Why do we run?: what the data says

Why do we run?Those of you who took part in the Big Running Survey may remember that it included a lot of questions about your motivations for running, and also a large section asking about the forms of running you took part in.

To non-runners I understand that the running community can look pretty homogenous – lots of people running around in shorts and t-shirts whatever the weather with slightly uncomfortable expressions on their faces. But of course as runners we know that isn’t the case. Running is an activity that encompasses a range of different sub-cultures and practises. From track sprinting to mountain marathoning, about the only they all have in common is running itself.

People run for a wide range of reasons, and these motivations (along with other factors) help to dictate the form of running they choose. So I thought it might be interesting to look at how motivations and ways of running related to each other statistically to start building a more nuanced picture of why we run.

Identifying key motivations

First of all, in order to manage the large number of motivational variables I have combined the scores for motivations that are strongly related to each other and make intuitive sense as clusters. Doing this we end up with these motivational clusters:

Competitive motivation
A combination of scores on motivations such as ‘to get the best possible times’ or ‘to do well in races’.

Psychological motivation
Includes questions like ‘it’s good for my psychological well-being’, ‘to escape my worries’ and ‘to have time to think’.

Aesthetic motivation
Focuses on questions around improving appearance and losing weight.

Social motivation
Combines questions on being motivated by social and community aspects of running.

Environment motivation
Includes motivations around enjoying being outside and interacting with the environment.

These categories don’t take in all the motivations examined in the survey, but these appear to be the most important and distinct categories. Using the data I can now give each runner who participated in the survey a score in each of these five dimensions.

Just as a summary, here are the average motivation levels (score out of 2) for men and women. I won’t comment on the differences we see here for now!

running motivations by gender
Table 1: Running motivations by gender

Identifying different ways of running

There were a lot of questions about specific details of how runners participate in the sport in the survey, but for now we’re going to stick to looking at the kind of races they like to take part in.

I used a similar process as for the motivations to identify clusters of related forms of running (types of race that were often attended by the same people) and generating over-arching categories of participation that each runner could be allocated a score in. They turned out to be:

Track running
Including all track races – whatever the distance.

Road running up to half-marathon
Self explanatory I hope!

Marathon and Ultra-marathon
It was difficult to draw a line between this category and road running because there is a high level of correspondence between half marathon and marathon runners. However, this pairing has some distinct characteristics and makes intuitive sense as a separate category.

Fell and trail running
Again, self explanatory. Quite a big overlap between this category and ultra-marathon too, but on balance it made sense to keep this pair in their own category.

Obstacle and mud running
A relatively recent phenomenon, this category is very distinct in terms of participation base.

Non-racers
Those who run but do not participate in races.

Again applying the theme of gender differences (which are important in running) we can see the level of participation in the last 12 months for one example of a form of running from each of these categories below:

Forms of running participation rates
Table 2: Forms of running participation rates

The above table deserves a couple of comments. First, the number of non-racing runners is probably an underestimate because less engaged runners may have been less likely to participate in the survey. I will address this later in the analysis by drawing on data from a huge survey by Sport England that will help measure the extent of this effect.

And secondly, I think the number of fell-runners and ultra-marathoners is probably a significant overestimate for the general running population. This is because these groups have been especially helpful in disseminating the survey and filling it in (thank you!). So these participation rates should be seen as reflecting the survey sample, not the overall running population.

Putting it together

Now we have scores for each runner in terms of their key motivations and their level of engagement with different forms of running. The next step is to combine these two sets of data to see how the different motivations correspond to each form of running.

In statistical terms I am looking for the degree of correlation between each type of running and each motivational cluster. Conducting this analysis gives us the following results, which I have simplified by giving each motivation a score to show the strength and direction of the relationship:

Running motivations by form of sport
Table 3: Running motivations by form of sport

A negative score (in red) indicates that the MORE someone is engaged in a particular form of running the LESS likely they are to be motivated by the relevant motivation.

A positive score indicates that MORE engagement with the form of running concerned is connected to a HIGHER level of the motivation.

The value of each score indicates the strength of the relationship.

What does this tell us?

I think the type of runner that stands out most clearly here is the ‘non-racer’. They really don’t seem to enjoy running much at all, and appear to be taking part mainly to lose weight – the stereotypical ‘jogger’.

mud runningMud and obstacle racing (despite the hype that surrounds it) appears to attract those with fairly low levels of all of our motivational variables, and is by far the least competitive form of racing. Interestingly given all the mud and mess, it’s also the only form of racing to be connected to the motivation to lose weight and look good.

