开始使用分级响应模型:R的介绍和教程
Rizqy Amelia Zein1,2, Hanif Akhtar3
1Department of Psychology, Ludwig-Maximilians-Universität, Munich, Germany.
概括
本教程解释了分级响应模型 (GRM),在物品响应理论 (IRT) 中的一个工具,用于评估心理尺度上的测量精度. 它指导研究人员使用R包进行GRM分析,以更好地评估项目和人.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 心理统计 心理统计
背景情况:
- 项目响应理论 (IRT) 提供了分析测量属性的先进方法.
- 心理尺度通常使用多种类型 (利克特式) 的项目,需要专门的分析模型.
- 准确测量物品和人的特征对于研究有效性至关重要.
研究的目的:
- 引入分级响应模型 (GRM) 作为测量精度的工具.
- 引导应用研究人员在R环境中进行一维GRM分析.
- 为了证明GRM的应用,使用来自右翼专制主义 (RWA) 规模的现实世界数据.
主要方法:
- 使用R包,如psych, mirt和ggmirt进行GRM分析.
- 概述分级响应模型的理论基础.
- 详细说明数据准备,假设测试和模型拟合的步骤.
主要成果:
- 在RWA尺度数据上演示GRM分析.
- 关于绘制项目参数和解释结果的指导.
- 说明GRM如何评估多种类型物品的心理测量属性.
结论:
- 分级响应模型 (GRM) 是在心理学研究中分析多种项目的一个有价值的工具.
- 应用研究人员可以有效地使用R包进行GRM分析.
- GRM分析提高了对物品和人体属性的理解,提高了测量精度.
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