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DAMES, 31/JAN/2012, T6 Opportunities and prospects in social research Paul Lambert, 31 st January 2012 Talk to the seminar Data management in the social.

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Presentation on theme: "DAMES, 31/JAN/2012, T6 Opportunities and prospects in social research Paul Lambert, 31 st January 2012 Talk to the seminar Data management in the social."— Presentation transcript:

1 DAMES, 31/JAN/2012, T6 Opportunities and prospects in social research Paul Lambert, 31 st January 2012 Talk to the seminar Data management in the social sciences and the contribution of the DAMES Node, a session organised as part of the Data Management through e-Social Science ESRC research Node

2 DAMES, 31/JAN/2012, T6 Start by thinking big… Landes (1969) analysis Knowledge-based revolutions Importance of standardising technology for cooperation (not just creating it) Importance of access to underlying materials – coal, cotton, etc. Uneven development (nationally) Landes, D.S. (1969). The Unbound Prometheus: Technological Change and Industrial Development in Western Europe from 1750 to the Present. Cambridge: Cambridge University Press. Emergent uses of computing and the internet, such as in e- Science traditions, arguably share similar characteristics Standardisation, communication, vast volumes of resources Social research data, e.g. large scale surveys and other large quantitative resources, exemplify these opportunities

3 DAMES, 31/JAN/2012, T6 E-Social Science / Digital Social Research ESRC & JISC initiatives as major UK investment in e-social science technology (see e-Science broadly involves using emergent computer technologies with enhanced capacities for communication/collaboration & data processing Handling and displaying large volumes of complex data E.g. GeoVue; LifeGuide; DReSS; Obesity e-labGeoVueLifeGuideDReSSObesity e-lab Resources for computationally demanding analyses CQeSS; MoSeS; eStat; NeISS CQeSSMoSeSeStatNeISS Standards setting in collaboration, data preparation, data and research support – DRS; MeRC; OeSS; DAMES DRSMeRCOeSS DAMES

4 DAMES, 31/JAN/2012, T6 Example: Understanding New Forms of Digital Records (DReSS) transcribed talk audio video digital records system logs location transcript code tree video system log

5 ..more examples.. (strategies for social scientists to tap into the e-Infrastructure) 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 StatJR 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, 31/JAN/2012, T6

6 e-Science, data management, and research revolutions (!) Data management through e-Social Science DAMES ( ) – developing services / resources using e- Science approaches which will help social scientists in undertaking data management tasks Information / data retrieval (e.g. GESDE systems) Storage and processing of data and metadata (e.g. secure portals and curation and fusion tools) …Data management is at the centre of transformations in the exploitation of information resources… Collaboration / standardisation in constructing empirical results Facility to host and distribute new forms of data Facility to discriminate between the masses of data DAMES, 31/JAN/2012, T6

7 Prospects in social research The changing terrain of social research and three exciting developments/frontiers: 1)Data access 2)Data management and analysis 3)Log books Some thoughts on the trajectory of social research developments DAMES, 31/JAN/2012, T6

8 1) Access to data.. Example: Accessing surveys via UK Data Archive Shibboleth authentication Download and analyse in Stata, SPSS, etc DAMES, 31/JAN/2012, T6

9 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, Such resources are often not widely known about, but have the ability to enhance analysis DAMES, 31/JAN/2012, T6

10 Steady accumulation of options / permutations / approaches in… 2a) Data Management Pre-analysis (and re- analysis) routines Sensitivity analysis Standardisation, harmonisation 2b) Data Analysis Descriptive tools Ongoing development of complex analytical models GLLMMs for structural data features, multi- process systems, etc DAMES, 31/JAN/2012, T6


12 E-Stat ebooks (image from doc in prep., Browne et al. 2011) (Links to product from StatJR)

13 3) Log books Software tools for logging work are increasingly well developed See our workshops on documentation/replication Other initiatives in sharing records of work E-Stat: Electronic workbooks for the data and model building process E-Stat MyExperiment: Depository for project files These havent yet been extensively exploited in survey research – but they should be! DAMES, 31/JAN/2012, T6

14 14 The idea of workflows Workflow modelling has an exciting future.. Workflow documentation oMyExperiment [] oSocial survey analysis Long, J.S. (2009) Workflow of Data Analysis using Stata. CRC press At present… Tool development in process Depositing workflows might impose constraints/burdens DAMES, 31/JAN/2012, T1

15 Example of using MS Excel for workflow documentation in survey research 15DAMES, 31/JAN/2012, T1

16 16 Who will take the initiative? Long, J. S. (2009). The Workflow of Data Analysis Using Stata. Boca Raton: CRC Press. 1-5: Programming in Stata; 6: Cleaning your data; 7: Analysing data and presenting results; 8: Protecting your work DAMES, 31/JAN/2012, T1 Because claims in published papers that additional materails are available from author usually prove false, at least after a few months, the California Center for Population Research at UCLA recently implemented a mechanism by which additional materials, for example, -do- and –log- files, can be attached to papers posted in its Population Working Paper archive. Other research centers are to be encouraged to do the same (p404 of Treiman (2009) Quantitative Data Analysis. NY: Jossey Bass) Bespoke solutions or the generic/dynamic approaches of e-Science?

17 Well-known challenges in survey research 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 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 DAMES, 31/JAN/2012, T6

18 Managed solutions? Data handling/analysis capacity-building ESRC programmes (NCRM, RDI, RMP); training workshops/materials; P/G funds; strategic research grant investment Documentation/replication policies 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) DAMES, 31/JAN/2012, T6

19 ..train and/or constrain the analysts.. Train them -> DAMES, 31/JAN/2012, T6

20 ..constrain the analysis.. DAMES, 31/JAN/2012, T6

21 Social solutions? Tools and infrastructure for better standards to are built up from within (aided by collaborative technologies) E.g. GESDE, P-ADLS, MethodBox, DAMES, 31/JAN/2012, T6

22 Summary e-Science would often be seen as about enabling effective research in conditions of abundant resources In practical terms, for social researchers, this means navigating through the vast array of data and analytical resources, and undertaking robust and replicable work Likely continuation of mix of generic and specific, managed and social, approaches DAMES, 31/JAN/2012, T6

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