GESIS - DBK - ZA6838
 

ZA6838: GLES Panel 2016-2019, Waves 1-11

Downloads and Data Access


On this page you find the complete metadata and an overview of all available data sets and documents for the requested study. The download of all files is possible from our central search page under the following link:
ZA6838 Downloads and Data Access

List of Files

List of Files
 

Datasets

  • ZA6838_data_access.pdf (User Contract) 74 KBytes
  • ZA6838_Datenzugang.pdf (User Contract) 75 KBytes

Questionnaires

  • ZA6838_Bildschirmansichten.zip (Questionnaire) 68 MBytes
  • ZA6838_fb.pdf (Questionnaire) 2 MBytes
  • ZA6838_fb_einzelne_Wellen.zip (Questionnaire) 6 MBytes
  • ZA6838_q.pdf (Questionnaire) 2 MBytes

Other Documents

  • ZA6800ff_GLES2017_Doku_insgesamt.zip (Codeplan) 705 KBytes
  • ZA6838_sd.pdf (Study Description) 945 KBytes
  • ZA6838_wr_w10_sA.pdf (Report) 1 MByte
  • ZA6838_wr_w10_sB.pdf (Report) 2 MBytes
  • ZA6838_wr_w11_sA.pdf (Report) 1 MByte
  • ZA6838_wr_w11_sB.pdf (Report) 1 MByte
Availability Availability C - Data and documents are only released for academic research and teaching after the data depositor’s written authorization. For this purpose the Data Archive obtains a written permission with specification of the user and the analysis intention.
Download of Data and Documents Download of Data and Documents All downloads from this catalogue are free of charge. Data-sets available under access categories B and C must be ordered via the shopping cart with a few exceptions. Charges apply! Please respect our Terms of use.


Bibliographic Citation

Citation Citation GLES (2020): GLES Panel 2016-2019, Waves 1-11. GESIS Data Archive, Cologne. ZA6838 Data file Version 2.0.0, https://doi.org/10.4232/1.13598
Study No.ZA6838
TitleGLES Panel 2016-2019, Waves 1-11
Current Version2.0.0, 2020-9-15, https://doi.org/10.4232/1.13598
Date of Collection06.10.2016 - 12.06.2019
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
  • Schoen, Harald - Universität Mannheim - ProjectLeader
  • Roßmann, Joss - GESIS – Leibniz-Institut für Sozialwissenschaften - ProjectManager
  • Bauer, Irina - GESIS – Leibniz-Institut für Sozialwissenschaften - ProjectMember
  • Bucher, Hannah - GESIS – Leibniz-Institut für Sozialwissenschaften - ProjectMember
  • Chalupa, Julia - GESIS – Leibniz-Institut für Sozialwissenschaften - ProjectMember
  • Gärtner, Lea - Universität Mannheim - ProjectMember
  • Kratz, Agatha - Universität Mannheim - ProjectMember
  • Kühn, Marie - GESIS – Leibniz-Institut für Sozialwissenschaften - ProjectMember
  • Preißinger, Maria - Universität Mannheim - ProjectMember
  • Wuttke, Alexander - Universität Mannheim - ProjectMember
  • Kaukal, Malte - GESIS – Leibniz-Institut für Sozialwissenschaften - DataCurator
  • Stroppe, Anne-Kathrin - GESIS – Leibniz-Institut für Sozialwissenschaften - DataCurator

Methodology

Geographic Coverage
  • Germany (DE)
Universe[Sample A] All German citizens with internet access who were eligible to vote in the 2017 election to the German Bundestag. [Sample B] All people 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
  • Non-probability: Quota
  • Probability: Stratified: Disproportional
Mode of Collection Mode of Collection
  • Self-administered questionnaire: Web-based (CAWI)
  • Self-administered questionnaire: Paper
  • Face-to-face interview: Computer-assisted (CAPI/CAMI)
Time Method Time Method
  • Longitudinal: Panel
Kind of Data Kind of Data
  • Numeric
  • Text
Data CollectorKantar Public / infratest dimap; University of Mannheim, Chair for Politial Psychology; GESIS – Leibniz Institute for the Social Sciences
Date of Collection
  • 06.10.2016 - 12.06.2019

