A Model-Free Approach for Analysis of Complex Contingency Data in Survey Research

A model-free approach is proposed which helps to identify differences in the response distribution of a criterion variable based on segments of respondents defined by characteristics of the predictor variables.

Abstract

Analysis and interpretation of higher order cross-tabulation data are of recurring concern in marketing research. The authors present a parsimonious new approach to this data analysis problem. Specifically, a model-free approach is proposed which helps to identify differences in the response distribution of a criterion variable based on segments of respondents defined by characteristics of the predictor variables. The approach, which relies on Bonferroni adjusted chi square statistics to direct a sequential search process, is illustrated in a segmentation analysis of data from a national consumer survey. The results of analyzing the same data by using AID and LOGIT procedures are also examined. The article concludes with a discussion of potential applications, limitations, and extensions of the new approach.