Sample Size Determination for Regression Models Using Monte Carlo Methods in R
DOI
https://doi.org/10.7275/d5pv-8v28
Abstract
A common question asked by researchers using regression models is, What sample size is needed for my study? While there are formulae to estimate sample sizes, their assumptions are often not met in the collected data. A more realistic approach to sample size determination requires more information such as the model of interest, strength of the relations among the variables, and any data quirks (e.g., missing data, variable distributions, variable reliability). Such information can only be incorporated into sample size determination methods that use Monte Carlo (MC) methods. The purpose of this article is to demonstrate how to use a MC study to decide on sample size for a regression analysis using both power and parameter accuracy perspectives. Using multiple regression examples with and without data quirks, I demonstrate the MC analyses with the R statistical programming language. Accessed 22,856 times on https://pareonline.net from August 23, 2014 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Beaujean, A. Alexander
(2019)
"Sample Size Determination for Regression Models Using Monte Carlo Methods in R,"
Practical Assessment, Research, and Evaluation: Vol. 19, Article 12.
DOI: https://doi.org/10.7275/d5pv-8v28
Available at:
https://scholarworks.umass.edu/pare/vol19/iss1/12