相互依赖的社交网络数据的Interrater可靠性:一个概括性理论方法
Debby Ten Hove1, Terrence D Jorgensen2, L Andries van der Ark2
1Department of Educational and Family Studies, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Multivariate behavioral research
|February 3, 2025
概括
我们为社交网络观测开发了新的interrater可靠性系数. 该方法量化了参与者,合作伙伴和关系效应,提高了观察性社交网络数据分析的概括性.
科学领域:
- 社会心理学 社会心理学
- 网络分析 网络分析
- 行为观察行为观察.
背景情况:
- 社交网络中的观测数据往往涉及相互依存的双向相互作用.
- 评估不同评级器的观察结果的可靠性对于有效的结论至关重要.
- 现有的模型可能无法完全捕捉评分者对二次观测的影响的复杂性.
研究的目的:
- 为观察性相互依赖的社交网络数据提出新的评分器间可靠性系数.
- 扩展社会关系模型,将评分器效应纳入全面的差异分解.
- 提供一个统计学上可靠的方法来评估在评分者之间观察到的社会行为的概括性.
主要方法:
- 使用概括性理论扩展了社会关系模型,包括评分效应.
- 分解二级观测数据的差异,分为参与者,合作伙伴,关系和评价者效应,包括相互作用.
- 采用马尔科夫链蒙特卡洛估计贝叶斯层次线性模型来计算类内相关系数 (ICC).
主要成果:
- 拟议的模型成功地将二级观测数据变异分解为关键组件.
- 开发的ICC量化了不同评级者之间的行为体,合作伙伴和关系效应的概括性.
- 模拟研究证实了拟议方法的低偏差和ICC估计的充分覆盖.
结论:
- 新的评价者间可靠性系数提供了一种可靠的方式来评估社交网络中的观测数据.
- 扩展的社会关系模型提供了更细致的对二极交互差异的理解.
- 这种方法提高了社交网络研究结果的严谨性和通用性,正如社交模仿数据所示.
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