Errata & Versions

VersionDate, Name, DOI
2.0.0 (current version)2020-9-15 Release 2-0-0 https://doi.org/10.4232/1.13598
1.0.02020-5-19 first archive edition https://doi.org/10.4232/1.13475
Errata in current version
DateSubjectDescription
2020-5-25Programming Error kp10_430* & kp10_650*Due to a programming error, participants who chose the value “0” when answering the party scalometer questions (“kp10_430*”) and the politician scalometer ques-tions (“kp10_650*”) were undistinguishable from “don’t know”-answers. The error was fixed dur-ing the first day of the field time. All answers who could not be assigned unambiguously to one of the categories were recoded as -92 “Error in data” on these variables.
2020-5-25Programming Error Time Response Variables Due to an unknown error, the export and processing of the response time measurements resulted in negative values for some cases. If this was the case, the response time variable was recoded as -92 “Error in data”.
2020-5-25Filter Error kp10_2100ne respondent was not directed to the follow-up question on party identification strength (“kp10_2100”), even though an answer about party identification was given. The variable was recoded in -92 “Error in the data” for this case.
2020-5-25Filter Error kp10_190bbOne respondent was not directed to the follow-up question on vote choice of small parties (“kp10_190bb”). The variable was recoded in -92 “Error in the data” for this case.
2020-5-25Data Input Error kp10_codingbeginIn one case the value of this variable (“kp10_codingbegin”) was not valid and recoded in -92 “Error in the data”.
2020-5-25Data Input Error kp10_5021One respondent did receive a code which could not be matched to one of the answer categories of “kp10_5021”. The variable was recoded in -92 “Error in the data” for this case.
2020-5-25Filter Error kp10_2101One respondent was not directed to the follow-up question on party identification strength (multiple) (“kp10_2101”), even though an answer about party identification (multiple) was given. The variable was recoded in -92 “Error in the data” for this case.
2020-5-25Filter Error kp10_ 3180, kp10_ 3190*, kp10_3140, kp10_650p1,q1,r1Three respondents were not directed to the follow-up question on the state elections in Bavaria (“kp10_3180”, “kp10_3190”, “kp10_3140”, “kp10_650p1,q1,r1”), even though they reported to live in Bavaria. The variables were recoded in -92 “Error in the data” for these cases.
2020-5-25Filter Error kp10_3290, kp10_3300*, kp10_3250, kp10_650s1,t1,u1One respondent was not directed to the follow-up question on the state elections in Hessen (“kp10_3290”, “kp10_3300*”, “kp10_3250”, “kp10_650s1,t1,u1“), even though he/she reported to live Hessen. The variables were recoded in -92 “Error in the data” for this case.
2020-9-16Filter Error kp11_2095Two respondents were not directed to the follow-up question on multiple party identification ("kp11_2095"), even though an answer about party identification was given. The variable was recoded in -92 “Error in the data” for these cases.
2020-9-16Programming Error kp11_2602The answers of respondents who reported that they were not able to fill in their correct postal code ("kp11_2602") were corrected if they stated their postal code again. The remaining 3 cases were recoded in -92 ”Error in the data”.
Version changes
Changes between version 2.0.0 and it's previous version
DateSubjectDescriptionCorrection Description
2020-9-16Wave 11 (Sample A and Sample B) has been added. Detailed information on this wave can be found in the wave reports.2020-9-16
2020-9-16The variables kpX_device, kpX_smartphone and kpX_tablet have been added to the data sets of wave 10 and will be available from wave 10 onwards. 2020-9-16
2020-9-16The variables kpX_flash, kpX_javascript and kpX_browser have been removed from the data sets of wave 1 to 10. If you are interested in accessing these variables, please contact the GLES team. 2020-9-16
2020-9-16The variable kpX_intstatus has been corrected for the data sets of wave 10. The calculation of the share of answered questions per respondents was based on an incorrect total number of questions in the questionnaire. 2020-9-16
2020-9-16Correction of typos in variable labels and value labels and completion of missing value labels2020-9-16
Changes between version 1.0.0 and it's previous version
DateSubjectDescriptionCorrection Description
2020-9-16Errata and Information Short-term Campaign Panel (GLES 2017) (ZA6804)Waves 1 to 9 of the GLES Panel were already published as Short-term Campaign Panel (GLES 2017) with the study number ZA6804. All errata for these waves can be found under this study number as well as the the study documentation.2020-9-16

Further Remarks

Number of Units: 25128
Number of Variables: 3836 (+ 2454 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.