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Trendspotting: Helping you make sense of large information sources

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1-hour version of the all day workshop

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Trendspotting: Helping you make sense of large information sources

  1. 1. Trendspotting: Helping you make sense of large information sources Marieke Guy, QAA Data Matters #HEDataMatters17 3rd November 2017
  2. 2. Your facilitator for the session… • Founded in 1997 • Offices in England, Scotland & Wales • Our mission is to safeguard standards and improve the quality of UK higher education, wherever it is delivered around the world • http://www.qaa.ac.uk/ About QAA
  3. 3. To begin to…. • Help you better organise and make sense of large information sources • Help you carry out language-based research and development • Help you with market research • Help you with business enhancement • Help you with report planning • Help you write better, more engaging reports Aims for the session
  4. 4. “To understand is to perceive patterns.” Isaiah Berlin
  5. 5. Practical: Thinking about order & organising
  6. 6. • One willing volunteer should empty their purse, wallet or bag on the table • Arrange and cluster the content into categories • Label each pile • Discuss In small groups (5 minutes)
  7. 7. Presentation: Introduction to qualitative data & thematic analysis
  8. 8. By Mark Johnstone, FlowingData
  9. 9. Qualitative data vs Quantitative data
  10. 10. • Information that is not in a numerical form i.e. language-based data, descriptive data… • Examples include: survey responses, diary accounts, open-ended questionnaires, unstructured interviews, unstructured observations, collections of reports • Often about interactions and relationships • Analysis of such data tends to be more difficult than looking at quantitative data (numbers) Qualitative data
  11. 11. • Collecting data: • Interviews • Surveys • Consultations • Focus groups • Polls • Existing data: • Case studies • Reports • Web content Remember the importance of context! Data sources
  12. 12. • To identify themes and patterns and share in the form of reports • To answer particular questions (or theories) • To help inform decision making and business planning How is it used?
  13. 13. • Using data that you have access to as an organisation to help guide decisions that improve success • Informed because should be based on more than just numbers – contextualised and use staff intelligence • Important part of strategic planning • Important to have data that backs up the decisions that are being made Data-informed decision making
  14. 14. • Anything more than you can easily read during the work time available • Perhaps more than 20 pages? • It’s all about organisation and process • It’s also about reproducibility and reuse • Big data – volume, velocity, variety • Tools, tools, tools… What are large volumes??
  15. 15. “If you do not know how to ask the right question, you discover nothing.” W. Edwards Deming
  16. 16. • Why have you been asked to do this work? • Who is it for? Who will see it? Where will it go? • Is there an agenda behind it? Where are the sensitivities? • Who is leading on the work? What about sign off? • What will be the output? • What is the business enhancement purpose? • How will success be measured? Starting point
  17. 17. • What do you need to produce? • Who is it for? • Is it for internal or external viewing? • When should it be delivered? • How long should it be? • How can it be promoted? End point
  18. 18. Presentation: Carrying out data analysis
  19. 19. What is a code? “A word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data.” Saldaña, J (2009). The Coding Manual for Qualitative Researchers.
  20. 20. • Gathering all the information about a topic together for further exploration – you code into nodes • Nodes can be topics, people, places, sections of a report, positive feedback etc. • Coding is heuristic • Different projects require different approaches • Need for consistency across projects • Can be carried out in cycles • Note that a theme is the outcome of coding Coding
  21. 21. • Common form of analysis in social science research • Involves examining and recording themes • Importance of organising data • Key element is ‘coding’ – recognising important moments in the data and highlighting them Thematic analysis familiarisation with data generating initial codes searching for themes among codes reviewing themes defining and naming themes producing the final report
