October 31, 2017 Elena Hristozova

NSS 2017: One data set, many dataviz approaches.

I often say that one dashboard cannot answer all the questions we may have for our data and that the way we visualise our data depends strongly on the question(s) we pursue to answer. On 24^th October 2017, the latest meeting of the Midlands Tableau User Group (#MidlandsTUG) community took place in Leicester and, as part of it, attendees had the opportunity to present their take on visualising a public dataset – the 2017 National Student Survey (NSS) results. As a result, a few the attendees presented their work, thus showcasing the variety of angles an analyst can take when trying to gain insight into their data.

Further in this post you will have the opportunity to see some of these examples, presented in no particular order, but first, please remember that the analysts whose work is referenced in this post work in a wide range of sectors. Some may be more familiar to the data than others, but they may not necessarily be an expert on it, and, therefore, their visualisations should not be interpreted as in-depth analyses!

The main purpose of the ‘data hackathon’ exercise during the event was to encourage creativity and a range of approaches when analysing a common data set, so please keep that in mind when exploring their visualisations.

About the data:

The 2017 NSS results were published in July and the data is available on the hefce website. The survey itself is ‘aimed at mainly final-year undergraduates, it gathers opinions from students about their experience of heir courses, asking them to provide honest feedback on what has been like to study on their course at their institution’ (http://www.thestudentsurvey.com/about.php).

Approach #1: Elena Hristozova

Link to interactive viz: https://public.tableau.com/profile/elena.hristozova#!/

Let me introduce you to my approach. I had worked with this data before which allowed me straightaway to bring together some context in my analysis by providing the themes in which the 27 questions are grouped. My main question for the data was: are there any trends that can be seen in the questions’ results across the subjects and the teaching institutions? In particular, I wanted to make it easier for the user to see whether any groups of questions tend to score lower than others.

I used a heatmap to show the results for all questions and subject or institutions, allowing users to make a choice. Based on no scientific evidence, I chose 75% agree score as my middle point and used colour to encode the ‘less than or higher than 75%’ results.

Approach # 2: Ali Motion

Link to interactive viz: https://public.tableau.com/profile/ali.motion#!/

Ali’s visualisation is a great example of a benchmarking type of dashboard where one can easily see how a selected institution has performed for each of the subjects taught at it, and how the results compare to those of other providers for a selected question. Ali has used a jitter plot where the results for each of the providers are plotted on a single axis, but the marks in the charts are given ‘some space to breathe’ and to show groupings a bit better.

Colour has been used to highlight the selected institution, which also changes between green and orange to indicate of the said institution’s score was above or below the benchmark, or the sector average. Furthermore, a user is also supported in the interpretation of the results through a very clear to read and understand set of tooltips that appear on hover.

Approach # 3: David Clutterbuck:

Link to interactive viz: https://public.tableau.com/profile/david.clutterbuck#!/

David’s approach was very straightforward: show the scores for the Top 10 and Bottom 10 providers, show movement from previous year and breakdown of satisfaction per question. He used both colour and shapes very effectively to show the insights and he even went a step further obtaining the previous year’s data to demonstrate the change over time in the overall score.

David has also added a couple of very small touches that make his visualisation easy to explore: by clicking on one of the providers in the table, the provider’s logo appears underneath and the provider’s results become highlighted in the detailed question breakdown.

Approach #4: James Linnett

Link to interactive viz: https://public.tableau.com/profile/james.linnett#!/

‘The way I approach it was for the end user (who may or may not have any analytical experience) to easily understand what the dashboard is portraying. By that I mean easily being able to compare their institution and/or subject to the national average.

The traffic light colours indicate how the specific institution, question group or individual question compare against the said average.’

Approach #5: Rob Radburn

Link to interactive viz: https://public.tableau.com/profile/robradburn#!/

Rob’s approach is very interesting in the sense that it has incorporated a way to visualise both, the mean score of students answers, which is a value from 1 to 5 where 1 = ‘Strongly Disagree’ and 5 = ‘Strongly Agree’ , and the level of agreement (or consensus), which is a measure he has calculated that has a value between 0 and 1, where 0 = disagreement and 1 = agreement.

