Gamer data: Fun preferences

After last week’s hair-pulling day of frustration, I’ve made I bit more progress. The survey contained seventeen questions which were based on the theory of four types of fun, set out by Nicole Lazzaro. These were 101 point  Likert scales, wherein the participant indicated their agreement with a statement, using a slider with no scale and the slider “handle” position set randomly, to reduce systematic bias. Of course, these being Likert disagree/agree scales, I was still expecting clumping at one end or the other despite my attempts to reduce that by making them 101 point scales. And so it proved, in many cases, as these histograms of the four questions I used as indicators of a preference for “Serious Fun” show.

Histx4SeriousFunIndicators

I never intended to do any correlations with the responses to the individual questions though. Instead my plan was to average out each individual’s responses to the indicator questions to create something more like a continuous variable which I could correlate with other responses. Doing that for the responses to the Serious Fun indicator questions, for example, turns the four clumpy histograms above into something a lot more like a “normal” curve.

histSeriousFun

 

And the distributions of all four “Fun preferences” look like this (as curve plots this time, in case you were getting bored of histograms):

plotx4FunProfiles

You’ll note straight away, that the “Hard Fun” curve is the one that most resembles a “normal” bell curve. Easy Fun has a distinct negative skew, and in fact all three others have a slight negative skew. And there’s a distinct preference apparent in this sample for Hard and Easy Fun over Serious and People Fun. In fact, the most popular preference in this sample is for Easy Fun where the mean stands at 70.8 and the median (in this most skewed of the four distributions) at 73.7. The mean of the Hard Fun distribution is 66.61, in third place is Serious Fun with a mean of 54.06 and trailing behind is People Fun with a mean of 42.22.

I was a bit surprised that People Fun scores so poorly in this sample, but I guess I shouldn’t be because one of the questions I used to indicate a preference for People Fun was “I don’t actually like playing games all that much” which I don’t suppose is going to find much agreement among gamers after all.

Which begs the question “would People Fun preference correlate negatively with the Ludic Interest vector I created last week?” But rather than look at that relationship on its own, lets see how all the derived variables I’ve created relate to each other.

plotCorrFunPrefsAndLudic

So, people fun correlates a bit with Serious Fun, but little else. Ludic Interest correlates less well with Hard Fun than I might have expected. Though the Ludic Interest variable was admittedly an afterthought and the selection of games from which it was derived by no means scientific. I might rethink that whole  section next time. The Serious Fun vector correlates with other variable more than I expected, and the little scatter plots look interesting, so next time I’ll investigate some of these relationships more deeply.


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I came to cultural heritage via five years working at Midland Bank when I left school. In my free time, I volunteered as a costumed interpreter at Kentwell Hall and, with re-enactment societies, at various medieval sites around the UK and France. When, one evening, a few of us said “we could make a business out of this” I left my job at the bank to go to college, first to get an Art Foundation and then to Manchester Polytechnic to join an innovative course called Design for Communications Media. I specialised in Educational Media Design, with the intention of applying what I was learning to cultural heritage. During my vacations and upon graduation I worked for the nascent company my friends had started, Past Pleasures, creating immersive living history festivals at Lancaster and Tunbridge Wells, as well as projects including: an exhibition for the centenary of the Commonwealth Institute; a design for a metafictional Sherlock Holmes exhibition in Croydon; and, a game that combined real-time investment advice from 300 year-old characters at the Bank of England Museum with a digital simulation, tracking the players’ investment portfolio from the founding of the bank to its tercentenary. In 1996 I helped found JMD&Co, and for two years I also lectured on Heritage Tourism and Visitor Management and Interpretation modules for a Portsmouth University validated HND/degree course at Farnborough Technical College. Subsequently, I enrolled in the new Distance Learning delivered Masters’ degree in Museum Studies at Leicester University, where I became interested in the social use of space, particularly Bill Hillier’s “space syntax,” and the increasing futility of cultural heritage sites trying to tell doggedly linear stories in three-dimensional spaces. Although my dissertation explored models for mapping interpretation, and particularly learning styles, onto spaces, a satisfactory reconciliation of linear story and three-dimensional space eluded me. After graduation, I decided my time in the “small business” end of cultural heritage was over for a while, and I left JMD&Co to join a cultural institution, the National Trust, as a Regional Community, Learning and Volunteering Manager. I brought the first National Trust iPad into use at Batemans, where, combined with a wax cylinder record player, and the help of renowned folk singer, Jon Boden, we’ve returned Rudyard Kipling’s voice back into his old home. However, one of the innovations which I am most proud of is the National Trust’s virtual tours. Working with a small company, and a range of disabled stakeholders, we created a touch-screen based human computer interface that could also, if required, be controlled with other input devices, and allowed visitors with a variety of disabilities to fully enjoy the virtual tour. The teams’ achievement was recognised with a Jodi Award for Excellence in accessible digital media in 2008.

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