Warren Pearce

Following a debate on the utility of social sciences cf. natural sciences as a basis for public policy (see here for some of the comments), I thought it might be time to sketch out some background on the differences between these two (very) broad areas of knowledge.

IS SOCIAL SCIENCE A 'SCIENCE'?
In 1853, Auguste Comte posited that all branches of human knowledge passed through three stages of development: theological, metaphysical and positive. Positive philosophy was the ultimate state for any area of knowledge, where observation and reasoning led to the discovery of laws. Comte regarded the natural sciences as having reached this state, whereas the social sciences were lagging behind - a position restated by Richard Feynman in the Horizon clip above.

Of course, "social science" is a term still in common usage today, which inevitably invites comparison with the natural sciences. Indeed many, if not all, branches of the social sciences have attempted to replicate scientific methods in order to achieve results that measure up to laboratory criteria such as validity, objectivity and generalisability. My argument is that in many cases these criteria are inappropriate for the social sciences, not because the latter is natural science's "poor relation" but because they are fundamentally *different* in character. How so?

LABORATORIES vs REAL WORLD
In the natural sciences artificial, closed environments are created in laboratories. This can not (usually)  be done in social sciences, the 'outside world' is an open system with multifarious potential variables to be measured.  These open systems preclude the discovery of generalised 'laws' which can predict human behaviour across range of circumstances.

WHY WE NEED SOCIAL *AND* NATURAL SCIENCES.
While natural sciences can help us understand the world, we need the social sciences to explain it. The pace of scientific discovery impacts ever more heavily on society, throwing up new questions for politics, religion etc. We need knowledge of the day-to-day language which people use and the personal, prior experiences with which they interpret their world, in order to show us how people come to diverse opinions on such discoveries (e.g. climate change, stem cell research, artificial intelligence). This is an argument made by a diverse range of thinkers such as Wittgenstein, Dilthey, Apel, and Oakeshott.  People do not necessarily come to such opinions by means of a 'rational', scientific process. 

SCIENTIFIC LAWS CAN BE DISPROVED
The world of science is not as 'fixed' as it sometimes appears. Kuhn's groundbreaking Structure of Scientific Revolutions uncovered the social processes inherent in practice of science. The Climategate emails provided a window into some of the less seemly language used by some scientists when discussing their field. Also, scientific results are always, by definition, provisional and waiting to be falsified. Significant assumptions which appear to have strong foundations can be disproved.

STATS IN THE SOCIAL SCIENCES
The world of social science's pursuing of scientific credibility has the potential to lead to the use of statistics to provide spurious certainty. How have the figures been calculated? Why have the researchers chosen to measure those variables rather than any others? This is not to repudiate the use of quantitative measures in social sciences; they have an important part to play. But they need to be treated with caution.

CRAVING CERTAINTY IN A CERTAIN WORLD
What I think this comes down to is (wo)man's aversion to uncertainty. The scientific method is seductive in holding out the promise of provable facts upon which we can base our decisions. A treatise in the nature of facts is beyond this post, but I would modestly propose that when the scientist takes off their white coat and leaves the lAb, they walk into a world dominated by uncertainty and unknown unknowns. Atoms are not the same as people, and any attempt to think we can come to know the two worlds in the same way are not doomed because social science is in some way deficient, it's because they are irrevocably different.

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Blog post to follow...

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Lucky to be attending an event called "Education for the Crisis" at De Montfort University later this month, which seeks to find a way forward for education in light of the crises and disruptions of the early 21st century. Thinking about these ideas, I've written down an idea that's been rattling around my head for a while, essentially how those opposing capitalism and the cuts are overlooking the environmental and resource crises which are intertwined with the degradation of capitalism, and how it's hard to argue against cuts when we all need to survive with *less* in the future.

This is a rough draft. If the words within it mean anything to you at all, please leave a comment below:

When we think about education and the crisis, we might think about what the former might say about capitalism. When capitalism becomes the subject of discussion, thoughts may turn to the swirl of street protests over the last two years. While some of this action has been unfocused (sometimes, deliberately so), a common thread through much of these actions has been resistance to cuts in services and funding by the Coalition Government. These ideas come together for many on the left, who argue both against the cuts and against capitalism. But this position fails to take into account the environmental and resource aspects of the crisis.

In common with other Western countries, the UK derived its wealth from its position at the forefront of the Industrial Revolution combined with the advantages of colonial power. This wealth came through the use of a disproportionate share of the world's natural resources, with environmental consequences across space and time. Consequently, the West can afford (once comfortably, now less so) annual welfare budgets which cost more than the total GDP of most countries.

If all the world's countries consumed resources at the same rates as the West, we would need well in excess of the one planet's resources we have at our disposal. This implies an urgent need to reduce our own consumption. This in turn implies a reduction in (material) wealth and the tax base, and with them the ability to support the existing scale of the welfare state. We can argue that cuts are uneven and unfair (in particular those aimed at the disabled) or that we could reduce spending in other areas of government (e.g. defence, international development), but if the pie gets smaller then so do the slices within it. To deny this is in fact an acceptance of the West continuing to consume a disproportionate share of natural resources in order to prop up GDP, and with it the welfare state papering over the ever-widening cracks.

As a result the environmental and economic crises throw notions of social justice into sharper focus. Squeezing the total national wealth threatens escalating poverty without more radical redistributive measures. But it also highlights the folly of 'business-as-usual' and defending the status quo, no matter what side of the political argument we come from.

What does this mean for education? Well, I think we need to find out. Not because I want to zero in on the welfare state, it is just an example of how the crisis cuts across the old left-right spectrum and needs to challenge our thinking on the institutions cherished within our political traditions. We need to fight for the goals of the present, not to protect the symbols of past struggles...

 

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Seen lots of linking to this Atlantic piece on the Modern Language Association's new guidance for citing a tweet, and felt moved to respond as I think the advice could be much better. I use Harvard APA rather than MLA, but whatever referencing system you use, the object of the exercise must be to enable your reader to cross-reference your source material for themsleves. MLA's proposal falls short on this score:

Last Name, First Name. (User name). "The tweet in its entirety". Date, Time. Tweet.

In his Atlantic piece, Alexis Madrigal describes the decision to omit the unique URL of the tweet as "curious", relying instead on a timestamp based on the timezone of the reader, not the author of the tweet. I'd say it's more than curious, it's just plain *wrong*.

Trying to guess the timezone of the paper's author and cross-reference it to an old tweet, especially, if the tweeter is prolific, would be a thankless task. While I'm sure Twitter addicts (myself included) would love to have their social media of choice further validated by a bespoke referencing method, it really is surplus to requirements.

Let's go back to basics and use the Harvard APA method for citing a web page:

Name of author (Year of publication). Title of website based article. Retrieved date accessed, from web address.

Here we gain two vital pieces of information. Firstly, the unique URL for the tweet which offers a one-click check of the source material for the reader, rather than having trawling through old Twitter timelines until getting back to the date of the tweet. Secondly, we know the date it was retrieved by the paper's author. This could be important if a tweet was deleted subsequent to its retrieval (a good academic author should keep an archive of retrieved web material as content often moves/changes/vanishes).

