Age, Gender and Sport in England

I apologise for the change from the advertised topic for this post. I was planning to talk about the relationship between childhood experiences and involvement in sport, but have been sidetracked by writing up my PhD thesis which is now taking up most of my time. Part of this process has been to crunch some numbers from Sport England’s Active People Survey, a massive survey of over 150,000 people across the country, which tracks sports participation changes from year to year. I’m using the data to contextualise running within the full range of sporting and active leisure activities popular in the UK today. In particular I’ve been looking at how running compares to other sports in terms of participant demographics.

I have posted a simple ranking of sports in terms of the occupational class of their participants before, but now that I have analysed more up-to-date data that includes extra sports as well as taking into account gender and age, I thought I’d come back to it again in a bit more detail.

First of all, here’s a chart showing the relative gender balance (x-axis) and mean age (y-axis) of participants in a number of popular sports. The dark red lines indicate the mean age and gender for the sample. The fact that the mean gender is just over 0.4 indicates that more women than men responded to the survey. Sports to the left of the dark red line are more popular with women, sports to the right more popular with men. The further they are from the red line the stronger this bias is. The mean age of the sports (on the y-axis) runs from 22 (basketball) to 60 (golf).

Sports participation by gender and age

The next two charts show occupational category data sorted in two ways. Each shows the percentage of participants in a selection of popular sports that have professional or managerial jobs (NS SEC 1-2, shown in blue) and the percentage that have traditionally working class jobs or are currently unemployed (NS SEC 5-8, shown in orange). The first chart shows the sports sorted by the percentage of ‘higher status’* occupations, the second is sorted by ‘lower status’* occupations.

I’m not going to comment on these findings at this stage, other than to say that they reinforce a strange fact about running. On the one hand it is a highly accessible sport with low barriers to entry that attracts men and women in almost equal quantities; on the other it attracts a disproportionately high level of middle class participants. In fact running sits above golf, tennis and mountaineering in terms of its proportion of higher status occupation participants. This is a paradox my research seeks to shed light on, and something I will come back to in future posts.

 

* The use of ‘higher’ and ‘lower’ here is related to the typical level of income and education of holders of these jobs – i.e. the socio-ecomomic status. They are not meant o imply that managerial jobs are inherently superior to working class ones.

 

Active People Survey:

Sport England. (2016). Active People Survey, 2014-2015. [data collection]. UK Data Service. SN: 8038, http://doi.org/10.5255/UKDA-SN-8038-1

 

Age and Gender Maps of Running

In a previous post I showed how different forms of running (road runners, fell-runners, obstacle course racers, ultra marathoners, track athletes) attracted different socioeconomic groups. We visualised the way in which participation was structured by plotting the mean income and education ranks for participation in each sport onto a chart with axes of education (x) and income (y). This was the result:

A quick look at the relative positions of the different forms of running suggests that the longer the distance of the event, the higher the average income level of those attracted to participate, and that education level is somehow connected to the type of environment people like to run in; lower education levels are associated with highly constructed, artificial spaces like the obstacle course and running track, and higher education levels are associated with participating in unstructured, natural environments and the wild. Perhaps there’s also a suggestion of a link between education level and preferences for communal or solitary running experiences.

But of course education and income can only explain so much. In fact there are other, more powerful drivers behind the choice of running form that need to be looked at.

In terms of their influence over our choices around sport, two of the most important social variables of all are gender and age. And of course these two factors are strongly linked to income (men and older people tend to earn more), so perhaps some of the effect we can see in the above chart can be explained simply by the age and gender of those taking part.

Below I have plotted the same five forms of running onto a similar chart, but this time with axes of gender and age. The age axis is self-explanatory, it’s simply the mean age of participants for each point plotted. The gender axis shows the relative proportion of male and female participants in the sport. The pink line marks the sample mean gender balance, so points to the right of this have more male participants than average, points to the left have more female. The further from the central line the more lopsided the gender balance gets.

I’ve also add some extra plots for key motivations (red) and included two extra running forms: Jogging [Jog] (non-competitive runners), and Orienteering [Ori] (for which I have just collected a booster sample of 300 respondents).

Key to motivations (red points): ‘Looks’ = strongly agree with ‘I run to improve my appearance’; Weight = ‘to lose or maintain weight’; ‘Social’ = ‘to socialise with friends”; Explore = ‘to explore the outside environment’; ‘Races’ = ‘to do well in races’.

