MonteCarloSEM: An R Package to Simulate Data for SEM
Abstract
Keywords
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/
Details
Primary Language
English
Subjects
Studies on Education
Journal Section
Research Article
Authors
Fatih Orçan
*
0000-0003-1727-0456
Türkiye
Publication Date
September 5, 2021
Submission Date
October 2, 2020
Acceptance Date
July 11, 2021
Published in Issue
Year 2021 Volume: 8 Number: 3