gambling machines research programme...gambling machines research programme report 2: identifying...

Gambling machines research programme

Report 2: Identifying problem gambling – findings

from a survey of loyalty card customers

Authors: Heather Wardle, David Excell, Eleanor Ireland, Nevena Ilic and Stephen Sharman

Date: 26.11.2014

Prepared for: The Responsible Gambling Trust

Acknowledgements

We would like to thank all operators who supported this project by providing access to their

loyalty card customers. A number of colleagues contributed to this report and our thanks are due

to:

Dan Philo and Nikki Leftly for helping with analysis and data management

David Hussey and Pablo Cabrera Alvarez for producing the weights

Sonia Shrivington and Claire Jones for overseeing the fieldwork

Peyman Damestani, Alessio Fiacco and Hannah Bridges for programming the questionnaire.

Finally, we thank all the participants who took part in each survey and made this report possible.

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Contents Executive summary .............................................................. 7

Aims and objectives ............................................................................................................. 7

Survey design and approach ................................................................................................ 7

Who are loyalty card holders? .............................................................................................. 7

Problem and at-risk gambling .............................................................................................. 8

Differences in machine gambling between problem and none problem gamblers ............. 9

Key themes .............................................................................................................. 9

1 Introduction .................................................................. 11

1.1 About the research ................................................................................................... 11 Policy context ...................................................................................................................... 11

About machines in bookmakers ........................................................................................... 12

The research process ........................................................................................................... 12

Report structure .................................................................................................................... 13

Measuring harm .................................................................................................................... 14

1.2 Unique contribution .................................................................................................. 14

1.3 Report conventions .................................................................................................. 15

2 Methods and research context .................................... 16

2.1 Loyalty card schemes............................................................................................... 16

2.2 Overview of methodological approach ..................................................................... 17

2.3 Profile of achieved sample ....................................................................................... 19

2.4 Use of loyalty cards .................................................................................................. 23 Use of loyalty cards – findings from focus groups and in-depth interviews ........................ 23

Use of loyalty cards – findings from the survey of loyalty card holders ............................... 26

2.5 Limitations ............................................................................................................ 28

3 Gambling participation ................................................. 29

3.1 Introduction ............................................................................................................ 29

3.2 Gambling participation by age and sex .................................................................... 29

3.3 Number of gambling activities, by age and sex ....................................................... 31

3.4 Gambling participation by socio-economic characteristics ..................................... 33

3.5 Frequency of gambling by age and sex.................................................................... 37 Most frequent activity ........................................................................................................... 37

Frequency of gambling on machines in a bookmaker’s ....................................................... 37

3.6 Frequency of gambling by socio-economic characteristics ..................................... 39

3.7 Summary ............................................................................................................ 42

4 Types of gamblers ........................................................ 43

4.1 Introduction ............................................................................................................ 43

4.2 Gambling types ......................................................................................................... 43

4.3 Factors associated with membership of each group ............................................... 46

4.4 Summary ............................................................................................................ 50

5 Problem and at-risk gambling ..................................... 51

5.1 Introduction ............................................................................................................ 51

5.2 Caveats ............................................................................................................ 52

5.3 Problem and at-risk gambling by age and sex ......................................................... 52

5.4 PGSI item endorsement by age and sex ................................................................... 54

5.5 Problem and at-risk gambling by gambler type, number of activities and sex ........ 57

5.6 Problem and at-risk gambling by income, area deprivation and economic activity 59

5.7 Factors associated with problem and at-risk gambling ........................................... 63

5.8 Problems with machine gambling by age and sex ................................................... 68

5.9 Problems with machine gambling by income, deprivation and economic activity .. 69

5.10 Factors associated with machine gambling problems ............................................. 72

5.11 Summary ............................................................................................................ 75

6 Motivations and attitudes............................................. 76

6.1 Attitudes towards machine gambling ...................................................................... 76

6.2 Motivations for machine gambling ........................................................................... 78

6.3 Summary ............................................................................................................ 85

7 Identifying problem gambling ...................................... 87

7.1 Introduction ............................................................................................................ 87

7.2 Profile of different player types by patterns of machine use ................................... 88 Metrics of machine use ........................................................................................................ 88

Types of gamblers and factor analysis of the PGSI .............................................................. 89

Machine gambling behaviour ............................................................................................... 91

7.3 Differentiating between ‘problem’ and ‘non-problem’ gamblers ........................... 101 Sensitivity and specificity: an illustration ........................................................................... 101