Also, note the information we gain by using the title of the page (i.e. the title displayed at the very top of the browser window and in the tab). This always takes the format: 'Twitter / username: first few words of tweet...'. So for this tweet:

Telegraph readers will be choking on their Grape Nuts. Greatest story/photo clash ever? #tomorrowspaperstoday yfrog.com/kgm5amej

— Warren Pearce (@WarrenPearce) March 1, 2012

The reference appears as follows:

Pearce, W. (2012, March 1). Twitter / @WarrenPearce: Telegraph readers will be ... . Retrieved March 6, 2012 from https://twitter.com/#!/WarrenPearce/status/175346628666994688

The reader can still identify the material as a tweet, as well as the user name, and we add in a straight-to-the source URL. This provides greater clarity than the MLA's proposal while maintaining the format already in use for web material.

Although of course you may feel differently. If there are any improvements to be made, do let me know...

 

 

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The end of January saw a supervision session for the second of three (planned) data chapters, which went pretty well. Even though I wrote 3-4,000w less than intended for both chapters, I've submitted both on time. It was very tempting to try and postpone the deadline and subsequent supervision by a week, but I am coming to believe that time is a more important variable in the writing-up process than word count. Three reasons for this:

1. It's important to get timely advice on draft chapters - no point postponing to write more words only to find that your supervisors think you've gone off track. It's much easier (both practically and psychologically) to add a few thousand words to an academically sound draft than rewriting the bulk of a longer draft that has run into the sand.
2. Sticking to deadlines lends you the air of competence - professionalism, even! Slipping deadlines are all too common in doctoral studies - why not surprise your supervisors by sticking to yours? Apart from avoiding the time-suck of rearranging supervision meetings,  your supervisors will be providing you with a reference (or maybe even interviewing you) for a job somewhere down the line. Proving to them you can carry off a big project on time can only be good news.
3. You are not only proving yourself to others, you are proving to you that you can stick to your timetable. The days leading up to the deadline are hard hard hard. For me, it doesn't seem to matter when I start writing, I always have '000s of words to get done in the last week. The temptation to postpone is immense. But my strong advice is to hand in on time, even if your submission is not quite what you were planning, for imposing some mastery over time during the writing up will give you some much needed self-confidence on the rock road to your doctorate.

Time is a pretty malleable concept within the PhD. Traditionally, students seem to take well over the idealised three year duration. The PhD is certainly a project that requires more work than any other in a student's academic career. Naturally, you want to get it right, but I strongly believe it needs to get done on time. Practically speaking, I know too many people who have gone into their fourth year and suddenly hit 'the wall' just when they thought they were on the home straight. Often, they've needed to take new jobs after the PhD funding has run out. Sometimes, the sheer length of time working on one topic has taken its toll, intellectual fatigue setting in.

But the big one for me is: how long do I really want to spend doing one thing? There's a lot of stuff in this world that needs fixing. Maybe my PhD will help with a very small part of that, maybe it won't. Whichever it is, I'm unconvinced that spending an extra 6 months, 12 months, 18 months writing and re-writing will improve it enough to make the extra time worthwhile. Even if you could *guarantee* that such an extension would improve your thesis, you may well gain in the long run by instilling some temporal self-discipline.

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I've written and delivered a two-hour session on Interpretive Policy Analysis (IPA) to both undergrad and postgrad students at University of Nottingham this semester. The first hour introduces the topic, looking at three aspects:

1. What difference does IPA make?
2. Why do we do IPA?
3. What are the problems of using IPA?

The lecture is based around the work of Yanow (1996; 2000) and Bevir and Rhodes (2006). The synopsis should be treated as lecture notes; they are not suitable for citation in essays.

While being highly recommended, if the books by Yanow and Bevir and Rhodes prove difficult to obtain, there are plenty of other excellent journal articles and conference papers, available online, in the bibliography below. Yanow (1992) is particularly good on the importance of metaphors in policy analysis.

Below the fold you can find the lecture synopsis, workshop materials and the bibliography.

1) What difference does IPA make?

The nineteenth and early twentieth century saw the application of scientific method to social science. When conducting research, this means collecting and analysing data in a manner which is repeatable in different circumstances, allowing the researcher to remain neutral and provide an objective view of a policy issue. This method enables research results to be comparable across cases and general principles or laws to be derived and applied to other cases.

The implication is that we can know the human world in the same way as we know the physical world, that we can understand the relationship between atomic particles in the same way as the relationship between actors in the policy process. In the physical world, if an apple falls from a tree, it does so in the same way in Nottingham or Nairobi. Can we predict how policy actors will behave in Nottingham and Nairobi, as determined by generalised principles or models in the policy literature?

Interpretative analysis recognises the differences between the physical and human (social) world as subjects of knowledge.

(adapted from Yanow, 2000, p.6)

Knowledge provides our own lens through which we see the world. We all have a different lens through which we make sense of the world around us; no one person’s set of circumstances is the same as another’s. The camera set-up in the ‘Making of Matrix’ video is a useful metaphor for this (the relevant passage is from 7m42s onwards):

Rather than the researcher looking through one ‘objective’ lens, they put themselves in the shoes of different communities within society to discover how they interpret a policy issue. The recognition of multiple voices may imply a more collaborative research approach and be less prescriptive about which views are ‘right’ or ‘wrong’.

Climatologists see climate change as a scientific issue, we know the world is very likely to warm up dangerously due to increased greenhous gas emissions from human activity. Republican candidates in the current US presidential election attach very different meanings to the issue. For them, scientists are fabricating results in order to gain research funding, and extreme green/socialist groups are using climate change as a Trojan horse for the introduction of higher taxes and greater state interviention into US public policy. We can come to conclusions about which views are the most legtimate, but we should not overlook that such different meanings and interpretations exist.

2) Why should we do IPA?

a) Epistemology

Actors interpret their situation through their own personal lens. Categorising people (e.g. by class, ethnic background, physical location) and then attempting to ‘read off’ their beliefs and behaviour from those categories is not a very fruitful way to analyse policy. It neglects the importance of local knowledge which can only be gained through much more in-depth research into people’s beliefs in order to find what meaning they place in a particular issue. This meaning may not be easily explained through ‘rational’ analysis. Yanow’s study of new Israeli community centres found that those living near the centres though they had been a great success, despite the policy having failed when measured against the initial aims of the policy. Yanow found that the community centres were an expression of cultural identity rather than....

b) Practise

Humans communicate meaning through story-telling, not through the abstract models and principles often found in the policy literature. The psychology literature demonstrates that humans have an innate dispensation to communicate through stories as we know it’s the most effective means of transferring information. Essentially, story structure allows us to mentally simulate situations in a way that abstract explanations do not. This has implications for both the methods we use to conduct research and the way results are presented.

3) What are the problems of using IPA?

a) Description, not explanation?

By moving away from producing generalised findings is public policy left only with (rich) descriptions of individual policy issues which leave the scholar saying “so what?!”

There are no comprehensive accounts of governance in the interpretive approach. Instead, researchers can look for Wittgenstein’s ‘family resemblances’ between policy issues or actors (Bevir and Rhodes, 2006, pp.167-8). Similarities can be identified between members of a family (e.g. eyes, nose, height) without using those similarities to explain behaviour. For example, a shift in governance from hierarchy to market to networks has been observed across public policy in recent years but does not manifest itself in a homogenous way across different policy areas.

The interpretive policy literature refers to traditions and dilemmas as broad categories within which we can analyse policy (Bevir and Rhodes, 2006; Bevir and Richards, 2009a). Actors draw on traditions of behaviour, manifested in their own store of knowledge, to make decisions when confronted by dilemmas.

b) The practise/advice paradox.