Track racers and shorter distance road runners seem to have a lot in common in terms of their motivations. They are a competitive bunch who also enjoy the social side of the sport – being part of a community of runners.

The really long distance runners and the fell- and trail- runners also share a lot of characteristics, although the ultra/marathoners appear to value the inner experience of running whereas fell-runners favour the experience of engaging with the world around them.

A missing variable

Earlier I touched on the fact that men’s and women’s motivations are quite different (see table 1). This is really important, as it changes the picture a bit when we conduct the same analysis on each group in isolation.

In the next post (next week) I will break these results down to show how each form of running has a subtly different meaning to men and women.

 

Data Analysis / Perceptions of Talent

Data collection has finally come to an end for the Big Running Survey. I’m pleased to say that we’ve exceeded our targets, obtaining a total of almost 2,500 responses. Thank you so much to everyone who participated.

Over the coming weeks I’m going to be embarking on the data analysis and will publish updates on this blog to let all of those who took part have access to what we’ve found. From beginning to end it will take a few months as I’m simultaneously writing the PhD thesis of which this is a part, so expect a drip-drip of findings rather than a deluge!

I’ve already done a bit of exploratory analysis on the data just to get a feel for where interesting patterns and relationships are likely to appear. I’ve already discovered some quite surprising relationships, and I’m looking forward to exploring them a bit more when we move on to the interviewing stage of the project.

For instance (and in the spirit of International Women’s Day) the little table below tells us something very interesting about one of the differences between men’s and women’s relationship to running.

The table shows the mean scores for runners’ self-perceptions of their running talent. Scores were on a scale of 1 to 7. As you can see I have split them down by respondents’ gender and by their frequency of winning a medal for running in the last year (not simply for participation, but because they finished high up the field).

Running talent by gender

The medal winning frequency is a rough indicator of how good the respondents really are at running. You would expect frequent medalists to be amongst the most talented runners.

What I find really interesting here is that men are significantly more sure of their natural talent than women. Even those men that don’t medal at all rate themselves as being (on average) only marginally less talented than medalling women, and not much less talented than the top female runners. And men who win the occasional medal rate themselves considerably more talented than the top women’s group.

What does this tell us? Is it a reflection of women’s tendency to underestimate themselves or to want to avoid appearing boastful or competitive? (something that men, perhaps, are less reticent about!) Or is it something to do with the fact that in a mass participation race the average woman will usually finish a little way behind the average man, giving the man the impression he has done better because his finishing place appears to be above average, with the woman having the opposite experience?

At this stage I’m not in a position to say, but more data analysis and the interviews might help to shed some more light on this. It’s important, because whether or not we consider ourselves good at something is one of the factors that determines whether and how much we participate. Could women’s lower self-perceptions of talent be one of the factors that contribute to their lower levels of participation in running in general and in competitive running in particular? (more on this in later articles)

If you have any other thoughts or ideas on how this gender difference could be interpreted I would love to hear them. Please let me know using the comments section below.

This really is just the tip of a rather massive (and intimidating) iceberg in terms of what we’re going to be able to get from the data. Future posts will cover a lot more ground, but I thought it would be worth posting this to mark the start of the results coming through.

If you haven’t done so, please sign up (using the form at the top of the sidebar) to receive regular updates on the research.

The Generosity of Runners

Thank you!Since I started out on the Big Running Survey project (i.e. my PhD research) a year or so ago I admit I’ve been swaying between wild optimism and gloomy pessimism on an almost weekly basis. I’m not naturally given to either extreme, but one unknown about the project has kept me up at night on many more than one occasion.

Specifically, after a year of research and writing and survey development, then the piloting, getting feedback and finally drawing up the final survey tool, I’ve wondered if, when the survey was finally launched, anyone would notice – or care. At my most pessimistic moments I’ve imagined running clubs refusing to pass it on to their members, social media shrugging its shoulders and moving on, race organisers throwing my requests for help in the bin… then where would I be? back to square one, and a year’s work down the drain.

However, at sunnier moments I’ve thought about all the nice people I’ve met through running. Their keenness to get others involved and to support them on their way to their own goals. Their pure joy in the sport, and the enthusiasm of their conversations about it. Surely these people will help me… some of them, at least.

