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.

中文翻译：

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在线客户评分的项目响应理论建模说明

摘要 在线消费品评级数据正在迅速增加。虽然当前的大多数图形显示主要代表平均评分，但 Ho 和 Quinn 提出了一种基于序数项目反应理论 (IRT) 模型的易于解释的图形显示，该模型成功地解释了系统的交互者差异。传统上，IRT 模型中的区分参数被限制为正数，特别是在教育测试评分数据的建模中。在本文中，我们使用真实世界的评分数据来证明这种约束会对参数估计产生很大影响。这种对估计的影响可以通过评估者的行为来解释。我们还讨论了评分者之间的相关性，并评估了受约束和不受约束模型的预测准确性。