A Monte Carlo simulation study is an essential tool for examining the behavior of various models in structural equation modeling (SEM). Recently, the tidyverse package in R is gaining popularity for data science because of its efficient data manipulation, exploration, and visualization capabilities. This article introduces how to write more parsimonious, readable, maintainable, and parallelizable R simulation codes using the tidyverse package. Specifically, this article (a) introduces some key functions and technical terminologies in the tidyverse package that are useful for implementing simulation studies in R, and (b) provides a concrete example to demonstrate how to generate datasets, run models, parallelize the simulation process, summarize results, and visualize results using the tidyverse package. By leveraging the power of the tidyverse package, researchers can conduct their simulation studies more efficiently.
|Number of pages||15|
|Journal||Structural Equation Modeling|
|Publication status||Published - 2020 May 3|
Bibliographical notePublisher Copyright:
© 2019, © 2019 Taylor & Francis Group, LLC.
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Modelling and Simulation
- Sociology and Political Science
- Economics, Econometrics and Finance(all)