I have some survey data that includes a Likert score as a response. I’d like to relate this likert score to some continuous outcome of interest. What’s the best way to include discrete ordinal features as predictors?
I understand using them as outcomes via
OrderedLogistic() likelihood functions. I also know I can include them as dummy variables and just use a standard linear regression, but this doesn’t take into account the ordinality.
TL;DR what’s the best way to use a likert/discrete ordinal feature as a predictor of a continuous outcome in PyMC3?
Thought about this a little more. Thinking about basically doing 2 models - one will model the Likert value itself as an outcome variable using the Ordered Logistic likelihood and update a set of coefficients for the other covariates. The goal of this is to get a model to predict the latent distribution behind the Likert. Then, use the predicted latent distribution as the predictor for my original question rather than the actual category itself. Any thoughts?