A comprehensive class of models is proposed that can be used for continuous, binary, ordered categorical and count type responses. The difficulty of items is described by difficulty functions, which replace the item difficulty parameters that are typically used in item response models. They crucially determine the response distribution and make the models very flexible with regard to the range of distributions that are covered. The model class contains several widely used models as the binary Rasch model and the graded response model as special cases, allows for simplifications, and offers a distribution free alternative to count type items. A major strength of the models is that they can be used for mixed item formats, when different types of items are combined to measure abilities or attitudes. It is an immediate consequence of the comprehensive modeling approach that allows that difficulty functions automatically adapt to the response distribution. Basic properties of the model class are shown. Several real data sets are used to illustrate the flexibility of the models.
翻译:提议全面的模型类别,用于连续、二进制、定单绝对和计数类型的响应。项目的困难在于困难功能,这些功能取代了项目响应模型通常使用的项目困难参数。它们关键地决定了响应分布,并使模型在所覆盖分布范围方面非常灵活。模型类别包含一些广泛使用的模型,如二进制Rasch模型和作为特例的分级响应模型,允许简化,并提供了计算类型项目的一种免费分配替代。模型的一大优点是,当不同种类的项目被组合起来以衡量能力或态度时,它们可以用于混合项目格式。这是综合模型方法的直接后果,使困难功能能够自动适应响应分布。示范类别的基本特性显示。一些真实的数据集用来说明模型的灵活性。