Longitudinal dataset of political issue-positions of 411 parties across 28 European countries (2009–2019) from voting advice applications EU profiler and euandi
REILJAN, Andres; FERREIRA DA SILVA, Frederico; CICCHI, Lorenzo ; GARZIA, Diego ; TRECHSEL, Alexander H.
Data in brief, 2020, Vol. 31, 105968
REILJAN, Andres, FERREIRA DA SILVA, Frederico, CICCHI, Lorenzo, GARZIA, Diego, TRECHSEL, Alexander H., Longitudinal dataset of political issue-positions of 411 parties across 28 European countries (2009–2019) from voting advice applications EU profiler and euandi, Data in brief, 2020, Vol. 31, 105968 - https://hdl.handle.net/1814/69627
Retrieved from Cadmus, EUI Research Repository
This data article provides a descriptive overview of the “EU Profiler/euandi trend file (2009–2019)“ dataset and the data collection methods. The dataset compiles party position data from three consecutive pan-European Voting Advice Applications (VAAs), developed by the European University Institute for the European Parliament elections in 2009, 2014 and 2019. It includes the positions of 411 parties from 28 European countries on a wide range of salient political issues. Altogether, the dataset contains more than 20 000 unique party positions. To place the parties on the political issues, all three editions of the VAA have used the same iterative method that combines party self-placement and expert judgement. The data collection has been a collective effort of several hundreds of highly trained social scientists, involving experts from each EU member state. The political statements that the parties were placed on, were identical across all the countries and 15 of the statements remained the same throughout all three waves (2009, 2014, 2019) of data collection. Because of the unique methodology and the large volume of data, the dataset offers a significant contribution to the research on European party systems and on party positioning methodologies.
First published online: 02 July 2020
Cadmus permanent link: https://hdl.handle.net/1814/69627
Full-text via DOI: 10.1016/j.dib.2020.105968
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