Presentation is loading. Please wait.

Presentation is loading. Please wait.

Guidelines for data preparation Social Science Data Archives: creating, depositing and using data Plymouth, 22 October 2004 Louise Corti.

Similar presentations

Presentation on theme: "Guidelines for data preparation Social Science Data Archives: creating, depositing and using data Plymouth, 22 October 2004 Louise Corti."— Presentation transcript:

1 Guidelines for data preparation Social Science Data Archives: creating, depositing and using data Plymouth, 22 October 2004 Louise Corti

2 ESRC Datasets Policy – what is expected of award holders? to preserve and share data from ESRC funded research funding allowed to prepare data for archiving all award-holders must offer data for deposit to the ESDS within 3 months of the end of the award any potential problems should be notified to the ESDS at the earliest opportunity final payment will be withheld if dataset has not been deposited within 3 months of the end of the award, except where a waiver has been agreed in advance ESRC Datasets Policy

3 Depositing data data should be deposited to a standard that would enable them to be used by a third party, including the provision of adequate documentation good housekeeping = good research = long-term shareable data any potential problems in archiving the data should be discussed with ESDS acquisitions as soon as possible issues of consent and confidentiality allowing archiving should be included in the project management plan and addressed before data collection starts unless a waiver on deposition has been agreed with ESDS and the ESRC, researchers should not make commitments to informants which preclude archiving their data

4 Data creation and deposit: best practice early advice to data creators: –high quality data and documentation –consent and ethical issues are taken on board –longer-term rights management in place –IPR issues considered promoting standards in: –research design –transcription techniques –data and project management –documenting data collection and analysis

5 Characteristics of a good archived research collection rich content accurate data, well organised and labelled files appropriate measurement of key concepts supporting documentation created –major stages of research recorded –research/measurement instruments documented data that can be stored in user-friendly dissemination formats, but can also be archived in a future-proof preservation format consent, confidentiality and copyright resolved

6 Intellectual content builds on previous research addresses new issues comparative potential topics not too specific or narrowly focused tried and trusted measures/scales used innovative approach to discipline and methodology

7 Extensive raw data types of research data assembled –survey data –in-depth interviews –focus groups –field notes/participant observation –case study notes images and audio-visual materials (supports textual transcripts) range of material – broad focus

8 Supporting documentation to produce catalogue record and user guide –funding application –description of methodology –communication with informants on confidentiality –coding schemes/themes –technical details of equipment –interview schedules –end of award report –documentation from CAQDAS software packages, e.g. analytical memos –bibliographies, resulting publications anything that adds insight or aids understanding and secondary usage


10 Q: Labelling and listing research list of contents of research collection acts as a point of entry for secondary user qualitative data: template approach – interviewee/case study characteristics


12 User Guides - qualitative studies to provide a deeper understanding of the study and research methods providing guidance on data resources and how to re-use them enhanced user guides: detailed notes on study methodology; behind the scenes interviews with depositors; FAQs thematic pages – combining interviews exemplars and case studies of re-use, including full bibliographies user support and training activities to support secondary analysis of data + online resources


14 Online access to qualitative data new emphasis on providing direct access to collection content –supports more powerful resource discovery –greater scope for searching and browsing content of data (supplementary to higher level study-related metadata) –since users can search and explore content directly… can retrieve data immediately providing access to qualitative data via common interface (EDSD Qualidata Online) supporting tools for searching, retrieval, and analysis across different datasets Means that data must be accurate and standardised

15 Q: Transcribing Research integrated into the ongoing research – budget accordingly full transcriptions or summaries costs and benefits –self transcription –internal team transcription –external transcription full transcriptions –consistent layout –speaker tags –line breaks –header with identifier/other details –checked for errors

16 Q: Identifiers removed scheme devised – different for each dataset ideally should reflect any pseudonyms used in publications confidentiality respected anonymisation? problems of anonymisation –applied too weakly –applied to strongly –timing –potential for distortion –examples user undertakings appropriate and sympathetic

17 Q: Accuracy of data: validation checks computer-aided surveys (CAPI, CATI or CAWI) these are the most accurate way of gathering survey data, but the software (e.g. Blaise) and hardware (e.g. a laptop for every interviewer) may be beyond project resources computer-aided surveys allow one to build in as many logical checks - on question routing and responses - as is possible at the point of data creation non computer-aided surveys less control over initial responses, but checks can performed: –at the point of data entry/transcription if data entry software is used. However, there are few cheap data entry packages around –the only feasible option may be to enter data without checks directly into a spreadsheet style interface (e.g. Excel worksheet, SPSS data view), and perform validation checks afterwards - via command files in statistical packages or Visual Basic code in Excel or Access

18 Q: An example of data seemingly untouched by the human eye : Originating error in text variables: OccupationDescription of Occupation sole traderpurveyor of seafood Propagated error in derived numeric variables: respondent was coded under the standard occupational (SIC) code relating to food retailers: 52.2 Retail sale of food, beverages and tobacco in specialised stores

