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GLES Sensitive Regional Data

Version
1.0.0
Resource Type
Dataset
Creator
  • GESIS - Leibniz-Institut für Sozialwissenschaften
Publication Date
2019-04-02
Contributor
  • Rattinger, Hans (Universität Mannheim) (Researcher)
  • Roßteutscher, Sigrid (Universität Frankfurt) (Researcher)
  • Schmitt-Beck, Rüdiger (Universität Mannheim) (Researcher)
  • Schoen, Harald (Universität Mannheim) (Researcher)
  • Weßels, Bernhard (Wissenschaftszentrum Berlin für Sozialforschung) (Researcher)
  • Wolf, Christof (GESIS – Leibniz-Institut für Sozialwissenschaften) (Researcher)
  • MARPLAN Media- und Sozialforschungsgesellschaft mbH (Pre- and Post-election Cross Section (GLES 2013)) Kantar Public (München) in cooperation with infratest dimap (Berlin) (Pre- and Post-election Cross Section (GLES 2017)) (Data Collector)
Language
German
Description
  • Abstract

    The variables, which are published in the data set GLES Sensitive Regional Data, contain sensitive information on the residence of a respondent and are therefore not included in the regular Scientific Use Files (SUF) for data protection reasons. The required regional variables of this data set can be merged with the individual datasets of the Pre- and Post-election Cross Section (GLES 2013) and Pre- and Post-election Cross Section (GLES 2017) as well as the cumulation of these studies via the identification number of the respondents. This data set contains the following sensitive regional variables (both key numbers and, if applying, names): • Administrative district (Regierungsbezirk, 2013-2017) • Spatial planning region (Raumordnungsregion, 2013-2017) • Urban and rural district/county (Land-/Kreis und kreisfreie Stadt, 2013-2017) • Municipality (Gemeinde, 2013-2017) • Postal/ZIP code (Postleitzahl, 2013 & 2017) • Constituencies (Wahlkreise, 2013-2017) • NUTS-3-Code (2013-2017) • INSPIRE ID (1km) • De-anonymized information on the size (2017) and the BIK-type of the respondent´s municipality (2013-2017) This sensitive data is subject to a special access restriction and can only be used within the scope of an on-site use in the Secure Data Center (SDC) in Cologne. Further information and contact persons can be found on our website: http://www.gesis.org/sdc. In order to take into account changes in the territorial status of the regional units (e. g. district reforms, municipality incorporations), the regional variables are offered as time-harmonized variables as of December 31, 2015 in addition to the status as of January 1 of the year of survey. If you want to use the regional variables to add additional context characteristics (regional attributes such as unemployment rate or election turnout, for example), you have to send us this data before your visit. In addition, we require a reference and documentation (description of variables) of the data. Note that this context data may be as sensitive as the regional variables if direct assignment is possible. Due to data protection it is problematic if individual characteristics can be assigned to specific regional units – and therefore ultimately to the individual respondents – even without the ALLBUS dataset by means of a table of correspondence. Accordingly, the publication of (descriptive) analysis results based on such contextual data is only possible in a coarsened form. Please contact the GLES User Service first and send us the filled GLES regional data form (see ´Data & Documents´), specifying exactly which GLES datasets and regional variables you need. Contact: gles@gesis.org As soon as you have clarified with the GLES user service which exact regional features are to be made available for on-site use, the data use agreement for the use of the data at an SDC guest workstation (Safe Room) in Cologne will be sent to you. Please specify all data sets you need, i.e. both the ´GLES Sensitive Regional Data (ZA6828)´ and the Scientific Use Files to which the regional variables are to be assigned. Furthermore, under ´Specific variables´, please name all the regional variables you need (see GLES regional data form).
Temporal Coverage
  • 2013-07-29 / 2017-11-30
Geographic Coverage
  • Germany (DE)
Sampled Universe
The 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.(Pre- and Post-election Cross Section GLES 2013 and 2017)
Sampling
Disproportional stratified multistage random sampling based on the ADM-design. Oversampling of population in East Germany. (Pre- and Post-election Cross-Section (GLES 2013)) Random sampling on the basis of local population registers. Oversampling of population in East Germany. (Pre- and Post-election Cross-Section (GLES 2017))
Collection Mode
  • Computer Assisted Personal Interview (CAPI) with standardized questionnairs. (Pre- and Post-election Cross Section GLES 2013 and 2017)
Data and File Information
  • Number of Variables: 34
Note
Please contact the GLES User Service first and send us the filled GLES regional data form (see ´Data & Documents´), specifying exactly which GLES datasets and regional variables you need. Contact: gles@gesis.org A Digital Object Identifier (DOI) for use in data citations is supplied as part of the data set.
Availability
Delivery
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.
Rights
All metadata from GESIS DBK are available free of restriction under the Creative Commons CC0 1.0 Universal Public Domain Dedication. However, GESIS requests that you actively acknowledge and give attribution to all metadata sources, such as the data providers and any data aggregators, including GESIS. For further information see https://dbk.gesis.org/dbksearch/guidelines.asp
Alternative Identifiers
  • ZA6828 (Type: ZA-No.)
  • WAHLEN (Type: FDZ)

Update Metadata: 2019-04-03 | Issue Number: 2 | Registration Date: 2019-04-02

GESIS - Leibniz-Institut für Sozialwissenschaften (2019): GLES Sensitive Regionaldaten. Version: 1.0.0. GESIS Datenarchiv. Dataset. https://doi.org/10.4232/1.13263