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

Yu-Cheng Ku (), Tsun-Feng Chiang () and Sheng-Mao Chang ()
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Yu-Cheng Ku: North Carolina State University
Tsun-Feng Chiang: Henan University
Sheng-Mao Chang: National Cheng Kung University

Marketing Letters, 2017, vol. 28, issue 1, No 3, 29-42

Abstract: Abstract 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.

Keywords: Bayesian residuals; Choice-based conjoint; Hierarchical Bayes; Outlier detection; Random utility theory; Respondent variability (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11002-015-9389-3

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