Mixed and Multilevel Models

Lately, I’ve gotten a lot of questions about learning how to run models for repeated measures data that isn’t continuous.

Mostly categorical. But once in a while discrete counts.

A typical study is in linguistics or psychology where (more…)

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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…)

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I love working with my clients.

I love working with my clients for many reasons, but one of them is I learn so much from them.

Just this week, one of my clients showed me how to get SPSS GENLINMIXED results without the Model Viewer.

She’s my new hero.

If you’ve ever used GENLINMIXED, the procedure for Generalized Linear Mixed Models, you know that the results automatically appear in this new Model Viewer.  (more…)

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How should I build my model?

I get this question a lot, and it’s difficult to answer at first glance–it depends too much on your particular situation.

There are really three parts to the approach to building a model: the strategy, the technique to implement that strategy, and the decision criteria used within the technique. (more…)

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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…)

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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. (more…)

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