Trendspotting: Helping you make sense of large information sources
Helping you make sense of large
Marieke Guy, QAA
Data Matters #HEDataMatters17
3rd November 2017
Your facilitator for the session…
• Founded in 1997
• Offices in England, Scotland
• Our mission is to safeguard
standards and improve the
quality of UK higher
it is delivered around the
To begin to….
• Help you better organise and make sense of large
• Help you carry out language-based research and
• 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
“To understand is to
• 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)
• Collecting data:
• Focus groups
• Existing data:
• Case studies
• Web content
Remember the importance of context!
• 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
How is it used?
• Using data that you have access to as an
organisation to help guide decisions that improve
• Informed because should be based on more than
just numbers – contextualised and use staff
• Important part of strategic planning
• Important to have data that backs up the decisions
that are being made
Data-informed decision making
• 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??
“If you do not know how to
ask the right question, you
W. Edwards Deming
• 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
• 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?
• 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?
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.
• 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
• Common form of analysis in social science
• Involves examining and recording themes
• Importance of organising data
• Key element is ‘coding’ – recognising important
moments in the data and highlighting them
• 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
• 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
• Think about cause and effect relationships
Trends and patterns
• 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
• Nvivo – from QSR
• Atlas-ti – from Scientific Software development GMbH
• MAXQDA – from VERBI
• 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
• 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
Individually (5 minutes)
Presentation: Outputs of
• If you ask for feedback you should act
• 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
• “You said – we did” campaign
• Based on NSS feedback
• 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
Combining with numbers
• Placing data in a visual context
• Helps users understand the significance of the data
• Want users to think about substance rather than
• Use the art of comparison: time-series, ranking,
ratios, deviation, frequency, correlation,
• Dangers of spurious accuracy – avoid 34.567%,
use about a third
• Think about story telling approaches
Data visualisation with numbers
• 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
Side by side
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What are students really dissatisfied
How can we engage our learners better
in discussions about technology?
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