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人的解释性多维物品响应理论与RR中的仪器包

Michael J Kleinsasser¹, Ritesh Mistry², Hsing-Fang Hsieh²

¹Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA. mkleinsa@umich.edu.

Behavior research methods

|August 26, 2024

概括

新的R包,仪器,使贝叶斯估计为多维项目响应理论模型. 它使用混合建模来促进复杂人体参数的分析,增强心理测量研究能力.

科学领域:

心理测量心理测量心理测量
统计建模统计建模
计算统计学计算统计学

背景情况:

多维物件响应理论 (MIRT) 模型对于理解复杂的响应模式至关重要.
对于MIRT,现有的贝叶斯估计方法可能是计算密集型和不那么灵活.
需要用户友好的R包来实现先进的MIRT模型.

研究的目的:

为贝叶斯估计解释性的多维项目响应理论 (MIRT) 模型引入R包"仪器".
在"仪器"包中实施探索性和更高阶 (确认性) MIRT 模型.
用混合效应模型来证明包装在解释人参数方面的能力.

主要方法:

贝叶斯估计使用汉密尔顿蒙特卡洛 (HMC) 在 Stan.
探索性和高级MIRT模型的实施.
固定和随机效应线性回归用于人参数解释的应用 (混合建模).

主要成果:

"仪器" R 包为贝叶斯 MIRT 分析提供了一个强大的框架.
该套件成功地适应了解释性的MIRT模型,对人体参数具有混合效应.
模拟研究证实了实施的模型的性能和可靠性.

结论:

"仪器"套件为心理测量和相关领域的研究人员提供了有价值的工具.
它简化了先进的贝叶斯MIRT技术的应用,包括混合建模.
该套件增强了在复杂的测量环境中解释和建模个体差异的能力.

关键词:

贝叶斯数据分析的贝叶斯数据分析.汉密尔顿式蒙特卡洛的蒙特卡洛的项目响应理论.潜伏回归的潜伏回归多维物品响应理论是多维物品响应理论.在R包中,R包是R包.

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