When is it appropriate to use a backward method in regression? I have read that it is permitted for exploratory model-building, but I have also read negative things about it.
I am making a model about streaming. The 12 predictors I chose are based on theory. The enter method is not an option, because when I put all independent variables (sociodemographic and others) in the regression with the enter method, only one independent variable is significant. When I use this independent variable and add an independent variable (which was significant from backward selection), both are significant, which means that the enter method filters variables out of the regression that are significant.
Can I use the backward method to test all variables first, then use the significant variables to rerun the regression with an enter method, or must I be consistent and use the backward method again? Or is the backward method out of the question?
May I select independent variables that have a significant effect on the dependent variable according to the correlation matrix to run the regression with, in this case maybe, the enter method?