Rob’s visualisation is very clear and he has provided the readers with a very simple explanation of how to read the chart: ‘The dot shows the average score for each question. The length of the line from the dot measures how in agreement students were in answering the question. This uses Tastle and Wierman’s measure of dispersal. The shorter the line, there is more agreement in the answers for the question. The longer the line, less agreement. The questions are then ordered from more to less agreement.’

Approach #6: Neil Richards

Link to interactive viz: https://public.tableau.com/profile/neil.richards#!/

‘For my visualisation I was aware that there were some great examples in Big Book of Dashboards chapter 3, so essentially I just wanted to recreate a simple version of the jitter plot of the start of the chapter, with one for each question. I didn’t actually look at the book until just now writing these bits, so I didn’t realise how well I’d remembered the look of the chart!

I added the overall average line after feedback on twitter, and fixed the chosen provider to always show in the middle (with all other x positions just placed at random).’

Approach #7: David Hoskins

Link to interactive viz: https://public.tableau.com/profile/hoskerdu#!/

‘I decided to take a more personal approach to the dataset and compare responses for three institutions where my step daughter Sophie is thinking of studying Social Work next year: Bournemouth, Leeds Trinity and Nottingham Trent.

I knew I had the perfect photo (taken at my partner’s graduation), and soon decided on a grid structure for my dashboard, with a column for each institution showing KPIs for overall comparison and the questions grouped into two categories to keep the layout uncluttered.

Narrowing the focus also allowed me to display the proportion of responses for each question as diverging stacked bars (using instructions at https://t.co/LyB0ikBSmB ) and show the detail behind the aggregated metrics.’

Approach #8: Jennie Holland

Link to interactive viz: https://public.tableau.com/profile/jennie.holland#!/

‘For my analysis I used the summary data at country level. The first sheet in the workbook shows survey results for the question groups at country level. As the data wasn’t too big I wanted to be able to show this all on one sheet, with highlights on country and UK comparison to show the variation at a glance.

The second page looks at the questions asked within each question group. To help with consistency between the two sheets, I grouped the questions into the same categories used in the first sheet, and allowed the user to select the group of questions they are interested in. I was quite keen on using one scale for all question groupings that were selected in the filter, to enable the viewer to see the distribution of scores across the question groups.’

Approach # 9: Neil Davidson

Link to interactive viz: https://public.tableau.com/profile/neild#!/

Neil has not had the chance to provide a quick summary of his approach but straightaway it is easy to see that he was familiar with the data and the HE sector. In his visualisation he has demonstrated the correlation between an institution’s overall score for each of the questions and the said institutions’ rank in the Complete University Guide (CUG) league table.

Not all providers who have results for the NSS appear in the league table and so only higher education establishments are visualised. Though Neil’s work is still a work in progress (as he has admitted it himself), it still demonstrates that the strongest correlation of the CUG ranks appears to be with Q15: The course is well organised and running smoothly (the question with line of best fit closest to 45 degrees). Of course, there is no argument against the fact that a league table is not based merely on the results of the NSS, but the bottom line is that visualising a set of small multiples to show correlation between two variables can work well.

Approach #10: Elena Hristozova & Dave Kirk

Link to interactive viz: https://public.tableau.com/profile/elena.hristozova#!/

The last approach to demonstrate is somewhat a joint approach between myself and Dave. Dave had the idea to create a visualisation that shows how many of the questions scored below the score for Q27 – Overall Satisfaction. Due to time constraint, he was unable to complete his visualisation but after a couple of exchanged messages I realised that this was an ideal question to answer using the sunburst chart I was currently learning how to make.

The end result is the visualisation below which compares the scores for all 26 questions in the survey to the overall satisfaction score for each of the country regions in the UK. The analysis is presented through encoding colour – negative difference presented in red and positive difference shown in blue. The intensity of the colour is an indicator of how big or small the difference is.

To sum up…

I hope you have enjoyed exploring the different ways in which the Tableau Community in the Midlands approached the 2017 NSS results. Some analysts drew inspiration from books and the literature, others took a very narrow focus on the data, and the rest just practised visualising survey data and they all had a different set of questions driving their analyses and visualisations!

Thank you,

Elena | #VisualisingHE

P.S. If there were any more submissions to the #MidlandsTUG data challenge that have been missed from this post, do get in touch!

