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The ASA Spring Methodology Conference Organized in Europe by the Department of Methodology and Statistics at Tilburg University, the Netherlands. SESSION: Statistical Social Network Analysis SUMMARY by Johan Koskinen (chair of the session) johan.koskinen@gmail.com Social network analysis (SNA) is concerned with the study of social interaction among social actors. Introduced as Sociometry, SNA was formalised using graph theory with the obvious analogue in the social world of nodes and edges being people connected by social relations. While visual and mathematical analysis, as well as rudimentary tests against simple null-models, has been in use since at least the thirties it is only in the last forty or thirty years that progress has been made in statistical modelling of networks and the dependencies these induce among observations. We deal here with small networks (under a thousand individuals) where we assume binary relational measurement for all of the pairs of individuals. Presenters: Josh Lospinoso lospinos@stats.ox.ac.uk Dept. of Statistics, University of Oxford, U.K. Marijtje van Duijn m.a.j.van.duijn@rug.nl Dept. Sociology, University of Groningen, the Netherlands. Mark Huisman and Christian Steglich j.m.e.huisman@rug.nl Dept. Sociology, University of Groningen, the Netherlands Nial Friel nial.friel@ucd.ie School of Mathematical Sciences, University College Dublin, Ireland
Author and presenter Lospinoso, Joshua A.; Dept. of Statistics, University of Oxford Title Joint inference on informant accuracy and social network dynamics Abstract To date, models for social network dynamics have been formulated (perhaps implicitly) in such a manner that complete trust is bestowed upon the observed data; the observed data panels are taken for the true state of relationships and data augmentation proceeds by constructing chains that could have connected them. This trend of placing complete trust in the data follows the general social networks literature. However, the extensive literature on informant accuracy suggests that placing such trust in the observed data as a representation of the true state of relations may be a tenuous proposition. In this talk, a flexible class of models is considered which relaxes the strict trustin observed network data. This intermediate model for informant accuracy can represent basic noise with some false positive rate and some false negative rate and be elaborated by random and fixed effects models. Bayesian ideas can be leveraged, as the notion of genuine prior belief about informant accuracy may be particularly appropriate. Further, the exponential random graph (ERG) family of models can be naturally employed for this purpose. In this sense, the model is a generalization of the Stochastic Actor Oriented Models of Snijders (2001) which permits joint inference on the social network dynamics and on the informant accuracy.
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The ASA Spring Methodology Conferen
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Friendships under study were formed
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the interplay of social background
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greatly inefficient. Only very few
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Data We use matched employer-employ
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KEY NOTE SPEAKER Snijders, Tom A.B.
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Presenter Hagenaars, Jacques A.P.;
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