Research in bilingualism often involves quantifying constructs of interest by the use of rating scales: for example, to measure language proficiency, dominance, or sentence acceptability. However, ratings are a type of ordinal data, which violates the assumptions of the statistical methods that are commonly used to analyse them. As a result, the validity of ratings is compromised and the ensuing statistical inferences can be seriously distorted. In this article, we describe the problem in detail and demonstrate its pervasiveness in bilingualism research. We then provide examples of how bilingualism researchers can employ an appropriate solution using Bayesian ordinal models. These models respect the inherent discreteness of ratings, easily accommodate non-normality, and allow modelling unequal psychological distances between response categories. As a result, they can provide more valid, accurate, and informative inferences about graded constructs such as language proficiency. Data and code are publicly available in an OSF repository at https://osf.io/grs8x.

中文翻译：

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评分量表分析：双语研究中普遍存在的问题和贝叶斯序数模型的解决方案

双语研究通常涉及通过使用评分量表来量化感兴趣的结构：例如，测量语言熟练程度、优势或句子可接受性。但是，评级是一种有序数据，它违反了通常用于分析它们的统计方法的假设。结果，评级的有效性受到损害，随后的统计推断可能会被严重扭曲。在本文中，我们详细描述了这个问题，并展示了它在双语研究中的普遍性。然后，我们提供了双语研究人员如何使用贝叶斯序数模型采用适当解决方案的示例。这些模型尊重评级的固有离散性，轻松适应非正态性，并允许对响应类别之间的不相等心理距离进行建模。因此，它们可以提供有关分级结构（例如语言能力）的更有效、准确和信息丰富的推论。数据和代码在 OSF 存储库中公开可用，网址为https://osf.io/grs8x.