Surveys that rely on ordinal polychotomous (Likert-like) items are widely employed to capture individual preferences because they allow respondents to express both the direction and strength of their preferences. Latent factor models traditionally used in this context implicitly assume that the response functions (the cumulative distribution of the ordinal outcome) are monotonic on the latent trait. This assumption can be too restrictive in several application areas, including in political science and marketing. In this work, we propose a novel ordinal probit unfolding model that can accommodate both monotonic and non-monotonic response functions. The advantages of the model are illustrated by analyzing an immigration attitude survey conducted in the United States.

翻译：依赖有序多分类（类李克特）条目的调查被广泛用于捕捉个体偏好，因为它们允许受访者表达其偏好的方向和强度。传统上在此背景下使用的潜在因子模型隐含地假设响应函数（有序结果的累积分布）在潜在特质上是单调的。这一假设在包括政治科学和市场营销在内的多个应用领域中可能过于严格。在本工作中，我们提出了一种新颖的有序概率展开模型，该模型能够同时容纳单调和非单调的响应函数。通过分析在美国进行的一项移民态度调查，阐明了该模型的优势。