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MonteCarloSEM: An R Package to Simulate Data for SEM

Yıl 2021, Cilt: 8 Sayı: 3, 704 - 713, 05.09.2021

Öz

Monte Carlo simulation is a useful tool for researchers to estimated accuracy of a statistical model. It is usually used for investigating parameter estimation procedure or violation of assumption for some given conditions. To run a simulation either the paid software or open source but free program such as R is need to be used. For that, researchers must have a good knowledge about the theoretical procedures. This paper introduces the R package called MonteCarloSEM. The package helps to simulate and analyze data sets for some simulation condition such as sample size and normality for a given model. Also, an example is given to show how the functions within the package works.

Kaynakça

  • Boomsma, A. (2013) Reporting Monte Carlo Studies in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 518-540. https://doi.org/10.1080/10705511.2013.797839
  • de Winter, J.C.F. (2013). Using the Student's t-test with extremely small sample sizes. Practical Assessment, Research, and Evaluation, 18, 10. https://doi.org/10.7275/e4r6-dj05
  • Fleishman, A. I. (1978). A method for simulating non-normal distributions. Psychometrika, 43, 521-532. https://doi.org/10.1007/BF02293811
  • Higham, N.J. (2009). Cholesky factorization. WIREs Computational Statistics, 1, 251-254.
  • Maechler, M., & Bates, D. (2006). 2nd Introduction to the Matrix package. URL: https://cran.r-project.org/web/packages/Matrix/vignettes/Intro2Matrix.pdf
  • Orçan, F. & Yanyun, Y. (2016). A Note on the Use of Item Parceling in Structural Equation Modeling with Missing Data. Journal of Measurement and Evaluation in Education and Psychology, 7 (1), 59-72. https://doi.org/10.21031/epod.88204
  • Orçan, F. (2020). MonteCarloSEM 0.0.1. https://CRAN.R-project.org/package=MonteCarloSEM
  • R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved September 10, 2020, from http://www.R-project.org/
  • Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48 (2), 1-36. URL: http://www.jstatsoft.org/v48/i02/
  • Schumacker, R. E., & Lomax, R. G. (2010). A beginner's guide to structural equation modeling (3rd ed.). Routledge.

MonteCarloSEM: An R Package to Simulate Data for SEM

Yıl 2021, Cilt: 8 Sayı: 3, 704 - 713, 05.09.2021

Öz

Monte Carlo simulation is a useful tool for researchers to estimated accuracy of a statistical model. It is usually used for investigating parameter estimation procedure or violation of assumption for some given conditions. To run a simulation either the paid software or open source but free program such as R is need to be used. For that, researchers must have a good knowledge about the theoretical procedures. This paper introduces the R package called MonteCarloSEM. The package helps to simulate and analyze data sets for some simulation condition such as sample size and normality for a given model. Also, an example is given to show how the functions within the package works.

Kaynakça

  • Boomsma, A. (2013) Reporting Monte Carlo Studies in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 518-540. https://doi.org/10.1080/10705511.2013.797839
  • de Winter, J.C.F. (2013). Using the Student's t-test with extremely small sample sizes. Practical Assessment, Research, and Evaluation, 18, 10. https://doi.org/10.7275/e4r6-dj05
  • Fleishman, A. I. (1978). A method for simulating non-normal distributions. Psychometrika, 43, 521-532. https://doi.org/10.1007/BF02293811
  • Higham, N.J. (2009). Cholesky factorization. WIREs Computational Statistics, 1, 251-254.
  • Maechler, M., & Bates, D. (2006). 2nd Introduction to the Matrix package. URL: https://cran.r-project.org/web/packages/Matrix/vignettes/Intro2Matrix.pdf
  • Orçan, F. & Yanyun, Y. (2016). A Note on the Use of Item Parceling in Structural Equation Modeling with Missing Data. Journal of Measurement and Evaluation in Education and Psychology, 7 (1), 59-72. https://doi.org/10.21031/epod.88204
  • Orçan, F. (2020). MonteCarloSEM 0.0.1. https://CRAN.R-project.org/package=MonteCarloSEM
  • R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved September 10, 2020, from http://www.R-project.org/
  • Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48 (2), 1-36. URL: http://www.jstatsoft.org/v48/i02/
  • Schumacker, R. E., & Lomax, R. G. (2010). A beginner's guide to structural equation modeling (3rd ed.). Routledge.
Toplam 10 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Eğitim Üzerine Çalışmalar
Bölüm Makaleler
Yazarlar

Fatih Orçan 0000-0003-1727-0456

Yayımlanma Tarihi 5 Eylül 2021
Gönderilme Tarihi 2 Ekim 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 8 Sayı: 3

Kaynak Göster

APA Orçan, F. (2021). MonteCarloSEM: An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8(3), 704-713.

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