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Tools of data analysis

Tools of data analysis. Paul Lambert, University of Stirling Presentation to the Scottish Civil Society Data Partnership Project (S-CSDP), Webinar 2 on ‘Popular tools of data analysis: MS Excel and online data analysis resources’ www.thinkdata.org.uk , 4 Mar 2016.

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Tools of data analysis

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  1. Tools of data analysis Paul Lambert, University of Stirling Presentation to the Scottish Civil Society Data Partnership Project (S-CSDP), Webinar 2 on ‘Popular tools of data analysis: MS Excel and online data analysis resources’ www.thinkdata.org.uk, 4 Mar 2016

  2. Selective views on history of data analysis • Up until the digital era, statistical analysis innovations keep a few steps ahead of data processing capacity • 20th-21st centuries witness exponential growth in capability for accumulation of electronic data and expansion and diffusion of software for both its analysis and its ‘management’ • By 2016, key characteristics are: • Storage of both quantitative, and qualitative data, and storage of important metadata • Capital costs of storing and analysing data have diminished, but not disappeared • A great many statistical analytical options are available, but specialist skills and knowledge may be a barrier to exploitation • Many relevant resources are online, such as data, metadata, training and guidance materials and analytical software Photo by Bertil Norberg (1932), http://www.stadsmuseum.stockholm.se and http://historyofwork.iisg.nl “A program like SPSS … has two main components: the statistical routines, which do the numerical calculations that produce tabulations and summary measures of various kinds, and the data management facilities. Perhaps surprisingly, it was the latter that really revolutionised quantitative social research.” (Procter, 2001: 253) S-CSDP, 4 Mar 2016

  3. Models of data analysis in different sectors • Conventionally, access, prepare and analyse a micro-level dataset on a local machine • Element of technical work in the organisation and processing of complex electronic data files with numeric data and metadata • Alternative models include online analysis of remote data using bespoke tools such as NESSTAR, and accessing secondary macrodata such as published social statistics • The academic model (from which most methodological guides emerge) generally presumes resources for data storage and analysis which may not be there in non-academic settings S-CSDP, 4 Mar 2016

  4. …Tools of data analysis and the context of research on civil society… With academic infrastructure • Secure data storage • Locally licenced copies of popular analytical software (e.g. SPSS, Stata, NVivo) • Access to expert support (statistical analysis; information technology) • Aside – options of academic collaboration for CSOs, see www.thinkdata.org.uk, including opportunities for affiliate HE membership and cross-sectoral collaboration Without academic infrastructure (today’s focus) • Widely owned software tools with analytical capabilities (e.g. MS Excel) • Free and low-cost online tools for accessing and analysing data • {Accessible} freeware analytical software {that can be used without extensive training and preparation} S-CSDP, 4 Mar 2016

  5. The impact of change in tools of data analysis is surprisingly varied • Digital evolutions revolutionised secondary survey research? • Access to data and its documentation • Capacity to process tasks on datasets using improved software • Digital evolutions have improved, or maybe ruined, survey research? • Enhanced scale and scope of data collection • ‘Big data’ perspectives • Digital evolutions have changed little in the world of secondary survey research activity? • Skills and activities little changed in 30 years (programming, processing, and understanding data & statistics) • See e.g.: • Purdham, K., & Elliot, M. (2015). The changing social science data landscape. In P. Halfpenny & R. Procter (Eds.), Innovations in Digital Research Methods (pp. 25-58). London: Sage. Reflections on digital data and social research using empirical data S-CSDP, 4 Mar 2016

  6. In all situations, attention to documentation files and ‘workflows’ are desirable Reproducible (for self) Replicable (for all) Paper trail for whole lifecycle Cf. Dale 2006; Freese 2007 • In survey research, this means using clearly annotated syntax files (e.g. Long 2009 – linked together in ‘workflows’) • …But not all tools of analysis have such neat routes to documentation… Background: Syntax Examples at: www.dames.org.uk/workshops S-CSDP, 4 Mar 2016

  7. Issues of skills, capacities, training resources… • Many situations where data resources exist but nobody in system is confident that they have the right skills to use the relevant tools required to analyse them • UK is rich in training initiatives – e.g. this CSDP activity – to support prospective users • Like all science, effort in clearly reporting and documenting activities and results is a major step forwards whenever it is feasible References cited • Dale, A. (2006). Quality Issues with Survey Research. International Journal of Social Research Methodology, 9(2), 143-158. • Freese, J. (2007). Replication Standards for Quantitative Social Science: Why Not Sociology? Sociological Methods and Research, 36(2), 153-171. • Long, J. S. (2009). The Workflow of Data Analysis Using Stata. Boca Raton: CRC Press. S-CSDP, 4 Mar 2016

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