Treatment effect measured by Likert scale I'm analysing data from a study with a control and a treatment group.
There are four questions on purchasing decisions for different products. Each question uses a 4-point Likert scale asking respondents about their attitude towards some products. For Example:
Control Group:





Strongly Disagree
Disagree
Agree
Strongly Agree




I would purchase A.1
4
3
1
2


I would purchase A.2
3
5
1
1


I would purchase B.1
1
1
6
2


I would purchase B.2
2
2
2
4




Treatment Group





Strongly Disagree
Disagree
Agree
Strongly Agree




I would purchase A.1
2
1
3
4


I would purchase A.2
1
2
4
3


I would purchase B.1
8
0
1
1


I would purchase B.2
6
3
1
0




I want to understand if treatment significantly influences the purchasing decision for one of the two product groups (A or B).
What is a suitable test for this?

Answer from my comments:
You probably want to use ordinal regression. This will allow you to test the effect of Treatment across all the data while still accounting for the effect of Group,  as well as .1 or .2 if those are meaningful.
But if you want something more simple, you might just do multiple Wilcoxon-Mann-Whitney tests, each for A.1, A.2, B.1, and B.2.. (In that case, you can just code your SD, D, N, A, SA responses as e.g. 1, 2, 3, 4, 5).
The appropriateness of either of these approaches may change depending on the answer to this question: Do you have multiple responses for the same person ? That is Respondent 1 answered about both A and B ? If so, did you record the data in way where you know the answer Respondent 1 gave to each of A and B ?
