2021/06/24 by Gerhard Tutz, Tutz, Gerhard
Computer Science · Decision Sciences · Mathematics · Psychology · #Advanced Statistical Modeling Techniques #Computer science #Econometrics #FOS: Computer and information sciences #Mathematics #Methodology (stat.ME) #Psychology #Psychometric Methodologies and Testing #Statistical Methods and Bayesian Inference #stat.ME
paper · pdf · doi:10.48550/arxiv.2106.12784
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/06/24 · openalex publication_date 2021/06/24 · arxiv updated 2021/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.