Interestingly the locations of the five forms we saw in the first chart are broadly similar in this one, even though we’re ostensibly measuring different things. This suggests that there may well be a relationship between gender/age and income/education.

We can see that the motivations associated more strongly with women are those around managing weight and improving their appearances, as well as social motivations. I should say that this is absolutely not to say that these are priorities for all female runners, the positions on the chart represent averages from my sample of almost 3,000 runners surveyed. Men are more likely to be motivated by competition (races) and exploring the outside environment.

To an extent these motivational tendencies are reflected in the forms that men and women participate in. Men are more likely to appear at a fell race or ultra marathon, both of which would often involve both competition and training in remote outdoor environments. Orienteering is the most male dominated of all the forms on the chart. Again we have a strong element of exploring the outside environment.

Women are more likely to go for OCRs (obstacle course races), which foster team spirit and camaraderie (i.e. feeding social motivations), and jogging, which is often practised to lose weight and involves no athletic competition. The location of sprinting is gendered female is harder to explain, but this may be because the sample of sprinters is relatively small, so the data may not be so reliable.

In terms of age, sprinting (unsurprisingly) attracts the youngest participants, with OCRs the second youngest group. Orienteering again stands out as by far the oldest of the forms.

Almost in the middle of it all sits the half-marathon. I think this reflects the open, easy access nature of this event. For many people it may be their entry point into running, and can be run as a motivation to stick to a weight loss plan or as a highly competitive race. It appears to be the one-size-fits-all event of running, attracting a gender and age balanced participant base.

Behind the Structure

How can we explain why different sports attract different social groups? This is a difficult question, and is key to anyone interested in promoting particular forms running and broadening their appeal. There are a number of possibilities:

  1. People prefer to join up with sports that are already populated by ‘people like them’, be that class, gender or age. This is certainly true, and would help explain why social differences harden, but not how they formed in the first place. This may require a historical account of how each form of running came into being.
  2. Some groups have greater physical access to the right infrastructure for a particular form of running, or have higher practical barriers to overcome to take part. For instance perhaps parents don’t have as much free time, so can’t fit in the training for doing ultra marathons, or self-employed people have more flexibility to fit in the large volumes of training for such events.
  3. Some forms of running cultivate an image that puts off or favours certain groups. Forms such as Ironman triathlon and races such as the Man versus Mountain appear to be masculinised through their names. Could this make them less appealing to women? Are they positioned as symbols of masculinity?
  4. People choose a form of running that suits their particular motivations, so competitive people choose more competitive forms, and people concerned about body image choose forms that they think will best address this. Again, this is certainly a factor, but sociologically speaking it’s quite a superficial answer. It fails to address why certain groups are more likely to have particular motivations than others. What is it about being a woman that makes you more likely to be motivated by losing weight; why are more educated people more interested in exploring?
  5. People’s life experiences, linked to their age, gender, social class and other factors lead them to develop particular tastes and identities that make some forms especially appealing and some less so. This is an important perspective. It pulls together ideas about motivation and taste and the demographic variables we’ve been looking at. By understanding how people’s class, gender or age result in different life experiences, and how these experiences lead to particular preferences or tastes, we might be able to get at the underlying cultural reasons for the structuring of the forms of running.

Picking up on the last point, in my next article I’ll be looking at some real examples of the ways in which running participation is influenced by people’s life experiences. In particular I want to look at how different experiences of sport and running in childhood are influenced by the kind of school people go to (state/private, rural/urban) and their gender. These differences, which we can explore using the interview data I’ve collected over the last year, show how variations in the opportunities and experiences of childhood can lead to quite different orientations to running in later life.

As always, any comments, ideas or suggestions would be very welcome! Just use the box below.

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.

 

Top 8 reasons to stop running

What – if anything – would stop you from running?reasons to stop running

A survey of frequent runners identified these 8 reasons as the things most likely to cause them to have to stop.

Interestingly, apart from items 3 and 4 which are both about motivation, the sequence is exactly the same for both frequent and occasional runners, although occasional runners rate all of the reasons as more likely to force them to stop than their keener counterparts. The percentages give the frequent runners’ response rate in red, occasional runners’ in blue).

