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the numbers of each group. Moreover, the hypothesis concerning the gender-differences is not significant. Therefore, we can say that there are no gender-differences between the four groups = 2.90 (df = 3; p =  .41).

--Table 2—

4.1.4. Interactions between PCB and gender

Because of the significant differences in three of the five personality factors, we also examined the existence of gender as a mediator variable for the correlations between the four PCB-categories with the five personality factors. For this purpose, we don’t only examine the interactions between gender and the PCB-categories by doing five ANOVAs, searching for effects of gender on the PCB-categories, but we also examine the interaction between these variables. The interaction between the two independent variables is not significant, so we can say that there are no significant gender-differences in relation to the PCB-categories and the five personality factors. Starting from this point in the analysis till the end of it, we will continue to work with the whole sample, i.e. without dividing the sample into two parts on ground of gender.

4.2. Primary Analysis

For the primary analysis, we used a between group-design (a one-way MANOVA). In the present study, the independent variables are the PCB-subscales and the dependent variable is the five personality factors together. First, we measured the general effect of the PCB-categories on the personality factors. The results are showing us a significant multivariate effect of the personality factors for the PCB quadrants. Using the Wilk’s Lambda can see this. The value of

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