fcirt:在项目响应理论中用于强迫选择模型的R包
Naidan Tu1, Sean Joo2, Philseok Lee3
1Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA.
Applied psychological measurement
|September 15, 2025
概括
fcirt包提供贝叶斯分析用于多维强制选择 (MFC) 评估,改进非认知特征测量. 它支持通用分级展开模型 (GGUM),并有助于评估评估质量.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 计算统计的计算统计.
背景情况:
- 多维强制选择 (MFC) 格式在评估非认知特征时,通过减轻响应偏差,比利克特类型尺度提供了优势.
- 越来越多地采用MFC格式,需要强大的分析工具来准确估计参数和评估模型.
研究的目的:
- 推出"强制选择"包,这是一款旨在促进多维强制选择 (MFC) 数据分析的新型工具.
- 为研究人员提供一个全面的套餐,用于估计基于一般化分级展开模型 (GGUM) 的多维单维对向偏好 (MUPP) 模型的参数.
主要方法:
- 该软件包采用贝叶斯估计方法,特别是使用汉密尔顿式蒙特卡洛 (HMC) 采样的软件包.
- 它实现了基于Generalized Graded Unfolding Model (GGUM) 的多维单维对称偏好 (MUPP) 模型进行参数估计.
- 该包包括计算项目和测试信息函数的功能,以及执行贝叶斯诊断绘图以进行模型评估.
主要成果:
- 该"fcirt"包允许在贝叶斯框架内估计MUPP模型参数.
- 它提供了通过信息功能来评估MFC评估的心理特征的工具.
- 贝叶斯诊断图可用于评估模型的融合和整体适应性.
结论:
- 该"fcirt"包为使用MFC格式的研究人员提供了宝贵的资源,提供先进的贝叶斯分析能力.
- 它支持对MFC评估质量的严格评估,有助于更可靠地测量非认知特征.
- 该方案通过综合诊断工具促进了改进的模型评估和趋同评估.
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