{"@context":"http://schema.org","@type":"Dataset","@id":"https://doi.org/10.13130/RD_UNIMI/ZSCGJ7","identifier":"https://doi.org/10.13130/RD_UNIMI/ZSCGJ7","name":"Replication Data for: Making sense of pollsters’ errors. An analysis of the 2014 second-order European election predictions","creator":[{"name":"Giuliani, Marco","affiliation":"University of Milano","@id":"https://orcid.org/0000-0002-6927-7177","identifier":"https://orcid.org/0000-0002-6927-7177"}],"author":[{"name":"Giuliani, Marco","affiliation":"University of Milano","@id":"https://orcid.org/0000-0002-6927-7177","identifier":"https://orcid.org/0000-0002-6927-7177"}],"datePublished":"2023-02-07","dateModified":"2023-02-07","version":"1","description":["Pollsters have been recently accused of delivering poor electoral predictions. We argue that one of the reasons for their failures lies in the difficulty of including an updated deep understanding of electoral behaviour. Even if pollsters’ predictions are not forecasts produced by models, the set of choices needed to produce their estimates is not indifferent to a theoretical comprehension of electoral dynamics. We exemplify this lack of theory by using an original dataset consisting of 1057 party*poll observations in the case of the last European election. Pollsters failed to account for what we know about second-order elections, thus overestimating government and big parties, which normally obtain poor results in European elections, and underestimating new and Eurosceptic ones, which usually perform well."],"keywords":["Social Sciences","European election","Polls","Accuracy","Prediction error"],"citation":[{"@type":"CreativeWork","text":"Giuliani, M. (2019) Making sense of pollsters’ errors. An analysis of the 2014 second-order European election predictions, Journal of Elections, Public Opinion and Parties, 29(2): 162-178. DOI: 10.1080/17457289.2018.1466786","@id":"https://doi.org/10.1080/17457289.2018.1466786","identifier":"https://doi.org/10.1080/17457289.2018.1466786"}],"license":{"@type":"Dataset","text":"CC0","url":"https://creativecommons.org/publicdomain/zero/1.0/"},"includedInDataCatalog":{"@type":"DataCatalog","name":"UNIMI Dataverse","url":"https://dataverse.unimi.it"},"publisher":{"@type":"Organization","name":"UNIMI Dataverse"},"provider":{"@type":"Organization","name":"UNIMI Dataverse"},"distribution":[{"@type":"DataDownload","name":"EP2014 polls do.do","fileFormat":"application/x-stata-syntax","contentSize":3137,"description":"Stata syntax","@id":"https://doi.org/10.13130/RD_UNIMI/ZSCGJ7/OI85JF","identifier":"https://doi.org/10.13130/RD_UNIMI/ZSCGJ7/OI85JF","contentUrl":"https://dataverse.unimi.it/api/access/datafile/17077"},{"@type":"DataDownload","name":"EP2014 polls.tab","fileFormat":"text/tab-separated-values","contentSize":346776,"description":"Stata main dataset","@id":"https://doi.org/10.13130/RD_UNIMI/ZSCGJ7/SVT6IY","identifier":"https://doi.org/10.13130/RD_UNIMI/ZSCGJ7/SVT6IY","contentUrl":"https://dataverse.unimi.it/api/access/datafile/17078"},{"@type":"DataDownload","name":"EP2014 pools codebook.pdf","fileFormat":"application/pdf","contentSize":456071,"description":"Codebook","@id":"https://doi.org/10.13130/RD_UNIMI/ZSCGJ7/WZKAW6","identifier":"https://doi.org/10.13130/RD_UNIMI/ZSCGJ7/WZKAW6","contentUrl":"https://dataverse.unimi.it/api/access/datafile/17076"}]}