Repeated Measures

One area in statistics where I see conflicting advice is how to analyze pre-post data. I’ve seen this myself in consulting. A few years ago, I received a call from a distressed client. Let’s call her Nancy.

Nancy had asked for advice about how to run a repeated measures analysis. The advisor told Nancy that actually, a repeated measures analysis was inappropriate for her data.

Nancy was sure repeated measures was appropriate. This advice led her to fear that she had grossly misunderstood a very basic tenet in her statistical training.

The Study Design

Nancy had measured a response variable at two time points for two groups. The intervention group received a treatment and a control group did not. Participants were randomly assigned to one of the two groups.

The researcher measured each participant before and after the intervention.

Analyzing the Pre-Post Data

Nancy was sure that this was a classic repeated measures experiment. It has (more…)

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There are three main ways you can approach analyzing repeated measures data, assuming the dependent variable is measured continuously: repeated measures ANOVA, Mixed Models, and Marginal Models. Let’s take a look at how the three approaches differ and some of their advantages and disadvantages.

For a few, very specific designs, you can get the exact same results from all three approaches.  This makes it difficult to figure out what each one is doing, and how to apply them to OTHER designs.

For the sake of the current discussion, I will define repeated measures data as repeated measurements of the same outcome variable on the same individual.  The individual is often a person, but could just as easily be a plant, animal, colony, company, etc.  For simplicity, I’ll use “individual.” (more…)

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