Fullerton Andrew S, Anderson Kathryn Freeman
Oklahoma State University, Stillwater, USA.
University of Houston, Houston, USA.
Prev Sci. 2023 Apr;24(3):431-443. doi: 10.1007/s11121-021-01302-y. Epub 2021 Nov 15.
Ordinal outcomes are common in the social, behavioral, and health sciences, but there is no commonly accepted approach to analyzing them. Researchers make a number of different seemingly arbitrary recoding decisions implying different levels of measurement and theoretical assumptions. As a result, a wide array of models are used to analyze ordinal outcomes, including the linear regression model, binary response model, ordered models, and count models. In this tutorial, we present a diverse set of ordered models (most of which are under-utilized in applied research) and argue that researchers should approach the analysis of ordinal outcomes in a more systematic fashion by taking into consideration both theoretical and empirical concerns, and prioritizing ordered models given the flexibility they provide. Additionally, we consider the challenges that ordinal independent variables pose for analysts that often go unnoticed in the literature and offer simple ways to decide how to include ordinal independent variables in ordered regression models in ways that are easier to justify on conceptual and empirical grounds. We illustrate several ordered regression models with an empirical example, general self-rated health, and conclude with recommendations for building a sounder approach to ordinal data analysis.
有序结果在社会科学、行为科学和健康科学中很常见,但目前尚无普遍接受的分析方法。研究人员会做出一些看似随意的不同重新编码决策,这意味着不同的测量水平和理论假设。因此,大量模型被用于分析有序结果,包括线性回归模型、二元响应模型、有序模型和计数模型。在本教程中,我们展示了一系列不同的有序模型(其中大多数在应用研究中未得到充分利用),并认为研究人员应以更系统的方式分析有序结果,既要考虑理论问题,也要考虑实证问题,并且鉴于有序模型所提供的灵活性,应优先选择它们。此外,我们考虑了有序自变量给分析人员带来的挑战,这些挑战在文献中常常被忽视,并提供了一些简单方法,以决定如何将有序自变量纳入有序回归模型,且在概念和实证依据上更容易说明其合理性。我们用一个关于一般自我健康评价的实证例子来说明几种有序回归模型,并最后给出关于构建更合理的有序数据分析方法的建议。