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Alive and kicking! Keeping data re-usable in the European Values Study
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Alive and kicking! Keeping data re-usable in the European Values Study


Alive and kicking! Keeping data re-usable in the European Values Study …

Alive and kicking! Keeping data re-usable in the European Values Study

Evelyn Brislinger, Astrid Recker
GESIS - Leibniz Institute for the Social Sciences

Repeated cross-national surveys generate huge amounts of cross-linked data and metadata. To enable replication and to make this data re-usable in new research contexts, thorough and standardized documentation of data and project workflow is indispensable. However, in the social sciences, data and documentation often undergo a continuous process of correction, refinement, and further development. These processes need to be documented too, especially to allow data providers to build on these results and experiences in preparation of the next wave.
In this paper, we use the European Values Study (EVS) 1981-2008 to illustrate the challenges to be met in the active curation of extensive amounts of data and documentation created, altered, and re-used across the survey life-cycle. Outlining how these challenges are met by the EVS, we will particularly discuss the following questions: Looking beyond the “standard” documentation of data and survey methods, what supporting contextual information should accompany data to ensure their effective “migration” and use across waves? Especially relevant in a project composed of 125 national surveys covering 49 countries and spanning almost 30 years is the question which preservation metadata is needed to achieve this objective and thus support the long-term accessibility of data and contextual information?

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  • 1. Alive and kicking!Keeping data re-usable in theEuropean Values StudyIASSIST Cologne, May, Evelyn.Brislinger@gesis.orgGESIS, Data Archive for the Social Sciences
  • 2. Overview Data and information flow in the EVS project Principles and workflows for managing data anddocumentation in survey projects
  • 3. GESIS Data ArchiveBasisInterplay between Principal Investigators (PI) and Data ArchiveAgreement on submission of data and information packagesGoalsEase access to data for a broad user communityProvide metadata for discovery, understanding, and good use of dataPreserve data and metadata for re-use and replicationsHoldingsStudies, study series, and complex survey programs as ISSP, Eurobarometer,ALLBUS, European Values Study (EVS), or election studies
  • 4. Data and information created in a survey projectTotal stock of data anddocumentation createdData and documentationsubmitted to an archiveFurther information necessaryfor the project(?)Selection processesManagement solutions for structuring data and information
  • 5. Example: European Values Study (EVS)9-year-period, 4 waves49 countries, 125 national surveysCross-national, longitudinalresearch programNational surveysWaves1981/1990/1999/2008Longitudinal data File1981-2008 (LdF)Integrated Values SurveysEVS/WVS (IVS)Harmonization and integration processNumber of filesSize of filesAtlas of European
  • 6. Collaboration of actors involved (EVS 2008)DatacreatedprocesseddocumentedNational teamDatastandardizedharmonizedintegratedCentral teamData Archive Secondary usersPrincipal InvestigatorsDatacheckeddocumentedpreservedreleasedDatare-usedAnalysesreplicatedResultsreported
  • 7. Users: analyze and evaluate outcomesQuestionsCheck trend questions and originalquestions ZACAT-Online Study CatalogueDataAnalyze data, report errors, monitorerror reporting GESIS Data CataloguePublicationsReplicate analysis of other projects EVS Repository…. and detect peculiarities inquestions or problems in data
  • 8. Peculiarities in question text spotted?Project DesignQuestionnaire DesignQuestionnaire TranslationData CollectionData DocumentationData ProcessingCheck question and translationMaster/field questionnaire, methodologicalquestionnaire, report ‘Translation History’Check source of questionTrend question from EVS and WVS,questions borrowed from other surveysIdentify consequences forCountries sharing/adopting affectedlanguage, languages belonging to a family,further languages used in a countryEVS 2008 Data lifecycle
  • 9. Data error detected?Standardization and harmonization process: check comparability of surveys,questions, variables  cumulate data and document each stepIntegratedValuesSurveysEVS/WVSLongitudi-nal dataFile1981-2008Wave2008NationaldataOriginaldata fileWave1999…..Nationaldata…..Retrace data processing steps across surveys: check data, syntaxfiles, and documentation  update data and highlight problems for next waveError detected
  • 10. Data and information createdDesignated communities Principal Investigator/Project Secondary userExperiences from EVS projectData and information packages Project package Archive packageSelection processes Within project Between project and archiveProjectArchiveTotal stock
  • 11. Communicating with the future: Activity on two levelsMacro levelDefining workflows, file and information paths on whichnecessary information is passed onMicro levelOrganizing information so that it isre-usable (RDM, metadata,systematic file structures)
  • 12. Begin by identifying principles for structuring and documenting files inthe project (Research Data Management)Selectwhich informationis relevantto whom?A tidy house, a tidy mind!Reference, don’tduplicate fileswhenever possibleIdentify andcapture “kinshiprelations”Capture processknowledgeclassesitineraries Make changestraceableversioningdocument revisions &annotationsminutesprotocols
  • 13. The magic wand Follow principles of good researchdata management (RDM) Use metadata to document processand content information Use standards wherever possible(e.g. DDI, Dublin Core, ISO codes,file naming conventions, etc.)(and not the one used by the sorcerer’s apprentice)
  • 14. DocumentDatecreatedLanguageVersionFormatResourceRightsDatemodifiedEnglishActorNameCollectionhasDatehasModifiercreatesmodifieshasAccessRightsisAhasVersionisAhasCreatorhasLanguagehasIdentifierisPartOfhasFormathasIdentifierhasRoledc:creatordc:createddc:modifieddc:identifierdc:formatdc:provenancedc:descriptiondc:languagedc:accessRightsdc:collection…isA
  • 15. Managing information flows in a collaborative, long-term project Which paths does information (data, documentation, othercontextual material) take from producers to users? Two models helped us clarify processes and paths, as well asidentify helpful terminology and concepts– Project life cycle– Open Archival Information System (OAIS) reference model(CCSDS 2012)CCSDS (2012). Reference Model for an Open Archival Information System (OAIS). Recommended Practice.
  • 16. Project RepositoryIngestData processingand enhancementDataManagementTemporaryStorageAccess(project-internaluse, PIs)Project DesignDataDisseminationQuestionnaireDesignQuestionnaireTranslationData CollectionDataDocumentationDataProcessingProject life cycle: Data flow during creation of a surveyGuidelines
  • 17. Data Archive(preservation service provider)DataManagementAccessArchival Storage(long-term)Preservation PlanningAdministrationIngestSecondaryUsers(future)PrincipalInvestigatorsSIP AIPAIP DIPProject Repository(content provider)IngestData processingand enhancementDataManagementTemporaryStorageAccess(project-internaluse, PIs)Project and Data Archive as distributed systemPIPPIPPIPPIPPIPPIPPIPPIPPIPPIP = Project Information Package, SIP = Submission Information Package,AIP = Archival Information Package, DIP = Dissemination Information PackageProject DesignDataDisseminationQuestionnaireDesignQuestionnaireTranslationData CollectionDataProcessingDataDocumentation
  • 18. Staying Alive! Where we are going from here Developing a guideline for projects– structuring and annotating of information on the micro level– issues to discuss with an Archive (preservation service provider) Testing our model– implementing our ideas in smaller projects with the aim ofmaking the results available to other projects
  • 19. Thank you for your attention!Evelyn Brislinger | Astrid ReckerGESIS – Leibniz Institute for the Social Sciences, Data |