Navigating the Range of Statistical Tools for Inferential Network Analysis

The last decade has seen substantial advances in statistical techniques for the analysis of network data, as well as a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of hi...

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Bibliographic Details
Published in:American journal of political science 2017-01, Vol.61 (1), p.237-251
Main Authors: Cranmer, Skyler J., Leifeld, Philip, McClurg, Scott D., Rolfe, Meredith
Format: Article
Language:English
Subjects:
AJPS WORKSHOP
Climate change
Computer networks
Criteria
Data
Data analysis
Environmental policy
Inference
Network analysis
Policy making
Policy networks
Political science
Political scientists
Social classes
Statistical analysis
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Summary:The last decade has seen substantial advances in statistical techniques for the analysis of network data, as well as a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis—the quadratic assignment procedure, exponential random graph models, and latent space network models—highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This article introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and it helps researchers choose which model to use in their own research.
ISSN:0092-5853
1540-5907
DOI:10.1111/ajps.12263