The technique of coarsening a variable by reducing it to a dichotomy based on the median is sometimes called the "nefarious median split" or the "invidious median split". This technique throws away information. I suggest you search the archives of this list for "median split".

How many cases do you have?

Are T1 and T2 pre and post measures?

Was treatment randomly assigned? I.e, is this actually a control group or just a contrast group?

Am I correct in assuming that your Hypothesis is that the change in the treatment group is larger than the change in the control/comparison group?

I suggest you start by visualizing your data. Use a series of perspectives.

scatterplots, boxplots, and ladder graphs.

-- a scatterplot of stress T1 (horizontal) by stress T2 (vertical) use different colors/shapes for the two groups. If you are doing this by hand connect the two markers. [Perhaps someone else on the list can suggest how to tie pairs of measures for cases in a scatter plot. ]

-- A scatterplot with Stress on the vertical and T1 vs T2 on the horizontal. Again use different colors/markers for the cases. In the output file try fitting regression and loess lines. Is there striking non-parallelism? Does the loess curve suggest a better fit than the regression line?

-- then EXPLORE creating 6 boxplots: for each group T1, T2 and a variable representing the change.

-- check the archives for "ladder graph" and "Andy". This list most likely has someone who has can help you adapt the example graph to have 2 colors for the lines. If you only have a few dozen cases you can use the example to do this by hand.

There are different ways to analyze change: e.g.,

-- a two way ANOVA with 1 dichotomous IV between the groups and 1 dichotomous IV within the groups. The interaction is what you would be interested in.

-- a REGRESSION with T2 stress as the DV, T1 stress on the first ENTER, and looking at the "variables not in the equation".

-- etc.

Art Kendall

Social Research Consultants