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| Author |
Wasserman, S. |
Title  |
Conformity of two sociometric relations |
Type |
Journal Article |
| Year |
1987 |
Publication |
Psychometrika |
Abbreviated Journal |
Psychometrika |
| Volume |
52 |
Issue |
1 |
Pages |
3-18 |
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| Abstract |
Abstract The problem of comparing two sociometric relations or measurements (A andB) recorded in distinct sociomatrices was originally discussed by Katz and Powell in the early 1950's and Hubert and Baker in the late 1970's. The problem is considered again using a probabilistic model designed specifically for discrete-valued network measurements. The model allows for the presence of various structural tendencies, such as reciprocity and differential popularity. A parameter that isolates the tendency for actors to choose other actors on both relations simultaneously is introduced, and estimated conditional on the presence of other parameters that reflect additional important network properties. The parameter is presented as a symmetric index but is also generalized to the predictive (A onB orB onA) situation. This approach to the problem is illustrated with the same data used by the earlier solutions, and the unique nature of the two relations in the data set (A = received choices,B = perceived choices), as it affects the modeling, is discussed. Significance tests for the parameter and related parameters are described, as well as an extension to more than two relations. |
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Equine Behaviour @ team @ |
Serial |
5027 |
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| Author |
Pattison, P.; Wasserman, S. |
| Title |
Logit models and logistic regressions for social networks: II. Multivariate relations |
Type |
Journal Article |
| Year |
1999 |
Publication |
The British Journal of Mathematical and Statistical Psychology |
Abbreviated Journal |
Br J Math Stat Psychol |
| Volume |
52 ( Pt 2) |
Issue |
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Pages |
169-193 |
| Keywords |
Humans; Markov Chains; *Models, Statistical; Organization and Administration; *Social Support |
| Abstract |
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. |
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Department of Psychology, University of Melbourne, Parkville, Victoria, Australia. pattision@psych.unimelb.edu.au |
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English |
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0007-1102 |
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PMID:10613111 |
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no |
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Equine Behaviour @ team @ |
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5030 |
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| Author |
Wasserman, S.; Faust, K. |
| Title |
Social Network Analysis : Methods and Applications |
Type |
Book Whole |
| Year |
1994 |
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bibtex-import |
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Cambridge University Press |
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Cambridge |
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Equine Behaviour @ team @ Wasserman1994 |
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5150 |
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| Author |
Iacobucci, D.; Wasserman, S. |
| Title |
Social networks with two sets of actors |
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Journal Article |
| Year |
1990 |
Publication |
Psychometrika |
Abbreviated Journal |
Psychometrika |
| Volume |
55 |
Issue |
4 |
Pages |
707-720 |
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Abstract Traditional network research analyzes relational ties within a single group of actors: the models presented in this paper involve relational ties exist beteen two distinct sets of actors. Statistical models for traditional networks in which relations are measured within a group simplify when modeling unidirectional relations measured between groups. The traditional paradigm results in a one-mode socionatrix; the network paradigm considered in this paper results in a two-mode socionatrix; A statistical model is presented, illustrated on a sample data set, and compared to its traditional counterpart. Extensions are discussed, including those that model multivariate relations simultaneously, and those that allow for the inclustion of attributes of the individuals in the group. |
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Equine Behaviour @ team @ |
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5036 |
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