Research Article
Year 2021,
Volume: 8 Issue: 3, 704 - 713, 05.09.2021
Abstract
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.
References
- 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.
Year 2021,
Volume: 8 Issue: 3, 704 - 713, 05.09.2021
Abstract
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.
References
- 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.
There are 10 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Studies on Education |
| Journal Section | Research Article |
| Authors | |
| Submission Date | October 2, 2020 |
| Publication Date | September 5, 2021 |
| Published in Issue | Year 2021 Volume: 8 Issue: 3 |