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A Note on Item Response Theory Modeling for Online Customer Ratings

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  • Chien-Lang Su
  • Sun-Hao Chang
  • Ruby Chiu-Hsing Weng

Abstract

Online consumer product ratings data are increasing rapidly. While most of the current graphical displays mainly represent the average ratings, Ho and Quinn proposed an easily interpretable graphical display based on an ordinal item response theory (IRT) model, which successfully accounts for systematic interrater differences. Conventionally, the discrimination parameters in IRT models are constrained to be positive, particularly in the modeling of scored data from educational tests. In this article, we use real-world ratings data to demonstrate that such a constraint can have a great impact on the parameter estimation. This impact on estimation was explained through rater behavior. We also discuss correlation among raters and assess the prediction accuracy for both the constrained and the unconstrained models. The results show that the unconstrained model performs better when a larger fraction of rater pairs exhibit negative correlations in ratings.

Suggested Citation

  • Chien-Lang Su & Sun-Hao Chang & Ruby Chiu-Hsing Weng, 2020. "A Note on Item Response Theory Modeling for Online Customer Ratings," The American Statistician, Taylor & Francis Journals, vol. 74(1), pages 53-63, January.
    • Handle: RePEc:taf:amstat:v:74:y:2020:i:1:p:53-63
    DOI: 10.1080/00031305.2017.1422804
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