I have spent two days grappling with this question, and the range of ambiguous answers online has driven me to ask. I am working with R.
I have a dataset where my dependent variable is an ordered categorical variable with 5 levels ("dislike very much" to "like very much"). I intend to use the
ordinal package for the actual regression analysis, but have been trying to decide on the best method to specify the model. From my reading it appears that stepwise methods are not a good option, and that a LASSO regression technique is a better method of selecting important variables. Is it acceptable to use a LASSO method to choose the variables to include and then to use these variables in a separate proportional odds regression? My primary interest is in the significance of the terms, rather than the size of the coefficient. I want to know which variables have a significant effect, in which direction, and in what order of importance. For this reason I would rather do the final modelling using a proportional odds glm than with a LASSO model.