GESIS - DBK - ZA6801
 

ZA6801: Post-election Cross Section (GLES 2017)

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Bibliographic Citation

Citation Citation    Roßteutscher, Sigrid; Schmitt-Beck, Rüdiger; Schoen, Harald; Weßels, Bernhard; Wolf, Christof; Wagner, Aiko; Melcher, Reinhold; Giebler, Heiko (2019): Post-election Cross Section (GLES 2017). GESIS Data Archive, Cologne. ZA6801 Data file Version 4.0.1, https://doi.org/10.4232/1.13235
Study No.ZA6801
TitlePost-election Cross Section (GLES 2017)
Current Version4.0.1, 2019-2-26, https://doi.org/10.4232/1.13235
Date of Collection25.09.2017 - 30.11.2017
Principal Investigator/ Authoring Entity, Institution
  • Roßteutscher, Sigrid - Universität Frankfurt
  • Schmitt-Beck, Rüdiger - Universität Mannheim
  • Schoen, Harald - Universität Mannheim
  • Weßels, Bernhard - Wissenschaftszentrum Berlin für Sozialforschung
  • Wolf, Christof - GESIS - Leibniz-Institut für Sozialwissenschaften
  • Wagner, Aiko - Wissenschaftszentrum Berlin für Sozialforschung
  • Melcher, Reinhold - Wissenschaftszentrum Berlin für Sozialforschung
  • Giebler, Heiko - Wissenschaftszentrum Berlin für Sozialforschung

Methodology

Geographic Coverage
  • Germany (DE)
UniverseThe population comprises all persons with German citizenship resident in the Federal Republic of Germany, who had a minimum age of 16 years and lived in private households at the time the survey was being conducted.
Analysis Unit Analysis Unit
  • Individual
Sampling Procedure Sampling Procedure
  • Probability: Stratified: Disproportional
Random sampling on the basis of local population registers. Oversampling of population in East Germany.
Mode of Collection Mode of Collection
  • Face-to-face interview: Computer-assisted (CAPI/CAMI)
Computer Assisted Personal Interview (CAPI)
Time Method Time Method
  • Cross-section
Kind of Data Kind of Data
  • Numeric
  • Text
Date of Collection
  • 25.09.2017 - 30.11.2017

Errata & Versions

VersionDate, Name, DOI
4.0.1 (current version)2019-2-26 Release4-0-1 https://doi.org/10.4232/1.13235
4.0.02018-9-19 Release4-0-0 https://doi.org/10.4232/1.13138
3.0.02018-8-3 Release3-0-0 https://doi.org/10.4232/1.13080
2.0.02018-2-23 Release2-0-0 https://doi.org/10.4232/1.12991
1.0.02017-12-21 first archive edition https://doi.org/10.4232/1.12954
Errata in current version
DateSubjectDescription
2017-12-22Programming error q92 – q104For questions concerning the familarity with and evaluation of constituency candidates, a technical error occured at the beginning of the field work, which led to an incorrect matching of candidate names to the respective electoral district in which the interview was conducted. This concerns 35 interviews in total, to which the code -92 (error in data) was assigned.
2017-12-22Filter error q17, q23There is one person in the dataset for whom information about the year of birth is missing. This person answered the questions about turnout (q17) and eligibility to vote for the German federal election 2013 (q23), although these questions contain age filters. Therefore, a flag variable q2c_flag was created, marking the respective respondent.
Version changes
Changes between version 4.0.1 and it's previous version
DateSubjectDescriptionCorrection Description
2019-2-26Release of an English version of the datasets and the questionnaire2019-2-26
Changes between version 4.0.0 and it's previous version
DateSubjectDescriptionCorrection Description
2018-9-19The weighting variables have been updated, now using the marginal distributions of the German micro census 2017 (previously micro census 2016).2018-9-19
2018-9-19Timestamps are provided in a separate data set.2018-9-19
Changes between version 3.0.0 and it's previous version
DateSubjectDescriptionCorrection Description
2018-8-6Codings of the open answers to the variables "Most important problem" and "Second most important problem" (q3_c1 – q4_c5) have been added to the data set.2018-8-6
2018-8-6The open answers to the variables "Most Important problem" and "Second most important problem" (q3s, q4s) have been deleted.2018-8-6
2018-8-6The serial number has been corrected.2018-8-6
2018-8-6The weighting variables have been updated, now using the marginal distributions of the German micro census 2016 (previously micro census 2013).2018-8-6
2018-8-6The variable Virtual Sample Points (vpoint) has been added to the dataset.2018-8-6
2018-8-6The variable bik10 has been removed due to data protection.2018-8-6
2018-8-6The flag variable q2c_flag has been added to the dataset.2018-8-6
Changes between version 2.0.0 and it's previous version
DateSubjectDescriptionCorrection Description
2018-8-6After quality assessment by the survey institute, several cases have been removed from the dataset.2018-8-6
2018-8-6The job and labour classification codes (ISCO08 and ISCO88) have been added to the dataset. The variables are as follows: q140_i88, q140_i08, q149_i88, q149_i08, q157_i88, q157_i08, q162_i88, q162_i082018-8-6
2018-8-6Variables q3s, q4s and q197 have been added to the dataset.2018-8-6

Further Remarks

Number of Units: 2112
Number of Variables: 602 (+ 455 Zeitvariablen)
Analysis System(s): SPSS, Stata

Publications

Relevant full texts
from SSOAR (automatically assigned)

Groups

Research Data Centre
Groups
  •  German Longitudinal Election Study (GLES)
    The German Longitudinal Election Study (GLES) is a DFG-funded project which made its debut just prior to the 2009 federal election. GLES is the largest and most ambitious election study held so far in Germany. Although the initial mandate is to examine and analyse the electorate for three consecutive elections, the aspired goal is to integrate the project within GESIS as an institutionalized election study after the federal election of 2017, and hence to make it a permanent study.