I am new to JMP and having a hard time running a repeated measures ANOVA. The problem is that I have two factors and BOTH are repeated measures. All of the examples find online show repeated measures ANOVA with one between subjects factor and one within. Can anyone help? Here is a description.
One group of subjects. This is a face recognition task with eye tracking. The dependent variable is the duration of looking time.
Repeated measures factor 1: Face Half (upper face vs lower face)
Repeated measures factor 2: Phase (Learning, Target, and Distractor)
In short, we are interested in seeing if subjects look longer to the upper or lower face and also if their duration of looking time differs across phase (when the learn the face, for target faces, or for distractor faces). Plus the interaction.
Solved! Go to Solution.
The add-in treats the subject effect as fixed, which is why you get the same results as you did with SPSS.
I believe that the add-in was created to reproduce the SPSS style of analysis for users who expect results when subject effects are treated as fixed. The note from the JMP Knowledge Base treats subject effects as random.
Repeated measures is a common study design that can use MANOVA. You measure the response of the same subject at multiple times. You can include one or more factors and covariates. The subject effect is again treated as random.
Sorry but it is difficult to keep up with you. I am having muliple and deep computer prolems - at the BIOS level!
I just wanted to clarify something about the full factorial repeated measures add-in. That add-in *does not* treat subject as a fixed effect. The model generated by the add-in treats subject as a random effect, and models all interactions with subjects as random (random slopes by subject). If you launch the model dialog after running the model (or from the add-in directly) you can see how this structure would be defined in the standard Fit Model dialog. Results from analyses set up with this add-in will certainly differ from treating subjects as a fixed effect (which is, of course, not recommended).
Jim solved your problem but I just want to make sure that his note in the JMP Knowledge Base is also part of your answer.
Why wouldn't you want to treat subject as a random effect? How is a fixed effect of subject meaningful?
The fact is that software was generally unable to properly estimate the random effects properly except in special cases until a recent times. So most analyses used a standard ANOVA or regression analysis and (incorrectly) treated all effects as fixed effects. Many textbooks have yet to catch up with more modern practice.
The choice between fixed or random is based on your inference about the effect of a source of variation. We are generally interested in the fixed effects of factors such as treatment. We see this effect as fixed because our inference is about specific levels and we believe that each level always produces the same effect. On the other hand, some sources of variation represent only a sample. The effect of subject is such an case. We are generally uninterested in the effect of an individual subject because it doesn't generalize but instead care about the population. In this case, we are interested in the variance across subjects. The random effect is this variance.
I don't know for certain that the majority of SPSS users treat subject as a fixed effect but I believe you. The majority is not always right and in statistics the majority is often simply behind in terms of adopting new methods. It takes about twenty years for methods to prevail and another decade for them to be covered in textbooks and taught in school.
You are correct: if one used a traditional MANOVA for a repeated measures analysis, the subject is a random effect.
It isn't wrong to treat subject as a fixed effect but what is the use of this interpretation? Subject has nothing to do with treatments. Subject is only part of the experimental unit.