A study into mechanisms of attitudinal scale conversion: A randomized stochastic ordering approach

Quantitative Marketing and Economics - This paper considers the methodological challenge of how to convert categorical attitudinal scores (like satisfaction) measured on one scale to a categorical...     

Conversion of ordinal attitudinal scales: An inferential Bayesian approach

The need for scale conversion may arise whenever an attitude of individuals is measured by independent entrepreneurs each using an ordinal scale of its own with possibly different numbers of (arbitrary) ordinal categories. Such situations are quite common in the marketing realm. The conversion of a score of an individual measured on one scale into an estimated score of a similar scale with a different range is the concern of this paper. An inferential Bayesian approach is adopted to analyze the situation where we believe the scale with fewer categories can be obtained by collapsing the finer scale. This leads to inferences concerning rules for the conversion of scales. Further, we propose a method for testing the validity of such a model. The use of the proposed methodology is exemplified on real data from surveys concerning performance evaluation and satisfaction.     

Multi level categorical data fusion using partially fused data

Data fusion poses challenging methodological issues for inferring the joint distribution of two random variables when the information available is mainly confined to the marginal distributions. When the variables are categorical, the challenges are even more severe. Applications of categorical data fusion are of top importance in marketing, especially in advertising. A great deal of categorical data fusion methods are confined to binary variables. In this paper we develop an innovative approach to categorical data fusion that extends previous methodologies and applies to categorical variables with any number of levels. We introduce a new concept of “evident dependence” that describes a variety of patterns of joint distributions given the marginals. Using information from partially fused data, our method smoothly accommodates a Bayesian approach based on mixtures of joint distributions constructed using evident dependence. The approach is illustrated using data from the advertising industry.