Summary .................................................................................................................... 103

8 Conclusions ................................................................ 106

Appendix A. Technical appendix...................................... 109

Survey processes .......................................................................................................... 109 Sample design .................................................................................................................... 109

Opt-out process .................................................................................................................. 110

Fieldwork .................................................................................................................... 110

Response rates ................................................................................................................... 111

Weighting .................................................................................................................... 113

Analysis .......................................................................................................... 116 Scoring the problem gambling screening instrument ........................................................ 116

Latent Class Analysis .......................................................................................................... 117

Logistic regression procedure for all models ..................................................................... 119

Factor analysis .................................................................................................................... 121

Data analysis and reporting ................................................................................................ 123

Appendix B. Focus group and in-depth interviews

methodology .................................................................... 125

Research aims and objectives ............................................................................................ 125

Methodology .................................................................................................................... 125

Recruitment and sample ..................................................................................................... 126

Ethical protocol ................................................................................................................... 127

Analysis .................................................................................................................... 127

Research challenges ........................................................................................................... 128

Appendix C Questionnaire .............................................. 129

NatCen Social Research | Loyalty card survey 7

Executive summary

Aims and objectives This study is part of the Responsible Gambling Trust’s machines research programme. This

programme aimed to examine whether industry data generated by machines in bookmakers

could be used to distinguish between harmful and non-harmful patterns of play.

To do this, a survey of people who have a loyalty card for Ladbrokes, William Hill or Paddy

Power was conducted. The survey included questions about gambling behaviour and

questions which measured whether someone was a problem gambler or not. Permission was

sought to link participant’s survey data with their loyalty card data. This linked data was then

analysed by Featurespace and RTI International to see if it was possible to predict who was

a problem gambler by looking at industry data alone. The results of that research are

presented in a separate report (see Report 3: Predicting Problem Gambling; Excell et al,

2014).

This report aims to document the survey process, give an overview of the broader gambling

behaviour of loyalty card holders, identify the prevalence of problem gamblers among loyalty

card holders, to introduce some key themes that are used in the predictive analysis (Report

3) and to highlight some caveats of the research.

Survey design and approach A random probability sample of 27,565 loyalty card holders who had gambled on machines in

a bookmaker’s was selected from industry registers. Loyalty card holders were the focus of

this study as it meant we had access to fuller information about their machine play behaviour.

Overall, 4,727 people took part in the survey and 4,001 people agreed that their survey

responses and their loyalty card data could be linked. Taking into account ineligible cases

(i.e., those where the contact details were incorrect), the response rate was between 17-

19%. Data was collected either via a web survey or a telephone interview.

Who are loyalty card holders? People who signed up for a loyalty card from a bookmaker’s were heavily engaged in

gambling. Compared with machines players identified in the British Gambling Prevalence

Survey 2010, loyalty card holders were more likely to gamble at least once a week and to

take part in more forms for gambling. They were also more likely to be of non-White ethnic

origin and to live in deprived areas.

Loyalty card survey participants took part, on average, in 4.8 different forms of gambling in

the past four weeks and 11% took part in more than 9 different forms of gambling.

72% of participants gambled at least twice a week on their most frequent gambling activity,

with 26% gambling nearly every day. 40% said that they gambled on machines in a


bookmaker’s at least twice a week and 10% played every day. This means loyalty card

survey participants were highly engaged in gambling generally and machine play specifically.

Those from lower income groups, those who were economically inactive or living in more

deprived areas gambled more frequently and played machines in a bookmaker’s more often

(for example, 16% of those who were unable to work because of illness or disability played

machines in a bookmaker’s every day compared with 7% of those in paid employment).

Despite loyalty card holders being generally more engaged in gambling, their behaviour

ranged between those who were less engaged in gambling, and tended to only play

machine’s in a bookmakers when visiting the venue (21% of participants) to those who were

heavily engaged in a range of gambling activities and both played machines and placed bets

when at a bookmaker’s (11% of participants).

70% of participants had only one loyalty card for a single operator. However, 21% had more

than one card. Most participants (68%) stated that they did not always use their loyalty card

when playing machines in a bookmaker’s. This means that loyalty card data is not showing

the full picture of play for most people.

