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.

 

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.

Proximity!

20140501-090244.jpg

My Gimbal beacons arrived yesterday. These are three tiny Bluetooth LE devices, not much bigger than the watch battery that powers them. They do very little more than send out a little radio signal that says “I’m me!” twice a second.

There are three very different ways of using them that I can immediately think of:

I’ve just tried leaving one in in each of three different rooms, then walking around the house with the the simple Gimbal manager app on my iPhone. It seems their range is about three meters, and the walls of my house cause some obstruction So with careful placing, they could tell my phone very simply which room it is in. And it could then serve me media like a simple audio tour.

Alternatively, as they are designed like key-fobs, they could be carried around by the user, and interpretive devices in a heritage space could identify that each user as they approach, and serve tailor media to that user. Straight away I’m thinking that a user might for example be assigned a character visiting, say, a house party at Polesden Lacey, the the house could react to the user as though they were they character. Or perhaps the user could identify their particular interests when they start their visit. If they said for example, “I’m particularly interested in art” then they could walk around their a house like Polesden Lacey, and when they pick up a tablet kiosk in one of the rooms, it would serve them details of the art first. Such an application wouldn’t hide the non-art content of course, it would just make it a lower priority so that the art appears at the top of the page. Or more cleverly, the devices around the space could communicate with each other, sharing details of the user’s movements and adapting their offer according to presumed interest. So for example, device a might send a signal saying “User 1x413d just spent a long time standing close to me, so we might presume they are interested in my Chinese porcelain.” Device b might then think to itself (forgive my anthropomorphism) “I shall make the story of the owner’s travels to China the headline of what I serve User 1x413d.”

But the third option and the one I want to experiment with, is this. I distributed my three Gimbals around the perimeter of a single room. Then when I stood by different objects of interest in my room, read of the signal strength I was getting from each beacon. It looks like I should be able to triangulate the signal strengths to map the location of my device within the room to within about a metre, which I think is good enough to identify which object of interest I’m looking at.

What I want to do is create a “simple” proof of concept program that uses the proximity of the three beacons to serve me two narratives, one about the objects I might be looking at, and a second more linear narrative which manages to adapt to the objects I’m by, and which I’ve seen.

I’ve got the tech, now “all” I need to do is learn to code!

Unless anybody wants to help me…?

Another look at my gamer data

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I’m still wrestling with R and wishing I was a natural (or maybe just a more experienced) coder. Everything takes so long to work out and to actually do. Last time I shared the results, I was just looking at the top-line data that iSurvey shares. This time I’ve downloaded the data and sucked it into R, the command line based stats language.

I start off looking at the basics. What is the size of my DataFrame (as it’s called in R)?

> dim(ghb)
[1] 193 89
> nrow(ghb)
[1] 193
> ncol(ghb)
[1] 89

There we go, its 193 by 89, or 193 rows by 89 columns. Now more that 200 people actually responded to the survey, but not everybody completed it, so to keep things simple, I only downloaded those who had completed it. But I discovered there were still gaps in the data, and here’s a case in point:

The first question I asked was a list of games, against which respondents could select from six categories:

When I composed this question I had two intentions in mind. Firstly, to offer a simple question to ease people into doing the survey, so they would be less challenged by the more esoteric questions I attempted later. Secondly, I just wanted to get an idea of the participants awareness of a number of different games and types of games. Thus the list of games was somewhat esoteric, with games I knew were popular, and games I’d only come across through my study. This is how that list appears in R:

[12] “Minecraft”
[13] “Red.Dead.Redemption”
[14] “Papa.Sangre”
[15] “I.Love.Bees”
[16] “Elder.Scrolls..Skyrim”
[17] “Cut.the.Rope”
[18] “Zombie.Run”
[19] “World.of.Warcraft”
[20] “The.Sims”
[21] “Just.Dance”
[22] “Ingress”
[23] “Dear.Esther”

I mentioned how the games compared in my earlier post. But since composing the survey I realized it should be quite easy to convert the categories into numbers and and total up individuals’ awareness of these games into a notional continuous numerical “game awareness score.” That might prove a statistically useful measure of a question I purposefully didn’t ask (which might have been: How interested in games are you? Not at all—–>Pro Gamer) against which I might be able to correlate certain play preferences, maybe even proving or disproving the oft-heard cry “Real gamers don’t play Angry Birds“! (An aside – I like this comic representation of a similar argument).

