Handling social science data: Challenges and responses

Handling social science data: Challenges and responses Paul Lambert, University of Stirling DAMES research Node, www.dames.org.uk DIR workshop: Handling Social Science Data 17/MAR/2010

What is social science data? 17/MAR/2010 DIR workshop: Handling Social Science Data Example: Accessing surveys via UK Data Archive Shibboleth authentication Download and analyse in Stata, SPSS, etc

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

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

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.

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

Example: Qualitative data used by ‘Digital Records for e-Social Science’ (DReSS)
transcribed talk
audio / video
digital records
system logs
location
DIR workshop: Handling Social Science Data transcript code tree video system log 17/MAR/2010

Three well-known challenges
We’re 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

‘ 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)

..train and/or constrain the analysts..
Train them ->

..constrain the analysis.. 17/MAR/2010 DIR workshop: Handling Social Science Data

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

..some examples..
E-Stat @
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

DAMES – online services for data coordination/organisation
Tools for handing variables in social science data
Recoding measures; standardisation / harmonisation; Linking; Curating

GESDE – Search and browse supplementary data on occupations; educational qualifications; ethnicity 17/MAR/2010 DIR workshop: Handling Social Science Data

Data curation tool (for collecting metadata)

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

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

References and Links
ADLS: http://www.adls.ac.uk/
ADMIN Node: http://www.ncrm.ac.uk/about/organisation/Nodes/ADMIN/
DAMES Node: http://www.dames.org.uk/
DReSS: http://web.mac.com/andy.crabtree/NCeSS_Digital_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.uk/810/

Handling social science data: Challenges and responses

by NeISSProject on Feb 04, 2011

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