Although we know the reasons policy-makers communicate through stories in their everyday practise, using stories as the basis for policy analysis may prove unpalatable for practitioners. Roe (1994) collects stories from various stakeholders in a pollution problem and uses his own interpretation to come up with a ‘meta-narrative’ which serves to provide advice to policy makers about what to do. Roe’s funding to continue this means of analysis was unexpectedly stopped shortly after the first report was delivered. without being sure of the reasons for this, Roe reflects that the language of narratives and meta-narratives that he used in his analysis may have been offputting for policy analysts used to more traditional models and toolkits. Ironically, Roe’s advice was ultimately heeded and was successful in tackling the problem. So a proven method of approaching policy analysis may be difficult to carry out on the ground due to cultural resistance. Roe suggests getting round this by writing up results in different ways for different audiences.

WORKSHOP

In the second hour, students discuss the different meanings that stakeholder groups might put on the extension of Nottingham's tram system - something that has this year impacted directly on any students driving to campus through the Workplace Parking Levy.

There is an excellent BBC webpage which looks at people's tram stories from around the city and beyond, as well as experiences of people living in Perth, Australia which introduced a parking levy in 1999. These were used as a springboard for assessing the very different criteria by which stakeholders would assess the tram extension policy.

There are more resources and press stories about the Nottingham tram extension at https://pinboard.in/u:warrenpearce/t:interpretiveresources/

BIBLIOGRAPHY

Bevir, M. & Rhodes, R.A.W. (2006a). Interpretive approaches to British government and politics. British Politics, 29(1), 84-112.

Bevir, M. & Rhodes, R.A.W. (2003). Interpreting British Governance. London: Routledge.

Bevir, M. & Rhodes, R.A.W. (2006). Governance Stories. Abingdon: Routledge.

Bevir, M. & Rhodes, R.A.W. (2008). The differentiated polity as narrative. British Journal of Politics & International Relations, 10(4), 729-734.

Bevir, M. & Richards, D. (2009a). Decentring policy networks: a theoretical agenda. Public Administration, 87(1), 3-14.

Bevir, M. & Richards, D. (2009b). Decentring policy networks: lessons and prospects. Public Administration, 87(1), 132-141.

Davies, J.S. (2009). The limits of joined-up government: towards a political analysis. Public Administration, 87(1), 80-96.

Dodge, J., Ospina, S.M. & Foldy, E.G. (2005). Integrating rigor and relevance in public administration scholarship: the contribution of narrative inquiry. Public Administration Review, 65(3), 286-300.

Durose, C. (2007). Beyond ‘street-level’ bureaucrats: re-interpreting the role of front line public sector workers. Critical Policy Analysis, 1(2), 217-234.

Durose, C. (2009). Front-line workers and ‘local knowledge’:  neighbourhood stories in contemporary local governance. Public Administration, 87(1), 35-49.

Finlayson, A., Dowding, K. Hay, C. Bevir, M. & Rhodes, R.A.W. (2004) The interpretive approach in political science: a symposium. British Journal of Politics and International Relations, 6(1), 129-164.

Gains, F. (2009). Narratives and dilemmas of local bureaucratic elites: Whitehall at the coal face? Public Administration, 87(1), 50-64.

Gains, F. & Clarke, K. (2007). Constructing delivery: implementation as an interpreted process. Critical Policy Analysis, 1(2),133–8.

Gordon, R., Kornberger, M. & Clegg, S.R. (2009). Power, rationality and legitimacy in public organiszations. Public Administration, 87(1), 15-34.

Hodgett, S. & Deneulin, S. (2009). On the use of narratives for assessing development policy. Public Administration, 87(1), 65-79.

Marsh, D. (2008). Understanding British government: analysing competing models. British Journal of Politics & International Relations, 10(2), 251-268.

Morgan, G. (1993). Imaginization. London: Sage.

Needham, C. (2009). Policing with a smile: narratives of consumerism in New Labour’s criminal justice policy. Public Administration, 87(1), 97-116.

Ospina, S.M. & Dodge, J. (2005). It’s about time: catching method up to meaning—the usefulness of narrative inquiry in public administration research. Public Administration Review, 65(2), 143-157.

Ospina, S.M. & Dodge, J. (2005). Narrative inquiry and the search for connectedness: practitioners and academics developing public administration scholarship. Public Administration Review, 65(4), 409-423.

Rein, M. (1976). Social Science and Public Policy. Harmondsworth: Penguin.

Roe, E.M. (1994). Narrative Policy Analysis: Theory and Practice. Durham, NC : Duke University Press

Stivers, C. (2008). Governance’s new spectacles. Public Administration Review, 68(5), 941-3.

Vickers, G. (1995). The Art of Judgement, Centenary Edition. Thousand Oaks: Sage.

Yanow, D. (1992). Supermarkets and culture clash: the epistemological role of metaphors in administrative practice. The American Review of Public Administration, 22(2), 89-109. (download link)

Yanow, D. (1993). The communication of policy meanings: implementation as interpretation and text. Policy Sciences, 26(1), 41-61.

Yanow, D. (1996). How Does A Policy Mean? Interpreting Policy and Organizational Actions. Washington D.C.: Georgetown University Press

Yanow, D. (2000). Conducting Interpretive Policy Analysis. Thousand Oaks, California: Sag

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Well, last Friday marked a milestone in this PhD, I submitted the first proper draft chapter, weighing in at a touch under 10,000 words. The chapter was about the idea of 'embedding' policy, but I'm not reflecting on that in this post, rather the fairly painful writing process that got me there.

1. Getting started.
It's unsurprising that, even after dividing it up into 'bitesize' chunks, the point at which you're beginning writing up feels like the foothills of Everest. I'd read plenty of advice about how to get started on writing. The reality turned out to be rather like one of those marathon runners who starts so far back that it takes them an hour just to cross the start line.

'Free writing' of the type proposed by 750 Words and others has helped me in the past. But under significant time pressure the 'just write anything and sort it out later' advice didn't really work for me this time. Instead, on one occasion I found myself recreating the experience of watching Dr. Who 30 years ago, peering through my fingers at the screen hoping it would all go away.

2. Out of practice.
Perhaps the problem is that the 750 Words approach isn't a 'break glass in emergency' option. You need to put the hard yards in by getting in the daily writing habit. That way, you've already won half the battle. I had deluded myself that I'd be doing a lot of writing in my second year, but I really hadn't. I wrote a ~2000w Project Memo for my monthly supervision meetings, which mostly contained some vignettes from my fieldwork with the odd update on policy developments. While these were important to reflect on fieldwork, and were supported by my supervisors, they were in no way the same kind of writing that I did in my first year, when I wrote two literature reviews and a research methods paper. Of course, it's difficult to fit in bigger pieces of writing in between the knowns and unknowns of organising and carrying out fieldwork. However, I'm not sure it's the length of the writing that was the problem. What was missing was...

3. Making an argument.
Structuring the arc of an argument, bringing in relevant fieldwork and relating it to theory is not an easy business, and paralysed me at the start. In particular, I was unsure whether I had done sufficient data analysis to get going. I had to take a practical decision on that one, and commit to continuing the analysis as I continue over the next few months. I certainly knew enough to write the kernel of the first chapter and work outwards. But even this, which had been rattling round my brain for a few months proved remarkably difficult to get down on paper in even a structure, let alone a fully fledged piece.