  22. 22. • Occur numerous times across the data – but frequency not always related to importance • Researcher judgement is key tool • Try to avoid preconceptions • Semantic and latent themes – look beyond what people say – underlying ideas • Themes and codes are different Themes
  23. 23. • Trends are the general direction of travel: “our customers are starting to prefer…” • Patterns are series of data that repeats: “Time has shown that customers like x” • Start to actively look for patterns • Look at how information is structured • Look for relationships between different pieces of information • Think about cause and effect relationships Trends and patterns
  24. 24. • Things that are similar • Things that are different • Things that are frequent • Things that are sequential or run in cycles • Things that are opposite • Things that are caused by one another • Things that are in relation to one another Recognising patterns Chronology Key events Settings People Places Processes Ideas
  25. 25. • Nvivo – from QSR http://www.qsrinternational.com/nvivo-product • Atlas-ti – from Scientific Software development GMbH http://atlasti.com/ • MAXQDA – from VERBI http://www.maxqda.com/ • Leximancer http://info.leximancer.com/ • Excel – Part of MS Windows • Many tools out there – some open source e.g. RQDA • None analyse the data – just help organise!! Language-based analysis tools
  26. 26. Practical: Thematic analysis and ‘coding’
  27. 27. • Look at the source material given • Decide on your coding approach • Start to code the text using the highlighter pens • Write a list of the codes you have identified • Feed back to the wider group • Compare Individually (5 minutes)
  28. 28. Presentation: Outputs of language-based analysis
  29. 29. • If you ask for feedback you should act on it • Pick the areas you can respond to • Offer a strategy for dealing with them • Don’t ask if you don’t want to hear the answer • “You said – we did” campaign • #YouSaidWeDid • Based on NSS feedback Feedback loop
  30. 30. • Reports look good with a few numbers in! • Think about key stats from your project:  How many data sources?  When were they collected?  How many participants?  What percentage of overall participants was this?  Answers to any yes/no questions? • Bar and pie charts • Graphs and sparklines • Tables Combining with numbers
  31. 31. • Placing data in a visual context • Helps users understand the significance of the data • Want users to think about substance rather than methodology • Use the art of comparison: time-series, ranking, ratios, deviation, frequency, correlation, geographical location • Dangers of spurious accuracy – avoid 34.567%, use about a third • Think about story telling approaches Data visualisation with numbers
  32. 32. • Think about story telling approaches • Word tags, bubble clouds, tree maps • Word counts • Venn diagrams • Cluster analysis • Using quotes • Using photos and icons Data visualisation with words https://www.behance.net/gallery/7526739/Nineteen-Qualitative- Data-Visualization https://infogr.am/
  33. 33. • Infographics Side by side https://visage.co/turn-qualitative-data-visual-storytelling-content/
  34. 34.  Where do students need extra help and support?  What are students really dissatisfied with?  How can we engage our learners better in discussions about technology? Jisc Student Digital Experience Tracker Go to www.jisc.ac.uk/rd/projects/student-digital-experience-tracker Sign up to participate http://bit.ly/trackersignup18 A survey of students' expectations and experiences of technology
  35. 35. Resources
  36. 36. • Thematic analysis: http://designresearchtechniques.com/casestudies/thematic-analysis/ • An introduction to Codes and Coding - . https://www.sagepub.com/sites/default/files/upm- binaries/24614_01_Saldana_Ch_01.pdf • HEA PTES survey responses https://www.heacademy.ac.uk/resource/their-own-words • Data visualisation beyond numbers: https://www.techchange.org/2015/05/27/data-visualization-beyond-numbers-tools-for- qualitative-data-visualization/ • Visualising data: http://www.visualisingdata.com/ • Thematic coding – video with Graham Gibbs https://www.youtube.com/watch?v=B_YXR9kp1_o • Better Evaluation – thematic coding: http://betterevaluation.org/en/evaluation-options/thematiccoding Useful resources
  37. 37. • All images from:  Pixabay – CC0 - pixabay.com/  or author’s own  Or url given Credits
  38. 38. qaa.ac.uk enquiries@qaa.ac.uk +44 (0) 1452 557050 © The Quality Assurance Agency for Higher Education 2017 Registered charity numbers: 1062746 and SC037786 Thank you

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