Re: Simple Main Effects Pairwise Comparisons vs Univariate Tests

Posted by Art Kendall on Jun 05, 2014; 1:11pm
URL: http://spssx-discussion.165.s1.nabble.com/Simple-Main-Effects-Pairwise-Comparisons-vs-Univariate-Tests-tp5726323p5726358.html

"I agree with Rich that Pre should be treated as a covariate"
As another look at the data especially  if you did not build in sufficient power.

Before you started what did you decide what would be a minimum meaningful difference in changes?  Did you do a power analysis?  

It may be that results will be statistically significant treating the pre-test as a covariate but not as repeated measures.  If so, use another grain of salt.  However, the visualization you include in your report would be more complicated.  

Try to work out an idealized visualizations of the  models before you run those models.  You can  create data sets with 9 means that show  meaningfully different.that have the patterns you expect. it is. You would be looking to see if there is a difference in changes, i.e., a non parallelism or interaction effect is what you would be hoping for. These will be an aid to set context when you eyeball the obtained results

Without knowing your complete design, an idealized visualization would have the pretest point all the same, with the two control groups parallel to each other.

The argument you can make about your results whether you end up reporting a repeated measure or covariance approach very much depends on whether the comparison groups are "control" groups.

In either the repeated measures model or the covariate model, to avoid irrelevant multiple tests, ONLY test the specific interactions effect(s) that you would be hypothesizing.
I.e, the two comparison/control groups have parallel profiles. The profile for the treatment group would NOT  be parallel to the (separate or pooled depending on your Hypothesis) profile(s).

Recall that statistically significant results may not be large enough to be meaningful.

HTH