Flexible Item Response Models for Count Data: The Count Thresholds Model.

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Applied Psychological Measurement Pub Date : 2022-11-01 Epub Date: 2022-08-07 DOI:10.1177/01466216221108124
Gerhard Tutz
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引用次数: 1

Abstract

A new item response theory model for count data is introduced. In contrast to models in common use, it does not assume a fixed distribution for the responses as, for example, the Poisson count model and extensions do. The distribution of responses is determined by difficulty functions which reflect the characteristics of items in a flexible way. Sparse parameterizations are obtained by choosing fixed parametric difficulty functions, more general versions use an approximation by basis functions. The model can be seen as constructed from binary response models as the Rasch model or the normal-ogive model to which it reduces if responses are dichotomized. It is demonstrated that the model competes well with advanced count data models. Simulations demonstrate that parameters and response distributions are recovered well. An application shows the flexibility of the model to account for strongly varying distributions of responses.

计数数据的灵活项目响应模型:计数阈值模型。
介绍了一种新的计数数据项目反应理论模型。与常用的模型相比,它并没有像泊松计数模型和扩展那样假设响应的固定分布。响应的分布由难度函数决定,该函数以灵活的方式反映项目的特征。稀疏参数化是通过选择固定的参数难度函数来获得的,更一般的版本使用基函数的近似。该模型可以被视为是由二元响应模型构建的,如Rasch模型或正态ogive模型,如果响应是二分的,则该模型可以简化为二元响应。结果表明,该模型与先进的计数数据模型具有很好的竞争性。仿真结果表明,参数和响应分布恢复良好。一个应用程序显示了该模型的灵活性,可以考虑强烈变化的响应分布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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