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A Tutorial on Covariance-based Structural Equation Modeling using R: focused on "lavaan" Package

R을 이용한 공분산 기반 구조방정식 모델링 튜토리얼: Lavaan 패키지를 중심으로

  • Received : 2015.08.25
  • Accepted : 2015.10.20
  • Published : 2015.10.28

Abstract

This tutorial presents an approach to perform the covariance based structural equation modeling using the R. For this purpose, the tutorial defines the criteria for the covariance based structural equation modeling by reviewing previous studies, and shows how to analyze the research model with an example using the "lavaan" which is the R package supporting the covariance based structural equation modeling. In this tutorial, a covariance-based structural equation modeling technique using the R and the R scripts targeting the example model were proposed as the results. This tutorial will be useful to start the study of the covariance based structural equation modeling for the researchers who first encounter the covariance based structural equation modeling and will provide the knowledge base for in-depth analysis through the covariance based structural equation modeling technique using R which is the integrated statistical software operating environment for the researchers familiar with the covariance based structural equation modeling.

본 튜토리얼은 R을 이용하여 공분산 기반의 구조방정식모델링을 수행하는 방법을 제시하고 있다. 이를 위해 본 튜토리얼에서는 기존 연구들에 대한 리뷰를 통해 공분산 기반의 구조방정식모델링을 위한 기준들을 정의하고, 하나의 예시 연구모형을 제시하여 공분산 기반의 구조방정식모델링을 지원하는 R 패키지인 "lavaan"을 이용하여 이 예시 모형을 분석하는 것을 보여준다. 결과물로 본 튜토리얼에서는 예시모형을 대상으로 한 R을 이용한 공분산 기반의 구조방정식모델링 기법과 실습 스크립트가 제시되었다. 본 튜토리얼은 공분산 기반의 구조방정식모델링을 처음 접하는 연구자들에게는 연구모형을 구조방정식 모델링으로 분석하는데 유용한 가이드가 될 것이며, 이미 공분산 기반의 구조방정식모델링에 익숙한 연구자들에게는 R을 이용한 새로운 공분산 기반의 구조방정식모델링 분석기법 제시를 통하여 R이라는 통합된 통계 소프트웨어 운영환경에서 심도 있는 연구를 위한 기반 지식을 제공할 것이다.

Keywords

References

  1. Gefen, D., Straub, D., & Boudreau, M. C., "Structural equation modeling and regression: Guidelines for research practice", Communications of the association for information systems, Vol. 4, Article 7, pp.1-78, 2000.
  2. Yoon, C., & Kim S., "Tutorial on PLS Structural Equating Modeling using R: (Centering on) Exemplified Research Model and Data", Information Systems Review, Vol. 16, No. 3, pp.89-112, 2014. https://doi.org/10.14329/isr.2014.16.3.089
  3. Rosseel, Y., "lavaan: An R package for structural equation modeling", Journal of Statistical Software, Vol. 48, No. 2, pp.1-36, 2012.
  4. Fox, J., "Teacher's corner: structural equation modeling with the sem package in R", Structural equation modeling, Vol. 13, No. 3, pp.465-486, 2006. https://doi.org/10.1207/s15328007sem1303_7
  5. Fox, J., Nie, Z., & Byrnes, J., "sem: Structural Equation Models", R package version 3.0-0, URL http://CRAN.R-project.org/package=sem, 2012.
  6. Steven Boker, S., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., Spies, J., Estabrook, R., Kenny, S., Bates, T., Mehta, P., & Fox, J., "OpenMx: an open source extended structural equation modeling framework", Psychometrika, Vol . 76, No. 2, pp.306-317, 2011. https://doi.org/10.1007/s11336-010-9200-6
  7. Bagozzi, R. P., & Yi, Y., "On the evaluation of structural equation models", Journal of the academy of marketing science, Vol. 16, No. 1, pp. 74-94, 1988. https://doi.org/10.1007/BF02723327
  8. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M., "A primer on partial least squares structural equation modeling (PLS-SEM)", Sage Publications, 2013.
  9. Anderson, J. C., & Gerbing D. W., "Structural equation modeling in practice: A review and recommended two-step approach", Psychological bulletin, Vol. 103, No. 3, pp.411-423, 1988. https://doi.org/10.1037/0033-2909.103.3.411
  10. Fornell, C., & Larcker, D. F., "Evaluating structural equation models with unobservable variables and measurement error", Journal of marketing research, Vol. 18, No. 1, pp. 39-50, 1981. https://doi.org/10.2307/3151312
  11. Henseler, J., Ringle, C. M., & Sinkovics, R. R., "The use of partial least squares path modeling in international marketing", Advances in International Marketing, Vol. 20, pp.277-320, 2009.
  12. Chin, W. W., "The partial least squares approach to structural equation modeling", Modern methods for business research, Vol. 295, No. 2, pp.295-336, 1998.
  13. Joreskog, K. G., & Sorbom, D., "LISREL 7: A guide to the program and applications", SPSS, 1989.
  14. Chin, W. W., & Todd, P. A., "On the use, usefulness, and ease of use of structural equation modeling in MIS research: a note of caution," MIS quarterly, Vol. 19, No. 2, pp.237-246, 1995. https://doi.org/10.2307/249690
  15. Hu, L. T., & Bentler, P. M., "Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives", Structural equation modeling: a multidisciplinary journal, Vol. 6, No. 1, pp.1-55, 1999. https://doi.org/10.1080/10705519909540118
  16. Steiger, J. H., "Structural model evaluation and modification: An interval estimation approach", Multivariate behavioral research, Vol. 25, No. 2, pp.173-180, 1990. https://doi.org/10.1207/s15327906mbr2502_4
  17. Bentler, P. M., "Comparative fit indexes in structural models", Psychological bulletin, Vol. 107, No. 2, pp.238-246, 1990. https://doi.org/10.1037/0033-2909.107.2.238
  18. Van der Heijden, H., "User acceptance of hedonic information systems", MIS quarterly, Vol. 28, No. 4, pp.695-704, 2004. https://doi.org/10.2307/25148660
  19. Yoon, C., Jeong, C., & Rolland, E., "Understanding individual adoption of mobile instant messaging: a multiple perspectives approach", Information Technology and Management, Vol. 16, No. 2, pp.139-151, 2014. https://doi.org/10.1007/s10799-014-0202-4