在社会关系中隐藏的相互参与和准确性变量结构方程建模结构方程建模
David Jendryczko1, Fridtjof W Nussbeck1
1Department of Psychology, University of Konstanz.
Multivariate behavioral research
|August 7, 2024
概括
这项研究增强了社会关系模型 (SRM) 用于分析二极交互. 研究结果显示,相互模仿会增加喜欢,而高估人气会降低它.
科学领域:
- 社会心理学 社会心理学
- 量化心理学 量化心理学
- 行为科学 行为科学
背景情况:
- 社会关系模型 (SRM) 是圆形连锁设计中二元数据的标准.
- SRM估计了个人和双层层次的判断的互惠性和准确性.
- 现有的SRM缺乏对互惠参与和准确性的个人间差异的详细分析.
研究的目的:
- 修改多重指标SRM以捕捉互动参与中的个人间和双人间差异.
- 在SRM框架内分析相互准确性的个体间差异.
- 将这些增强模型应用于现实世界的二极交互数据.
主要方法:
- 在社会关系框架内利用结构方程建模.
- 应用随机测量和经典测试理论原理.
- 修改了多个指标SRM,以加强对二次数据的分析.
主要成果:
- 参与相互模仿的人在互动后更受欢迎.
- 过度估计自己的受欢迎程度与被更少地喜欢有关.
- 增强的SRM有效地捕捉了个体间和双节间的差异.
结论:
- 修改后的SRM提供了一个更细致的了解二级社会动态.
- 相互模仿和对人气的准确自我认知是社会喜爱的关键.
- 改进的模型为研究社会互动提供了有价值的应用.
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