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使用IRTree模型在存在极端响应风格的情况下促进选择有效性

Victoria L Quirk¹, Justin L Kern¹

¹Department of Educational Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.

Journal of Intelligence

|November 24, 2023

概括

项目响应理论 (IRT) 模型,特别是IRTree模型,在存在噪音特征时,与通用部分信用模型 (GPCM) 相比,提高了选择准确性并减少了响应风格的不利影响.

科学领域:

心理测量心理测量心理测量
心理测量心理测量
项目响应理论.

背景情况:

用利克特类项目进行自我报告测试是测量心理构造的常见方法.
现有的模型可能无法充分区分内容和噪声特征 (例如响应风格).
项目响应理论 (IRT) 为心理测量数据提供了先进的建模技术.

研究的目的:

使用IRTree模型与通用部分信用模型 (GPCM) 进行选择有效性和噪声特征的不利影响的比较.
在有或没有噪声特征影响的条件下评估模型性能.
通过使用现实世界的心理测量数据,评估IRTree模型的实际应用.

主要方法:

进行了一项模拟研究,以在不同噪声特征条件下比较IRTree和GPCM.
分析了选择决策的准确性和与极端响应风格相关的不利影响.
来自费舍尔气质清单的经验数据集被用来评估模型适应和影响比率.

主要成果:

模拟显示,IRTree模型在存在噪声特征时产生更高的准确性和更少的不利影响,其他情况下的性能与GPCM相似.
IRTree模型显示出与经验数据更好的匹配.
尽管更适合,但模型决策导致IRTree和GPCM在应用程序中的影响比率几乎相同.

结论:

在选择环境中,IRTree模型比GPCM具有优势,因为响应风格 (噪声特征) 可能会影响得分.
IRTree模型对选择公平性的实际影响需要仔细考虑特定的数据集和决策规则.
进一步的研究应该探索更广泛的应用,并完善IRTree模型方法.

关键词:

在IRTree模型中,利克尔特等级是指利克尔特等级的等级.负面影响的负面影响.分类的准确性分类的准确性.极端的响应风格极端的响应风格项目响应理论是物品响应理论.项目响应树是物品响应树.非内容变化性的非内容变化.响应过程响应过程选择的有效性选择的有效性

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