The plot indicates a significant interaction between gender and stereotype threat. An error bar plot (lines representing 1 SE) reveals that this is the case: Let's review the hypothesis of the experiment: It assumes that women exhibit a decrease in microworld performance under stereotype threat.

It turns out that the decision about which type of sums of squares to use is based on the question whether it is reasonable to report main effects in the presence of an interaction. But why all these differences? What does options(contrasts=c('contr.sum', 'contr.poly')) actually do and what the heck are Type-III sums of squares? I surely did not learn about these things at my university. I dug a little deeper and found another line I needed to add to the R command in order to get exactly the same result:Īs you can see, these results are identical. In SPSS, the main effect of gender is still significant. DESIGN=reasonz GENDER STTHREAT GENDER*STTHREAT. UNIANOVA MWP BY GENDER STTHREAT WITH reasonz However, that is still not what SPSS produces: What a difference: The main effect of the participants' gender on thir microworld performance does not reach statistical significance. I do not want order to matter and adjust my calculation accordingly: In a nonorthogonal design with more than one term on the right hand side of the equation order will matter (i.e., A+B and B+A will produce different results)! We will need use the drop1( ) function to produce the familiar Type III results.

WARNING: R provides Type I sequential SS, not the default Type III marginal SS reported by SAS and SPSS.

Unianova spss code