So far so good.

So, we have alot of variables in our database

1: Age

2: Sex

3: Priming or control-group

4: Centrality of the elements (is the mean of 2 questions about the centrality asked in different ways) (12 variables)

5: How different it makes them (12 variables)

6. How "roly" it makes them (12 variables)

7: How seperated it makes them (12 variables)

So, the way i have tackles this so far:

Found the mean of centrality for each element, multiplied it by variable 5-6-7 (look up) casewise and squarerooted the number (gives a 1-7 number). Then the mean of these three new variables (CentralityXDifference, CentralityXRole, CentralityXSeperateness) gives us a number of "Total distinctiveness". Unfortunately these means doesnt differ depending on priming or controlgroup, but that's okay. We can probably argue why.

Here comes some questions

1. What do i do if I want to check for tendencies that the priming or controlgroup accordingly have high scores in variable 5-6-7 when they have high in variable 4; Do the priming group tend to rate elements of high centrality as highly distinctive? I cannot figure out a way to do this in a smart way, so if you have an idea i'll be grateful.

2. Have I done something wrong the way i've done it already, or are there room for improvement in my method?