So after some frustration I come up these two lines of code for R:

ghb$ludic.interest <- round(rowSums(ghb[12:23])/72, 2)
hist(ghb$ludic.interest, col = “firebrick3”, xlab = “Notional score”, main = “Ludic Interest”)

Which creates a new array of values(rounded to two decimal places) between zero and one (where one = “true gamer”), then plots the results in a histogram thus:

hist(ghb$ludicity)

Not entirely “normal” but getting there, with a positive skew, but nothing too dreadful. A set of data I can work with.

Or can I? Because when I look at the values in the vector itself I find that a small number of values are coming up “NA”. Whats going on? It turns out that some respondents didn’t select any of the categories for some of the games. And if they miss out just one game, their Ludic Interest value is screwed. It’s not too bad for this vector, but I can only assume there are other questions, where other respondents have chosen not to select an answer. And I try to correlate those vectors with this one, more and more answers will come up “NA”.

What should I do? The easiest thing to do would be to remove any respondent who has has any missing data:

> newdata <- na.omit(ghb)
> dim(newdata)
[1] 94 90

And bamm! At a stroke my sample size tumbles down from 193, to 94. How badly will that effect my analysis? Lets redraw that histogram with the reduced dataset:

hist(newdata$ludicity)

Hmmm, a bit more comb-like, almost bi-modal. Worrying.

So, can I deal with the missing data in other ways, changing it to zero for example? That might be (just about) acceptable for converting the categorical data in this particular question into a Ludic interest score, but may not be acceptable for the other instances of missing data. Ohhhhh maths is hard!

Oh curse you, respondents! Why could you just have answered all the questions properly? And why didn’t iSurvey remove you when I asked it to strip out incomplete surveys?

This post on Stack Exchange is the most useful introduction I’ve discovered so far about the mysteries of imputation.  But I’ll leave that for another day. In the meantime, I’ll work with my 94 complete responses.

International, interdisciplinary and “on the move”

Today, I’ve been at Southampton University’s interdisciplinary week, for a session on the World University Network, of which, Southampton is a part. WUN sponsors my trip last year to the the US to attend and speak at the the Decoding the Digital Conference at University of Rochester.

After a brief introduction to the session from my supervisor Graham Earl, and another one to the WUN from Elanora Gandolfini, Professor Leslie Carr, of the University’s Web Science Institute, kicked off by trying to claim that universities are old and more sustainable than the countries in which they are based. (I’m not going to agree or disagree.) He does make a compelling case however that there were attempts to make things like the World Wide Web before this academic and open initiative actually succeeded and was given free to the world.

He contrasts this with the rise of for profit academic publishing since the war, and recognizes the tension between the two methods of distribution and sharing of knowledge. But he concludes that universities are more than places to learn, but a vital engine for better worlds, woven into the social fabric, and more sustainable the Johnny-come-lately technology companies.

Then Chris Phethern, a third year PhD candidate, talked about a couple of exchange trips he has made alongside other Southampton students to Tromso and Korea, facilitated by WUN. Graeme Earl explained a little about the Research Mobility Programme (which got me to Rochester) and another programme that makes awards to specific projects.

He then went on to challenge us on various methods of interdisciplinary work, making me realize that though I work collaboratively on all sorts of written work, I do it by sharing multiple copies of the work on email, not by working on a single shared document like GoogleDocs.

I was on more comfortable ground when the discussion turned to social networking and blogging, two fellow PhD candidates I was sitting next to turned out to be far more nervous that I am about sharing this sort of stuff. Partly, I think, because they felt very few other people would be interested in their area. I countered that in the great scheme of things, I don’t expect VERY many people to be interested I this blog. But I feel I’ve already made useful contacts out of sharing my work here and on Twitter. However, justas we turned back to the front, one of the highlighted the concern he had about opening himself up to abuse on social networks. I think this is a very real concern for many, especially (it seems) women, as we transition from a pseudonominous internet society to a real-name one.

I have an action to take away from this session, to find out more about the University’s Internal Communications Network and SMuRF (and CalIT2). As someone who doesn’t spend much time on campus, I do feel I still rely too much on face-to-face real-world networking with my university cohort, and I might be missing the person also working at Southampton on a project that might perfectly compliment my own research.

Overall though, I left the session feeling very excited about the digital future of Universities. We may still be feeling our way nervously through the digital forest, but when the “find it” we’ll look back and realize that we changed the world.

