This contribution deals with effect measures for covariates in ordinal data models to address the interpretation of the results on the extreme categories of the scales, evaluate possible response styles, and motivate collapsing of extreme categories. It provides a simpler interpretation of the influence of the covariates on the probability of the response categories both in standard cumulative link models under the proportional odds assumption and in the recent extension of the Combination of Uncertainty and Preference of the respondents models, the mixture models introduced to account for uncertainty in rating systems. The article shows by means of marginal effect measures that the effects of the covariates are underestimated when the uncertainty component is neglected. Visualization tools for the effect of covariates are proposed, and measures of relative size and partial effect based on rates of change are evaluated by the use of real data sets.

该贡献涉及序数数据模型中协变量的效果度量，以解决对尺度的极端类别的结果的解释，评估可能的响应样式并激发极端类别的崩溃。它提供了协对无论是在比例优势假设下的标准累计链路模型，并在最近扩展的响应类别的概率的影响的一个简单的解释ç的ombination ü ncertainty和P参考受访者模型，引入混合模型以说明评级系统的不确定性。该文章通过边际效应测度表明，当忽略不确定性成分时，协变量的作用被低估了。提出了针对协变量影响的可视化工具，并通过使用实际数据集评估了基于变化率的相对大小和部分影响的度量。