In many cases, ordinal data, for example rating objects on a scale from 1 to 5, is observed only for those objects that have been chosen from a set of discrete alternatives, with no ratings for unchosen objects. An example is customer ratings of goods sold by online retailers. The joint modelling of choice and rating is made difficult by the missing ratings for unchosen alternatives. A method of jointly modelling choice and rating data termed a choice-ordered logit (COL) model is presented. Two types of COL model are defined: two-step, which places a positive probability on the chosen alternative not having the highest rating, and one-step, where the highest rated alternative is always chosen. Three case studies exemplifying the use of COL models are given. One uses simulated data and two use data from discrete choice experiments. It is shown that COL models can produce robust estimates. Two-step models provided a better fit than one-step, and most participants seemed to use two-step decision-making. However, a sizeable minority used one-step decision-making in one case study. It is argued that COL models have benefits over standard approaches, in particular adding information on strength-of-preference to discrete choices.

在许多情况下，序数数据，例如从 1 到 5 的等级对对象进行评级，只观察到那些从一组离散备选对象中选择的对象，没有对未选择的对象进行评级。一个例子是客户对在线零售商所售商品的评分。由于缺少未选择的替代品的评级，选择和评级的联合建模变得困难。提出了一种联合建模选择和评级数据的方法，称为选择排序逻辑 (COL) 模型。定义了两种类型的 COL 模型：两步，它对没有最高评级的所选替代方案赋予正概率，以及一步，其中始终选择最高评级的替代方案。给出了三个案例研究，举例说明了 COL 模型的使用。一种使用模拟数据，两种使用来自离散选择实验的数据。结果表明，COL 模型可以产生稳健的估计。两步模型比一步模型更适合，大多数参与者似乎使用两步决策。然而，在一个案例研究中，相当多的少数人使用了一步决策。有人认为，COL 模型优于标准方法，尤其是将有关偏好强度的信息添加到离散选择中。