Dear expert listers,
I have a dataset where the variables are overall satisfaction(7 point scale),Value for money comparison(7 Point Scale) and also a variable with 13 attributes which contains Description ratings(7 point scale),One variable called attribute(7 point scale),Rating performance(7 Point Scale),Rating quality of Communication (7 point scale).
Now client needs a robust Driver analysis based on these variable. What I think first
1) I have to make all the variables as Binary variable as I need to know only Top 2 Box performance.
2) To check the strength of relationship - I want to do correlation with overall satisfaction to all other variables.
Or:
To check which factor drives the overall satisfaction very well:
1) I have to make all the variables as Binary variable.
2) Then for removing the multicolinearity I have to do factor analysis on the base of 13 attributes.
3) After getting a good factor solution -taking those factor solution and other 4 variables as an independent variable; overall satisfaction as a dependent variable.
4) Finally wants to run a linear regression analysis with enter method.
Can anyone suggest me which one would be more robust method?
Thanks!
Mou.
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