
  10. Browne, M. W., "Topics in applied multivariate analysis", pp.72~141 (1982), Cambridge University Press
  11. Chou, C. P., "Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: A Monte Carlo study", British Journal of Mathematical and Statistical Psychology, 44, pp.347~357 (1991)
  12. Coenders, G., "Alternative approaches to structural modeling of ordinal data", A Monte Carlo study, Structural Equation Modeling, 4, pp.261~282 (1997)
  13. Curran, P. J., "The robustness of test statistics to non-normality and specification error in confirmatory factor analysis", Psychological Methods, 1, pp.16~29 (1996)
  14. Cuttance, P., "Structural modeling by example: Applications in educational. sociological, and behavioral research", pp.241~279 (1987), Cambridge University Press
  15. 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)
  16. Fabrigar, L. R., "Evaluating the use of exploratory factor analysis in psychological research", Psychological Methods, 4, pp.272~299 (1999)
  17. Fan, X, "Effects of potential confounding factors on fit indices and parameter estimates for true and misspecified models", Structural Equation Modeling, 5, pp.701~735 (1998)
  18. Finch, J. F., "Effects of sample size and nonnormality on the estimation of mediated effects in latent variable models", Structural Equation Modeling, 2, pp.87~105 (1997)
  19. Flora, D. B., "An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data", Psychological Methods, 9, pp.466~491 (2004)
  20. Gerbing, D. W., "Viability of exploratory factor analysis as a precursor to confirmatory factor analysis", Structural Equation Modeling, 3, pp.62~72 (1996)
  21. Hoyle, R. H., "In Structural equation modeling: Concepts, issues, and applications", pp.1~15 (1995), Sage Publications, Inc
  22. Hu, L. T., "Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification", Psychological Methods, 3, pp.424~453 (1998)
  23. Hu, L. T., "Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives", Structural Equation Modeling, 6, pp.1~55 (1999)
  24. Johnson, D. R., "Ordinal measures in multiple indicator models: A simulation study of categorization error", American Sociological Review, 48, pp.398~407 (1983)
  25. Jöreskog, K. G., "A general approach to confirmatory maximum likelihood factor analysis", Psychometrika, 34, pp.183~202 (1969)
  26. Jöreskog, K. G., "A general method for analysis of covariance structures", Biometrika, 57, pp.239~251 (1970)
  27. Jöreskog, K. G., "LISREL 7: A guide to the program and applications" (1989), SPSS
  28. Kelloway, E. K., "Structural equation modeling in perspective", Journal of Organizational Behavior, 16(3), pp.215~224 (1995)
  29. Kieffer, K. M., "An introductory primer on the appropriate use of exploratory and confirmatory factor analysis", Research in Schools, 6, pp.75~92 (1999)
  30. Lei, M., "The effect of varying degrees of nonnormality in structural equation modeling", Structural Equation Modeling, 12, pp.1~27 (2005)
  31. MacCallum, R., "Some problems in the process of model modification in covariance structure modeling", Paper presented in the European meeting of psychometric society (1985)
  32. MacCallum, R. C., "Sample size in factor analysis", Psychological Methods, 4, pp.84~89 (1999)
  33. Mardia, K. V., "Measures of multivariate skewness and kurtosis", Biometrika, 57, pp.519~530 (1970)
  34. Marsh, H. W., "In search of golden rules : Comments on hypothesis-testing approaches to setting cut-off values for fit indexes and dangers in overgeneralizing Hu and Bentler’s(1999)findings", Structural Equation Modeling, 11, pp.320~341 (2004)
  35. Marsh, H. W., "Contemporary psychometrics. A Festschrift for Roderick P. McDonald", pp.275~340 (2005), Erlbaum
  36. Muthen, B., "A comparison of some methodologies for the factor analysis of non-normal Likert variables", British Journal of Mathematical and Statistical Psychology, 38, pp.171~189 (1985)
  37. Muthen, B, "A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model", British Journal of Mathematical and Statistical Psychology, 45, pp.19~30 (1992)
  38. Nevitt, J., "Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling", Structural Equation Modeling, 8, pp.353~377 (2001)
  39. Pearson, K., "On lines and plans of closest fit to system of points in space", Philosophy, 2, pp.559~572 (1901)
  40. Preston, C. C., "Optimal number of response categories in rating scales : Reliability, validity, discriminating power, and respondent preferences", Acta Psychologica, 104, pp.1~15 (2000)
  41. Rigdon, E. E., "SEMNET discussion" (1997)
  42. Rigdon, E. E., "Scale scores with 3-point scale items", SEMNET discussion (2000)
  43. Rigdon, E. E., "The performance of the polychoric correlation coefficient and selected fitting functions in confirmatory factor analysis with ordinal data", Journal of Marketing Research, 28(4), pp.491~497 (1991)
  44. Satorra, A., "Scaling corrections for chi-square statistics in covariance structure analysis", ASA 1988 Proceedings of the Business and Economic Statistics, pp.308~313 (1988), American Statistical Association
  45. Satorra, A., "A scaled difference chi-square test statistic for moment structure analysis", Psychometrika, 66(4), pp.507~514 (2001)
  46. Spearman, C., "General intelligence objectively determined and measured", American Journal of Psychology, 15, pp.201~293 (1904)
  47. Velicer, W. F, "Effects of variable and subject sampling on factor pattern recovery", Psychological Methods, 3, pp.231~251 (1998)
  48. West, S. G., "Structural equation modeling: Concepts, issues and applications", pp.56~75 (1995), Sage
  49. Widaman, K. F., "Common factor analysis versus principal component analysis: Differential bias in representing model parameters?", Multivariate Behavioral Research, 28, pp.263~311 (1993)

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