STUNNING PIECES OF ADVICE
...are in short supply here. But here's a couple of tips you might want to consider when getting going on analysis:

1. Get in the writing habit EARLY.
Umpteen bloggers would have said this before me, but it bears repeating. If you can do a 750 Words every day, you're giving yourself some rather sturdier crampons when it comes to climbing Everest than writing a couple of thousand every month as I did.

2. Start with the data.
For qualitative research, this is kind of like free writing but for analysis. Don't be afraid to put in chunks of quotes from interviews etc in the body of your text. You'll probably find yourself with a 'brick wall' piece of writing in the early stages, where large bricks of quotes are separated by thin strips of analysis and linking text. As you review, you'll find yourself being able to put more of the quotes into your own words, and expand on the analysis as you make the links with the theory. But starting with the data - that's what your chapter is about after all - is a good way to jump start your writing, as well as the not inconsiderable benefit of shifting that pesky word count of zero.

LINKS
750 Words http://750words.com/
(picture comes from stats on one day's "free writing")

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image credit: Spiff_27

As I've mentioned before, ths blog has been in something of an hiatus while I've been doing fieldwork, as the project has mushroomed in size.

Fieldwork began with a focus group in June 2010, since when I've been conducting interviews and observing meetings with people from around the East Midlands (and beyond). The last piece of fieldwork will take place at the end of August, leaving 13 months to analyse and write before thesis submission.

During that time, I'm planning to reboot this blog and explore (in short form) some potential themes from the research.

What are the potential risks of doing this? Some have warned me about the possibility of academic material being...err...nicked before it's put into a paper or thesis.

If any readers have any experience, advice or articles they can provide me with, it would be much appreciated. Particuarly as I can expect my supervisors to be rather sceptical...

 

UPDATE - some Twitter feedback on the question to go with the comments here on the blog.

 

[View the story ""How can I blog on research themes?" " on Storify]

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Caught up with Oliver on New Walk, en route from the Climate Week event to an afternoon meeting. The whole thing was done on the hoof (literallY), so fairness dictates that I mention the other organisations at the event who weren't mentioned in the Audioboo: Mark Group, ThankQ, B&Q and De Montfort University Retail Lab.

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Just a note to say that the Thunderfly blog will be returning later in the year, we're both up to our eyes in other projects at the moment and unfortunately the full Thundefly has had to go into temporary hibernation. We're both still around on Twitter @warrenpearce and @triplewicky where we link to intereseting dataviz and presentation stuff (amongst other things) and you can check a ton of useful bookmarks on effective communication (also still being updated) at https://pinboard.in/u:WarrenPearce/t:effcomm (also available as a RSS feed).

Also, we've been checking over the stats and thought we would recap the Top 5 most read posts over the last few months:

1) Gesalt and The Guardian: the chart obscuring GMG's dwindling revenue

2) Starting from the beginning: using cognitive psychology to decide how you present information

3) Stephen Few and the dark arts of dataviz

4) Microsoft's worst ever infographic

5) Look for the hook: a well-chosen image makes your research memorable

To date, the blog has been far more successful than we imagined. Thanks you to all our readers and commenters (that's you!)

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Thunderfly's thirst for improving presentations is rooted in many hours spent trapped in front of energy-sapping, text-heavy Powerpoint decks. But while getting the slides right is important, and structuring your content is crucial, they're not the whole story. As an individual, you have to try to act as 'naturally' as possible, something that's not easy when you're nervous and struggling with technology to get the right slides onscreen.

A few weeks ago I was at a conference where the keynote speaker punctuated their talk with a distracting 'next slide!' to a hapless lackey offstage manning the laptop. And I've seen all too many academic presenters inhibiting their efforts over the years by remaining half hidden behind the lectern in order to intermittently tap the keyboard. Either way, these distractions act as a barrier between presenter and audience and make it all the more likely that the people who are supposed to be listening to your pearls of wisdom will more likely be engaging their brain on their next meal.

The problem here is that the presenter is having to concentrate on computers, not content. You'll likely be nervous standing up in front of an expectant (hopefully) crowd, don't amplify the chance of something going wrong by going all fingers and thumbs at the keyboard. If you have the chance to walk natually round the stage, you'll put yourself and your audience at ease and come across as more human, and competent, than someone chained to the lectern.

The little gadget that goes a long way to solving these problems is still the exception rather than the norm, so a plea to presenters everywhere - try and get your hands on a wireless presenter. There are loads on the market, and you can spend well in excess of 50 quid on a large box festooned with baffling buttons. We like our stuff faff-free on Thunderfly and when you're worrying about what to say next, you need something that's easy to use and hard to mess up. After three years of use, the wireless presenter from Kensington (affiliated link) definitely does the job in the field.

It has only four buttons: two to move back and forward between slides, one to black out the screen - very useful if you want to get your audience's full attention - and a laser pointer if you like that sort of thing (or are a cat owner). A little USB stick pops in the computer, turn your Powerpoint/Keynote presentation on. It just works does a simple job very well.

Crucially, it allows you to get out from behind the lectern and move around the stage and the remote is simple enough to operate by touch without having to continually check you're pressing the right button.

If you've got an iPhone/iPod and a Mac, you can do a similar job for much less money...in fact just 69p with the Keynote Remote (iTunes link, not affiliated) which will connect over wi-fi or bluetooth and allow you to remotely change slides as well as look at your speaker notes. Nifty, although potentially not as reliable as the infra-red goodness of the Kensington, particularly when working with unfamiliar technology. Although Apple gear is rapidly increasing in popularity, you're still far more likely to be presented with a Windows set-up when presenting at a conference etc. Of course you could take a Macbook along and plug it into the projector, but that's for another post...

Preparation remains the big deal when it comes to giving good presentations, but giving yourself the freedom to move around the stage and free yourself from the lectern will do wonders for your confidence and engagement with the audience. So beg, borrow or buy a wireless presenter - you might be surprised how much difference it makes.

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Over the past few months, Warren and I have shared some thoughts (and hopefully some useful tips) about presentations, and visualising data.  Today, I'd like to suggest that one of the most valuable tools you'll use in your work isn't something you learned on a course, and isn't an object provided by your employer (if you have one) - it's a camera.

Now, if you're a regular reader, you'll know that both Warren and I feel that improving the quality of your images - whether graphical figures, or illustrative pictures - can have a huge impact on the efficacy of your argument.  But while the internet has revolutionised our ability to access images, I think it's well worth considering pictures you can take yourself.  

We've all seen the stock images of well groomed people in suits, shaking hands in airy, light-filled offices.

But who wouldn't prefer to see the meeting they're actually in?

It's not unusual for me to dig out my camera when I want to use images to illustrate a concept in a presentation.  Here are a couple of examples.

Scenario 1

When working with researchers or analysts, they often find it tricky to step back from the detail of their work, in order for them to decide what the key messages are, and how best to present them.  In order to communicate this message, I decided to use the metaphor of a recipe.  If you wanted to create a tasty dish, it would be a mistake to use all of the ingredients at your disposal.

You need to trust your experience and expertise to pick out the really important stuff.

Thank God, a custard and kiwi-free tomato sauce.

