项目响应模型用于评级关系数据.
Chih-Han Leng1, Ulf Böckenholt2, Hsuan-Wei Lee3
1Department of Psychology, https://ror.org/05bqach95National Taiwan University, Taipei, Taiwan (ROC).
Psychometrika
|June 30, 2025
概括
本研究引入了新的项目响应模型,用于在网络中分析关系数据. 这些模型有效地比较网络参与者,并捕捉诸如互惠和集群等关系模式.
科学领域:
- 社交网络分析分析
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 关系数据,通常来自定向网络中的发送接收器评级,存在独特的分析挑战.
- 现有的模型可能无法完全捕捉这些网络中互惠和同类关系的细微差别.
- 了解网络结构和个体属性在各种社会科学领域至关重要.
研究的目的:
- 引入针对评级关系数据量身定制的新项目响应模型.
- 为了使发射器和接收器的单维隐性尺度比较.
- 为了解释网络内的未观察到的同性恋关系.
主要方法:
- 为定向网络开发项目响应模型.
- 贝叶斯参数估计.贝叶斯参数估计.
- 马尔科夫链蒙特卡洛 (MCMC) 方法用于后部分布近似.
主要成果:
- 拟议的模型有效地捕捉了关系数据中的互惠和聚类.
- 模拟研究证实了令人满意的参数恢复,即使在小的网络尺寸.
- 这些模型证明了对完整和不完整的网络数据的实用性.
结论:
- 引入的项目响应模型为分析关系网络数据提供了一个强大的框架.
- 该方法促进了对网络参与者的细微比较,并揭示了潜在的关系动态.
- 这些模型为社会网络分析中的实证应用提供了有价值的见解.
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