Forms of Dependence: Comparing SAOMs and ERGMs from Basic Principles
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| Title: | Forms of Dependence: Comparing SAOMs and ERGMs from Basic Principles |
|---|---|
| Language: | English |
| Authors: | Block, Per, Stadtfeld, Christoph, Snijders, Tom A. B. |
| Source: | Sociological Methods & Research. Feb 2019 48(1):202-239. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 38 |
| Publication Date: | 2019 |
| Document Type: | Journal Articles Reports - Descriptive |
| Descriptors: | Statistical Analysis, Social Networks, Models, Network Analysis, Probability, Differences, Comparative Analysis |
| DOI: | 10.1177/0049124116672680 |
| ISSN: | 0049-1241 |
| Abstract: | Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM are defined globally but are nested in actors in the SAOM. Consequently, dependence assumptions in the SAOM are generally stronger than in the ERGM. Resulting from both, meso- and macro-level properties of networks that can be represented by either model differ substantively and analyzing the same network employing ERGMs and SAOMs can lead to distinct results. Guidelines for theoretically founded model choice are suggested. |
| Abstractor: | As Provided |
| Number of References: | 40 |
| Entry Date: | 2019 |
| Accession Number: | EJ1203786 |
| Database: | ERIC |
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| Abstract: | Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM are defined globally but are nested in actors in the SAOM. Consequently, dependence assumptions in the SAOM are generally stronger than in the ERGM. Resulting from both, meso- and macro-level properties of networks that can be represented by either model differ substantively and analyzing the same network employing ERGMs and SAOMs can lead to distinct results. Guidelines for theoretically founded model choice are suggested. |
|---|---|
| ISSN: | 0049-1241 |
| DOI: | 10.1177/0049124116672680 |
