A p* primer:: logit models for social networks
A p* primer:: logit models for social networks
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DOI：
10.1016/s0378-8733(98)00012-4
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发表时间：
1999-01-01
期刊：
影响因子：
3.1
通讯作者：
Crouch, B
中科院分区：
文献类型：
--
作者：
Anderson, CJ;Wasserman, S;Crouch, B
对 Holland、Leinhardt 等人开发的用于分析社交网络的统计模型的主要批评 [Holland, P.W., Leinhardt, S., 1977。社交网络数据统计分析的注释； Holland, P.W., Leinhardt, S., 1981。有向图概率分布的指数族。美国统计协会杂志。 76，第 33-65 页（带讨论）； Fienberg, S.E., Wasserman, S., 1981。单一社会计量关系的分类数据分析。在：莱因哈特，S. （主编），《社会学方法论》，1981 年，旧金山：Jossey-Bass，第 156-192 页； Fienberg, S.E.、Meyer, M.M.、Wasserman, S.，1985。多重社会计量关系的统计分析。美国统计协会杂志，80，第 51-67 页； Wasserman, S.、Weaver, S.，1985。二元关系数据的统计分析：参数估计。数学心理学杂志。 29，第 406-427 页； Wasserman, S.，1987。两种社会计量关系的一致性。心理测量学。 52，第3-18页]是对网络或团体内互动的个人或单位做出的非常强的独立性假设。鉴于 Frank 和 Strauss 提出的随机图模型的最新发展，这种限制性假设不再是必要的 [Frank, O., Strauss, D., 1986。马尔可夫图。美国统计协会杂志。 81，第 832-842 页] 以及 Strauss 和 Ikeda [Strauss, D., Ikeda, M., 1990。社交网络的伪似然估计。美国统计协会杂志。 85，第 204-212 页]。生成的模型非常灵活且易于适应数据。尽管 Wasserman 和 Pattison [Wasserman, S., Pattison, P., 1996。社交网络的 Logit 模型和逻辑回归：I. 马尔可夫随机图和 p* 简介。心理测量学。 60，第 401-426 页]介绍了这些模型的派生和扩展，本文是关于如何使用这些重要突破来建模单个网络中的参与者（个人、单位）之间的关系的入门读物，并提供了模型到多个网络的扩展。多个网络的模型允许研究人员研究群体如何相似和/或如何不同。使用 Parker 和 Asher [Parker, J.G., Asher, S.R., 1993] 的研究中小学生的友谊数据来说明单个和多个网络的模型以及建模过程。童年中期的友谊和友谊质量：与同伴群体接受度以及孤独感和社会不满的联系。发展心理学。 29，第 611-621 页]。(C) 1999 Elsevier Science B.V. 保留所有权利。
A major criticism of the statistical models for analyzing social networks developed by Holland, Leinhardt, and others [Holland, P.W., Leinhardt, S., 1977. Notes on the statistical analysis of social network data; Holland, P.W., Leinhardt, S., 1981. An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association. 76, pp. 33-65 (with discussion); Fienberg, S.E., Wasserman, S., 1981. Categorical data analysis of single sociometric relations. In: Leinhardt,S. (Ed.), Sociological Methodology 1981, San Francisco: Jossey-Bass, pp. 156-192; Fienberg, S.E., Meyer, M.M., Wasserman, S., 1985. Statistical analysis of multiple sociometric relations. Journal of the American Statistical Association, 80, pp. 51-67; Wasserman, S., Weaver, S., 1985. Statistical analysis of binary relational data: Parameter estimation. Journal of Mathematical Psychology. 29, pp. 406-427; Wasserman, S., 1987. Conformity of two sociometric relations. Psychometrika. 52, pp. 3-18] is the very strong independence assumption made on interacting individuals or units within a network or group. This limiting assumption is no longer necessary given recent developments on models for random graphs made by Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association. 81, pp. 832-842] and Strauss and Ikeda [Strauss, D., Ikeda, M., 1990. Pseudolikelihood estimation for social networks. Journal of the American Statistical Association. 85, pp. 204-212]. The resulting models are extremely flexible and easy to fit to data. Although Wasserman and Pattison [Wasserman, S., Pattison, P., 1996. Logit models and logistic regressions for social networks: I. An introduction to Markov random graphs and p*. Psychometrika. 60, pp. 401-426] present a derivation and extension of these models, this paper is a primer on how to use these important breakthroughs to model the relationships between actors (individuals, units) within a single network and provides an extension of the models to multiple networks. The models for multiple networks permit researchers to study how groups are similar and/or how they are different. The models for single and multiple networks and the modeling process are illustrated using friendship data from elementary school children from a study by Parker and Asher [Parker, J.G., Asher, S.R., 1993. Friendship and friendship quality in middle childhood: Links with peer group acceptance and feelings of loneliness and social dissatisfaction. Developmental Psychology. 29, pp. 611-621].(C) 1999 Elsevier Science B.V. All rights reserved.