Top 8 reasons why runners would give up their sport

  1. Injury or physical disability (73% / 83% of respondents agreed)

  2. Less free time available for running (31% / 58%)

  3. Loss of motivation (29% / 48%)

  4. Wanting to spend time doing other sports instead (21% / 52%)

  5. Running partner stops (8% / 16%)

  6. Financial cost of running too great (8% / 16% )

  7. Break-up of running group (8% / 12%)

  8. Departure of coach (4% / 6%)

On average we can see that frequent runner rate themselves as 40% less likely to quit in the face of setbacks or difficulties than occasional runners.

And only physical injury or disability rate as sufficient reason to stop running for most serious runners. No surprise there.

Data from Scheerder et al (2009) quoted in Borger et al (2015), in Scheerder et al (2015).

Sport and Social Class – The Rankings

This is a follow-up to an article I published earlier this week that looked at how people’s socioeconomic background (crudely, their ‘class’) was a great predictor of the kinds of sports they got involved in.Sports by socioeconomic group

Based on a large scale survey of sports participants, running came in just below sports like sailing, yoga and windsurfing, but above cycling, basketball and football in terms of the socioeconomic status of its enthusiasts.

But the data I used for that study was from Belgium, and I only included a handful of sports. So this post is designed to provide an English perspective, as well as much wider coverage in terms of the sports included in the comparison.

I’ve taken data from Sport England’s massive ‘Active People Survey‘, an annual sports participation survey of over 160,000 people, and done a bit of number crunching to compile a list of popular sports ranked by their relative popularity to high and low status groups.

More precisely, I generated a ratio of the rate of participation in each sport by high socioeconomic group people to the rate of participation for the low socioeconomic group. Sorry if that sounds confusing, but what it means is:

If a sport gets a score of 2 on the ranking that would mean it is twice as popular with the high status group as it is with the low status group. Or, if a sport gets 0.5 then the likelihood of a high status person participating in the sport is half that of a low status person.

So this is about comparing the appeal of each sport to the two groups, not comparing the total numbers in each group participating.

The two socioeconomic status categories are defined using the N-SEC classification system used in the UK census. Here’s a list of those included in each group:

Higher Status

  1. Higher managerial and professional occupations
  2. Lower managerial and professional occupations
  3. Intermediate occupations (clerical, sales, service)
  4. Small employers and own account workers

Lower Status

5. Lower supervisory and technical occupations
6. Semi-routine occupations
7. Routine occupations
8. Never worked and long-term unemployed

‘Participation’ is defined as taking part in a sport at least once per week.

The UK’s ‘Poshest’ Sport Rankings

Rank

Sport

Participation Rate Ratio

1 Tennis 3.89
2 Squash 3.00
3 Keep-fit Classes 2.43
4 Golf 2.42
5 Mountaineering 2.40
6 Running 2.28
7 Road Cycling 2.25
8 Swimming – Outdoor 2.09
9 Athletics – Track & Field 2.08
10 Aerobics 2.05
11 Badminton – Indoor 1.87
12 Hockey 1.67
13 Swimming – Indoor 1.60
14 Netball 1.53
15 Fitness & Conditioning 1.50
16 Gym 1.49
17 Table Tennis 1.29
18 Boxing 1.20
19 Karate 1.06
20 Equestrian 1.05
21 Bowls 1.03
22 Shooting 1.00
23 Cricket 0.97
24 Football 0.94
25 Rugby Union – 15-a-side 0.73
26 Tenpin Bowling 0.71
27 Basketball 0.63
28 Snooker 0.60
29 Pool 0.56
30 Angling 0.54
31 Darts 0.40

Note: A high rank doesn’t mean better! The sports that are doing the best to encourage as wide a range of participants as possible are those towards the middle of the table with scores around 1. These are equally attractive to both ends of the social spectrum.

You can see that most sports are more popular with the higher status group than the lower status group (i.e. they have a participation ratio above 1). This reflects the fact that participation in sport as a whole is more common amongst the middle class than the working class.

Looking at the detail, there are a quite few surprising results. As with the Belgian results, running is pretty high up the list – only just behind golf and mountaineering, but I wouldn’t have guessed rugby or cricket would be in the lower half of the table,or that equestrian sports and shooting have such similar levels of appeal across the classes. But the data is from a very reliable source and from a huge sample, so we have to take it seriously.

I’d love to hear your interpretations for any of these figures.