Is what you choose what you want?—outlier detection in choice-based conjoint analysis
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- 作者：Yu-Cheng Ku ; Tsun-Feng Chiang ; Sheng-Mao Chang
- 关键词：Bayesian residuals ; Choice ; based conjoint ; Hierarchical Bayes ; Outlier detection ; Random utility theory ; Respondent variability
- 刊名：Marketing Letters
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：28
- 期：1
- 页码：29-42
- 全文大小：
- 刊物类别：Business and Economics
- 刊物主题：Marketing;
- 出版者：Springer US
- ISSN：1573-059X
- 卷排序：28
文摘
Choice-based conjoint (CBC) analysis has long been a popular technique in market research. Because CBC is dependent upon respondents’ stated preferences, respondent variability should be taken into account in part-worth estimation. In the spirit of Bayesian residuals within the probit framework, this paper proposes a novel respondent variability measure for CBC, called the “utility deviation” (UD), to detect outliers who have unusually high respondent variability. UD is constructed based on the standardized deviation between a respondent’s true and representative utilities on the made choices. We compare UD with the largest absolute realized deviation (LARD) statistic and the typically used metric, root likelihood (RLH), in the performance of outlier detection using simulated and empirical data. The results show that UD performs slightly better than LARD and significantly outperforms RLH. Finally, we show that performing outlier detection to exclude misleading data can significantly improve the quality of estimation and resultant applications.