Maximum difference scaling (MaxDiff) has experienced a surge in popularity to measure preferences of items in a list on a common scale. This scaling approach is similar to conjoint analysis, where respondents are presented with multiple alternatives described by features of interest. However, unlike a conjoint analysis where price is included as a product feature and can be used to convert part-worth utility to a monetary scale, MaxDiff measures preferences on an ordinal scale. Ordinally scaled preferences give rise to the need to employ an arbitrary origin in an analysis for estimation and inference. We propose an integrative model that combines MaxDiff and conjoint data by assuming that the coefficients come from the same generative process but provide different information about product features. We show how our integrative model can be used to estimate the null level of product attributes useful for assessing the drivers of demand and menu pricing.
