The 2017 German elections (and its aftermath) were extraordinary. At the campaign’s outset, the SPD experienced the strongest surge in public approval ever observed in German polling but months later attitudes had shifted again and the party ended up with its lowest election result in decades. The decline of Germany’s oldest democratic party was accompanied by the re-entry of the Free Democrats into German parliament and the rise of a populist right-wing party propagating stances on immigration and German history that for long were treated as „taboo“ in public discourse. Finally, the elections brought a new parliament but, for the first time in German history, no government.
With the publication of the 2017 GLES campaign panel you now have data to investigate these elections that might once mark a historic transition in German politics.
- Eight survey waves (more to come)
- 22,526 respondents
- side-study on parallel provincial snap election
- extensive documentation (English translation soon)
- harmonized with all other GLES components (face 2 face survey, media analysis, RCS etc.)
- A few weeks after the federal election in Germany, the citizens of Lower Saxony had the chance to vote again in a provincial snap election. Shortly after the provincial snap elections were called, we included an extensive survey battery on attitudes towards politicians and parties in Lower Saxony to assess how the parallel campaigns on the federal and on the provincial levels impact level-specific attitudes (survey wave 5ff.). These survey measure are included in the published data file. We conducted an additional post-election survey wave, exclusively for respondent from Lower Saxony. This wave will be added soon.
- After a decision has been made about whether our country will either have a government again or will face new elections, we will conduct another survey wave (wave 9), which then will be added to the data set.
- English translations will follow.
- More information on survey measures (which instrument in which waves etc.) will follow.
This looks like a fabulous dataset. Looking forward to English documentation.