Scenario 2

As Warren has described previously, there is something of a magical (and memorable quality about grouping items in threes.  Setting aside ancient rules of rhetoric, I wanted to use a mnemonic to help people remember that I'd make three points; remembering that there are three points is the first step to remembering what they were, after all.  As conceptual points, I lacked an obviously visual way of communicating them.  So I decided to use an image which focused on the 'three-ness' instead.

You might think that you need a big swanky camera to take pictures good enough to be used in a presentation or report, but in fact the majority of the images I've used are taken on a small digital point-and-click camera.  What's more important is getting reasonable lighting (which is why, in both of my examples, the pictures were taken on my kitchen worksurfaces, where I could add all sorts of lights).  I then used a free image editor (Microsoft Picture Editor, in my case) to crop the images and increase the saturation, or make other changes.  And if you really don't have time to conduct a mini photoshoot, don't forget the power of the cameraphone - if you're out and about, and spot a nice image, perhaps an entertaining contrast or even a 3D exploded pie chart in the wild, you can quickly snap it and put it into your next presentation; after all, when you're taking your own pictures, you can be certain there are no issues with permission to use them!

 

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Bikes are a longstanding love of mine.  When I last worked in an office that wasn't part of my own house, I became an enthusiast for cycling to work, even when it involved changing into office wear in the building's disabled loo, not being able to have a shower, and taking an elderly bike up to the fourth floor.  I started writing about biking to work, the obstacles and the incentives.  Now that my morning commute involves ambling upstairs from my kitchen with my fifth cup of tea, I have no need to cycle to work, so I find myself biking somewhere for lunch, or trying to do my grocery shopping by bike.  And after questioning the usefulness (and beauty) of infographics in data visualisation in last week's post, I thought it was only right to show the attractive image below.

Created by Nau from the data collected by Bikes Belong, this image is more of a poster than an infographic - in fact, the only section of this which I would describe as a true infographic is the 'bar chart' about three times as many new bikes as cars being sold annually.  For the rest, it is simply an attractively designed way of communicating data; a less thoughtful design could simply have involved typing these numbers onto a stock image of people on bikes.  So, given that this demonstrates all the worst criticisms of infographics (what's the point of it?), why am I showing it here in the hallowed halls of Thunderfly?

Well, I think it is important in this context to consider the audience for this image.  This is the kind of picture that could be printed out and posted up on a noticeboard at work; the bright colours and cartoon-y images draw attention and could start the discussion about transport between workplace and the home.  It acts as a tool for advocacy, showing that there is the possibility of reducing the reliance on cars and increasing bike travel, whilst asking us to consider the need for more facilities (secure bike parking, bike lanes, showers or just increased consideration from drivers) and how that might be achieved.  And it is inclusive, by showing cycling as a real alternative (particularly given the financial costs of running a car), rather than simply as a leisure activity, or as something only done by people in tight lycra.

So, this isn't really an infographic.  It's more than that.  It's an illustrated thought-provoker. So what do you think?

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A few weeks ago, the Guardian's datablog published an article on the backlash that seems to be gathering against infographics.  Although they describe it as "gathering steam", some of the sentiments they describe aren't new: it's just a pretty picture but it doesn't actually tell you anything; the design choices obscure the actual data; is this going to be the pie chart that looks like Pac Man again?  In fact, here's a critique of infographics which is nearly two years old.

The main point of the Guardian's article is that tools that have developed in recent years allow people with all sorts of expertise and specialisms to create infographics (rather than just those who know about data, or about design).  Sometimes, this means that the folks putting those infographics together don't follow some of the rules that you'll hear about often at Thunderfly; and sometimes an infographic is used in the same way as stock photography - simply a colourful illustration to break up the text, rather than communicating a message on its own terms.

Now, I know that infographics are just one branch of visualising data, and I'm not averse to a bit of colour.  But I found myself in sympathy with the critics when I encountered this infographic, created for the political social media aggregator, Yatterbox.

Here are the issues as I see it.  First of all, though, you'll need to take off those sunglasses.  No, I don't know why the designer has chosen a tartan background, unless he particularly wanted to bring out the way it clashes horribly with the Union Jack header.  So, first issue: the designer wants to pack in all sorts of information, using a number of different techniques, but the background image draws immediate attention to itself at the expense of communicating their insight.

The stacked bar chart of social media usage isn't too bad; the colours used are related to the service in question (turquoise for Twitter, dark blue for Facebook, poor old Flickr gets white) so the design draws upon our pre-existing brand knowledge as a design shortcut.  But adding white space between the bars makes it tricky to draw comparisons, and the rounded corners also change the shape, and therefore the accuracy, of the sections.  I'm not convinced that the orange bar for RSS feeds looks more than twice the size of the blue Facebook bar.

The bar chart on 'When they joined Twitter' has left me questioning the scale.  Is that a percentage of the people they follow? Raw numbers? I can see that early 2009 was a popular time to get a Twitter account, but how easy is it to compare the second half of 2009 with the first half of 2010?

I'm not sure how relevant - or indeed reliable - the infographics are that are generated from Klout data.  Listing the Twitter handles of the 'most influential' or 'most influenced' means that I don't know whether they're real people or just bots that retweet from elsewhere.  And finally, although the last few snippets are interesting, they're not really 'Trends to Watch'.

What do you think? Are infographics just treated like pictures? What are the positives of the example I've picked out? And does it matter whether infographics are accurate?

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There's a cracking (and lengthy) post by Christopher Fahey and Timothy Meaney building on their talk from this year's SXSW conference: Conversation is the New Attention. There's a lot of good stuff in there, including an inspiring story about a presenter using only his smartphone for speaking notes and wandering amongst the audience as he spoke. While it's worth a read just for that, I want to highlight something towards the end of the piece, which draws on John Medina's fine book Brain Rules.

Fahey and Meaney talk about the problems of keeping your audience's attention in a presentation - at a tech-y conference, people may start to fiddle more with their phones, in a university tutorial a student may simply fall asleep (happened to me last week). In an attempt to avoid this fate, the authors talk about Medina's four characteristics of 'attention':

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I have a longstanding penchant for taking data visualisation into the real world, whether taking pictures on my holidays of 3D pie charts in the wild, or being enchanted by handmade visualisations with bar charts in the flower beds.  So I was delighted to see that there's a data visualisation side to the #Occupy protests. Occupy George is a project to use the ubiquitous dollar bill as a way of circulating infographics about the distribution of wealth within the United States.  Templates are provided to enable participants to print the infographics (onto their own money!) and enter them into circulation, though I'd hazard a guess that they might not last long, either being withdrawn as defaced currency, or kept as a souvenir by somebody who ends up with one in their pocket.

The graphics are simple, clear and get their message across perfectly.  Top marks also for accompanying each of the infographics with a link to the original source of the data, whether that's a conclusions from a think tank report, analysis of public data, or even a 'fact check' piece on a Michael Moore speech.  It strikes me as an elegant way to protest about a matter dear to all our hearts - money.

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I've just got back from spending four weeks in North America, taking in California, Oregon and spending some time in my second home of Vancouver, British Columbia.  Driving through California provides a chance to consider how much the culture of the west has influenced perceptions of America (especially via Hollywood).  I also had plenty of time to think about quite how big California is, and as a consequence how big (and diverse) the USA is.  Of course, as soon as I got home, I spotted this amazing map, which led me to similar conclusions from the comfort of my desk.

