Is what you choose what you want?—outlier detection in choice-based conjoint analysis

Yu Cheng Ku, Tsun Feng Chiang, Sheng Mao Chang

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)29-42
期刊Marketing Letters
出版狀態Published - 2017 三月 1

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Economics and Econometrics
  • Marketing

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