Effect of Level of Disaggregation on Conjoint Cross Validations: Some Comparative Findings*
研究了数据聚合程度对联合分析模型预测准确性的影响，发现当属性单调时聚合模型预测市场份额较好，但属性非单调时个体模型更优。
ABSTRACT Early formulations of conjoint models focused on part‐worth estimation at the individual level. As the methodology's popularity grew so did industry demands for increasingly larger numbers of attributes and levels. In response to these demands, new approaches, based on partial or full data aggregation (such as clusterwise/latent class conjoint and choice‐based conjoint), have appeared. This paper suggests that pooled‐data models will often be successful in predicting market shares when researchers employ monotonic attributes. In these cases more of a good attribute (or less of a bad attribute) is always more preferred. In the more realistic case, in which some of the attributes may be nonmonotonic, we find that data aggregation does not predict holdout sample preferences as well as individual part‐worth models.