The analysis of social networks

被引：155

作者：

O'Malley A.J. ^{[1
]}

Marsden P.V. ^{[2
]}

机构：

[1] Department of Health Care Policy, Harvard Medical School, Boston, MA 02115-5899

[2] Department of Sociology, Harvard University, Cambridge, MA 02138

来源：

Health Services and Outcomes Research Methodology | 2008年 / 8卷 / 4期

关键词：

Correlation; Exponential random graph model; Latent-space model; Network autocorrelation model; Social network; Social relationship;

D O I：

10.1007/s10742-008-0041-z

中图分类号：

学科分类号：

摘要：

Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships. Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. More recently, interest in the analysis of social network data has grown among statisticians, who have developed more elaborate models and methods for fitting them to network data. This article reviews fundamentals of, and recent innovations in, social network analysis using a physician influence network as an example. After introducing forms of network data, basic network statistics, and common descriptive measures, it describes two distinct types of statistical models for network data: individual-outcome models in which networks enter the construction of explanatory variables, and relational models in which the network itself is a multivariate dependent variable. Complexities in estimating both types of models arise due to the complex correlation structures among outcome measures. © Springer Science+Business Media, LLC 2008.

引用

页码：222 / 269

页数：47

共 117 条

[71]

Laumann E., Marsden P., Prensky D., The boundary specification problem in network analysis, Applied Network Analysis A Methodological Introduction, pp. 18-34, (1983)

[72]

Laumann E., Mahay J., Paik A., Youm Y., Network data collection and its relevance for the analysis of STDs: The NHSLS and CHSLS, Network Epidemiology: A Handbook for Survey Design and Data Collection, pp. 27-41, (2004)

[73]

Leenders R., Modeling social influence through network autocorrelation: Constructing the weight matrix, Soc. Networks, 24, 1, pp. 21-47, (2002)

[74]

Marsden P., Core discussion networks of Americans, Am. Sociol. Rev., 52, 1, pp. 122-131, (1987)

[75]

Marsden P., Network data and measurement, Annu. Rev. Sociol., 16, pp. 435-463, (1990)

[76]

Marsden P., Egocentric and sociocentric measures of network centrality, Soc. Networks, 24, pp. 407-422, (2002)

[77]

Marsden P., Network methods in social epidemiology, Methods in Social Epidemiology, pp. 267-286, (2006)

[78]

McGrath C., Blythe J., Krackhardt D., The effect of spatial arrangement on judgments and errors in interpreting graphs, Soc. Networks, 19, 3, pp. 223-242, (1997)

[79]

McPherson M., Smith-Lovin L., Cook J., Birds of a feather: Homophily in social networks, Annu. Rev. Sociol., 27, pp. 415-444, (2001)

[80]

Miguel E., Kremer M., Networks, Social Learning, and Technology Adoption: The Case of Deworming Drugs in Kenya, (2003)

← 3 4 5 6 7 8 9 10 11 12 →