Ordinal measurements are common outcomes in studies within psychology, as well as in the social and behavioral sciences. Choosing an appropriate regression model for analysing such data poses a difficult task. This paper aims to facilitate modeling decisions for quantitative researchers by presenting the results of an extensive simulation study on the inferential properties of common ordinal regression models: the proportional odds model, the category-specific odds model, the location-shift model, the location-scale model, and the linear model, which incorrectly treats ordinal outcomes as metric. The simulations were conducted under different data generating processes based on each of the ordinal models and varying parameter configurations within each model class. We examined the bias of parameter estimates as well as type I error rates ($α$-errors) and the power of statistical parameter testing procedures corresponding to the respective models. Our findings reveal several highlights. For parameter estimates, we observed that cumulative ordinal regression models exhibited large biases in cases of large parameter values and high skewness of the outcome distribution in the true data generation process. Regarding statistical hypothesis testing, the proportional odds model and the linear model showed the most reliable results. Due to its better fit and interpretability for ordinal outcomes, we recommend the use of the proportional odds model unless there are relevant contraindications.

翻译：序数测量是心理学以及社会与行为科学领域中常见的研究结果。为分析此类数据选择合适的回归模型是一项困难的任务。本文通过呈现一项关于常见序数回归模型推断性质的广泛模拟研究结果，旨在为定量研究者提供建模决策参考。这些模型包括：比例优势模型、类别特定优势模型、位置偏移模型、位置尺度模型以及将序数结果错误视为度量数据的线性模型。模拟基于各序数模型的不同数据生成过程进行，并在每个模型类别内设置了多种参数配置。我们检验了参数估计的偏差、I类错误率（$α$误差）以及各模型对应统计参数检验程序的功效。研究发现呈现出若干亮点：在参数估计方面，当真实数据生成过程中存在较大参数值和结果分布高度偏斜时，累积序数回归模型表现出较大偏差；在统计假设检验方面，比例优势模型和线性模型展现出最可靠的结果。鉴于其对序数结果具有更好的拟合优度和可解释性，我们建议除非存在明确的反向指征，否则应优先使用比例优势模型。