Using a dataset from the US Geographical Service, the National Hydrography Dataset, the author has colour coded the frequency of different terminology for waterways across the US.  'River' and 'creek' are ubiquitous, and so are symbolised in grey.  The colours clearly show the patterns of settlement and expansion throughout history.  As Watkins himself comments,

Lime green bayous follow historical French settlement patterns along the Gulf Coast and up Louisiana streams. The distribution of the Dutch-derived term kill (dark blue) in New York echoes the colonial settlement of “New Netherland”... Similarly, the spanish-derived terms rio, arroyo, and cañada (orange hues) trace the early advances of conquistadors into present-day northern New Mexico. Washes in the southwest reflect the intermittent rainfall of the region, while streams named swamps (desaturated green) along the Atlantic seaboard highlight where the coastal plain meets the Appalachian Piedmont at the fall line.

The area I visited, the west coast, shows its Spanish influences in Southern California through the appearance of 'rio' and 'arroyo', with 'slough' and 'fork' appearing further north as I travelled into Northern California (wetter, forested) and on to Oregon (the start of the Pacific Northwest, home of the rainforest, i.e. wettest).  It is fascinating to see the patterns emerge in other parts of the country, and reminds me that the history of this country has been action-packed during the short time since Europeans first arrived on its shores.

A similar map has been pulled together for the UK, by James Cheshire at Spatial Analysis.  

The debate is already on as to terms that Cheshire might have included, issues of rerouting of water courses and whether local waterways are even named in the original dataset.  Why not check it out and join the debate?

 

 

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Last week, Warren wrote about Apple's underwhelming visualisation of the carbon footprint of the iPhone 4S.  Of course, the launch of the product was almost immediately overshadowed by the untimely death of one of Apple's founders, Steve Jobs.  Here at Thunderfly, we have often held up Steve as an example of an excellent presentational approach, and so it seems fitting to mark his passing by pointing out a tribute based on data visualisation, put together by Twitter - the place where I, and many others, found out the news.  In the days after Jobs' death, as many as 5 of the top 10 trending hashtags referred to him, so Twitter collated the following image, made up of tweets under the "#thankyousteve" hashtag.  Although not without its faults, this image is a moving demonstration of the impact that Jobs has had on the modern world.

 

 

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The new iPhone was unveiled this week with the usual frenzied discussion about its merits, or lack thereof. Omitted from the media coverage was any conisderation of the phone's environmental impact. Unsurprising perhaps, when there is the sexier fare of talking assistants and errr, debating the model number, but correspondants can't be excused by a lack of data to go on.

Following a run-in with Greenpeace a few years ago, Apple now publish an environmental report for every new product they launch. The reports are not particularly detailed, but they do include estimated greenhouse gas (GHG) emissions for a product over its entire life cycle (i.e. including those caused by using the phone).

Here is the data published for the new iPhone 4S *pie chart klaxon*:

Pie charts are, at best, overused as a data visualisation technique (at least this one isn't 3D). This  does quickly convey some useful information: while production unsurprisingly accounts for the biggest chunk of emissions, customer use makes up almost a quarter of the total. The presence of recycling is also of note, a handy reminder that breaking down and resuing electronics is not an energy-free, or even safe, activity.

So we have a mildly interesting breakdown of the emissions sources alongside a total figure of 70kg of GHGs. Overall, I go away with a relatively good feeling about the product's environmental credibility, mainly as the pie chart seems to be made of wood.

What is lacking from this display is a comparison over time, in particular we have no context for the total GHG figure which is the data central to any environmental analysis. Apple have already published comparable data for the previous two iPhone models, it would have been very easy to include them all in one place. It only took me about three minutes to do the bar chart below, which is very revealing. Indeed, if one were cynical maybe it would be a bit too revealing for Apple to publish themselves:

This chart doesn't tell us what a kg of GHG really means (one of the climate change agenda's difficulties is the intangibility of its core principle) but we do know that the figures should be decreasing over time. 

Compiling the data from three pie charts in separate reports into one bar chart shows that across most parts of the lifecycle, iPhone emissions are on the rise, with a 27% jump between the iPhone 4 and 4S.

Not good.

What's more the seemingly relentless rise in production emissions was only offset on the iPhone 4 by a huge drop, of almost 50%, in those estimated from customer use. Sadly, there isn't much detail published in the methodology for these figures. I have no reason to doubt them, other than to point out that such a large change is... well... surprising.

So what has this simple exercise told us?

1. Pie charts can convey a very limited amount of information and, like other data visualisations, are easily sullied by daft colour schemes. Bar charts almost always trump pie charts.
2. Context is key: by adding in comparison over time we get a much better understanding of the numbers, even for pretty abstract stuff like GHG emissions.
3. Apple's environmental reporting might be a (small) step in the right direction, but poor visualisation and manipulation of the data is obscuring an increasingly disturbing trend in their environmental record.

[Thanks to Darragh Browne from Carbon Calculated, whose initial spot prompted this post.]

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We are shameless magpies here at Thunderfly, and so I am never happier than when passing on a tool or tip that I think people might find useful.  And so it is that I'd like to highlight a tool which Warren and I have mentioned during our courses, but not yet on the blog - the Junk Charts Trifecta.

First things first, time to give credit where it is due.  Junk Charts is a reference to Edward Tufte's concept of 'chartjunk', coined in the classic (and much recommended by us) Visual Display of Quantitative Information.  As Wikipedia tells us, Tufte says:

The interior decoration of graphics generates a lot of ink that does not tell the viewer anything new. The purpose of decoration varies — to make the graphic appear more scientific and precise, to enliven the display, to give the designer an opportunity to exercise artistic skills. Regardless of its cause, it is all non-data-ink or redundant data-ink, and it is often chartjunk.

The blogger behind Junk Charts, Kaiser Fung, has said he aims to recycle the chart junk he encounters into junk art. The blog showcases the occasions on which heinous crimes against data visualisation are brought to his attention, and he does his best to extract the meaning (like sunlight from a cucumber, as Farquarson would have it) and display it in a more meaningful way.  

Now, we've covered checklists (including one from the estimable Stephen Few, which Warren put to good use) before, but Fung has developed this triangular checklist, which he calls the Trifecta, and which might conceivably be easier on the eye if you print it and put it up by your desk.  Successful data visualisations have all three elements in harmony, and Fung checks both the original visualisations, and the ones he develops to replace them, to ensure they meet the criteria.

You can address the three elements in any order, but perhaps it makes sense to start at the top and ask what the question being asked is - what question is the data answering?  This was something mentioned by John Kay in his series of superhero recommendations.  You could be overstepping the mark if you use data to assert something that it doesn't actually show.

The second point of the triangle asks what the data says.  This is the section where your expertise or area of interest guides you to pick out the important information that you wish to communicate.  It is hard to offer concrete guidance in this area; this is where we remind you that you are the expert, and to have the confidence in the decisions you make.

Finally, the trifecta asks you to check whether the chart or figure you've designed communicates both of those aspects.  If all of these three elements are working for you, you're onto a winner.

 

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Having decided to treat blogging as a weekly habit, rather than an occasional dalliance, I've started taking a bit more notice of the page view stats available on the 'back end' of this blog, which is hosted by a company called Posterous. You can also see the often measly, occasionally huge, page views on the left of this very page.

Through this sporadic glancing at the numbers, a couple of things jumped out, so I thought I'd throw it into a Thunderfly five-minute special barchart to illustrate the issues.

