聚合关系数据的测量属性和NSUM估计的网络大小
Miranda J Lubbers1, Michał Bojanowski1,2, Nuria Targarona Rifa1
1Department of Social and Cultural Anthropology, Autonomous University of Barcelona, Barcelona, Spain.
概括
聚合关系数据 (ARD) 和网络扩展方法 (NSUM) 可以对社交网络大小进行排名,但缺乏一致的测量. 网络大小,特别是"枢纽",在采访之间大幅波动.
科学领域:
- 社交网络分析 社交网络分析
- 社会学 社会学 社会学
- 计算社会科学 计算社会科学
背景情况:
- 聚合关系数据 (ARD) 和网络扩展方法 (NSUM) 常用于社交网络分析.
- 需要进一步研究ARD问题和NSUM衍生网络大小的测量特性.
研究的目的:
- 评估ARD问题和NSUM估计的网络大小的测试-重新测试可靠性.
- 为了评估NSUM估计的网络大小的收有效性.
主要方法:
- 与西班牙巴塞罗那50名参与者的混合方法研究.西班牙巴塞罗那.
- 两次采访超过1015天使用ARD问题.
- 定性解答,以了解响应行为.
主要成果:
- NSUM准确地对受访者网络大小进行了排名,但在所有测量中显示出不一致的估计.
- 网络大小,特别是"枢纽",在采访之间大幅波动.
- NSUM估计的网络规模与总和方法和Facebook朋友数量有中等的相关性.
结论:
- 虽然NSUM可以对网络大小进行排名,但对估计绝对值的可靠性是有限的.
- 为ARD项目选择和NSUM仪器设计提供了建议.
- 需要进一步的研究来改善ARD和NSUM的测量特性.
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