Hi @seoleelvjs,
I was able to run the Manova script in your table and it reproduced the repeated response you specified just fine. You'll see that jsl in your saved script:
Response Function( "Sum", Repeated( 1 ), Title( "Between Subjects" ) ),
Response Function(
"Contrast",
Repeated( 2 ),
Prefix( "Time*" ),
Title( "Within Subjects" )
)
As for interpretation, I know the output from Manova seems a bit verbose, but once you know where to look it'll be easy! For your within-subject effect of Time (whether there is evidence that the means differ from pre/post/30day, averaging over academic program) look in the time section for your F and associated DF and p value:
For your test of academic program (whether the means for the programs differ, averaging over time), look in the between-subjects section under "academic_program_new":
For the interaction between your within and between subjects factors, the test of whether you have evidence the time effect differs for the different academic programs (or equivalently, whether you have evidence that the academic program difference is different and different time points), look in the Within-Subjects section under Time*academic_program_new, and choose your favorite multivariate f statistic to use (whatever is most common in your field -- I would guess Lambda):
A few additional notes before we get to calculating partial eta squared. The output you requested is for the true multivariate analysis of variance where we are constructing a linear contrast across the multiple within-subject responses. These tests are not the traditional univariate repeated measures tests. If you wish to request the univariate repeated measures tests (what you might get from SPSS repeated measures) you can do so when you call the repeated contrast -- simply check the box for univariate tests also:
When you do this, you will receive additional output in each section. Notice for each of your within-subject tests, there are Univar tests, both uncorrected and epsilon corrected. There is also the familiar Sphericity Test.
Now, to your final question about partial eta squared. You won't be able to get the particular values you need from this output, but you will with a slight restructuring of your data and then by running this model through Fit Model a different way.
First, we'll use Tables > Stack to Stack your data. We'll tell jmp to stack your three time columns, and then label the columns appropriately in the dialog:
This returns a table where your repeated measurements are across rows rather than separate columns (dataset attached here):
Next, we'll return to Analyze > Fit Model, but rather than specifying multiple Y columns, we'll define your repeated structure completely in the model effects section, and we'll utilize your user_id column to tell JMP there is a random effect present. To do this, we'll first add a full factorial of your "fixed" effects, academic program and time, and then we'll add user_id, and with it selected click the red triangle next to attributes, and select random. This will tell JMP that this effect is a random effect (which is what subjects always are). To be completely formal we need to tell jmp that your subjects are nested inside of the between subjects conditions, so we select the user_id factor in the model effects, and then select academic program in the columns list, and then click "nest." Finally, if you wish to run this model in a way that gives you SS for your calculation of eta squared, change the "Method" at the top right to EMS Traditional (I'll come back to this choice in a minute). Your dialog should look like this:
When you click Run, you'll obtain the traditional expected mean squares repeated measures output with all the terms you need to calculate partial eta squared:
By changing your method to EMS, you were telling JMP to calculate this model using expected mean squares, a traditional and older way of fitting these repeated measures models. The original default, REML, is the much preferred (for many statistical reasons) method for these models, and would return a linear mixed effects models. If you have complete data, a mixed model and ems model will return identical p-values. When there are missing data (at random) the mixed model will outperform EMS. For this and other reaons I would certainly recommend REML over EMS, though you will not be able to calculate an easy effect size measure for your factors (you can read up on the controversy over effect sizes in mixed effects models if you're interested).
If you wish to simplify the specification of these repeated measures models via a mixed model approach, you can download my Full-Factorial Repeated Measures Add-in here: Repeated Measures Add-In. The only requirement is that your data be in tall/stacked form (what we did above).
I hope this helps!
@julian