Reasons people gave for not using their loyalty card ranged from forgetting about it or not

thinking that it was worth it to not wanting to be tracked or thinking that it would affect the way

the machine played. There may be some systematic differences between those who do and

do not use their loyalty card all the time. For example, those who were younger (29%) were

even less likely to always use their loyalty card than those who were older (42%).

On the whole, loyalty card participants said that they played machines to win money or

because it was exciting. They displayed fairly balanced views towards machine gambling,

with most disagreeing that it was a harmless activity. Participants had mixed views as to

whether gambling should be discouraged though most felt that people should have the right

to gamble if they wanted.

Problem and at-risk gambling Problem gambling is defined as gambling to a degree that that compromises, disrupts or

damages family, personal or recreational pursuits.

In this survey, problem gambling was measured using a series of nine questions called the

Problem Gambling Severity Index (PGSI). The PGSI groups people into the following

categories based on their responses to the questions: non-problem gambler; low risk

gambler; moderate risk gambler and problem gambler.

Overall, 23% of loyalty card survey participants were problem gamblers, 24% were moderate

risk gamblers, 24% low risk gamblers and 29% non-problem gamblers. It should be

remembered that loyalty card survey participants were highly engaged in gambling and

therefore these estimates are not representative of all machine players.

Problem gambling estimates were higher among men than women (24% vs 18%), though the

magnitude of the difference was much smaller than observed in other studies. Rates were


also higher among those aged 24-44 and those from lower income groups (31%), those living

in more deprived areas (28%) and those who were economically inactive (39%).

Problem gamblers tended to gamble on a greater range of activities than non-problem

gamblers; they were not just machine gamblers.

Questions were also asked whether the participant felt they had problems with their machine

gambling. 14% said they had problem at least most of the time that they played. Rates were

also higher among those with lower incomes (18%), those living in more deprived areas

(18%) and those who were economically inactive (22%).

Differences in machine gambling between problem and none problem gamblers

Overall, 4,001 participants agreed that their survey responses could be linked to their loyalty

card data; of which 951 were problem gamblers.

Looking at key patterns of behaviour recorded by industry shows that, on average, problem

gamblers bet at higher stakes than non-problem gamblers (£7.43 per bet vs £4.27); they

deposit more cash into the machine when gambling (£41.28 per session vs £22.77); they

gamble more often (41% gambled every day vs 16%) and, correspondingly had fewer days in

between visits to a bookmaker’s to play machines. They also had a higher number of discrete

gambling sessions per day (2.2) than non-problem gamblers (1.8).

Problem gamblers, however, had lower income levels than non-problem gamblers (31% had

an income of less than £10,400 per year compared with 24% for non-problem gamblers)

suggesting that differences in spend are less likely to be related to increased levels of

disposable income.

Whilst these broad variations were evident, there was also a great deal of overlap between

the behaviour of problem and non-problem gamblers. Of those who staked, on average, 53

pence per bet or lower, 19% were problem gamblers whilst 18% of those who staked, on

average, £13.40 per bet or higher were non-problem gamblers. This shows that it is difficult

to clearly distinguish between the behaviours of non-problem and problem gamblers; when it

comes to their machine behaviour, they are not mutually exclusive groups.

Key themes Simply looking at a single behaviour alone is unlikely to have enough discriminatory power to

distinguish between problem and non-problem gamblers. This is because there is significant

overlap in the behaviour of the two groups.

Because of this overlap, policy makers and other stakeholders need to think carefully about a

range of trade-offs. When implementing new measures aimed at protecting gamblers from

harm it may mean that stakeholders have to accept that some non-problem gamblers will

also be included in the intervention in order to reach as many problem gamblers as possible.


This needs careful testing. On the one hand, an intervention may have unintended

consequences if it affects too many non-problem gamblers (i.e., it is not very specific). On the

other hand, some interventions, no matter how well intentioned, may not have the desired

impact because they are simply not effective at capturing all problem gamblers (i.e., they are

not very sensitive). New policies to be thoroughly tested and evaluated, with this evaluation

built into the policy development and design process from the very start.

To help improve the identification and prediction of problem gamblers, operators should look

to collect more contextual information about their customers. This could include demographic

information when people sign up for a card or ways to link staff interactions and observations

with loyalty card records.

Finally, this study suggests that those who sign up for a loyalty card under the current

schemes have elevated risk of experiencing problems from gambling. Gambling operators

should think carefully about the level and type of promotions offered to these customers.


