The proportional odds with partial proportionality constraints model for ordinal response variables
The proportional odds with partial proportionality constraints model for ordinal response variables
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DOI：
10.1016/j.ssresearch.2011.09.003
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发表时间：
2012-01-01
影响因子：
2.5
通讯作者：
Xu, Jun
中科院分区：
文献类型：
--
作者：
Fullerton, Andrew S.;Xu, Jun
有序logit模型中的比例优势假设是一个限制性假设，在实践中经常被违反。违反假设表明，一个或多个自变量的影响在模型中的临界点方程之间存在显著差异。为了放松累积优势模型的假设，研究人员可以使用放松变量子集假设的“部分”模型或放松每个自变量假设的“广义”模型。在本文中，我们提出了一个相对较新的和未充分利用的第三种选择，比例优势与部分比例约束（POPPC）模型，它允许一个子集的变量的影响，通过一个共同的因素，在不同的临界点方程。我们改进了早期制定的POPPC模型，提供了一个额外的概念理由的模型和估计方法，不需要使用的人的阈值数据。我们用2008年综合社会调查的两个例子来说明POPPC模型。(C)2011 Elsevier Inc. All rights reserved.
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. (C) 2011 Elsevier Inc. All rights reserved.