1. Examples of using big datasets for third sector research Orian Brook, University of Stirling Webinar 1 “Dealing with data: Defining 'Big data' and research opportunities in studying civil society with secondary and administrative data” www.thinkdata.org.ukwww.thinkdata.org.uk 3 Mar 2016

2. Big datasets For the purposes of this presentation, can be – Survey data: large, government surveys spanning many years – Administrative data: eg pupil census, GP registrations, pensions data, commercial records – Big, unstructured data: eg social media, digitalised text eg all of Hansard, GPS records S-CSDP, 3 March 20162

3. Example 1: Effects on children of benefit sanctions Nick Bailey, Urban Big Data Centre, University of Glasgow Facilitated by the Administrative Data Research Centre, University of Edinburgh Linking (anonymously) details of: – c100,000 people whose benefits were sanctioned – Pupil Census, to understand effects on school attendance, behaviour and exam results S-CSDP, 3 March 20163 https://adrn.ac.uk/research-projects/approved-projects/project052/

4. Example 2: Milo Database managed by SCVO on behalf of Scottish TSI & VC network, with c35,000 voluntary orgs recorded Office of the Scottish Charity Regulator has c24,000 – only registered charities are represented – registered office of the charity is represented We are cleaning, coding and analysing Milo – making it available to researchers – better understanding of 3 rd sector esp locally S-CSDP, 3 March 20164 www.thinkdata.org.uk

5. Example 3: Longitudinal Analysis of Volunteering Daiga Kamerāde, Third Sector Research Centre, University of Birmingam Using Understanding Society (British Household Panel Survey) Looking at how longitudinal analysis changes our understanding of participation in volunteering S-CSDP, 3 March 20165 www.thinkdata.org.uk

6. How many people get involved in voluntary organisations?

8. Example 4: Cultural participation in Scotland Orian Brook, University of Stirling S-CSDP, 3 March 20168 Previously understood using survey data, lacks detail – Policymakers concluded that level of supply made little difference to participation – Disinvestment in local facilities: participation explained by social stratification Analysed transactional (box office) data from Scottish arts venues

9. Established significant distance decay of proximity to venue on attendance 9

10. Example 5: Estimating epidemics Google used user searches to predict flu outbreaks since 2009 However ran into problems as was later over- predicting cases by as much as 100% in some weeks Due in part to searches reflecting fear of flu as much as actual flu cases Constant need to week out spurious seasonal correlations eg high school basketball – winter detector or flu detector? S-CSDP, 3 March 201610 http://gking.harvard.edu/publications/parable-Google-Flu%C2%A0Traps-Big-Data-Analysis

11. Example 6: Tweeting the IndieRef Ana Langer (Politics), Michael Comerford (UBDC) & Des McNulty (Policy Scotland), University of Glasgow Analysing tweets during referendum, to understand role of social media in political mobilisation S-CSDP, 3 March 201611 http://policyscotland.gla.ac.uk/how-the-yes-social-media-strategy-helped-snp-effect-seismic-change-in-scottish- politics/