1 Introduction

1.1 About the research

Policy context This report forms part of a series of research projects commissioned by the Responsible Gambling

Trust (RGT) to explore the extent to which industry data generated by gambling machines in

bookmakers can be used to identify harmful patterns of play. In recent years, there have been

increasing calls to use transactional data recorded by bookmakers’ machines to better understand

how consumers play these machines. It is hoped that by doing this, patterns of play that indicate if a

consumer is experiencing problems or harm from their gambling can be identified. Industry and

regulators alike are keen to see if this is possible. If so, a potential new range of responsible

gambling measures, tailored towards and intervening with the individual, could be developed.

To date, regulation of gambling machines tends to be conducted at a fairly blunt level and focuses

on restrictions of stake, prize, speed and numbers of machines in certain venues. There is no

regulation that is tailored to individual gamblers. The Gambling Commission (the industry regulator)

considers that a mix of macro (e.g., stakes and prizes) and micro (e.g., the individual) regulatory

approaches may be effective. Therefore, a critical question is whether industry data can identify

‘harmful’ patterns of play at an individual level and if so, what types of interventions could be

introduced that intercede with gamblers experiencing problems. A further concern is to ensure that

any individual-led policies intervene with those experiencing problems, whilst allowing those who are

not experiencing problems to gamble without onerous intervention.

The objectives set by the Responsible Gambling Strategy Board (RGSB)1 for the broader research

programme were:

can we distinguish between harmful and non-harmful gaming machine play? and;

if we can, what measures might limit harmful play without impacting on those who do not

exhibit harmful behaviours?

To meet these objectives, a series of research projects were planned by the research team, a

consortium of NatCen Social Research, Featurespace, Geofutures and RTI international. These

projects focus mainly on the first objective, though consideration of the second is also given. Other

research projects (called ‘contextual projects’ in the broader research programme) contribute to the

second objective, for example by looking at how people understand certain types of player

messaging (see Collins et al., 2014).2

1 The Responsible Gambling Strategy Board is the body responsible for setting strategic objectives for gambling research

in Great Britain. 2 Collins, D., Green, S., d’Ardenne, J., Wardle, H., William, S-K. (2014) Understanding of Return to Player Messages:

Findings from user testing. London: Responsible Gambling Trust.


About machines in bookmakers This project focuses on machines available in bookmakers only. In Great Britain, there are a range

of different gambling machines available. They are broadly differentiated based on the maximum

permitted stake per bet, the prizes they offer and where they are located. Bookmakers in Great

Britain are allowed to have up to four gambling machines. These are interactive terminals where a

range of different types of games can be accessed. The first game type is classified by regulators as

a B2 game. These tend to offer more casino style content (like roulette, the most popular type of

game hosted on these machines) and allow a maximum stake of £100 per bet and a maximum prize

of £500. However, more traditional slot style games are also available (based on spinning reels and

lines) and these are typically known as B3 games which have a maximum stake of £2 per bet and a

maximum prize of £500. Other game content is also offered, which tend to have lower stakes and

prizes than this. Gamblers can switch content whilst gambling on these machines and change

stakes. However, the B2 casino-style content is the most popular.

Overall, it is estimated that there are over 9000 bookmakers in Great Britain and most have their full

allocation of machines. Figures from the Gambling Commission estimated that there were 33,526

machines in bookmakers in 2012-13.3 This project focuses on gaining insight about who uses

machines in bookmakers and how they play them to see if it is possible to distinguish between

harmful and non-harmful players based on their patterns of gambling.

The research process To meet the objectives set by the RGSB, a number of project steps were planned and three related

reports have been published. These are shown in Figure 1.1.

Figure 1.1 Research project stages and reports

The first step was to consider what patterns of play might indicate that someone was experiencing

harm from their machine gambling. In order to look at whether industry data can identify harm, it is

first necessary to think about what patterns of play might show that someone is gambling in a

harmful way. This involved a theoretical review, a rapid evidence review and consultation with key

3 See Gambling Commission (2014) Industry statistics: April 2009- September 2013. Birmingham: Gambling Commission.

This data is based on regulatory returns and shows that only 153 machines in bookmakers are not of the style described

above.


stakeholders to identify a set of metrics (or markers) that may exist within industry data to indicate

that someone was experiencing harm. The results of this stage are published in Wardle, Parke &

Excell (2014) and this is called ‘Report 1’ in this series (see Wardle, Parke & Excell, Report 1:

Theoretical Markers of Harm).

