GESIS - DBK - ZA6817
 

ZA6817: Longterm-Online-Tracking T37 (GLES)

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ZA6817 Downloads and Data Access

List of Files

List of Files
 

Datasets

  • ZA6817_missing.do (Dataset) 2 KBytes
  • ZA6817_v2-0-1.dta (Dataset Stata) 2 MBytes
  • ZA6817_v2-0-1.sav (Dataset SPSS) 2 MBytes
  • ZA6817_v2-0-1_open-ended.csv (Dataset) 64 KBytes

Questionnaires

  • ZA6817_fb.pdf (Questionnaire) 261 KBytes

Other Documents

  • ZA6800ff_GLES2017_Doku_insgesamt.zip (Codeplan) 705 KBytes
  • ZA6817_sb.pdf (Study Description) 356 KBytes
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Bibliographic Citation

Citation Citation GLES (2019): Longterm-Online-Tracking T37 (GLES). GESIS Data Archive, Cologne. ZA6817 Data file Version 2.0.1, https://doi.org/10.4232/1.13295
Study No.ZA6817
TitleLongterm-Online-Tracking T37 (GLES)
Current Version2.0.1, 2019-5-7, https://doi.org/10.4232/1.13295
Date of Collection12.09.2017 - 23.09.2017
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
  • Dietz, Melanie - Universität Frankfurt - ProjectManager
  • Bieber, Ina - Universität Frankfurt - ProjectMember
  • Scherer, Philipp - Universität Frankfurt - ProjectMember
  • Blinzler, Katharina - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Kaukal, Malte - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Kratz, Sophia - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Jungmann, Nils - GESIS - Leibniz-Institut für Sozialwissenschaften - DataCurator

Methodology

Geographic Coverage
  • Germany (DE)
UniverseThe population comprises all German citizens who were eligible to vote when the survey was conducted. Due to the fact that this was an online survey not everybody who was eligible to vote had a probability to get selected for this Online Tracking. Therefore the population comprises all persons with German citizenship resident in the Federal Republic of Germany and being part of the forsa omninet, who had a minimum age of 18 years, who lived in Germany and used at least once a week the Internet for private reasons. This group comprised nearly 75000 active Panel members. Active Panel members had been recruited exclusively by phone, had filled out a basic questionaire and had attended to at least one survey within the last twelve months.
Analysis Unit Analysis Unit
  • Individual
Sampling Procedure Sampling Procedure
  • Non-probability: Quota
Mode of Collection Mode of Collection
  • Self-administered questionnaire: Web-based (CAWI)
Time Method Time Method
  • Cross-section
Kind of Data Kind of Data
  • Numeric
  • Text
Data CollectorForsa.main Marktinformationssysteme GmbH, Frankfurt am Main
Date of Collection
  • 12.09.2017 - 23.09.2017

Errata & Versions

VersionDate, Name, DOI
2.0.1 (current version)2019-5-7 missing definitions added https://doi.org/10.4232/1.13295
2.0.02018-12-17 Release2-0-0 https://doi.org/10.4232/1.13205
1.0.02017-11-2 first archive edition https://doi.org/10.4232/1.12917
Errata in current version
DateSubjectDescription
2017-11-2Programming error t920Due to a programming error respondents were given the possibilty to check more than one answer instead of just one. Eventually, only one respondet named more than one party. Thus, the answers were able to be combined into on variable, following the party variable schema of version A and B.
2017-11-2Shortend time of collectionDue to a technical problem regarding the invitation to the survey the survey had to be restarted. Therefore, compared to former long-term online trackings the time of collection is shorter.
Version changes
Changes between version 2.0.0 and it's previous version
DateSubjectDescriptionCorrection Description
2018-12-17The weighting variables have been updated based on the marginal distributions of the Microcensus 2017 and the (N)onliner Atlas 2016 (previously Microcensus 2013 and (N)onliner Atlas 2014).2018-12-17
2018-12-17The "elecdist" variables now contain the electoral districts of 2017.2018-12-17

Further Remarks

Links
Number of Units: 1085
Number of Variables: 783
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.