A Model-Free Approach for Analysis of Complex Contingency Data in Survey Research
提出一种无模型方法，利用Bonferroni校正卡方统计量进行顺序搜索，识别预测变量划分的受访者段落在响应变量分布上的差异，并通过全国消费者调查数据与AID、LOGIT方法对比验证。
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.