The next step was to review whether the markers of harm identified from this review were actually

evident in the data that industry collects. This part of the research was conducted by Featurespace,

a company specialising in behavioural analytics. Information on this phase of work is reported in

Report 3 in this series (See Excell et al., Report 3: Using industry data to identify gambling-related

harm).

Preliminary analysis of industry data suggested that some of the markers of harm described in the

theoretical review could be identified within that data and that further exploration was warranted. A

main question for the next stage of the research was whether the potential patterns of harm

identified through theory were actually patterns of play exhibited by those experiencing harm from

gambling. A critical aspect of this was determining the extent to which potential patterns of harm

differentiate between those who are experiencing harm and those who are not. To explore this, more

detail is needed about the player and the extent to which they are experiencing gambling-related

problems. This information can only be obtained by speaking with players.

This current study (Report 2) fills that gap. It reports findings of a survey of people who have loyalty

cards for Ladbrokes, William Hill or Paddy Power. Using loyalty card holders as a sampling frame for

a survey meant that we could link their survey responses with data collected and recorded against

their loyalty card. The loyalty cards for bookmakers operate in much the same way as other loyalty

cards (like those for grocery stores or other retail businesses such as Tesco clubcards or Nectar

cards) where every transaction (where the card is used) is recorded against the record for an

individual. This means it is possible to track how often and how much people spend on machines, so

long as they used their loyalty card when doing so. Using this data has considerable benefits over

traditional survey approaches as it is widely accepted that estimates of gambling expenditure

obtained through surveys are inaccurate.

The survey had one main aim which was to identify machine gamblers who may be experiencing

problems with their gambling and to link this information with loyalty card data. Once this data was

linked, a further objective was to explore the extent to which those with gambling problems have

distinct patterns of machine play. The results of this process are documented in two separate

reports, this one (Report 2) and Report 3: Using industry data to identify gambling-related harm.

Report structure This is the first time in Great Britain that loyalty card holders for bookmakers have been surveyed

and their survey responses linked to their objective machine play data. Therefore, it is important to

fully understand who loyalty card holders are, what other types of gambling they engage in, what

their attitudes and motivations to gambling are and how these things vary from machine gamblers

more broadly. This is so we can fully understand the circumstances of this group of people and how

this might impact on the results we see. Therefore, the aims of this report are to:

document the survey process and outline the limitations of the research (Chapter 2)

explore the broader gambling behaviour of people who hold loyalty cards for a bookmaker’s

(Chapters 3 and 4) and their motivations for gambling (Chapter 6);


conduct initial exploration of the factors that distinguish problem gamblers from non-problem

gamblers (Chapter 5); and

introduce key concepts used in Report 3, which explores patterns of machine play using

industry data in much more detail (Chapter 7).

This report and Report 3 should be viewed together and it is these two reports in combination which

meet the research objectives set out by the RGSB.

Measuring harm The research objectives set out by the RGSB focus on identifying harmful patterns of play.

Increasingly, the term ‘gambling-related harm’ is being used in British gambling policy. It is felt that

this term is preferable to ‘problem’ gambling as it includes:

‘the adverse financial, personal and social consequences to players, their families and

wider social networks that can be caused by uncontrolled gambling’. 4

To date, there has been little work aimed at quantifying and measuring this broader range of

gambling harms and there are no validated survey questions which can be used. This research

project had a very tight timetable; there was less than five weeks between the project being formally

commissioned and the survey being launched. Therefore, there was no time to develop new survey

questions aimed at measuring gambling-related harm. It was agreed with the client, the RGT, and all

major stakeholders (the RGSB and the Gambling Commission) that this study would measure

problem and at-risk gambling instead, using a set of questions called the Problem Gambling Severity

Index. We recognise that this changes the aims of the research, as this now examines the extent to

which industry data can be used to identify problematic patterns of play rather than harmful patterns

of play. However, to date, there has been no attempt to examine this in Great Britain and therefore

this study fills an important gap in knowledge.

1.2 Unique contribution Despite the change in focus from gambling harms to gambling problems noted above, this study

makes an important contribution to the evidence base in a number of ways:

It is the first time in Great Britain that access to loyalty card customers has been obtained

and that responses to a survey, including measurement of gambling problems, have been

linked to industry data.

It is also the first time that the major bookmakers in Great Britain have opened up their data

to scrutiny by independent researchers.

