I am using CBC / HB version 5.5.4 and I have a question regarding differences in resulting relative importances when running the estimation with effects vs. dummy coding. My burn-in phase consists of 200k iterations and I use 1800 draws for each respondent with a skip factor of 50 (I cannot increase this number since the software crashes otherwise. My data set is very large, I include up to 50 interaction terms and one dummy-coded covariate. I also tried increasing the number of draws by running it in batch mode, so the graph is turned off).
Previously, I estimated many different models (main effects only, with a few interaction terms, with all interaction terms, with/without covariates etc.) always using dummy coding. The resulting relative importances for my five attributes were very similar across all models (I calculated importances on individual level first and averaged them second). Now, I estimated a model using effects coding and the relative importances changed by up to 5%-points. If I calculate importance for the interaction terms too, I notice that all interaction terms lose importance and the main attributes gain importance (up to 9%-points). What do you think could be the reason for that? I also wonder which results are more reliable.
Thank you very much in advance and kind regards.