When analyzing the results of my ACBC study, there was one interaction effect identified. This interaction proved to (only just) significantly improve the model fit if it was to be included in the analysis. However, the amount of parameters to be estimated in the HB analysis would increase from 28, to 48. Is it worth it to include the interaction effect anyway, despite the massive increase in complexity of the model? I know the complexity of the model is a big factor in CBC, but how does this work for ACBC? Since the amount of parameters are often much larger by nature, due to more included attributes.
I'd also like to add that i'm using the ZC individual part-worth utilities to create segments, using Latent Class Clustering. Including the additional levels of the interaction levels might be troublesome.
Additionally, when analyzing the HB results (interaction included), i noticed something strange. When examining the part-worth utilities of the interaction levels, is saw one combination of attributes which indicated as a prohibition with a large and positive utility value. I'd really like to know how this is possible, and what could have caused this? Thank you in advance!