一个相关的特征相关的 (方法 - 1) 多特征-多方法模型,用于增强的循环数据
David Jendryczko1, Fridtjof W Nussbeck1
1Department of Psychology, University of Konstanz, Konstanz, Germany.
The British journal of mathematical and statistical psychology
|October 16, 2023
概括
这项研究引入了一种新的统计模型来分析二元循环数据,增强多特征多方法分析. 该模型准确地估计参数并检测模型匹配,即使在增强设计中具有互惠性.
科学领域:
- 心理测量 心理测量 心理测量
- 社交网络分析 社交网络分析
- 统计建模 统计建模
背景情况:
- 多特征多方法 (MTMM) 模型对于评估构造有效性至关重要.
- 双向的圆形连环设计捕捉了复杂的社会互动.
- 现有的模型可能无法完全解释增强的圆环数据中的依赖关系.
研究的目的:
- 导出和呈现一个相关的特征相关的 (方法 - 1) [CTC(M - 1) ]多特征-多方法 (MTMM) 模型用于增强的二圆环数据.
- 扩展CTC (M - 1) 模型以处理评级者和目标之间的依赖关系.
- 提供评估模型合适性和解释结果的方法.
主要方法:
- 为增强的二圆环数据开发结构方程模型.
- 包括互惠互差参数来考虑相互依赖.
- 变量分解,一致性和可靠性系数的呈现.
- 模拟研究,以评估参数估计和模型合适检测.
主要成果:
- 拟议的CTC(M - 1) 模型准确地估计了参数,并使用已建立的指数检测模型匹配.
- 即使在小组中也观察到令人满意的参数估计偏差和覆盖率.
- 建议使用较大的群体大小,以尽量减少参数估计的不准确性.
- 忽视互惠协差并没有严重偏差其他参数估计.
结论:
- 衍生出的CTC(M - 1) 模型提供了一个强大的框架来分析增强的二圆环数据.
- 该模型为复杂的社会数据的一致性和可靠性提供了有价值的见解.
- 这些发现支持这种模型在心理测量研究中的准确应用.
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