总和与估计的因子得分:在使用观察得分时考虑不确定性
1Department of Human Development and Quantitative Methodology, University of Maryland.
Psychological methods
|February 8, 2024
概括
本研究评估了不同的观察得分如何估计潜变量 (LVs) 和恢复结构关系. 结果指导心理科学中选择最佳分数,考虑可靠性和模型错误.
科学领域:
- 心理学科学 心理学科学
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 观察到的分数经常被用来代表心理研究中潜在的构造.
- 潜变量 (LVs) 是通过测量模型,通常是共同因子模型来操作的理论构造.
- 了解不同观察得分的表现对于准确表示LVs至关重要.
研究的目的:
- 评估各种观察得分的表现,以估计潜在得分和分类个体.
- 评估不同观察得分能够恢复潜在变量之间的结构关系的能力.
- 为了考虑到观察到的得分表现的评估中的采样错误和模型错误.
主要方法:
- 在古典测试理论和常见因子模型中,对观察到的分数的心理测量属性的审查.
- 进行模拟研究以检查在不同条件下的得分表现.
- 分析两个实证示例,以展示具有不同可靠性,样本大小和模型错误级别的得分行为.
主要成果:
- 不同观察得分的性能因具体应用而有很大差异 (例如,估计与结构恢复).
- 可靠性,样本大小和模型错误极大地影响了隐性得分估计和结构恢复的准确性.
- 某些观察到的得分在特定的不确定性和测量质量条件下显示出优异的性能.
结论:
- 提供基于证据的建议,用于在心理学研究中选择适当的观察得分.
- 强调在使用观测得分时考虑测量误差和模型错误规范的重要性.
- 建议未来的研究方向,以推进潜变量建模中观察到的得分的理解和应用.
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