在分级响应模型中对潜在特征的可靠估计
Audrey Filonczuk1, Ying Cheng2
1Department of Psychology, University of Notre Dame, 390 Corbett Hall, Notre Dame, IN, 46556, USA.
Behavior research methods
|January 14, 2025
概括
这项研究为分级响应模型 (GRM) 引入了一种新的可靠估计器,以提高心理评估的准确性. 这种新方法有效地减少了利克特类项目中异常反应引起的偏差.
科学领域:
- 心理测量 心理测量 心理测量
- 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 异常反应,如疏忽或错误的答案,可能会损害心理评估的有效性.
- 现有的强大的估计器对二分制物品响应理论 (IRT) 模型是有效的,但对于具有多个响应类别的利克尔特类型项目是不有效的.
- 对于广泛使用的利克尔特类型尺度而言,有必要采用可靠的估计方法.
研究的目的:
- 为分级响应模型 (GRM) 提出和评估一种新的可靠估计器.
- 为了解决心理调查中异常反应的挑战,使用利克特类型的项目.
- 在存在响应干扰的情况下,提高潜在特征估计的准确性.
主要方法:
- 为分级响应模型 (GRM) 开发一个可靠的估计器.
- 实施两个权重函数 (Huber和bisquare) 以减轻可疑响应的权重.
- 进行不同测试长度,响应类别数量和干扰类型的模拟研究.
主要成果:
- 拟议的强大估计器显著降低了在不同的模拟条件下隐性特征估计的偏差.
- 观察到稳定的标准误差,表明估计者的可靠性.
- 该方法在减轻响应干扰对评估得分的影响方面表现出有效性.
结论:
- GRM的强大估计器有效地提高了使用利克特类项目进行心理评估的准确性.
- 这种方法为研究人员和从业人员处理响应干扰提供了宝贵的工具.
- 这些发现表明在调查方法和心理测量分析中具有更广泛的应用.
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