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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model |
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Identification Number: |
doi:10.7910/DVN/K3D1M2 |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2017-04-21 |
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Version: |
1 |
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Bibliographic Citation: |
Morgan, Jason; Box-Steffensmeier, Janet M.; Christenson, Dino P., 2017, "Replication Data for: Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model", https://doi.org/10.7910/DVN/K3D1M2, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication Data for: Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model |
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Identification Number: |
doi:10.7910/DVN/K3D1M2 |
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Authoring Entity: |
Morgan, Jason (The Ohio State University) |
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Box-Steffensmeier, Janet M. (The Ohio State University) |
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Christenson, Dino P. (Boston University) |
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Producer: |
Political Analysis |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Morgan, Jason |
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Depositor: |
Morgan, Jason |
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Date of Deposit: |
2017-02-28 |
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Series Name: |
Volume #, Issue # |
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Holdings Information: |
https://doi.org/10.7910/DVN/K3D1M2 |
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Study Scope |
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Keywords: |
Social Sciences |
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Abstract: |
In the study of social processes, the presence of unobserved heterogeneity is a regular concern. It should be particularly worrisome for the statistical analysis of networks, given the complex dependencies that shape network formation combined with the re- strictive assumptions of related models. In this paper, we demonstrate the importance of explicitly accounting for unobserved heterogeneity in exponential random graph models (ERGM) with a Monte Carlo analysis and two applications that have played an important role in the networks literature. Overall, these analyses show that failing to account for unobserved heterogeneity can have a significant impact on inferences about network formation. The proposed frailty extension to the ERGM (FERGM) generally outperforms the ERGM in these cases, and does so by relatively large mar- gins. Moreover, our novel multilevel estimation strategy has the advantage of avoiding the problem of degeneration that plagues the standard MCMC-MLE approach. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Forthcoming, Political Analysis |
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Bibliographic Citation: |
Forthcoming, Political Analysis |
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Label: |
replication-materials-20170414.zip |
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Text: |
Replication materials, stored in a file hierarchy. See the included README files for instructions on how to replicate the study. (Updated April 2017.) |
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Notes: |
application/zip |