Voila:

Clearly, there is an enormo-spike (technical term) in pageviews on the 4th August post, compared to those directly before and after. A strange looking anomaly on the face of it, but explained by some heavyweight web-celeb support; both Guido Fawkes and Ben Goldacre linking to my dissection of the data presentation in the Guardian's annual report.

So far, so good. Less easy to explain is the trend in the first half of the chart, prior to 27th April. Here, we have a downwards trend, but at a much higher level than subsequent posts. Indeed, if the Guardian post had been in this sequence, its pageviews wouldn't have looked out of place at all.

What's odd is that while the content of these posts is - of course - fascinating, my guess is that they have limited appeal. They certainly had limited publicity; many of the pre-April posts are links to training resources not circulated beyond a few attendees and a solitary tweet.

So was there really a drop of 80-90% in pageviews after March? As mentioned previously on Thunderfly, if you find a surprising result, the likelihood is the data is wrong. While I can explain the Guardian spike, there's no explanation through content or linkage for the inflated figures in the first half of the post. Ever the sceptic - my working assumption is that the pre-March data is in some way, incorrect. Unless someone at Posterous can tell us otherwise (there's no mention of any changes on their official blog).

The irony of this is that I have been receiving Google Analytics reports for this blog for a long time now but rarely looked at them. Why? Because I was already seeing pageview data every time I logged into Posterous. As my lizard brain followed the Principle of Least Effort, I was happy to stick with those figures, rather than open a weekly email attachment from Google, even though if I'd thought about it, I probably would have guessed the latter provide more accurate figures. *If* a change did happen in the way pageviews were calculated, I'm surprised no announcement was made to that effect, although other users have noticed the discrepancy.

A 'live' chart simlar to the one above might focus bloggers on their audiences more, enabling them to *easily* compare the relative success of their posts without delving into the Google Analytics.

This is also a cautionary tale of not taking the data at face value. Only when the methodology (apparently) changed, did it become clear that something might have been wrong with the previous data. Cross-checking with a different source - in this case, Google Analytics - would have highlighted this much earlier and provided me with a more realistic picture of this blog's audience.

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A much belated post from me this week - apologies.  I'm blaming interference on the line.  That aside, I had a chance to reflect this week on the more artistic end of the visualisation of data, as I was able to get up close and personal with an artwork created by Chris Jordan.

Warren has introduced Chris's work previously on this blog. Chris himself says:

Sociologists tell us that the human mind cannot meaningfully grasp numbers higher than a few thousand; yet every day we read of mass phenomena characterized by numbers in the millions, billions, even trillions.

Chris's work therefore aims to enable each of us, as individuals, to reconnect with the numbers beyond the "anaesthetising" language of statistics, to find meaning and to act on what we find within his art.  Although his approach certainly isn't one I'd advocate to everyone, it is interesting that his aim is ultimately the same as everyone who presents data of any kind - to communicate, and to spur action.

The piece of work I was able to see is called "Shark Teeth", and here it is as you round a corner in the Monterey Aquarium, having just encountered the juvenile Great White shark in their tank.

Based on a watercolour painting by Sarah Waller, the painting is made up of a depiction of fossilised shark teeth, equal in number to the number of sharks killed *each day* for their fins.  If I tell you that the picture is 64"x94", and show you what it looks like up close, you can begin to understand Chris's mission to communicate the message as well as the numbers.

Chris has depicted 270,000 teeth in the picture, which is mind-boggling both for the work that it has entailed for him, and for the number of sharks killed - and that surely is what he's after.

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Advice, evidence, everyone's got some, including us (that's presumably why you're here, after all). Nicola shared some good advice from John Kay on Monday. All very useful but it did get me thinking - sometimes we can't always take advice, even if we want to. The problem with evidence-based advice is that it has to overcome the barriers presented by tradition and long-established practice.

Kay's words on the passive voice struck a chord with me, having experienced its vice-like grip on academic writing. Even in the social sciences, which supposedly pay more philosophical attention to the problems of the dispassionate author, the passive voice remains the norm. When I first wrote a research paper directly relating *my* opinions and experiences - "I think that...I conclude etc" - my (otherwise excellent) supervisor got to work with the biro:

Use the passive voice when writing academically

I'm now clear in my own mind that the faux-science of the passive voice obscures the part the author plays in interpreting the data, and with it the possibility for alternative explanations. However, at the time of writing my paper I wasn't strong enough to argue the case, so beat a temporary retreat from my supervisor's ire. I resolved to get a better understanding of the literature in order to be better placed to argue my case when the issue arose again.

Whether adopting the first person own voice, purging pie charts or binning bullet points, the lesson is worth remembering: change is hard enough for ourselves, persuading someone else to do so is even harder.

Particularly when it's the boss.

They will almost certainly resist change in the short term, even after you've laid out the evidence. Highly embedded presentation practices are not revolutionised overnight - it's a long haul. Tradition holds sway over evidence.

So if your attempts to overturn the established order are rebuffed, don't be (too) disappointed. It's normal. What's more, it will force you to sharpen your arguments for your next try. It may take a long campaign for you to get your way. But if you do it you will have made a worthwhile change to the way you or your organisation communicate.

Unfortunately, I've no magic bullets to provide in this endeavour other than persistence and hard work.

But maybe you have some tips on how new advice and evidence can overcome tradition?

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Much of the advice that Warren and I offer seems faintly reminiscent of the Green Cross Code.  For anyone who didn't grow up in Britain during the 1980s, Dave Prowse (later to don the mask and cape of Darth Vader) towers over small children in a white and green jumpsuit, urging them to, "Stop! Look! Listen!"

Perhaps we need to introduce a memorable character to ask people to "Stop! Close down PowerPoint! Go analogue!"?  If so, I'd like to propose John Kay as my superhero of choice.  Recently asked by Business Secretary Vince Cable to conduct a review of the performance of UK equity markets, Kay has had a long and distinguished career as an academic economist.  In recent times, his written work has aimed to make economics both accessible and relevant to the wider public, and just a few weeks ago he offered some valuable tips for dealing with data that I'd like to share.  The man writes a mean title (that's his that I've shamelessly used), and you can read the full article (which I would highly recommend) here.

Recommendation number 1: Always ask yourself where the data has come from.  Underpinning the headline figures might be assumptions, bias or a questionable methodology.  Kay cautions that using a passive voice (such as "it is expected that...") reduces the personal responsibility of anyone making this assertion.  It sounds lovely and objective, doesn't it?

Recommendation number 2: Ask yourself "What is the question to which this data is the answer?".  If you are a number wrangler yourself, this may be something that you need to communicate to others: they would like to use your data to assert one thing is the case, but your data actually answers a completely different question.  

Recommendation number 3:  Ensure definitions are clear.  This is particularly important if your data or information is going to be communicated to the public, who might not be familiar with technical definitions of what your numbers actually mean.  He also counsels: "probe descriptions that define what a statistic is not, rather than what it is", giving the example which so often accompanies reports on the performance of investments: 'these statements are not forecasts, and should not be relied upon'.  What are they, if not forecasts, and why should they not be relied upon?

Recommendation number 4: Be careful of references to things you are expected not to have read.  Statistics are often compiled 'in accordance with guidance provided by...' but that is not a helpful footnote if you don't know what that means (or whether it is meaningful at all).  A good example of this is sorting out which statistics fulfil the criteria of a "National Statistic" and which don't.  The UK Statistics Authority has a handy guide to what the various levels of official statistics mean, as well as showing the quality mark which help identify what's what.

