Fair national election studies
How well are we doing
Bibliographic Data
| ID | 12869647 |
|---|---|
| Authors | Christina Eder (0000-0002-7703-4108, GESIS - Leibniz Institute for the Social Sciences, corresponding author), Alexander Jedinger (0000-0003-3511-0368, GESIS - Leibniz Institute for the Social Sciences) |
| Year | 2018 |
| Volume | 18 |
| Issue | 4 |
| Pages | 651-668 |
| Publication date | 2018-11-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | European Political Science (JOURNAL) |
| Journal identifiers | ISSN: 1680-4333 • E-ISSN: 1682-0983 |
| Publisher | Palgrave Macmillan (PUBLISHER • GB) |
| DOI | 10.1057/s41304-018-0194-3 |
| OpenAlex | W2901141439 |
| Language | EN |
| Citations received | 3 |
| References cited | 9 |
Election studies are an important data pillar in political and social science, as most political research investigations involve secondary use of existing datasets. Researchers depend on high-quality data because data quality determines the accuracy of the conclusions drawn from statistical analyses. We outline data reuse quality criteria pertaining to data accessibility, metadata provision, and data documentation using the FAIR Principles of research data management as a framework (Findability, Accessibility, Interoperability, and Reusability). We then investigate the extent to which a selection of election studies fulfils these criteria using studies from Western democracies. Our results reveal that although most election studies are easily accessible and well documented and that the overall level of data processing is satisfactory, some important deficits remain. Further analyses of technical documentation indicate that while a majority of election studies provide the necessary documents, there is still room for improvement
Business · Comparative politics · Data management · Data quality · Data science · Database · Documentation · Interoperability · Metadata · Political science · Politics · Quality (philosophy · Reuse · World Wide Web · Computer Science · Data Quality and Management · Engineering · Law · Privacy-Preserving Technologies in Data · Research Data Management Practices
| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |