Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.
When the model tries to estimate their unique effects, it goes wonky (yes, that’s a technical term).
So for example, you may be interested in understanding the separate effects of altitude and temperature on the growth of a certain species of mountain tree.
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…)
I have recently worked with two clients who were running generalized linear mixed models in SPSS.
Both had repeated measures experiments with a binary outcome.
The details of the designs were quite different, of course. But both had pretty complicated combinations of within-subjects factors.
Fortunately, both clients are intelligent, have a good background in statistical modeling, and are willing to do the work to learn how to do this. So in both cases, we made a lot of progress in just a couple meetings.
I found it interesting, through, that both were getting stuck on the same subtle point. It’s the same point I was missing for a long time in my own learning of mixed models.
Once I finally got it, a huge light bulb turned on. (more…)
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…)
Repeated measures ANOVA is the approach most of us learned in stats classes for repeated measures and longitudinal data. It works very well in certain designs.
But it’s limited in what it can do. Sometimes trying to fit a data set into a repeated measures ANOVA requires too much data gymnastics.
The ICC, or Intraclass Correlation Coefficient, can be very useful in many statistical situations, but especially so in Linear Mixed Models.
Linear Mixed Models are used when there is some sort of clustering in the data.
Two common examples of clustered data include: