Multi-Variate Regression - I think!
- From: isporter@xxxxxxxxx
- Date: 10 Feb 2006 12:00:19 -0800
Experts,
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
.
- Follow-Ups:
- Re: Multi-Variate Regression - I think!
- From: Paige Miller
- Re: Multi-Variate Regression - I think!
- Prev by Date: IVs and DV
- Next by Date: eliminate noise from a regression model
- Previous by thread: IVs and DV
- Next by thread: Re: Multi-Variate Regression - I think!
- Index(es):
Relevant Pages
|
|