September 9, 2017 Elena Hristozova

Universities in UK: From Research to Patents

Higher Education Providers (HEPs) in the UK pride themselves with delivering excellent teaching and world class research, but here is a question: how can we measure how ‘innovative’ these institutions are? One way to look at this is to see how many patents each institution has using data from the Higher Education Business Community Interaction (HE-BCI) return. Though this is nowhere nearly as comprehensive a method as are the numerous rankings, league tables, and other excellence framework exercises, it is still a simple way of showing who invests in protecting their intellectual property.

What are the caveats?

First, this is not a sign of research strength or quality but rather shows what the universities choose to do with their intellectual property.

Second, whether universities have a large patent portfolio or not depends on a range of factors: some universities do not focus on research as much as others do (e.g. some HEPs are traditionally research focused whilst other, more recently established ones, put more emphasis on teaching); some areas of research are less likely to be commercialised (e.g. research in the social sciences and humanities subjects would less frequently lead to practical inventions as would research in the applied scientific subject areas); some universities may choose not to file any patents if they do not seek to commercialise their inventions, and so on.

These are only a few out of many possible explanations, so keeping this information in mind, have a look at the insights.

The Size of UK’s Patent Portfolio:

In 2016 the 162 UK higher education providers held a total of 18,723 live patents! 18% of those, or 3,357 were owned by a single institution: the University of Oxford. What is also striking is that the university with the second largest patent portfolio, the University College London, has approximately half the number of patents that Oxford has.

By applying the Pareto principle to the data we see that 80% of the patents were owned by the top 15% of institutions (or the top 24 out of 162 HEIs). If you would like to find out more about the Pareto principle, have a look at this article: https://betterexplained.com/articles/understanding-the-pareto-principle-the-8020-rule/

The Region Split

London is the region with the largest number of patents overall – 5,152 followed by the South East with 4,265. Whilst London has twice as many HEPs than the South East (38 vs. 19), the region only has 20% more patents.

Another interesting region is Northern Ireland – whilst there are only four HEPs, only two of them held any live patents as of 2016. These were the ‘Queen’s University of Belfast’ and the ‘University of Ulster’.

The Management of Intellectual Property

The last set of charts do not focus solely on patents but rather, on the overall management of the intellectual property generated by an institution. This may include any licenses, designs, and trademarks, etc. When comparing whether the management of IP is dealt-with in-house or outsourcing, it is clear to see the difference between the top 24 institutions and the remaining institutions with at least one active patent. Amongst the top 24, the most preferred method is through a combination of both in-house and outsourcing the expertise whilst amongst the remaining universities, outsourcing is the most popular choice.

One very interesting observation from the data is that amongst the 97 HEIs that held at least 1 patent in 2016, all but two have disclosed that members of staff whose research generate the intellectual property are rewarded in some form. Although it is not easy to compare what these rewarding methods are, remember that you can still find out more about them by clicking on an institution in either of the charts.

Is This All the Data Could Tell Us?

Absolutely no. The data is very rich and it allows for a great depth of analyses which may be covered in other #VisualisingHE projects. Some of the questions that weren’t answered due to limitations of the data include:

any 5-10 year trend analyses (not all data for the last 10 years is made freely available by HESA).
any cost-revenue insights (this topic explored patents in particular and the cost-revenue analysis available in the dataset is for the entire intellectual property of an institution);
any breakdown of the science field in which patents are held (a topic that may be explored with data from the UK patent office, if such open dataset exists).

Link to interactive viz:

https://public.tableau.com/profile/elena.hristozova#!/vizhome/UKHEIsIP/IP

Elena

#VisualisingHE

June 14, 2017 Elena Hristozova

Destination: Europe

In today’s globally connected world we experience the notion of a border mainly when we go through passport control at the beginning or the end of a journey. For the education sector, it means that now studying abroad has never been easier, especially for those who live in Europe. So what does the student mobility within Europe look like?

To answer this question, I have decided to look at the Higher Education data from Eurostat and find out the number of enrolled students within each European country who are from a different European country. The full dataset contains student enrolments from some countries outside of Europe, too, but for the purpose of this exercise only European origin and destination countries have been included.

Why visualising student mobility within Europe?

Choosing the topic wasn’t easy. In fact, Dave Kirk helped me with this a few months ago when Dave, Adam , Stephen and I launched #VisualisingHE. He and Adam were enthusiastically listing topics and datasets they were looking forward to exploring as part of the project but I was struggling to come up with something that I could talk about with so much excitement.

