If you’ve ever worked with multilevel models, you know that they are an extension of linear models. For a researcher learning them, this is both good and bad news.
The good side is that many of the concepts, calculations, and results are familiar. The down side of the extension is that everything is more complicated in multilevel models.
This includes power and sample size calculations. (more…)
If you learned much about calculating power or sample sizes in your statistics classes, chances are, it was on something very, very simple, like a z-test.
But there are many design issues that affect power in a study that go way beyond a z-test. Like:
Regular sample size software can accommodate some of these issues, but not all. And there is just something wonderful about finding a tool that does just what you need it to.
Especially when it’s free.
Enter Optimal Design Plus Empirical Evidence software. (more…)
That is a very good reason. But there are others, and it can be helpful to keep these in mind when you’re tempted to skip this step or are grumbling through the calculations you’re required to do.
It’s easy to base your sample size on what is customary in your field (“I’ll use 20 subjects per condition”) or to just use the number of subjects in a similar study (“They used 150, so I will too”).
Sometimes you can get away with doing that.
However, there really are some good reasons beyond funding to do some sample size estimates. And since they’re not especially time-consuming, it’s worth doing them. (more…)