When NOT to Center a Predictor Variable in Regression
There are two reasons to center predictor variables in any type of regression analysis–linear, logistic, multilevel, etc. 1. To lessen the correlation between a multiplicative term (interaction or...
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How to Combine Complicated Models with Tricky Effects
Need to dummy code in a Cox regression model? Interpret interactions in a logistic regression? Add a quadratic term to a multilevel model? This is where statistical analysis starts to feel really hard....
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Multilevel, Hierarchical, and Mixed Models–Questions about Terminology
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...
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February 2020 Member Training: A Gentle Introduction to Multilevel Models
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...
View ArticleConcepts in Linear Regression you need to know before learning Multilevel Models
It seems very many researchers are needing to learn multilevel and mixed models, and I have to say, it’s not so easy on your own. I too went to graduate school before it was taught in classes–we did...
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Covariance Matrices, Covariance Structures, and Bears, Oh My!
Of all the concepts I see researchers struggle with as they start to learn high-level statistics, the one that seems to most often elicit the blank stare of incomprehension is the Covariance Matrix,...
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Sample Size Estimates for Multilevel Randomized Trials
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...
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Three Issues in Sample Size Estimates for Multilevel Models
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...
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The Difference Between Random Factors and Random Effects
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...
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Member Training: Elements of Experimental Design
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,...
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