HI there: I have a one likert item I would like to analyze. I know that most scales require multiple likert items, but this is just an exploratory exercise of an existing survey. It's out of my control.
When I treat the likert categories (strongly support - support - oppose -strongly oppose) as a numeric variable (coding 0 to 1) and conduct a multiple regression, I get some predictors in my models as statistically s ignificant. When I try to create some predicted values based on different configurations of my indendent variables, however, some combinations of values create predicted values larger than 1, which is impossible given the original scale.
Incidentally, when I recode the four categories to two (support - oppose) and rerun it with a binomial logistic regression, the same predictors do not appear as statistically significant.
I have three questions: in the first scenario, is it meaningful and / or appropriate to rescale the predicted values so that the maximum predicted value is 1 and the minimum is 0? Or is that not appropriate?
Second question, can someone explain why the predictors would not be showing up as statistically significant when turning to logistic regression? Is there important variance at the poles of the DV (between strongly support and support) that is being lost when squishing them into binomial?
Third question, should I just analyze this as ordinal?
Thank you very much for your time.
I am conducting this on R 3.0.2. Simon