Multiple-Membership Multiple-Classification Models for Social Network and Group Dependences

• DOI: 10.1111/rssa.12021
• Publication Date: 2013-08-09T11:17:17Z
• Authors: Mark Tranmer; David Steel; William J. Browne
• Keywords: Multilevel models; name=Jean Golding; 330; 000; 4. Education; membership; 05 social sciences; social; Original Articles; Auto-correlation; Science and Technology Studies; /dk/atira/pure/core/keywords/jean_golding; name=Jean Golding; Social networks; /dk/atira/pure/core/keywords/jean_golding; multiple; models; dependences; Engineering; classification; 0504 sociology; network; group; Linear regression

SummaryThe social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications.