GESIS - DBK - ZA6802
 

ZA6802: Pre- and Post-election Cross Section (Cumulation) (GLES 2017)

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ZA6802 Downloads and Data Access

List of Files

List of Files
 

Datasets

  • ZA6802_de_missing.do (Dataset) 2 KBytes
  • ZA6802_de_v3-0-1.dta (Dataset Stata) 5 MBytes
  • ZA6802_de_v3-0-1.sav (Dataset SPSS) 5 MBytes
  • ZA6802_de_Zeitvariablen_v3-0-1.dta (Dataset Stata) 9 MBytes
  • ZA6802_de_Zeitvariablen_v3-0-1.sav (Dataset SPSS) 11 MBytes
  • ZA6802_en_missing.do (Dataset) 2 KBytes
  • ZA6802_en_timestamps_v3-0-1.dta (Dataset Stata) 9 MBytes
  • ZA6802_en_timestamps_v3-0-1.sav (Dataset SPSS) 11 MBytes
  • ZA6802_en_v3-0-1.sav (Dataset SPSS) 5 MBytes
  • ZA6802_en_v3-0-1_en.dta (Dataset Stata) 5 MBytes
  • ZA6802_Zeitvariablen_zuspielen.do (Dataset) 1 KByte
  • ZA6802_Zeitvariablen_zuspielen.sps (Dataset) 2 KBytes

Questionnaires

  • ZA6802_fb.pdf (Questionnaire) 498 KBytes
  • ZA6802_Listenheft.pdf (Questionnaire) 6 MBytes
  • ZA6802_q.pdf (Questionnaire) 485 KBytes

Other Documents

  • ZA6800-02_Berufsvercodung.pdf (Codeplan) 279 KBytes
  • ZA6800ff_GLES2017_Doku_insgesamt.zip (Codeplan) 705 KBytes
  • ZA6802_sb.pdf (Study Description) 298 KBytes
Availability Availability A - Data and documents are released for academic research and teaching.
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Bibliographic Citation

Citation Citation GLES (2019): Pre- and Post-election Cross Section (Cumulation) (GLES 2017). GESIS Data Archive, Cologne. ZA6802 Data file Version 3.0.1, https://doi.org/10.4232/1.13236
Study No.ZA6802
TitlePre- and Post-election Cross Section (Cumulation) (GLES 2017)
Current Version3.0.1, 2019-2-26, https://doi.org/10.4232/1.13236
Date of Collection31.07.2017 - 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
Contributor, Institution, Role
  • Roßteutscher, Sigrid - Universität Frankfurt - ProjectLeader
  • Weßels, Bernhard - Wissenschaftszentrum Berlin für Sozialforschung - ProjectLeader
  • Bieber, Ina - Universität Frankfurt - ProjectManager
  • Wagner, Aiko - Wissenschaftszentrum Berlin für Sozialforschung - ProjectManager
  • Blumenberg, Manuela - GESIS - Leibniz-Institut für Sozialwissenschaften - ProjectMember
  • Dietz, Melanie - Universität Frankfurt - ProjectMember
  • Förster, André - GESIS - Leibniz-Institut für Sozialwissenschaften - ProjectMember
  • Giebler, Heiko - Wissenschaftszentrum Berlin für Sozialforschung - ProjectMember
  • Melcher, Reinhold - Wissenschaftszentrum Berlin für Sozialforschung - ProjectMember
  • Roßmann, Joss - GESIS - Leibniz-Institut für Sozialwissenschaften - ProjectMember
  • Scherer, Philipp - Universität Frankfurt - ProjectMember
  • Stövsand, Lars-Christopher - Universität Frankfurt - ProjectMember
  • Blinzler, Katharina - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Blumenberg, Manuela - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Chalupa, Julia - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Jungmann, Nils - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Kratz, Agatha - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Kratz, Sophia - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Roßmann, Joss - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Stroppe, Anne-Kathrin - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator

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
Mode of Collection Mode of Collection
  • Face-to-face interview: Computer-assisted (CAPI/CAMI)
Time Method Time Method
  • Longitudinal: Trend/Repeated cross-section
Kind of Data Kind of Data
  • Numeric
  • Text
Data CollectorKantar Public Germany
Date of Collection
  • 31.07.2017 - 23.09.2017 (pre-election)
  • 25.09.2017 - 30.11.2017 (post-election)

Errata & Versions

VersionDate, Name, DOI
3.0.1 (current version)2019-2-26 Release3-0-1 https://doi.org/10.4232/1.13236
3.0.02018-9-19 Release3-0-0 https://doi.org/10.4232/1.13139
2.0.02018-8-3 Release2-0-0 https://doi.org/10.4232/1.13081
1.1.02018-3-2 Release1-1-0 https://doi.org/10.4232/1.12997
1.0.02018-2-23 first archive edition https://doi.org/10.4232/1.12989
Errata in current version
DateSubjectDescription
2018-2-23Error in data vn41i, vn43i, vn45i, vn46i, vn48i, vn49i, vn50i, vn51iThe respondents of the Pre-election Cross Section have been coded -92 "error in data" for variables vn41i, vn43i, vn45i, vn46i, vn48i, vn49i, vn50i and vn51i, as these variables are missing in the Pre-election Cross Section.
2018-2-23Coding error v77b-fContrary to specifications in the questionnaire, several respondents received code -97 "doesn´t apply" in variables v77b-f "Knowledge of Constituency Candidates". This concerns 55 respondents for SPD, DIE LINKE, and FDP, as well as 60 respondents for GRÜNE and 140 respondents for AfD. For 55 respondents, this is probably due to a coding error, as they received code -99 "not stated" in v77a "Knowledge of Constituency Candidates of CDU/CSU". For the remaining respondents who received -97 "doesn´t apply" for knowledge of the constituency candidates of GRÜNE and AfD, probably no candidates of the respective parties stood for election in their constituencies.
2018-2-23Programming error n77a-f – vn89For 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.
2018-8-6Filter error n10, n36There is one person in the dataset for whom information about the year of birth is missing. This person answered the questions about turnout (n10) and eligibility to vote for the German federal election 2013 (n36), although these questions contain age filters. Therefore, a flag variable vn2c_flag was created, marking the respective respondent.
Version changes
Changes between version 3.0.1 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 3.0.0 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 2.0.0 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" (vn22_c1 – vn23_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" (vn22s, vn23s) 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 (vnvpoint) 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 variable survey1 has been added to the dataset.2018-8-6
2018-8-6The flag variable vn2c_flag has been added to the dataset.2018-8-6
Changes between version 1.1.0 and it's previous version
DateSubjectDescriptionCorrection Description
2018-3-2The value labels of vn7, v10, v17, vn18, v33, n33, vn64 and vn166 were corrected.2018-3-2
2018-3-2Revision of variable and value labels.2018-3-2

Further Remarks

Number of Units: 4291
Number of Variables: 758 (+ 911 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.