mixed model

The Difference Between Random Factors and Random Effects

January 9th, 2019 by

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. (more…)

Member Training: Generalized Linear Models

September 3rd, 2018 by
In this webinar, we will provide an overview of generalized linear models. You may already be using them (perhaps without knowing it!).
For example, logistic regression is a type of generalized linear model that many people are already familiar with. Alternatively, maybe you’re not using them yet and you are just beginning to understand when they might be useful to you.

Member Training: Power Analysis and Sample Size Determination Using Simulation

July 30th, 2018 by
This webinar will show you strategies and steps for using simulations to estimate sample size and power. You will learn:
  • A review of basic concepts of statistical power and effect size
  • A simulation-based approach to power analysis
  • An overview of how to implement simulations in various popular software programs.

Six Differences Between Repeated Measures ANOVA and Linear Mixed Models

January 22nd, 2018 by

As mixed models are becoming more widespread, there is a lot of confusion about when to use these more flexible but complicated models and when to use the much simpler and easier-to-understand repeated measures ANOVA.

One thing that makes the decision harder is sometimes the results are exactly the same from the two models and sometimes the results are (more…)

Member Training: Crossed and Nested Factors

May 1st, 2017 by

We often talk about nested factors in mixed models — students nested in classes, observations nested within subject.

But in all but the simplest designs, it’s not that straightforward. (more…)

Examples for Writing up Results of Mixed Models

September 12th, 2014 by

One question I always get in my Repeated Measures Workshop is:

“Okay, now that I understand how to run a linear mixed model for my study, how do I write up the results?”

This is a great question.

There are many pieces of the linear mixed models output that are identical to those of any linear model–regression coefficients, F tests, means.

But there is also a lot that is new, like intraclass correlations and (more…)