Department of Sociology, Oklahoma State University, 431 Murray, Stillwater, OK 74078, United States.
Soc Sci Res. 2012 Jan;41(1):182-98. doi: 10.1016/j.ssresearch.2011.09.003. Epub 2011 Sep 16.
The proportional odds assumption in ordered logit models is a restrictive assumption that is often violated in practice. A violation of the assumption indicates that the effects of one or more independent variables significantly vary across cutpoint equations in the model. In order to relax this assumption for the cumulative odds model, researchers may use either a "partial" model that relaxes the assumption for a subset of variables or the "generalized" model that relaxes the assumption for every independent variable. In this paper, we propose a relatively new and under-utilized third alternative, the proportional odds with partial proportionality constraints (POPPC) model, which allows the effects of a subset of variables to vary across cutpoint equations by a common factor. We improve upon an earlier formulation of the POPPC model by offering an additional conceptual justification for the model and an estimation method that does not require the use of person-threshold data. We illustrate the POPPC model with two examples from the 2008 General Social Survey.
有序逻辑回归模型中的比例优势假设是一个严格的假设，在实践中经常被违反。违反该假设表明，模型中一个或多个自变量的效应在临界点方程之间显著变化。为了放宽累积优势模型中的该假设，研究人员可以使用“部分”模型（该模型放宽了模型中一部分变量的假设）或“广义”模型（该模型放宽了每个自变量的假设）。在本文中，我们提出了一个相对较新且未充分利用的第三个替代方案，即部分比例约束的比例优势（POPPC）模型，该模型允许一组变量的效应通过一个共同因素在临界点方程之间变化。我们通过为模型提供额外的概念性理由和一种不需要使用个体阈值数据的估计方法，改进了早期的 POPPC 模型的公式。我们用来自 2008 年综合社会调查的两个例子来说明 POPPC 模型。
