A Repeated measures ANOVA in StatView requires that the data be organized into a compact variable, i.e. each row is a subject and you have one column for each level of each factor.  Recall that the factorial ANOVA can be done with either file organization; this is not the case for the RM design.

 

Therefore, if necessary you should take the time to review making compact variables, e.g. using the Wine sample file, which has one factor with 7 levels.  (Though note that we will see later that there is a problem with doing a RM ANOVA on such a dataset.)
 

If subjects provided multiple repetitions of some measure, in your RM design you can have a Repetitions factor, or you can first average your repetitions and just use the mean value.

When you have an appropriate file with a compact variable open, go to Analyze - ANOVA and t-tests - Repeated Measures ANOVA.   The dialog box opens confusingly, with the "between factor" boxhighlighted, ready for your entry.  But you do not have to have any between-subjects factors to run the analysis.  Instead ignore that box and just drag your "compact" variable into the "Repeated measurement" box underneath, and run your analysis.  Unlike in a factorial analysis, you do not open up the compact variable.  (Do not put Subjects into the Between box, else the analysis will not run.)
 

The results table is not explicitly labeled as resulting from a RM analysis, but you can tell that it does because all the error terms used as denominators for the F ratios are interactions with your subject factor (not Residual), and the resulting degrees of freedom are correspondingly low. 

 

Try the following example: 

 

-first do a 2-way factorial analysis using my file factorial.xls (so, 2 between-subjects factors, assuming 24 different subjects)

-then do the same ANOVA, using the same data, but in a compact variable, in my file compact.xls (the result should be exactly the same, right?)
 

-finally, do a RM ANOVA using the same compact file (so, 2 within-subjects factors, assuming 6 different subjects)

 

I hope this isn’t confusing to use the same data file for the two analyses – obviously if these were real data, only one of these analyses would be appropriate and the other not.  This is just to show the mechanics of doing these two kinds of analyses, and to compare the results.

result of RM:
 

 

result of factorial:

 

 

Question: How can you tell, just by looking, which analysis was the factorial one and which the RM? 
 

>>The error terms, and the way they are displayed in the table (and also the associated degrees of freedom) are different.

You'll see that the p values are generally lower in the second table.  If you do a factorial analysis on RM data, then you are overestimating the significance of the effects.
 

MIXED DESIGNS

If some factors are within-subjects and some are between-subjects, then you use the RM option.  Your data file should have any Within factors in a compact variable (these will be the columns) and any Between factors, plus subjects, as the rows.  Drag factors which are not part of your compact variable into the Between box, and drag your compact variable into the Repeated measurements box.  The StatView sample file Teaching Effectiveness is a straightforward mixed design, with Time as the Within factor.  Also try my files mixed.svd
and  mixed2.svd.