There are two options available for computing a repeated measures analysis. One is using the multivariate ANOVA, the other is a nested design form.
To compute the analysis using the multivariate ANOVA option, your repeated observations must be entered as separate variables. For example, day 1, day 2, day 3, and so on. You need to have a variable that records treatment type for each subject in the study where each
subject is a row in your dataset.
Select your observations as Y variables and your treatment as an X variable. Choose Calc > Linear Models. Data Desk opens the Linear Model design view. Click on the button next to "Type of analysis:" that says MANOVA and select Repeated Measures from the pop-down menu. To compute and view the results click the arrow next "Results" to open the Results panel inside the Linear Models design view.
Repeated measures can also be computed using a nested form. You must have one variable that records observations, a variable that records the corresponding treatment, a subject variable, and the repeat variable, which names the repeats. (See pages 29/8 and 29/9 for a schematic representation and an example.)
Select the observations variable as Y and the other three
variables as X. Choose Calc > Linear Models. Data Desk opens the
Linear Models design view. In the Factors panel, nest the Subject
factor inside the Treatment factor.
Next open the Interactions panel (click on the arrow next to "Custom Interactions") and specify Treatments*Repeats interaction term. To compute and view the results click the arrow next "Results" to open the Results panel inside the Linear Models design view.
Advantages of multivariate repeated measures:
* Easier to specify.
* Faster and smaller; may be able to compute under memory limits
when nested form cannot.
* Offers dotplots of responses in repeat order with lines
connecting subjects; a useful diagnostic display.
Disadvantages of multivariate repeated measures:
* Less flexible; can't omit interactions.
* Can't compute expected cell means, coefficients, or post-hoc
* Cases that miss even one Repeat are omitted from the
* One Repeat factor only.
Advantages of the nested calculations:
* Greater flexibility; can omit interactions.
* Can compute expected cell means, coefficients, and post-hoc
tests for all terms.
* Missing observations are omitted only for the repeat on which
they are missing; the subject can be kept in the analysis.
* Multiple Repeat factors are possible.
Disadvantages of the nested calculations:
* More complex to specify; may require data manipulation to put
variables in the correct form.
* Slower and larger. May have difficulty completing the
calculation for large files without a large amount of memory.