19 Q: Labelling of data all variables should be named. Variable names should not exceed 8 characters where possible, as the most common format for disseminating data is SPSS all variables should be labelled. Labels should be brief (preferably < 80 characters), but precise and always make explicit the unit of measurement for continuous (interval) variables. Where possible, all variable labels should reference the question number (and if necessary questionnaire). For example, the variable q11bhexc might have the label q11b: hours spent taking physical exercise in a typical week. This gives the unit of measurement and a reference to the question number (q11b), so the user can quickly and easily cross-reference to it for categorical variables, all codes (values) should be given a brief label (preferably < 60 characters). For example, p1sex (gender of person 1) might have these value labels: 1 = male, 2 = female, -8 = dont know, -9 = not answered where possible, all such labelling should be created and supplied to the UKDA as part of the data file itself. This is the expectation with data supplied in one of the three major statistical packages - SPSS, STATA or SAS.

20 Q: Documentation Core documentation: questionnaire methodology: details of sample design, response rate, etc. Codebook, i.e. a comprehensive list of variable names, variable descriptions, code names and variable formatting information. This is essential If the package being used for data management does not allow the sort of variable and code labelling to be stored within the data file technical report describing the research project Other useful documentation that is seldom supplied: code used to create derived variables or check data (e.g. SPSS, STATA or SAS command files).


22 Consent for archiving anonymity and privacy of research participants should be respected explicit informed consent gained information for research participants should be clear and coherent and include: –purpose of research –what is involved in participation –benefits and risks –storage and access to data –usage of data (current and future uses) –withdrawal of consent at any time –Data Protection and Copyright Acts N.B. Additional measures are needed when participants are unable to consent through incapacity or age reflect needs and views of all works in practice

23 Ethical practice - history The Nuremberg Code –principles for conducting research with human subjects arises from the Nuremberg Trials, which took place after the Second World War –sets out statements of certain moral, ethical and legal principles relating to research involving human subjects Declaration of Helsinki –adopted in 1964 by the World Medical Assembly adopted –provides guidance for physicians in biomedical research with human subjects –most recently amended in 1989 and 1996 the ethical guidelines of many professional organisations endorse these principles

24 Legal issues in data preparation Duty of confidentiality Law of Defamation Data Protection Act 1998 and EU Directive Copyright Act 1988 Freedom of Information

25 Duty of Confidentiality disclosure of information may constitute a breach of confidentiality and possibly a breach of contract not governed by an Act of Parliament not necessarily in writing can be a legal contractual exemptions are: –relevant police investigations or proceedings –disclosure by court order –public interest - defined by the courts –ethical obligations in cases of disclosure of child abuse

26 Law of Defamation a defamatory statement is one which may injure the reputation of another person, company or business

27 Data Protection Act 1998 eight principles: –fairly and lawfully processed –processed for limited purposes –adequate, relevant and not excessive –accurate –not kept longer than necessary –processed in accordance with the data subject's rights –secure –not transferred to countries without adequate protection allows for secondary use of data for research purposes under certain conditions

28 Options for preserving confidentiality anonymisation consent to archive at the time of field work researcher contacts informants retrospectively user undertakings in exceptional circumstances - permission to use or closure of material

29 Copyright Act 1988 developed for the broadcasting industry not research! protection of authors rights multiple copyrights apply: –automatically assigned to the speaker –researcher holds the copyright in the sound recording of an interview obtain written assignment of copyright from interviewee, or oral agreement (licence) to use –employer holds the copyright in research data obtain copyright clearance from employer) copyright lasts for 70 years after the end of the year in which the author dies copying work is an infringement unless it is for the purposes of research, private study, criticism or review or reporting current events, and if the use can be regarded as being in the context of 'fair dealing seek legal advice on problem issues

30 Conclusion: archivable research suitable for electronic dissemination suitable formats for re-use and long-term preservation in-house data processing –cleaning up research/ documenting –repairing minor errors –meeting user expectations –cannot engage in major processing tasks unless destined for publishing into online systems meeting users needs –building an expansive and varied data portfolio –creating online exploratory/data browsing systems good housekeeping = good research = good archives

31 Depositing data with ESDS provide details of all data collected, together with three samples of qualitative data, if applicable to do this, complete the Data Submission form on the Deposit pages on ESDS web site dataset will then be formally reviewed for archiving by the UKDA Acquisitions Review Committee if accepted, complete the UKDA Deposit and Licence forms, and send the data, documentation and forms to the UK Data Archive within the required time-scales you will be notified when your data are being released via the UKDA online catalogue access to data will be granted to registered bona fide researchers only via Athens authentication

32 Creating or depositing data Tel: 01206 872572/872974

Download ppt "Guidelines for data preparation Social Science Data Archives: creating, depositing and using data Plymouth, 22 October 2004 Louise Corti."

Similar presentations

Ads by Google