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repeated measures anova

An Example of Specifying Within-Subjects Factors in Repeated Measures

by Karen Grace-Martin  Leave a Comment

Some repeated measures designs make it quite challenging to  specify within-subjects factors. Especially difficult is when the design contains two “levels” of repeat, but your interest is in testing just one.

Let’s look at a great example of what this looks like and how to deal with it in this question from a reader :

The Design:

I want to do a GLM (repeated measures ANOVA) with the valence of some actions of my test-subjects (valence = desirability of actions) as a within-subject factor. My subjects have to rate a number of actions/behaviours in a pre-set list of 20 actions from ‘very likely to do’ to ‘will never do this’ on a scale from 1 to 7, and some of these actions are desirable (e.g. help a blind man crossing the street) and therefore have a positive valence (in psychology) and some others are non-desirable (e.g. play loud music at night) and therefore have negative valence in psychology.

My question is how I can use valence as a within-subjects factor in GLM. Is there a way to tell SPSS some actions have positive valence and others have negative valence ? I assume assigning labels to the actions will not do it, as SPSS does not make analyses based on labels …
Please help. Thank you.

[Read more…] about An Example of Specifying Within-Subjects Factors in Repeated Measures

Tagged With: linear mixed model, repeated measures anova, within subject factor

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Six Differences Between Repeated Measures ANOVA and Linear Mixed Models

by Karen Grace-Martin  13 Comments

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 [Read more…] about Six Differences Between Repeated Measures ANOVA and Linear Mixed Models

Tagged With: ANOVA, clustered data, linear mixed model, Missing Data, mixed model, Repeated Measures, repeated measures anova, unbalanced data

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When Does Repeated Measures ANOVA not work for Repeated Measures Data?

by Karen Grace-Martin  25 Comments

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. [Read more...] about When Does Repeated Measures ANOVA not work for Repeated Measures Data?

Tagged With: crossed random effects, generalized linear mixed model, GLMM, linear mixed model, Marginal Model, Repeated Measures, repeated measures anova, time-varying covariates

Related Posts

  • Six Differences Between Repeated Measures ANOVA and Linear Mixed Models
  • An Example of Specifying Within-Subjects Factors in Repeated Measures
  • Linear Mixed Models for Missing Data in Pre-Post Studies
  • Why Mixed Models are Harder in Repeated Measures Designs: G-Side and R-Side Modeling

Five Advantages of Running Repeated Measures ANOVA as a Mixed Model

by Karen Grace-Martin  22 Comments

There are two ways to run a repeated measures analysis.The traditional way is to treat it as a multivariate test–each response is considered a separate variable.The other way is to it as a mixed model.While the multivariate approach is easy to run and quite intuitive, there are a number of advantages to running a repeated measures analysis as a mixed model.

First I will explain the difference between the approaches, then briefly describe some of the advantages of using the mixed models approach. [Read more…] about Five Advantages of Running Repeated Measures ANOVA as a Mixed Model

Tagged With: Missing Data, mixed model, post hoc test, repeated measures anova

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