Thank you, everybody who completed my survey

I took the survey off line today. 226 people responded, though 33 didn’t answer all the questions. Still that isn’t a bad sample size. Thank you to everyone that participated, even if you didn’t manage to answer all the questions. A quick scan though the answers tell me these things:

  • Mobile games have an awareness issue. Eleven people had never heard of Minecraft, 112 people had’t heard of Cut the Rope. 178 people haven’t heard of Ingress, the AR game that according to some sites, is “taking the world by storm.” Just 32 people said they’ve played a game that uses their device’s location.
  • Specialist gaming handhelds are the least popular play medium. 121 respondents said they “never” used a device like a Nintendo 3DS or Sony PSVita. And only 47 said they “sometimes”, “mostly” or “always” used one. By way of comparison only 51 people said they “never” used a phone to play games, and 124 said they “sometimes”, “mostly” or “always” used one.
  • Of the 202 respondents that answered the question about their phone, 97 have an Android, 67 an Apple, 22 don’t have a smartphone, eleven use a windows phone, and five a Blackberry.
  • SMS is the most popular method of messaging, 109 said they use it daily, compared to 30 for iMessage, 23 for Google, nineteen for WhatsApp, and 11 for Snapchat.
  • Facebook is the most popular social network still. 110 people said they use it daily.
  • Of the 193 people who answered the question on age, one was over 60, 59 between 41-60, 83 were 26-40, 47 were 16-25 and three claimed to be under 16.
  • 101 said they loved in the UK

But that’s just a first look through. The real interesting stuff will come after I’ve crunched the numbers on gaming motivations.

Another Conspiratorial meeting

Focus group

On the sixth day of March, in the town of Eastleigh, I met with a group of potential conspirators… Not really, but I did run a focus group to inform the development of the nacsent “Conspiracy 600” project (or whatever it ends up being called). So what did I learn? First of all, don’t rely on the SoundNote app. It crashed half-way through my focus group, and I’ve lost the recording of the first half of the session. Luckily I took notes.

The group was six people, from mid-teens to 30, all living or studying relatively local to Eastleigh. We began by discussing the games they enjoyed, and they mentioned everything from Draughts to Flappy Bird, with card games, Monopoly, Call of Duty and the Sims in between. They also talked unprompted about how and why they play games and in doing so, showed a great deal of diversity within the group, some played casual games to pass the time, others didn’t touch them. One or two said that before they invested any time in a game, two or three people had to tell them it was worth playing. One person spoke about playing (physically) alone but with on-line friends. Another use a term I hadn’t heard before, but which everybody understood: Rage games.

I had planned to ask specifically about why they played games, and what they got out of them, but a couple couldn’t stay long and so (given that they’d discussed it a bit already) I cut it out of my plan. As it turns out one left and covered for the other who stayed to the end, so in retrospect, I’d have liked to include that section after all. Never mind … next time.

Instead I moved onto a discussion of game mechanics. I’d printed out a number a playing cards, each naming feature of videogames culled from the “choices and feedback” lists in Navarro’s article on games and emotion. I asked them to work together to group these, however they saw fit, as I recorded the conversation. (This is the bit of the recording I most miss, as I didn’t make much in the way of notes.)

They ended up with seven groups,  the two which were the easiest for them to pull out where those referring to rewards and goals. The discussion around this was bast very much on achievement and the dextrous skills and game-knowledge that allows a competant player to show off, at one point one of them added “spectacle for others” to that group, talking about people who like to post videos of their brilliance on-line for others to watch. Later on they were quite disparaging about those people that boasted about their competence on-line.

The second, and largest group was around role-playing, and here, the discussion was about creating your own avatar, refining every details and immersing yourself in a rich fantasy world. Interestingly, there was a slight tendency to corral RPGs into the mediaval worlds of Skyrim and Zelda and talk of Mass Effect about which there was some discussion) as something other, but I think it was conceptually folded into this genre with a discussion about the immersive, filmic qualities of games like The Last of Us. Nurturing games, like the Sims or the many pet-care variants were counted by the focus group as a subgroup of Roleplaying mechanics.

The third group turned out to have a number of the “serious fun” mechanics: study, work out, rhythm etc.  And that was how they indentified it (without of course using the “serious fun” tag. A small group was made almost exclusively of “people fun” mechanics: Lead; Mentor; Cooperate; Communicate and Compete. These were seen as applying to physical team sports as well as (or more so than) computer games. And the focus-group’s unprompted creation of these groupings made me think there might well be something in Navarro’s work, and that it might apply to more than just digital games.

