Description

Author

Prathiba Natesan (University of North Texas, Denton, TX, USA)

Abstract

Statistical simulation studies are becoming more common given advances in modern computing and increased software availability. However, in general simulation methodology appears to be underutilized by applied researchers, possibly because advanced work can entail complex programming. Therefore, the purpose of this article is to introduce statistical simulation as a multiple methods research approach and provide a primer to (I assume uninitiated) researchers on how to conduct simulations. This article is based on my many years of teaching statistical simulation for researchers and graduate students, and builds upon what I have learnt from various colleagues who have published with me, as well as from the authors whom I cite in this article. In addition to sharing some basic guidance on the conduct of simulation procedures, I describe resources that can help researchers to learn simulation techniques. These resources include key readings, a basic demonstration based on the law of large numbers, and an appendix containing the R code that was used in this primer. As a final point, international researchers and students who read this journal might especially benefit from this primer because the R programming environment is free. Familiarity with R can, of course, support standard empirical analyses, but learning how to perform statistical simulation work via R can lead to inexpensive ways to contribute to the methodological literature.