Customizing Bayesian multivariate generalizability theory to mixed-format tests.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-07-29 DOI:10.3758/s13428-024-02472-7
Zhehan Jiang, Jinying Ouyang, Dingjing Shi, Dexin Shi, Jihong Zhang, Lingling Xu, Fen Cai
{"title":"Customizing Bayesian multivariate generalizability theory to mixed-format tests.","authors":"Zhehan Jiang, Jinying Ouyang, Dingjing Shi, Dexin Shi, Jihong Zhang, Lingling Xu, Fen Cai","doi":"10.3758/s13428-024-02472-7","DOIUrl":null,"url":null,"abstract":"<p><p>Mixed-format tests, which typically include dichotomous items and polytomously scored tasks, are employed to assess a wider range of knowledge and skills. Recent behavioral and educational studies have highlighted their practical importance and methodological developments, particularly within the context of multivariate generalizability theory. However, the diverse response types and complex designs of these tests pose significant analytical challenges when modeling data simultaneously. Current methods often struggle to yield reliable results, either due to the inappropriate treatment of different types of response data separately or the imposition of identical covariates across various response types. Moreover, there are few software packages or programs that offer customized solutions for modeling mixed-format tests, addressing these limitations. This tutorial provides a detailed example of using a Bayesian approach to model data collected from a mixed-format test, comprising multiple-choice questions and free-response tasks. The modeling was conducted using the Stan software within the R programming system, with Stan codes tailored to the structure of the test design, following the principles of multivariate generalizability theory. By further examining the effects of prior distributions in this example, this study demonstrates how the adaptability of Bayesian models to diverse test formats, coupled with their potential for nuanced analysis, can significantly advance the field of psychometric modeling.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":" ","pages":"8080-8090"},"PeriodicalIF":4.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior Research Methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13428-024-02472-7","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/29 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Mixed-format tests, which typically include dichotomous items and polytomously scored tasks, are employed to assess a wider range of knowledge and skills. Recent behavioral and educational studies have highlighted their practical importance and methodological developments, particularly within the context of multivariate generalizability theory. However, the diverse response types and complex designs of these tests pose significant analytical challenges when modeling data simultaneously. Current methods often struggle to yield reliable results, either due to the inappropriate treatment of different types of response data separately or the imposition of identical covariates across various response types. Moreover, there are few software packages or programs that offer customized solutions for modeling mixed-format tests, addressing these limitations. This tutorial provides a detailed example of using a Bayesian approach to model data collected from a mixed-format test, comprising multiple-choice questions and free-response tasks. The modeling was conducted using the Stan software within the R programming system, with Stan codes tailored to the structure of the test design, following the principles of multivariate generalizability theory. By further examining the effects of prior distributions in this example, this study demonstrates how the adaptability of Bayesian models to diverse test formats, coupled with their potential for nuanced analysis, can significantly advance the field of psychometric modeling.

Abstract Image

为混合格式测试定制贝叶斯多元概括性理论。
混合形式测验通常包括二分项目和多分任务,用于评估更广泛的知识和技能。最近的行为学和教育学研究强调了它们的实际重要性和方法论的发展,特别是在多元概括性理论的背景下。然而,这些测验的反应类型多样,设计复杂,在同时建立数据模型时,给分析工作带来了巨大挑战。由于对不同类型的反应数据分别处理不当,或在不同反应类型中施加相同的协变量,目前的方法往往难以得出可靠的结果。此外,很少有软件包或程序能针对这些局限性为混合格式检验建模提供定制解决方案。本教程提供了一个详细示例,说明如何使用贝叶斯方法对从混合形式测验(包括多项选择题和自由回答任务)中收集的数据进行建模。建模是使用 R 编程系统中的 Stan 软件进行的,Stan 代码是根据测试设计的结构定制的,遵循了多元概括性理论的原则。通过进一步研究先验分布在本例中的影响,本研究展示了贝叶斯模型对不同测验形式的适应性,以及其进行细致分析的潜力,如何能极大地推动心理测量建模领域的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信