Big Map 3.png

Then Dave asked me: what are you passionate about? And it didn’t take me long to come up with an answer – I am an Eastern European and I cannot imagine who and where I would be today if I hadn’t been given the opportunity to undertake my bachelor’s degree in the UK! Therefore, I decided to look into the topic and develop a pretty, colourful and informal visualisation that represents the beauty of studying abroad… in Europe.

What influenced the design and choice of charts?

Probably the better question here is not ‘what’ but ‘who’ influenced the choices I have made as part of the design process. Having decided on the overall ‘feeling’ I wanted to my visualisation to convey, I had to think about whether I wanted my dashboard to be in a #coffeetableviz (i.e. poster) style or be leaning towards the exploratory side. I will leave it to you to decide where precisely on the informative vs. exploratory spectrum the end result lies, but I will let you know whose work gave me the inspiration:

for poster style infographic I would always go to Pooja Gandhi’s fantastic work first: if you haven’t visited her tableau public profile, do it! I find her work truly inspiring – she knows how to deliver a clear message in a dataviz by seamlessly combining text and graphs.
for visualising the inflow vs. outflow concept I was inspired by Neil Richard‘s viz on the Eurovision song contest. When I first saw his viz, I remember thinking: ‘Oh, well it is clear to me what the visualisation is telling me.’ Plus, I really liked the flags.
for colours I got inspired by Jeffrey Shaffer‘s Beautiful Trash.

Then the idea of a Sankey sprung to my mind and that was it – I was going to do a Sankey and practice my great arc curves on a map. I started with the great arc curves as I had already done these for a project at work. For the data structure I followed this summary and to get the lines curved, I found this post useful. Building the Sankey wasn’t too difficult either – there are plenty of blog posts written by the community that explain how to do it. I read a few and after deciding I did not want to use custom SQL, I found this post helpful.

What were the challenges?

Just as I was thinking I was making some really good progress, I came across Ravi Mistry and Nicco Cirone‘s very interesting work on ERASMUS. It was great! I loved it… but it also had a map of Europe with curves, and a Sankey, and was talking about students studying in Europe… Oh dear, I didn’t want to copy their work… Even though I hadn’t seen it until I was alredy halfway there with my viz, if I were to use exactly the same combination of graphs would have still made me feel as if I had copied it. At this point I had two options: drop the topic, or think really hard how to make my visualisation different. A few days later I was going through some of my favourite dashboards on Tableau Public and then, inspired by Adam McCann’s beautiful Beatles Analysis, I decided to develop an ‘hourglass’, or a double Sankey, chart (I don’t know if this chart has a name) to show the inflow vs. outflow relationship. Not long after I had my first draft:

Student Mobility

Another big decision making point in the process of developing this dashboard was around my choice of font. It crossed my mind that there is this thing about safe fonts for tableau public. After a quick check on Google I thought I should try and use a web safe font. My choice was Comic Sans. Yes, Comic Sans, because I was going for a fun artistic / informal look. Little did I know that there was something about Comic Sans… When I asked for some feedback on the way my dashboard was coming together, I got mixed reactions. I particularly like the contrast in Dave and Stephen’s comments:

Comic Sans & Fonts

After doing some reading on the internet and quite a few lengthy discussions with friends and colleagues at work, I decided that though I see nothing wrong in using Comic Sans for my dashboard (especially, since I am not saying: ‘Danger, Danger, the world is about to collapse’ … or something in those lines), I decided to change it. Simply because I didn’t want the font to distract from its overall purpose.

What did I learn?

How to build a Sankey
How to build an hour glass visualisation
How to crop an image in GIMP (and add transparency to it)
There is something about Comic Sans…
How to use A LOT of containers (with very few of them floating!)

What are my personal challenges for the next viz?

Don’t use a long-form dashboard
Produce a dashboard with a non-black background

Thank you for reading and I hope you enjoy exploring the dashboard!

Link: https://public.tableau.com/profile/elena.hristozova#!/vizhome/EuropetoEuropeStudentMobility/InflowOutflow

Inflow & Outflow (6)

VisualisingHE

Using data to deliver insight into the higher education sector

Author: Elena Hristozova