Hi dear board members,

I have got a dataset of a psychological study, in which several variables were collected.

As the study is meant to indicate some causality, I have classified the variables as independent and dependent variables:

Independent variables:

Personality trait factors 1 to 5 (a scale from 0 to 48 each), age, gender, current mood (scale from 1 to 7). I.e., there are eight independent variables in the dataset that may be used for the analysis.

Dependent variables:

Evaluation of several emotional stimuli (on a scale from 1 to 10).

The dataset comprises 86 cases to evaluate the influence of the indep. variables on the dependent ones.

Still, an initial correlation matrix yields only few significant results. I believe that particularly the interaction effects of the independent variables may be meaningful.

However, these interaction effects could be of such nature that they appear when one independent variable has a very low value while another shows a very high value (e.g., when one personality trait has a very low value while another is very pronounced).

Now I really need help in terms of which methods to employ in order to detect such relationships with SPSS 20. A simple computation of interaction variables by means of multiplication of two independent variables does not seem feasible if the interaction effects are of such nature as described above.

Are there any methods or techniques that may reveal something more about my data?

I'm very grateful for your support!

Kind regards,

Christian