1 Contents Overview of approach Part 1: key findings Part 2: key findings Discussion Next steps
2 Overview of project design Aim to fully explore BGPS data in relation to machine players Iterative and exploratory analysis – meeting in August to discuss key findings Some further changes due to limitations of what is possible Findings presented in two parts: Part 1 – changes over time Part 2 – In-depth profile of machine player sub-types using 2010 data
3 Part 1: Changes over time Past year prevalence of playing slots, by survey year and sex
4 Part 1: Changes over time Past year prevalence of playing machines in bookmaker’s, by survey year and sex
5 Part 1: Changes over time Past year prevalence of playing machines in bookmaker’s, by survey year, age and sex
6 Part 1: other profile changes [slots] Slot machine gamblers: Age and sex profile different Decreases among those aged 16-35; increases among those aged 35+ Some variations by marital status (likely age related) Changes in profile by educational attainment (reflecting age changes and changing profile of education status in past decade?) Changes in profile by income – greater % of slot machine gamblers from lower income groups. But reflective of broader changes? Need to question how all of the above are related to policy change
7 Part 1: other profile changes [slots] Slot machine gamblers: Slot machine gamblers in 2010 more engaged in gambling than in 1999 and 2007 Take part in more activities per yr and per wk 9% 7+ activities 1999; 21% 7+ activities 2010 Increased frequency of gambling (25% vs. 30%) Increased frequency of playing machines (15% vs. 20% Mean DSM-IV scores higher in 2010 than 1999 Potentially reflection of changing profile of slot machine players
8 Part 1: other profile changes [Fobts] Fobt machine gamblers: Increasingly male, increasingly younger Higher levels of educational attainment Lower income groups BUT Unlike slots, no evidence of any change in levels of engagement in gambling Heavily engaged in other activities – majority take part in 7+ activities in past year & over two thirds gambler once a week or more
9 Part 2: Machine gambler sub-types Methods Used Latent Class Analysis to identify machine gamblers sub-types Number of approaches tried to determine which method was most robust Past year participation & Frequency Venue of play Used standard criteria to identify the best approach Key part of the criteria is also how useful and how interpretable resultant groups are.
10 Machine gambler sub-groups Based on venue of participation in the past year:
11 Machine gambler sub-groups: categorisation Class 1: Mainly Pubs (46.7%; n=483) Class 2: Only AGCs (17.1%; n=194) Class 3: Mainly bookmaker’s (15.2%; n=147) Class 4: Other venues (11.3%; n=130) Class 5: Multi-venues (9.7%; n=94)
12 Machine gambler sub-groups: categorisation Class 1: Mainly Pubs (46.7%; n=483) Class 2: Only AGCs (17.1%; n=194) Class 3: Mainly bookmaker’s (15.2%; n=147) Class 4: Other venues (11.3%; n=130) Class 5: Multi-venues (9.7%; n=94)
13 Machine gambler sub-groups: context Mainly used regression to model membership of belonging to each group Need context of understanding what differentiates machines gamblers from other gamblers first: Variables entered into model: Age Sex Ethnicity Marital status Educational qualifications General health Smoking status Alcohol consumption Personal Income NS-SEC of household reference person Economic activity Significant variables in model: Age (younger) Sex (men) Educational qualifications (lower quals) Smoking status (smokers) Alcohol consumption (heavier drinkers)
14 Machine gamblers: gambling characteristics Variables entered into model: Age Sex No of gambling activities in past week PGSI categorisation Age first gambled Parental gambling status Significant variables in model: Age (younger) Sex (men) No of gambling activities in past week (more activities) PGSI categorisation (low, mod, PGs) Age first gambled (younger)
15 Machine gamblers: summary Compared with other gamblers (excl NL only): Machine gamblers more likely to be younger, male, to engage in other risk behaviours, to be highly engaged in gambling generally, to have started younger and to experience some problems with behaviour
16 Machine gamblers sub-groups How does this vary among our machine gambler sub-groups? Are all machine players alike?
19 Regression 1: socio-demographics PubsAGCsBookiesOtherMulti Age Sex Ethnicity Marital status Educational qualifications General health Smoking Alcohol Income Economic activity NS-SEC of household reference person
20 Regression 2: Gambling characteristics PubsAGCsBookiesOtherMulti No of activities in past week PGSI categorisation Parental gambling status Age first gambled Pubs – odds lower among PGs AGCs – odds lower among more engaged gamblers Bookies – odds higher among PGs Multi – odds higher among PGs and more engaged gamblers
21 Discussion What are most salient findings from GC perspective? How do we refer to B2/Fobt players? What implications for stakeholders – how do we manage this? Need to fully quality assure statistics (double verification and checking process)
22 Next steps Agree timescale for production of final report Agree publication strategy Agree stakeholder management and impact strategy
Your consent to our cookies if you continue to use this website.