I'm performing a psychological study about gender differences in attraction. Participants select between a number of figures that vary in Waist-To-Hip Ratio (WHR) or Shoulder-To-Hip Ratio (SHR). Figures varying in WHR/SHR also covary in BMI, so I ask participants to estimate the BMI of their chosen figure, and plan to factor this variance out.

Thus, my aim is to find out if, with variance due to BMI removed, the genders vary in their selection of the most attractive figure - does this sound ok up to this point?

I initially planned to use a Two-Way ANOVA, as this allows you to factor out a variable. However, I think that this can only be used for nominal variables, such as gender? And EstimatedBMI and FigureChoice are scale variables (there are 9 figures to choose between).

I then thought of multi-variate regression as another technique that allows one to isolate how much of the variance is explained by different factors. My thought is that my correlation table would consist of FigureChoice, EstimatedBMI, and Gender. How does this sound - am I on the right track?

If so, does this translate in SPSS13 into Analyze > General Linear Model > Multivariate...? If so, are FigureChoice and EstimatedBMI dependent variables and gender a Fixed Factor? Or perhaps EstimatedBMI would be a covariate?

Many thanks for your help,

Iain