{"title":"Digital Module 37: Introduction to Item Response Tree (IRTree) Models","authors":"Nana Kim, Jiayi Deng, Yun Leng Wong","doi":"10.1111/emip.12665","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <h3> Module Abstract</h3>\n \n <p>Item response tree (IRTree) models, an item response modeling approach that incorporates a tree structure, have become a popular method for many applications in measurement. IRTree models characterize the underlying response processes using a decision tree structure, where the internal decision outcome at each node is parameterized with an item response theory (IRT) model. Such models provide a flexible way of investigating and modeling underlying response processes, which can be useful for examining sources of individual differences in measurement and addressing measurement issues that traditional IRT models cannot deal with. In this module, we discuss the conceptual framework of IRTree models and demonstrate examples of their applications in the context of both cognitive and noncognitive assessments. We also introduce some possible extensions of the model and provide a demonstration of an example data analysis in R.</p>\n </section>\n </div>","PeriodicalId":47345,"journal":{"name":"Educational Measurement-Issues and Practice","volume":"44 1","pages":"109-110"},"PeriodicalIF":2.7000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational Measurement-Issues and Practice","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/emip.12665","RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
摘要
模块 摘要 项目反应树(IRTree)模型是一种包含树形结构的项目反应建模方法,在测量领域的许多应用中已成为一种流行的方法。IRTree 模型采用决策树结构来描述基本的反应过程,其中每个节点的内部决策结果都用项目反应理论(IRT)模型来参数化。这类模型提供了一种灵活的方法来研究和模拟基本的反应过程,这对于研究测量中个体差异的来源和解决传统 IRT 模型无法解决的测量问题非常有用。在本模块中,我们将讨论 IRTree 模型的概念框架,并举例说明其在认知和非认知评估中的应用。我们还将介绍该模型的一些可能扩展,并提供一个用 R 进行数据分析的示例。
Digital Module 37: Introduction to Item Response Tree (IRTree) Models
Module Abstract
Item response tree (IRTree) models, an item response modeling approach that incorporates a tree structure, have become a popular method for many applications in measurement. IRTree models characterize the underlying response processes using a decision tree structure, where the internal decision outcome at each node is parameterized with an item response theory (IRT) model. Such models provide a flexible way of investigating and modeling underlying response processes, which can be useful for examining sources of individual differences in measurement and addressing measurement issues that traditional IRT models cannot deal with. In this module, we discuss the conceptual framework of IRTree models and demonstrate examples of their applications in the context of both cognitive and noncognitive assessments. We also introduce some possible extensions of the model and provide a demonstration of an example data analysis in R.