Gambling Commission looks to personal stories in new report on gambling harms
In a first-of-its-kind qualitative study, the Gambling Commission (UKGC) has analysed 25 interviews with people affected by gambling-related harm.
This new analysis is part of an effort to gain new, in-depth insights into how gambling impacts players. Specifically, it relates to the Commission’s ‘Evidence theme 3: Gambling-related harms and vulnerability’.
Gambling Survey of Great Britain
The 25 participants were respondents to the Gambling Survey for Great Britain (GSGB), an annual survey first carried out in 2023 and introduced as part of the White Paper Review.
The GSGB, whose methodology has been praised, collects data from 20,000 respondents and is the largest survey of its kind. So far, it has revealed that:
- 48% of British adults had engaged in gambling in the preceding four weeks
- The National Lottery remains the most popular form of gambling
- Younger people and men gamble more often than other groups
- The prevalence of problem gambling is small but significant
UKGC goes ‘in-depth’ with qualitative methods
The UKGC selected 25 individuals who had experienced adverse effects from gambling in the 12 months prior to responding to the GSGB.
When discussing problem gambling and related phenomena, it’s usually the case that large datasets are considered, so this personal approach had the potential to provide a new perspective.
The advantage of these methods is that they can reveal nuances that are often lost when just interrogating numerical data. The report’s main objectives were:
- To explore experiences that lead to problem gambling
- To identify factors that might contribute to problem gambling
- To understand experiences of the consequences
- To understand which behaviours help to treat/avoid problem gambling
Player experiences
Based on the interviews, the UKGC identified three key areas of concern, each of which were referred to in both positive and negative terms by participants.
Financial impact
Positive: “sometimes I won what are considerable sums of money – not that would change my life – but they're... It was worthwhile.” (male aged 55+)
Negative: “I've used the money for food [on gambling] where I've had to go and borrow money so I could eat.” (female aged 35 to 54)
Negative: “I lost my house, I lost my job, so I was homeless on the streets [...] I still gamble but nowhere near as bad as I used to.” (female aged 35 to 54)
Health and wellbeing
Positive: “When I gamble and I can see that my analysis, my calculation is right, I'll be very proud of myself.” (male aged 25 to 34)
Negative: “I used to be a very fit person [...] but now it's just... I just find it difficult to even eat because I'm thinking what I've done in terms of gambling.” (male aged 35 to 54)
Impact on relationships
Positive: “We'll make a whole night of [going to bingo] and just lately the last few times my granddaughter who is now 19, she's been coming with us as well, so 3 of us are going and making a night of it.” (female aged 55+)
Negative: “She was like, ‘You're not paying the bills.’ The arguments were really intense. I went, ‘“Right, we'll just move on. We'll split.’” (male aged 35 to 54)
What does this tell us?
The datasets of qualitative studies are necessarily smaller than those of sweeping quantitative studies. However, this small sample means that the experiences of these participants won’t necessarily be representative of the wider population of gamblers.
The three areas of focus are likely relevant to most problem gamblers, but more data is needed if they are to infer anything really meaningful and that is actually actionable, especially when each area is commented on in both a positive and negative light.
Nuanced exploration
That said, qualitative methods are suited to nuanced exploration of personal experiences like these, capturing patterns that would be treated reductively or missed altogether by alternative methods.
The bottom line is that a study like this can prove very informative when viewed alongside a wider, more quantitative study, and when not generalised too much. These are individual experiences, not those of every problem gambler.
What could/should they have done?
Whilst the study sheds some light, it can’t be considered enlightening. The sample is too small to produce results that can be generalised in any significant way.
Critics may also suggest that the Commission is simply doing the bare minimum to avoid accusations of being too data-focused, or that it’s a thinly-veiled effort to appear more humanistic.
Even large, robust datasets like the GSGB can have their downsides, so qualitative versions absolutely should be carried out, just on a larger scale than this.
What next?
The UKGC should consider this a pilot study, and seek to build on it. Expanding the study would improve the quality of the data significantly, and allow for the identification of wider trends, rather than individual experiences.
The Commission should also ensure that the sample is representative of British gamblers. Demographics such as race, age and gender must all be factored in.
Future interview-based studies can be refined based on the results of this one. The next round of interviews should probe deeply but respectfully. The Commission should identify blind spots, and allow these to inform the design and structure of future interviews.
This would, of course, require resources, but a combination of research methods should be harnessed if the UKGC is to continue prioritising its data-first approach to legislation.
Summary
This study is well-intended but limited by its sample size. It provides a few small insights, but more data is required if the Commission is to be able to act on its findings. A truly meaningful and valuable qualitative survey would surely mean gathering data from several hundred people, not 25.
The issue here is not the direction that the Commission has taken, but how tentatively they have dipped their toe into qualitative waters.
This sort of data can be rich and highly valuable, but the UKGC will need to put its hand in its pocket and splash out on the resources to collect and process significantly from more than 25 data points.