Despite the widespread use of ordinal measures in HCI, such as Likert-items, there is little consensus among HCI researchers on the statistical methods used for analysing such data. Both parametric and non-parametric methods have been extensively used within the discipline, with limited reflection on their assumptions and appropriateness for such analyses. In this paper, we examine recent HCI works that report statistical analyses of ordinal measures. We highlight prevalent methods used, discuss their limitations and spotlight key assumptions and oversights that diminish the insights drawn from these methods. Finally, we champion and detail the use of cumulative link (mixed) models (CLM/CLMM) for analysing ordinal data. Further, we provide practical worked examples of applying CLM/CLMMs using R to published open-sourced datasets. This work contributes towards a better understanding of the statistical methods used to analyse ordinal data in HCI and helps to consolidate practices for future work.

翻译：尽管在人机交互领域广泛使用序数测量（如李克特量表项目），但人机交互研究者对分析此类数据的统计方法尚未形成共识。该学科既广泛使用参数方法，也大量采用非参数方法，却鲜少反思这些方法的前提假设及其对序数数据分析的适用性。本文系统考察了近期报告序数测量统计分析的人机交互研究，重点梳理了主流分析方法，探讨其局限性，并揭示那些削弱方法洞察力的关键假设与常见疏漏。最终，我们倡导并详细阐释使用累积链接（混合）模型分析序数数据的优势。此外，我们通过R语言对已公开的开源数据集进行CLM/CLMM建模，提供了实际应用范例。本研究有助于深化对人机交互领域序数数据分析方法的理解，并为未来研究实践提供方法论整合基础。