Navigating the Range of Statistical Tools for Inferential Network Analysis
Bibliographic Data
| ID | 7236147 |
|---|---|
| Authors | Skyler J Cranmer (The Ohio State University), Sue Cranmer (0000-0002-6213-6400, The Ohio State University), Philip Leifeld (0000-0002-6834-5705, University of Glasgow Swiss Federal Institute of Aquatic Science and Technology (Eawag) University of Bern), Scott D Mcclurg (0000-0002-1022-6180, Southern Illinois University), Meredith Rolfe (University of Massachusetts Amherst) |
| Year | 2016 |
| Volume | 61 |
| Issue | 1 |
| Pages | 237-251 |
| Publication date | 2016-07-13 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Journal of Political Science (JOURNAL) |
| Journal identifiers | ISSN: 0092-5853 • E-ISSN: 1540-5907 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/ajps.12263 |
| OpenAlex | W2464294608 |
| Language | EN |
| Citations received | 94 |
| References cited | 44 |
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
Contingency table · Data mining · Data science · Exponential random graph models · Graph · Inference · Machine learning · Network analysis · Random graph · Range (aeronautics · Statistical inference · Statistics · Strengths and weaknesses · Artificial Intelligence · Computer Science · Electoral Systems and Political Participation · Engineering · Mathematics · Psychology · Social Capital and Networks · Social Media and Politics · Theoretical Computer Science
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| Unique citing works | 94 |
|---|---|
| Citations per year | 10,44 |
| Citation span | 2017 - 2026 (10) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 93 |