Handling social science data: Challenges and responses

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Handling social science data Challenges and
responses

• Paul Lambert, University of Stirling
• DAMES research Node, www.dames.org.uk

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What is social science data?
Example Accessing surveys via UK Data
Archive Shibboleth authentication Download and
analyse in Stata, SPSS, etc
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Principal forms of data

• Large and complex social surveys
• Longitudinal cross-national hierarchical
• Small scale social surveys
• Administrative data (e.g. ADMIN node ADLS
  commercial data)
• Supplementary (digital) data
• E.g. GESDE services at DAMES
• Qualitative material auido / video / textual

17/MAR/2010
DIR workshop Handling Social Science Data
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Large and complex social surveys

• several thousand variables
• tens of thousands of cases (micro-data)
• additional complex survey data features (e.g.
  household clustering)

17/MAR/2010
DIR workshop Handling Social Science Data
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Complex data example British Household Panel
Survey dataset SN 5151

• This example shows BHPS being analysed in Stata.
  BHPS re-contacts subjects annually (since 1991)
• 4294 interviewed as adults every year for 17
  years.
• Analysis methods, and measurement issues over
  time, are challenging.

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Supplementary (digital) data

• E.g. Occupational information resources data
  files within information on occupations, which
  can be usefully linked to micro-data about
  occupations
• e.g. GEODE acts as a
• library of OIRs,
• www.geode.stir.ac.uk
• Such resources are often
• not widely known about,
• but have the ability to
• enhance analysis

17/MAR/2010
DIR workshop Handling Social Science Data
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Example Qualitative data used by Digital
Records for e-Social Science (DReSS)
video

• transcribed talk
• audio / video
• digital records
• system logs
• location

code tree
transcript
system log
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Three well-known challenges

• Were data rich, but analysts poor
• UK Data Forum (2007) Wiles et al (2009)
• Under-use of suitably complex statistical models
• Coordination and communication on data processing
  
• Recodes / Standardisation / harmonisation /
  documentation
• Not rewarded/incentivised to researchers
• Lack of generic/accessible representation of
  tasks
• Limited disciplinary/project/researcher
  cross-over when dealing with data
• Specific software orientations
• These are not generally problems of scale, but of
  organisation

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Managed responses?

• Data handling/analysis capacity-building
• ESRC programmes (NCRM, RDI, RMP) training
  workshops/materials P/G funds strategic
  research grant investment
• Documentation/replication policies
• Dale (2006)
• Software for data access and analysis
• NESSTAR UK Data Archive data/metadata browser
• Long (2009) on the Stata software
• Remote access to data (e.g. SDS)

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..train and/or constrain the analysts..

• Train them -gt

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..constrain the analysis..
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Non-hierarchical responses?

• Technological collaborative services might
  support effective, unmanaged data access,
  coordination and exploitation
• (in principle)
• UK e-Social Science investment in data oriented
  social science research support
• NeISS E-Stat DAMES Obesity e-Lab CQeSS

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..some examples..

• E-Stat _at_

• National e-Infrastructure for Social Simulation
• Expert led simulation demonstrations
• Combining data resources
• Workflows for the simulation analysis
• Modify and re-specify existing simulation
  templates

• Design a tool to specify complex statistical
  models in generic / visual terms
• Multilevel models
• Multiple data permutations and analytical
  alternatives
• Ready access to a suite of complex modelling tools

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DAMES online services for data
coordination/organisation

• Tools for handing variables in social science
  data
• Recoding measures standardisation /
  harmonisation Linking Curating

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GESDE Search and browse supplementary data on
occupations educational qualifications ethnicity
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• Data curation tool (for collecting metadata)

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Handling data analysis-oriented data management
priorities

• Data collection or creation
• Data preservation or curation
• Data enhancement/modification
• Data analysis
• Multiple permutations of related analyses
• Documentation and replication

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Ideas on the future of social science research
data

• Enduring challenges of documentation for
  replication, and coordination
• More and more comparative analysis
• Harmonisation and standardisation
• Data linkage and data enhancement
• Models for complex multiprocess systems
• Fluency increasing uptake by more users

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References and Links

• ADLS http//www.adls.ac.uk/
• ADMIN Node http//www.ncrm.ac.uk/about/organisati
  on/Nodes/ADMIN/
• DAMES Node http//www.dames.org.uk/
• DReSS http//web.mac.com/andy.crabtree/NCeSS_Digi
  tal_Records_Node/
• Secure Data Service http//securedata.ukda.ac.uk/
  
• UK Data Archive http//www.data-archive.ac.uk/
• Dale, A. (2006). Quality Issues with Survey
  Research. International Journal of Social
  Research Methodology, 9(2), 143-158.
• Long, J. S. (2009). The Workflow of Data Analysis
  Using Stata. Boca Raton CRC Press.
• Wiles, R., Bardsley, N., Powell, J. L. (2009).
  Consultation on research needs in research
  methods in the UK social sciences. Southampton
  University of Southampton / ESRC National Centre
  for Research Methods, and http//eprints.ncrm.ac.u
  k/810/