One collection was all about strategy, and challenging environments, puzzle solving in an immersive situation. Lara Croft, Far Cry and exploratory games were mentioned in this discussion, as well as the Monkey Island series.

I then asked a stupid question, about whether one set of mechanics appealed more than any other. Of course no-one said one did. They pointed out that a good game, Skyrim was the example they used, has a mix of all the mechanical groups. It offer levels and increasing skills, you are also creating your own character. One member described playing it socially, though that functionality isn’t available yet. Three players would start playing at the same time, and compare their progress as they played. Next time I’ll ask instead if there’s a group of mechanics that they could do without, that might be more insightful.

This group placed a lot on emphasis on the story, even though they also said they didn’t like being on rails – they decided in the end that they wanted the illusion of not being on rails. The Last of Us, one said, is not designed to be played again and again. Another was reminded about controversy over mass effect, that there was only one ending. This prompted discussing of Elder Scrolls on-line, and the difference between Far Cry 3’s single player and multi-player modes – the multi-player mode focused more on points scored, kills and achievements, and less on narrative.

Bioshock and Borderland’s worlds came up in discussion, one player explained that part of the attraction of those games was just being in their worlds. Immersion is a word that gets mentioned a lot, and its going to be a challenge for our “real-world” game. Something to thing more upon.

The group then returned narrative, and the importance of a good ending. I think that’s easier for us to provide.

Then I moved the discussion to communications. Everyone had a smartphones – a couple with iPhones, the rest with Android. There was very little love for Windows phone and no mention of Blackberry. I shared a set of cards featuring the logos of most of the famous networking/messaging tools as well as some more surprising ones that had been mentioned in the survey, and asked them to comment on them.

The first thing somebody pointed out was the lack of Microsoft branding, this was said with no surprise. One of girls and one of the boys pointed out the lack of Facetime, which was more surprising them them because it was something they used. One person mentioned using Steam as a communication method. This prompted a discussion of Google Hangouts and Skype for conferencing. One young man uses Skype a lot, for all his gaming conversations and talking to his girlfriend.

I asked about preferences for text messaging.  First to the bottom of the list was Facebook “Its just annoying now” “The only thing I didn’t miss when I spent a month off-line” However it was useful for keeping track of family and friends abroad, getting notifications from clubs and the messaging function was still the primary method of communication for at least one group-member. He said it was because he spent a lot of time on his computer at home and wanted to carry over conversations to his phone when out and about. Other preferred SMS and chose to ignore a backlog of Facebook messages.

Snapchat came in for a lot of criticism, one girl uses it quite a lot, but hates it because its so, slow. Viber came in for special praise because of the quality of international voice calling.

Something that became apparent is the way young people use different networks to segment their social contacts, keeping family and friends separate. The challenge they present us is the one thing everybody uses is SMS, rather than data. Because SMS is now unlimited for most users.

Then we got round to discussing the early concepts for the conspiracy game itself. There wasn’t overwhelming enthusiam for the game as described. One young man warned that “I don’t think I’d get out enough to enjoy it.” One person raised concerns about trying to achieve too much in any day, and worried about letting other people do the work for him. He wanted to be there, not to turn up at the end without any sense of achievement because he’d left it to others to find the clues.

This lead to a discussion about team work “You need to have equal participation”, one said or there’s no sense of achievement. This led to a discussion about a mechanic about people in a team having to be (say three) particular places at the appointed time. There was a concern though about being let down by a friend who didn’t turn up. The group preferred a mechanic where if  a number of people turned up at a target place within a certain timeslot everyone there might get a points multiplier, but if you turned up without anybody else, you’d still succeed, and the story will still progress.

A mention of fliers at the locations brought the discussion round to marketing. I asked how we’d bring it to local young people’s attention. Word of mouth is the main vector of marketing to the group, they also mentioned on-line adverting, which can be localised and personalized relatively cheaply(but its something we need to budget for). No-one admits to reading magazines. Youth-clubs and other networks may be effective. We talked about early adopters convincing others to use it, one person suggested that the early phases of the game should be at busy locations.

It makes me wonder if the game should be a modern version of the Southampton Plot, rather like an updated version of a Shakespeare play. So our players can be conspirators on a modern, immersive conspiracy, that mirrors the 600 year old plot: Hal as a Dave Cameron, rousing the rabble to war against France, with the conspirators choosing to work for or against him… The costumes would be cheaper.

