I have a survey analysis data which has responses regarding Consumer Satisfaction (on a scale of 1 to 5)and I am trying to fit a linear regression model to it. As per my understanding, the basic assumption for linear regression is that the independent variables must not show significant correlation.
In my case however, since the responses are filled by people (homo-sapiens), the responses are showing correlations within a category and across categories (Food, Facility etc).
Is this a cause for concern? Can I still go ahead and apply linear regression or should I combine the correlated responses? Also if I were to combine responses, how should I go about it?
I have had to make changes to the responses (independent and dependent variables) based on a scorecard:
Excellent 100
Very good 90
Good 75
Fair 25
Poor 0
I have two approaches in mind:
- I can run a linear regression based on the scorecard
- I keep the responses to my dependent variable as "Excellent, Very good, Good, Fair, Poor", change the responses to the independent variables according to the scorecard and apply an Ordinal Logistic Regression
Can you guys help me pick the most proper approach.
Thanks!
Best Answer
If I understood correction, you want to do a simultaneous regression on several independent variables (multivariable regression). You can still do it if the independent variables correlate strongly among each other, just remember the interpretation of the results will be very difficult. The problem of correlation between the independent variable is called collinearity or multicollinearity, in your case. The bottom line, as explained in the link, is that it does not reduce the predictive power of the model, but interpretation of the variables and their usage, especially if you want to manipulate them independently, is virtually impossible because of the correlation among them.