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Conducting Simulation Studies in the R Programming Environment

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Doi: 10.20982/tqmp.09.2.p043

Hallgren, Kevin A.
Keywords: statistics , simulation studies
Tools: R
(no sample data)   (Appendix)

Simulation studies allow researchers to answer specific questions about data analysis, statistical power, and best-practices for obtainingaccurate results in empirical research. Despite the benefits that simulation research can provide, many researchers are unfamiliar with available tools for conducting their own simulation studies. The use of simulation studies need not be restricted toresearchers with advanced skills in statistics and computer programming, and such methods can be implemented by researchers with a variety of abilities and interests. The present paper provides an introduction to methods used for running simulationstudies using the R statistical programming environment and is written for individuals with minimal experience running simulation studies or using R. The paper describes the rationale and benefits of using simulations and introduces R functions relevant for many simulation studies. Three examples illustrate different applications for simulation studies, including (a) the use of simulations to answer a novel question about statistical analysis, (b) the use of simulations to estimate statistical power, and (c) the use of simulations to obtain confidence intervals of parameter estimates throughbootstrapping. Results and fully annotated syntax from these examples are provided.

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