Analyzing structural relations in multivariate dyadic binary data.

Statistica Neerlandica, 2004

A random effects model is proposed for the analysis of binary dyadic data that represent a social network or directed graph, using nodal and/or dyadic attributes as covariates. The network structure is reflected by modeling the dependence between the relations to and from the same actor or node. Parameter estimates are proposed that are based on an iterated generalized least-squares procedure. An application is presented to a data set on friendship relations between American lawyers.

Communication Methods and …, 2010

Social Network Analysis

Actors and their actions are viewed as interdependent rather than independent, autonomous units 0 Relational ties (linkages) between actors are channels for transfer or "flow" of resources (either material or nonmaterial) 0 Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action 0 Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors In this section we discuss these principles further and illustrate how the social network perspective differs from alternative perspectives in practice. Of critical importance for the development of methods for

Network Science, 2019

We consider the specification of effects of numerical actor attributes, having an interval level of measurement, in statistical models for directed social networks. A fundamental mechanism is homophily or assortativity, where actors have a higher likelihood to be tied with others having similar values of the variable under study. But there are other mechanisms that may also play a role in how the attribute values of two actors influence the likelihood of a tie between them. We discuss three additional mechanisms: aspiration, the tendency to send more ties to others having high values; attachment conformity, sending more ties to others whose values are close to the “social norm”; and sociability, where those having higher values will tend to send more ties generally. These mechanisms may operate jointly, and then their effects will be confounded. We present a specification representing these effects simultaneously by a four-parameter quadratic function of the values of sender and rec...

Social Networks, 1999

British Journal of Mathematical and Statistical Psychology, 1999

The research described here builds on our previous work by generalizing the univariate models described there to models for multivariate relations. This family, labelled p*, generalizes the Markov random graphs of Frank and Strauss, which were further developed by them and others, building on Besag's ideas on estimation. These models were first used to model random variables embedded in lattices by Ising, and have been quite common in the study of spatial data. Here, they are applied to the statistical analysis of multigraphs, in general, and the analysis of multivariate social networks, in particular. In this paper, we show how to formulate models for multivariate social networks by considering a range of theoretical claims about social structure. We illustrate the models by developing structural models for several multivariate networks.

British Journal of Mathematical and Statistical Psychology, 1986

Social interaction data record the intensity of the relationship, or frequency of interaction, between two individual actors. Recent methods for analysing such data have treated'these relational variables as continuous. A more appropriate method, described here, views these dyadic interactions as variables in multidimensional discrete cross-classified arrays, thus permitting analysis by log-linear models.

2011

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Relational Sociology: From Project to Paradigm

Social network analysis offers a myriad of measures to operationalize social interactions—some measure similar ideas, but with varying definitions and scope conditions. Although there is some theoretical import for keeping these measures separated, the purposes of much social science do not often warrant these distinctions. In this chapter, I illustrate how network data can be used to construct broad, social interaction measures associated with dyadic, interpersonal relationships. These broad constructs often get closer to the underlying ideas outlined in many sociological theories. Principal components factor (PCF) analysis methods are used to construct these more parsimonious measures.