Well, D-Day was last Monday, 8 days ago: The moment my survey was announced to the world. I contacted a few dozen running clubs – the first of a long list of contacts I’ve compiled by scouring the internet, I emailed all the runners I knew and called in a few favours, and then I took to twitter and facebook to try to get a snowball rolling. Then I just had to wait and see what would happen next.

Within a few hours a trickle of responses was coming in. Day 1 reaped 9 responses. Day 2, a further 7. At this stage I was feeling positive. At this rate I could reach my minimum target of 500 in just a couple of months – well within schedule.

But then it started to take off. A friend of a friend posted a link on his running club facebook page and sent out a tweet. Day 3 garnered 33 responses. After a quiet day 4 one of my tweets was picked up by #ukrunchat and another couple of retweeters and in flooded 56 responses. Two days later and more retweets from various running groups and individuals led to 72 responses… Surely it couldn’t go on like this?!

It didn’t… A day later it went off the scale! I’d had far fewer women respondents than men, so tweeted #womensrunninguk to ask for help finding female runners who would be interested in participating. A day or two later they retweeted it, setting off a minor viral cascade, with dozens of retweets and resulting in an incredible 469 responses on the day.

So, I’ve already surpassed my minimum sample size of 500 by 50%, and the responses are still coming in. I had allocated four months for data collection, and this all happened in only 8 days! The only ‘problem’ I have now is that I need more male runners, as the call for female runners was so successful they’ve put the men in the shade!

I’m going to keep up the publicity on social media (without getting annoying, I hope!) and keep contacting the clubs, many of whom have already been so helpful. I’m just so thankful for the generosity of the running community – all those hundreds of strangers – who have helped me promote the survey and who have completed it themselves. Also for all the encouraging comments that have redoubled my enthusiasm for the project.

Thank you all!

Survey can be accessed via www.bigrunningsurvey.co.uk

Results will be published on this website in due course.

What are psychographics?

If you’ve been visiting my site regularly you’ve no doubt come across the word ‘psychographics’ a few times. Not everyone is familiar with this term, so to save myself (and you) from having to explain the word every time I use it I’ve added this reference page.psychographics definition

What are psychographics?

To describe the characteristics of a particular group we’re used to the idea of using demographic data such as age, gender, education level or location. This information helps us to give the group some defining features that allow us to compare it to other groups, to work out if other people or populations are part of the group or to focus marketing activities on the right people.

For example: the group ‘night clubbers’ has the demographic characteristics of being mostly under 25, evenly split between males and females and mostly single.

In this case we’ve identified a particular group based on a known behaviour (going to night clubs) and made some generalisations about the typical demographic characteristics of the group as a whole.

But demographic data can also be used to segment a particular group into subgroups to help us understand variations in behaviour or outcomes within the larger group. This can be done in a number of ways, including simply splitting the group in terms of one variable (for example, into male and female night clubbers) or by using statistical techniques to look for clusters of demographic factors that, when they occur together, tend to correspond to a certain ‘target’ behaviour or other variable of interest to the researcher.

So, let’s say we want to split the night clubbers group into segments that correspond to their scores on a variable about how often they go clubbing. Using a statistical cluster analysis we can find out how the main group can be segmented into subgroups made up of people that tend to frequent nightclubs at a similar regularity. So it might turn out that the subgroup that goes clubbing 2-3 times per week are best defined as predominantly female, low income students, and under the age of 23; the group who go less than once per month might turn out to be predominantly male, high income and over the age of 30.

Demographics can be an excellent way of describing and segmenting populations, but because they only look at relatively objective factors they can miss out on a lot of rich and important detail about what makes people tick. It’s all very well saying people under 40 are more likely to buy an ipod than those over 40, but surely we’d have a much better predictor of purchase if we split a group by factors like whether or not they are big music fans or technophobes (for which age might be an imperfect proxy). This is where psychographics comes in.

Psychographics takes the study of groups into new areas: interests, attitudes, opinions, motivations, values, personality and lifestyle choices. It enables us to describe and segment  populations by the way they think rather than by demographic factors, which is often very useful to market researchers looking to develop a detailed picture of existing or potential customers.

A simple example of a psychographic segmentation is a political opinion poll. Here a population is divided up according to an opinion or intention: who will you vote for?

In combination, psychographics and demographics can provide a powerful way of defining groups and predicting behaviour. In my research I am using both types of data to help create a set of profiles of different types of runner based on their relationship to the sport. Each profile will have a different combination of factors (e.g. values, attitudes, motivations, age, gender, socioeconomic status) that correspond to – and help explain – the ways they practice their sport.