Mixed Models for Logistic Regression in SPSS

by Karen Grace-Martin


Can I use SPSS MIXED models for (a) ordinal logistic regression, and (b) multi-nomial logistic regression?

Every once in a while I get emailed a question that I think others will find helpful.  This is definitely one of them.

My answer:


(And by the way, this is all true in SAS as well.  I’ll include the SAS versions in parentheses).

You can think of SPSS Mixed (SAS proc mixed) as the clustered-data version of SPSS GLM (proc glm).  They have a lot of similarities in both their syntax and the kinds of models they can run.

Any model you can run in GLM, you can run in Mixed (but not vice-versa).

But both require an outcome variable that is unbounded, continuous, and measured on an interval or ratio scale.

So logistic regression, along with other generalized linear models, is out.

But there is another option (or two, depending on which version of SPSS you have).

You can run a Generalized Estimating Equation model for a repeated measures logistic regression using GEE (proc genmod in SAS).  It has a repeated statement, and can run equivalent models to a model in Mixed with a repeated statement.

These are called population averaged or marginal models in both procedures, because you’re fitting a single model to all clusters, but controlling for within-cluster correlation.

In contrast are true Mixed Models, which actually fit a variance parameter for random effects, usually random intercepts and slopes.  Rather than just control for within-cluster similarity in responses, they model it.  Mixed models are run in Mixed using the Random statement.

(One of the reasons this gets so confusing is that for some designs, you can get the exact same results with either type of model.  But they’re taking different routes to the same destination).

Mixed Models have a lot more flexibility than Population Averaged Models–you can, for example, run a 3-level mixed model, but Population Averaged Models are restricted to two levels.

To run a true Mixed Model for logistic regression, you need to run a Generalized Linear Mixed Model using the GLMM procedure, which is only available as of version 19.

(In SAS, use proc glimmix).

If you want to learn more about Mixed Models, check out our webinar recording: Random Intercept and Random Slope Models.  It’s free.

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