- Thread starter Marvin85
- Start date
- Tags median difference

These tests assume that both groups come from the same distribution, whatever shape it is, but they differ only with respect to their medians. OK, just my initial thoughts. Hope that helps!

Steve

Thank you so much for your reply. Just to give you some context of my design: My main task is to "test" if staff member who participated in the counseling sessions had fewer "Use of Force (UOF)" incidents in the 6 moths after the counseling session. I have a data set with the number of UOF incidents for the staff member in the 6 months before (pre) and after the counseling (post). I also have a control group (staff who didn't participate in the counseling sessions). So in total I have the pre UOF count the post UOF count and a group variable.

By conducting sign rank tests for each group separately, I concluded that UOF incidents significantly decreased in the two groups from pre to post. Now I would like to know if the staff who received the counseling (experimental group) reduced the number of UOF in a greater rate that staff in the control group. I just want to see if the counseling is more effective in reducing UOF incidents than no counseling at all. Someone recommended Poisson regression, but I guess this only test if the POST UOF incident is higher in the the experimental vs control group , controlling for pre UOF incidents. I know that that UOF at pre and post is high in the experimental group since they are the "trouble" staff. I just want to test if the difference between pre and post is statistically significant higher in the experimental compare to the control.

Any ideas?

6 months pre (control)

6 months post (control)

6 months pre (experimental)

6 months post (experimental)

I'm not an expert on ANCOVA, but it's what jumped out at me as I was understanding your design and research question. There are additional checks you should do to confirm if your ANCOVA model is valid, such as homogeneity of variance, normality, correlation of your covariate between independent and dependent variables, etc. If you can send me your file, I would like to run it just as a learning exercise. Hope that helps, Marvin.

Steve

Code:

```
. poisson PostUOF PreUOF Group,irr
Iteration 0: log likelihood = -875.85322
Iteration 1: log likelihood = -875.61156
Iteration 2: log likelihood = -875.61138
Iteration 3: log likelihood = -875.61138
Poisson regression Number of obs = 338
LR chi2(2) = 125.19
Prob > chi2 = 0.0000
Log likelihood = -875.61138 Pseudo R2 = 0.0667
------------------------------------------------------------------------------
PostUOF | IRR Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
PreUOF | 1.082344 .008646 9.91 0.000 1.06553 1.099423
Group | 1.249379 .0812125 3.43 0.001 1.099928 1.419137
_cons | 1.853692 .111252 10.28 0.000 1.647978 2.085085
------------------------------------------------------------------------------
```