What’s the difference between Mixed and Multilevel Models? What about Hierarchical Models or Random Effects models?
I get this question a lot.
The answer: very little.
random effect
Confusing Statistical Term #10: Mixed and Multilevel Models
April 20th, 2021 by Karen Grace-MartinMultilevel, Hierarchical, and Mixed Models–Questions about Terminology
October 11th, 2019 by Karen Grace-MartinMultilevel 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 (more…)
The Difference Between Random Factors and Random Effects
January 9th, 2019 by Karen Grace-MartinMixed 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. (more…)
Member Training: Meta-analysis
October 31st, 2018 by guest contributerMeta-analysis is the quantitative pooling of data from multiple studies. Meta-analysis done well has many strengths, including statistical power, precision in effect size estimates, and providing a summary of individual studies.
But not all meta-analyses are done well. The three threats to the validity of a meta-analytic finding are heterogeneity of study results, publication bias, and poor individual study quality.
What is the Purpose of a Generalized Linear Mixed Model?
September 10th, 2018 by Kim LoveIf you are new to using generalized linear mixed effects models, or if you have heard of them but never used them, you might be wondering about the purpose of a GLMM.
Mixed effects models are useful when we have data with more than one source of random variability. For example, an outcome may be measured more than once on the same person (repeated measures taken over time).
When we do that we have to account for both within-person and across-person variability. A single measure of residual variance can’t account for both.
Is there a fix if the data is not normally distributed?
February 19th, 2018 by Karen Grace-MartinIn this video I will answer another question from a recent webinar, Random Intercept and Random Slope Models.
We are answering questions here because we had over 500 people live on the webinar so we didn’t have time to get through all the questions.