Network autocorrelation modeling
| Autor: | Dino Dittrich, Joris Mulder, Roger Th. A. J. Leenders |
|---|---|
| Přispěvatelé: | Department of Organization Studies |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Sociology and Political Science
Computer science 05 social sciences Bayesian probability Autocorrelation 050401 social sciences methods Bayes factor order hypotheses 01 natural sciences 010104 statistics & probability 0504 sociology network autocorrelation model hypothesis testing Econometrics 0101 mathematics empirical Bayes Social influence Statistical hypothesis testing |
| Zdroj: | Sociological Methodology, 50(1), 168-214. Wiley-Blackwell |
| ISSN: | 0081-1750 |
| Popis: | The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However, currently available methods cannot be applied to test a specific order of social influence in a network. In this article, the authors first present flexible Bayesian techniques for estimating network autocorrelation models with multiple network autocorrelation parameters. Second, they develop new Bayes factors that allow researchers to test hypotheses with order constraints on the network autocorrelation parameters in a direct manner. Concomitantly, the authors give efficient algorithms for sampling from the posterior distributions and for computing the Bayes factors. Simulation results suggest that frequentist properties of Bayesian estimators on the basis of noninformative priors for the network autocorrelation parameters are overall slightly superior to those based on maximum likelihood estimation. Furthermore, when testing statistical hypotheses, the Bayes factors show consistent behavior with evidence for a true data-generating hypothesis increasing with the sample size. Finally, the authors illustrate their methods using a data set from economic growth theory. |
| Databáze: | OpenAIRE |
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