My first statistics course primarily consisted of plugging numbers into formulas. I did not leave that course with any real idea of how statistics differed from basic algebra. The next course I took is what put it all together, and I’ve loved statistics ever since. In that course, we examined the relationships between a population and its samples. I learned that using just a small amount of data, statistics enables us to make inferences about the entire population. Now that is cool! Read more…
In our last four posts in this series, we showed you how to calculate power for a t test using Monte Carlo simulations, how to integrate your simulations into Stata’s power command, and how to do this for linear and logistic regression models and multilevel models. In today’s post, I’m going to show you how to estimate power for structural equation models (SEM) using simulations.
Our goal is to write a program that will calculate power for a given SEM at different sample sizes. We’ll follow the same general procedure as the previous two posts, but the way we’ll go about simulating data is a bit different. Rather than individually simulating each variable for our specified model, we’ll be simulating all our variables simultaneously from a given covariance matrix. Means for each of the variables can also be used to simulate the data if your SEM has a mean structure, such as in group analysis or growth curve analysis. Read more…