Evaluation of Political Party Cohesion Using Exponential Random Graph Modeling
| Autor: | John Piorkowski, Shambavi Sadayappan, Ian McCulloh |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
media_common.quotation_subject
05 social sciences Polarization (politics) 050109 social psychology House of Representatives 01 natural sciences Democracy 010104 statistics & probability Politics Political economy Political science Voting Exponential random graph models 0501 psychology and cognitive sciences 0101 mathematics media_common |
| Zdroj: | ASONAM |
| DOI: | 10.1109/asonam.2018.8508333 |
| Popis: | The United States is becoming increasingly politically divided. In addition to polarization between the two-major political parties, there is also divisiveness in intra-party dynamics. In this paper, we attempt to understand these intraparty divisions by using an exponential random graph model (ERGM) to compute a political cohesion metric to quantify the strength within the party at a given point in time. The analysis is applied to the 105th through 113th congressional sessions of the House of Representatives. We find that the Republican party not only generally exhibits stronger intra-party cohesion, but when voting patterns are broken out by topic, the party has a higher and more consistent cohesion factor compared to the Democratic Party. |
| Databáze: | OpenAIRE |
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