How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power

TLDR

Researchers often ask how large a sample is needed, but no universal rule exists because sample size depends on model size, variable distribution, missing data, reliability, and relationship strength. The article demonstrates how researchers can use a Monte Carlo study to decide on sample size and determine power. The authors illustrate this with two example models—a confirmatory factor analysis and a growth model—analyzed in Mplus.

Abstract

Abstract A common question asked by researchers is, "What sample size do I need for my study?" Over the years, several rules of thumb have been proposed. In reality there is no rule of thumb that applies to all situations. The sample size needed for a study depends on many factors, including the size of the model, distribution of the variables, amount of missing data, reliability of the variables, and strength of the relations among the variables. The purpose of this article is to demonstrate how substantive researchers can use a Monte Carlo study to decide on sample size and determine power. Two models are used as examples, a confirmatory factor analysis (CFA) model and a growth model. The analyses are carried out using the Mplus program (Muthén& Muthén 1998).