Maydeu-Olivares Alberto, Brown Anna
a Faculty of Psychology , University of Barcelona.
b SHL Group.
Multivariate Behav Res. 2010 Nov 30;45(6):935-74. doi: 10.1080/00273171.2010.531231.
The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally presented as scaling models, that is, stimuli-centered models, they can also be used as person-centered models. In this article, we discuss how Thurstone's model for comparative data can be formulated as item response theory models so that respondents' scores on underlying dimensions can be estimated. Item parameters and latent trait scores can be readily estimated using a widely used statistical modeling program. Simulation studies show that item characteristic curves can be accurately estimated with as few as 200 observations and that latent trait scores can be recovered to a high precision. Empirical examples are given to illustrate how the model may be applied in practice and to recommend guidelines for designing ranking and paired comparisons tasks in the future.
排名任务和配对比较任务中使用的比较格式可以显著减少通常与评分量表相关的一致反应偏差的影响。瑟斯顿（1927年，1931年）的模型为建模比较数据（如配对比较和排名）提供了一个强大的框架。尽管瑟斯顿模型通常被视为量表模型，即以刺激为中心的模型，但它们也可以用作以人为中心的模型。在本文中，我们讨论了瑟斯顿的比较数据模型如何可以被公式化为项目反应理论模型，以便能够估计受访者在潜在维度上的得分。使用广泛使用的统计建模程序可以很容易地估计项目参数和潜在特质得分。模拟研究表明，只需200个观察值就可以准确估计项目特征曲线，并且潜在特质得分可以高精度地恢复。给出了实证例子来说明该模型在实践中如何应用，并为未来设计排名和配对比较任务推荐指导方针。
