Organizational research increasingly relies on online review data to gauge perceived valuation and reputation of organizations and products. Online review platforms typically collect ordinal ratings (e.g., 1 to 5 stars); however, researchers often treat them as a cardinal data, calculating aggregate statistics such as the average, the median, or the variance of ratings. In calculating these statistics, ratings are implicitly assumed to be equidistant. We test whether star ratings are equidistant using reviews from two large-scale online review platforms: Amazon.com and Yelp.com. We develop a deep learning framework to analyze the text of the reviews in order to assess their overall valuation. We find that 4 and 5-star ratings, as well as 1 and 2-star ratings, are closer to each other than 3-star ratings are to 2 and 4-star ratings. An additional online experiment corroborates this pattern. Using simulations, we show that the distortion by non-equidistant ratings is especially harmful in cases when organizations receive only a few reviews and when researchers are interested in estimating variance effects. We discuss potential solutions to solve the issue with rating non-equidistance.

中文翻译：

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五是最亮的星。但到底增加了多少呢？测试在线评论中星级的等距性

组织研究越来越依赖在线评论数据来衡量组织和产品的感知价值和声誉。在线评论平台通常会收集顺序评分（例如，1 到 5 星）；然而，研究人员经常将它们视为基本数据，计算总体统计数据，例如评分的平均值、中位数或方差。在计算这些统计数据时，隐含地假设评级是等距的。我们使用来自两个大型在线评论平台：Amazon.com 和 Yelp.com 的评论来测试星级评级是否等距。我们开发了一个深度学习框架来分析评论文本，以评估其整体估值。我们发现 4 星和 5 星评级以及 1 星和 2 星评级彼此之间的距离比 3 星评级与 2 星和 4 星评级之间的距离更接近。另一项在线实验证实了这一模式。通过模拟，我们表明，当组织只收到少量评论以及研究人员有兴趣估计方差影响时，非等距评级造成的扭曲尤其有害。我们讨论解决评级非等距问题的潜在解决方案。