More maths

plotCorrFunPrefsAndLudic

Last time I finished with this matrix of scatter-plots, ordered by the magnitude of correlation. But what does it actually mean? Lets take a step back, and look at those derived variables. I ask R to describe the table of variables that I created previously, which include the notional ludic.interest variable and the Hard, Serious, Easy and People fun preference variables. These are handily additional columns created by R on the end of the table of original data, so I ask R to describe just those columns:

> describe(newdata[90:94])

This gives me a little table describing the variables. It’s where the mean values I quoted last week came from. Looking at it again this week its interesting to note the ranges of some of the scores, but the first thing I notice is that the Standard Deviation (SD) of the ludic.interest variable is noticeably lower than the fun preference variables. Those range between 15.31 for the Hard fun variable, and 16.75 for the Serious fun variable. While the ludic.interest variable is 11 (actually 0.11, but remember that the other fun variables are between 0-100 and ludic.interest between 0-1). The range of score for ludic.interest  is tighter too:

VARIABLE RANGE
ludic.interest 51
H 66
E 76
S 89
P 76

The Serious fun preference questions thus showed the most division among gamers. What’s particularly interesting is that the lowest score in that range is zero, so at least one respondent vehemently disagreed with all the statements associated with that preference. The same is true of the People fun variable.

That matrix at the top of the post suggests that despite (or because of?) the wide range of the Serious fun variable, its one that shows some correlation with all the other variables. Stronger correlation, in fact, than the People fun variable, which correlates poorly with the all the variables except Serious fun.

Lets look at that in more detail. The Serious fun variable correlates most with the Easy fun variable,  the value of the correlation coefficient  (r) = 0.52, plot the two variables with a regression line and it looks like this:
plotSeriousEasy

Not a bad, shall we say “moderate” relationship. For every point up the Easy fun preference scale somebody scores, they are likely to score 0.54 higher on the Serious fun scale. With a standard error of 0.09 the T value for this relationship is 5.9, and the corresponding p value is very low at 0.00000006. So this appears to be a statistically valid relationship.

(You can see that respondent who disagreed with all the Serious fun statements in the bottom left, they weren’t that keen on Easy fun either, but at least scored 22 for that. Looking at the table of data I find its the same respondent who also disagreed with all the People fun preferences, and scored 43.5 for hard fun, and 33 (0.33) for ludic.interest.)

Lets compare how that looks with the plot for the relationship between preferences for Hard fun and People fun, where the correlation coefficient is just 0.02:

plotHardPeople

Hardly any relationship at all then.

 


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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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