Recommendation number 5: if the data seems counterintuitive, bear in mind that there might be a problem with the data, rather than the finding itself being surprising.  As Kay says:

Statistics are only as valid as the sources from which they are drawn and the abilities of those who use them. When I discover something surprising in data, the most common explanation is that I made a mistake.

 

 

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The lizard brain likes the familiar, and will tend to our 'default setting' whenever possible. Google is great for finding information, but relying on it for our image needs can lead to some pretty ppor results which wil reflect badly on you and your research. So why do we need to unGoogle when searching for snaps?

1. Quality. Search engines will scour the internet for any images they can find which may be related to your enquiry. These will generally have been optimised to fit onto a web page. That is, they will be low resolution 'thumbnail' images specifically designed to be viewed on a small monitor.

When you blow that image up to fit on a Powerpoint slide, you'll find yourself with something blocky and distinctly amateurish which will detract from the information you're presenting.

2. Copyright. Google Image searches will list *any* images it can find. As such, they are likely to contain a lot of copyrighted material which you have no right to lift and use in your presentation. It only takes one email of your slides to an interested audience member and they could easily end up in the public domain, with or without your knowledge. If those slides contain copyrighted images, you could find yourself in hot water.

Even if you vow never to email your slides to anyone, there's no sense in getting in bad habits you'll just have to undo later. Learning how to use images properly gives you the skill you'll need for a publication or big conference presentation.

3. Ditch Google, get Creative. Images under Creative Commons (CC) licence are your friends. There are a variety of licences under the CC umbrella, but as long as you credit the image creator (generally, the photographer) and aren't planning to do any major re-editing of the original, you'll be OK to re-use any images with a CC licence.

Where to find them? Flickr has a huge range of images, many of which are Creative Commons. A good way to search them is through the Compfight search engine. Just click on 'creative commons' in the sidebar, enter your search term and away you go. Clicking an image takes you to its Flickr page:

Check the image licence (1, above) - it's summarised on the page but you can get more details by clicking through. This one says you are free to copy and transmit the work as long as you attribute the creator of the image, don't use it for commercial benefit and agree not to alter it.

Then move up to 'actions' and click on 'view all sizes' (2):

Here's where you get your hands on the image. You need a pretty big one if you're using for a full Powerpoint slide. In this case, choose original (3) as it's the biggest on offer - remember you want it to look good on the 'big screen'. Go large!

Then just download (4) and paste it into your Powerpoint with some text crediting the image. For this image I would suggest: Flickr user 'mobile disco': "I Love Peckham post-its" (there's no real name given for this user, if there is one, use that instead).

Ta-da! You have a high quality, correctly licensed image from the very creative Flickr community, rather than the lo-res, copyrighted fare of Google Images.

Further reading:

11 ways to use your image poorly - classic Presentation Zen post on the best way to use your image in a slide (hint: *fill* the screen).

Easy-peasy guide to crediting images - great flowchart to help you check you're doing it right.

Where can you find good images? - more Presentation Zen goodness, list of alternative sites for good, roylaty-free images.

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Last week, I talked about the lizard brain, and the way it tempts you to follow the line of least resistance whenever you encounter a challenge, or try to do something new.  This week, I’d like to suggest one way that you can break out of your normal routine, by reverting to what Warren and I call one of the ‘tools of the trade’ – pen and paper.

It is easy to start a project – presentation, report or figure – by switching on the computer and opening up a familiar piece of software.  The first thing that many people do when preparing a presentation is open up Powerpoint, and click onto those inviting text boxes: title here; bullet points go here.  Copy and paste the slides from the last presentation you gave, tweaking the date and venue on the title page.

We've all been there. Picture from alice_c, flickr creative commons. 

 But when you fall into this pattern, the medium takes over, and it becomes more important than the message you’re trying to get across.  When you find yourself going onto autopilot, it’s a great chance to stop, reflect, and go analogue – get out paper and pens.

Paper allows you the chance to really explore what it is you’re trying to achieve, before you get bogged down in the technicalities of making that happen.  We often ask people on our courses to have a go at drawing a figure which shows data that we’ve given them, with only some coloured pens and A1 paper.  They’re often uncomfortable at sacrificing accuracy, but going analogue provides them with a chance to think through the decision-making process of which data to focus on, and how to present it most effectively.  Those are the key concerns, rather than worrying about how to get the chart wizard to show what you want.

Getting out a pen and paper also stimulates bits of your brain that might otherwise be neglected; research suggests that writing by hand has a powerful impact on the parts of your brain involved with cognitive processing and working memory, and using techniques such as drawing pictures or mindmapping can help you see new connections, highlight priorities and can also suggest new ways of communicating your message. 

Once you start adding in coloured pens and post-it notes, you can really embrace the flexibility and creativity of going analogue.  And we practice what we preach!  Planning for our most recent course (contact us if you’d like to know more, or book us to help your organisation) looked like this:

 Post-it notes naturally bring out everyone’s playful side.  Bring the joy of the analogue into your work!

From the brilliant postitwar.com.

 

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Last week I delivered some closing remarks to the (very successful!) ENQUIRE postgraduate conference. Various PhD deadlines, attending conference sessions and err (whisper it) my lizard brain meant that the task of writing the remarks began at 2pm. I was speaking at 4pm, so this definitely falls into the "sub-optimal" class of presentation preparation. I'd picked up a couple of ideas during the conference and was helped out enormously by a friendly tweet, but how to put it all together - intelligibly - in a very short space of time?

A device used by my academic supervisor popped into my head. When asking a class to read a chapter, he always asks them to come back next week with three key points. As well as focusing in on key aspects of the text, it provides a ready-made structure to note taking.

Why three? Well, instinctively it seems a neat number of things to look at. Whether that's because of its mathematical qualities or its prevalence in literature. Jokes often often rest on a ternary rhythm. Essentially, it just feels neater than two or four (five, I would argue, is too many for a presentation).

So I went with three sources for the inspiration mentioned in the friendly tweet:

1. academic peers
2. academic 'heroes' (our plenary speakers)
3. the ENQUIRE journal

This structure makes it much easier for your audience to follow you, as you can simply explain at the beginning what you're going to cover. It also makes it much easier for you, as the speaker, to drop in the vital signposts which help keep your audience engaged. So before talking about academic peers, say something like "the first source of inspiration, our academic peers" then after that section "so that was the first source of inspiration, our academic peers, now for the second: our academic heroes". This seems fairly banal when written on the page, but it makes a huge difference in keeping an audience with you.

After these three points, I attempted to draw it all together, turning the idea of sources of inspiration on its head and suggested something that the audience could be inspired to go and do by all the great stuff they'd seen in the last two days. My suggestion was to go and start a postgraduate journal and/or conference in their own university, as they still seem to be the exception rather than the norm amongst PhD students. Then there was just time to thank the speakers, organisers and finally the attendees, suggesting they finish by giving each other and themselves a big round of applause. Always a safe note to end on...

This is by no means a 'set in stone' template for how to do a presentation - just an example of how we can reach for familiar patterns to structure our talks, especially when prepaation time is tight. While dividing your presentation up into three parts is a handy way of signposting to help your audience, it doesn't follow that they will remember all three of those things once you've finished talking. You might have to set your sights even lower if you want your ideas to stick. But that's for another post...

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