In this Stat’s Amore Training, Marc Diener will help you make sense of the strange terms and symbols that you find in studies that use multilevel modeling (MLM). You’ll learn about the basic ideas behind MLM, different MLM models, and a close look at one particular model, known as the random intercept model. A running example will be used to clarify the ideas and the meaning of the MLM results.
Multilevel models and Mixed Models are generally the same thing. In our recent webinar on the basics of mixed models, Random Intercept and Random Slope Models, we had a number of questions about terminology that I’m going to answer here.
If you want to see the full recording of the webinar, get it here. It’s free.
Q: Is this different from multi-level modeling?
A: No. I don’t really know the history of why we have the different names, but the difference in multilevel modeling [Read more…] about Multilevel, Hierarchical, and Mixed Models–Questions about Terminology
Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies.
The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set that requires mixed or multilevel models has some of these design elements. [Read more…] about Member Training: Elements of Experimental Design
Mixed models are hard.
They’re abstract, they’re a little weird, and there is not a common vocabulary or notation for them.
But they’re also extremely important to understand because many data sets require their use.
Repeated measures ANOVA has too many limitations. It just doesn’t cut it any more.
One of the most difficult parts of fitting mixed models is figuring out which random effects to include in a model. And that’s hard to do if you don’t really understand what a random effect is or how it differs from a fixed effect. [Read more…] about The Difference Between Random Factors and Random Effects
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.
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:
- repeated measures
- clustering of individuals
- including covariates in a model
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. [Read more…] about Sample Size Estimates for Multilevel Randomized Trials