I have a large set of survey data. I'm looking at trying to find out which variables are the most important to impacting a DV (call it "happiness"). I'm not looking to find a beta number like a typical regression. I theorize that some of my ~14 variables will have co-linearity. Is it appropriate to do the following in SPSS, and am I interpreting this correctly?
Analyze->Regression->Automatic Linear Modeling Predictors = all my ~14 variables Target = my DV ("happiness)
Build options - automatically prepare data OFF Model selection = forward stepwise Criteria for entry/removal = F Statistics, including effects with p-values less than 0.05 and removing p-values greater than .1.
Scroll down in the model to "coefficients, and expand window.
Variable 1 - coefficient .16, sig .000, importance .52 Variable 2 - coefficient .10, sig .000, importance .19 Variable 3 - coefficient .07, sig .000 importance .16 Variable 4 - coefficient .06, sig .000, importance .08 Variable 5 - coefficient .06, sig .001, importance .05
The coefficient I assume is beta - i.e. if this variable increased by 1, "happiness" should increase by the coefficient #. Now, what I'm really interested in is the importance #. Am I correct in interpreting this for variable 1 as, "Our model suggests 52% of the variance of happiness is due to this variable."
Further, am I correct that in that this modeling indeed removes colinearity from variables? Or do I need to do that in a different way. I'm hopeful SPSS is essentially doing a "relative weights analysis" here.
Thanks, and hopefully this makes sense!