 

Questions for tech SMEs and cultural heritage institutions on working together

I’ve been putting it off for weeks even months, finding distraction activities rather than tackling the challenge that appears so simple, but feels incredibly complex. Even now, I’m wrestling the impulse to go and see if the chickens have laid eggs that need collecting, 0r to try one of the new games that I’ve downloaded.

But my task is to plan the structure for the interviews that I need to conduct with technology SMEs (small and medium enterprises) that have worked with heritage organisations, and cultural heritage personnel who have worked with tech SMEs.

My intellectual paralysis is due to the fact that while I’m sure the interview structure will change as I conduct more interviews and discover what makes my interviews open up and offer real insights, I don’t want my initial interviews to be “just practice.” I want to be reasonably confident that I’ll get something worthwhile from these first ones, and not wish, twenty interviews down the line, that “I wish I’d asked that question!”

My opening  is easy:

“I’d like to ask you about one particular project where you’ve (been commissioned by a heritage organisation/commissioned a digital technology provider). I’d like to keep that one project in mind as well talk, though of course please feel free to refer to other projects to illustrate particular points of alternative ways of working. If you have a number of projects to choose from, I’d like to to select one that is relatively recent, and ideally one that you think might have gone better if you (and/or of course your client/supplier) had handled things differently. Of course, if every project has been a perfect (or alternatively, and absolute disaster) feel free to select one of those. All the details of the project(s) will be anonymised, and these notes will be secure and confidential, so I hope you to feel able to speak freely about even difficult issues.”

But then I have a muddle of questions, and a huge amount of doubts over order. So I thought I might share where I am so far, the hope that writing this post will help me, or force me, to make some sense of it all. Of course any comments will be thansfully received.

  • What was the objective of the project? (I’d hope that is is understood the same way by clients and suppliers?)
  • What organisational strategic aims did the project realize? (For suppliers this might be: What was your perception of your client’s strategic aims?)
  • What were the specific outputs of the commission? (talking here about the specific outputs of the client/supplier contract/partnership)
  • Roughly what was the budget or contract price for the work?
  • Did the outputs change over the course of the commission?
  • What was the contracted timescale of the commission?
  • Did the timescale/milestones change during the commission?
  • Tell the about the project management regime?
  • What were your measures of success?
  • How is the project now? or What were the results?
  • What were the real successes of working with this (supplier/client)?
  • What difficulties did you overcome?
  • What persistent difficulties were there?
  • What (or who!) was the greatest barrier to progress?
  • How did you attempt to overcome these difficulties?
  • How did you feel about the difficulties?
  • Why did the difficulties persist? (if any did)
  • What actions did you try to solve these problems?
  • Why do you think the solutions (being the actions in the question above) didn’t work?
  • What frustrated you most about these?
  • What other options were there?
  • Would you work with that (supplier/client) again?
  • What advice would you give somebody planning to work with that (supplier/client)?
  • If attempting a similar project again, what would you do differently?
  • If you had no constraints on time. money, power, etc what else might you have done?
  • What was the final cost of the work?
  • What comparable projects have you seen?
  • What do think your project did better than those?
  • What did you learn from those comparable projects?

Putting them all down here, and re-arranging some of them as I did so, makes me think how similar they are to the sort of questions one gets taught on coaching/leadership or managing people courses. I guess that’s no surprise as I’ve tried to write them to be open and not leading. If fact I’ve just pulled out an old manual from a managing people course I did (woah!) ten years ago, and there, the questions are similar in style but actually quite different, as its about coaching somebody through a dilemma, rather than reviewing a working relationship around a project that is likely (though I guess possibly not) to be completed. However there is one question which I’ve just nicked and added to the list.

Can you tell which it is?

Questions, questions

My head is full of questions today. On the one hand, I need to get some front end evaluation data on young people and mobile gaming together, in just a month, so I’m composing an online survey about that.

On the other hand it is the deadline for Bodiam Castle to submit bespoke questions for the National Trust’s visitor survey, so I need to get my head around what questions to try and persuade them to add. It can’t be everything that I’ll eventually ask on site, because the National Trust visitor survey is already pretty long. The most obvious one is did the visitor actually do (what I’m currently calling) “the thing” (because I don’t yet know what they’ve decided to call it)?

With my third hand (if only) I need to crack on the with composing the interview questions for my planned research into the relationships between tech companies and heritage organisations…

But I’m going to leave that and  Bodiam to one side for a moment and concentrate on the other survey. I need to ask about the target audience’s social media use, but before I do that, I ought to review what we already know. And I know very little. I hear from the papers that Facebook use is on the decline among young people because all us oldies are spoiling their fun. To which I want to say “It was always meant to be for us oldies anyhow, to keep in touch with our University friends as we got older and drifted apart. Your place, my young chums, was meant to be MySpace, but like a teenager’s bedroom you let it get messier and messier before you moved out.”

