Analysing multiattribute ranking data: Joint and conditional approaches

Rankings, in contrast to ratings, eliminate effects of individual differences in scale usage and avoid arbitrary definitions regarding the number of response categories and category labels. However, despite the appeal and popularity of this technique, few methods are available for the analysis of rankings on several attributes. This paper presents extensions of Thurstonian and logistic models for the joint analysis of multiattribute ranking responses and for conditional analyses where rankings on one of the attributes are modelled as a function of the rankings on the other attributes. These extensions are based on two approaches proposed to account for associations among the ranking responses. Empirical applications of the Thurstonian and logistic ranking models indicate that one of the two approaches appears particularly promising for the analysis of multiattribute ranking data.