But actually my 12 year old is counting down the days to her birthday when she’ll be able to comply with Facebook’s terms and conditions and open an account (which all her friends with more relaxed parents have apparently already done). So it seems there’s life in the old network yet. My first point of call of course was to ask her what “the young people” were using nowadays, but she didn’t say anything that was new to me. And actually she’s a bit younger than my target market, so I had better turn to some published data.

The Pew Research Center tells me that 90% of all internet users aged 18-29 (which is pretty close to my target market) in the US (which is not) use Social Media. They also report proportions of the the 18-29 age band using particular social platforms. In 2013 they asked 267 internet users in that age band about what they used:

84% used Facebook

31% used Twitter

37% Instagram

27% Pintrest, and

15% LinkedIn.

I think its interesting that there’s such a steep difference between Facebook and the also-rans. The curve leaves very little room for other networks like Foursquare.

Meanwhile the Oxford Internet Surveys show us that use of social media is begging to plataux at around 61 % of internet users generally. They also show us that Social Network use gets less the older the respondent is, with 94% of 14-17 year olds using networks,  dropping t0 the mid-80% (the graph isn’t that clear) for 18-24 year olds.

The full report of their 2013 survey concentrate on defining five internet “cultures” among users.

Although they overlap in some respects these cultures define distinctive patterns. While these cultural patterns are not a simple surrogate for the demographic and social characteristics of individuals, they are socially distributed in ways that are far from random. Younger people and students are more likely to be e-mersives, but unlike the digital native thesis, for example, we find most young students falling into other cultural categories.

The group of young people that I’m interested in here falls especially into two of those cultures: The e-mersive and the cyber-savvy. Both of which might be worth looking at in more detail later. What I can see now, though, is that these two groups are the most likely to post original creative work on-line (rather than simply re-post what others have created. Interestingly, between the 2011 and 2013 surveys, the proportion of users putting creative stuff online has dipped a little, except for photographs. I guess that may be the Instagram effect. In fact the top five Social Network activities recorded in the survey are updating status; posting pictures; checking/changing privacy settings; clicking though to other websites; and leaving comments on someone else’s content.

Its an interesting report, but nothing novel comes out of it about young people’s use of the social networks. That should be reassuring I suppose, but it doesn’t particularly inform our front-end evaluation for a mobile game based around the Southampton Plot. So we’re going to have to ask young people themselves.

How to we ask, first of all, what sort of games they are playing? There are too many to list, so I’m toying with a “dummy” question that simplying gets respondents into the mood, by asking about a relatively random selection of games, but trying to include sandbox games like Minecraft, story games like Skyrim, MMORPGs like World of Warcraft, social games like Just Dance, etc. (And throwing in I Love Bees, as a wild card just to see if anybody bites at the Augmented reality game that seems to be closes to our very loose vision for the Southampton Plot. But the real meat is a free-text question that simply asks what is their favourite game that they’ve been playing recently.

My next thought has a bit more “science” behind it. Inspired by the simple typology put together by Nicole Lazzaro, I’ve taken seventeen statements her researched players used to illustrate the four types of fun she describes, and asked respondents to indicate how much they agree with them. My plan is to use some clever maths to identify what sort of mix of fun our potential gamers might enjoy.

Then I plan to ask them about the social networks they use, including the top three from the OIS data (Facebook, Instagram and Twitter) but also throwing Pintrest (which the US data also highlighted) and Foursquare (which I wanted to include because it is inherently locatative (though Facebook and Instagram are too, slightly more subtly). We’ll see how much our sample matches the published data in terms of users. I’ve also asked them to name another network if they are using that and its not one of my listed ones. Just in case MySpace is making its comeback at last 🙂 or G+ is finally getting traction.

Then I’ve suggested a similar question about messaging networks, like What’sApp and Snapchat.

I have also included a question about smartphones, whether they have one, one sort (iOS, Android etc) it is. And I’ve tried to create a question about how much of their social networking is mobile vs desk (or laptop) based, but it’s the one I’m least happy about.

Finally, as we’re trying to use this game to get people to places, I’ve asked about transport: walking; cycling; public transport; catching lifts; and being about to drive themselves. We’ll see how mobile they turn out to be.