The resulting survey data provides information from the largest sample of problem gamblers

living within the general population to date – with over 1000 problem gamblers identified in

this survey. Other surveys, such as the British Gambling Prevalence Survey (BGPS),

typically only interview around 50-60 problem gamblers. This means more analysis can be

undertaken of who these problem gamblers are and how their patterns of machine gambling

vary.

4 Responsible Gambling Strategy Board (2012) Strategy. Birmingham: Responsible Gambling Strategy Board.

Available at: http://www.rgsb.org.uk/publications.html.


As with any research, there are a number of limitations to be considered. These are discussed in

Section 2.5.

1.3 Report conventions The following conventions are used in this report:

Unless otherwise stated, the tables are based on the responding sample for each individual

question (i.e., item non-response is excluded): therefore bases may differ slightly between

tables.

The group to which each table refers is shown in the top left hand corner of each table.

The data used in this report have been weighted. The weighting strategy is described in

Appendix A. Both weighted and unweighted base sizes are shown at the foot of each table. The

weighted numbers reflect the relative size of each group of the population, not the number of

interviews achieved, which is shown by the unweighted base.

The following conventions have been used in the tables:

- No observations (zero values)

0 Non-zero values of less than 0.5% and thus rounded to zero

[ ] An estimate presented in square brackets warns of small sample base sizes. If a

group’s unweighted base is less than 30, data for that group are not shown. If the

unweighted base is between 30-49, the estimate is presented in square brackets.

* Estimates not shown because base sizes are less than 30.

Because of rounding, row or column percentages in the tables may not exactly add to 100%.

A percentage may be presented in the text for a single category that aggregates two or more

percentages shown in the table. The percentage for that single category may, because of

rounding, differ by one percentage point from the sum of the percentages in the table.

Some questions were multi-coded (i.e., allowing the respondent to give more than one answer).

The column percentages for these tables sum to more than 100%.

The term ‘significant’ refers to statistical significance (at the 95% level) and is not intended to

imply substantive importance.

Only results that are significant at the 95% level are presented in the report commentary.


2 Methods and research context

2.1 Loyalty card schemes This study is a survey of customers who have a loyalty card for Ladbrokes, William Hill or Paddy

Power. Currently, there is no regulatory requirement for any bookmakers to monitor which players

are using their machines. However, some bookmakers are increasingly implementing loyalty card

schemes to obtain player insight and to use in marketing. The longest running loyalty scheme is the

‘Odds On!’ programme which has been run by Ladbrokes since 2008. Both Paddy Power and

William Hill introduced their loyalty schemes in 2013, and Gala Coral introduced their Coral Connect

scheme early in 2014. An example of the player cards provided by the industry is shown below. At

the time this research was commissioned, loyalty card data was only available from Ladbrokes,

Paddy Power and William Hill.

Because each loyalty card scheme is different, operators’ requirements in order to register for a

loyalty card vary. However, for the three operators included in this study, there was no legal

requirement to provide names, addresses or other personal details in order to register for a card.

Cards were given out to those who wanted one in bookmakers’ offices by staff. In practice, most

operators attempted to obtain a telephone number for each registered card, but systematic checking

of the contact details provided was not undertaken at the point of registering for a card, meaning the

quality of this data is variable. Therefore, at the time when this research was commissioned,

information about the total number of loyalty cards given out was available but details such as who

the card belonged to and information about their age and sex, for example, was not necessarily

known. Furthermore, some people can have more than one card for the same operator. The

implications of this are discussed below.

Figure 2.1 Example loyalty cards


2.2 Overview of methodological approach This section gives a brief overview of the methodological approach for this survey; full technical

details are provided in Appendix A.

This study was a survey of people who held at least one loyalty card either for Ladbrokes, William

Hill and/or Paddy Power and had used it at least once whilst gambling on machines in bookmakers

between September and November 2013.5 The primary aim of this study was to collect problem

gambling information from these players and obtain consent to link their survey responses with their

loyalty card data.

The study was designed to be as representative as possible of loyalty card holders who had played

machines. First, the three main operators provided information about the total number of loyalty

cards held and whether contact details were available for each registered card. Overall, there were

131,275 cards with contact details available. This list also contained some basic information about

how often the loyalty card had been used when gambling on machines between September and

November 2013. From this information, a random probability sample (n=47,268) was drawn, with

those cards which had been used most often being oversampled. This was to try to boost the

number of gamblers who might be experiencing problems in the survey.

Operators first contacted each potential participant via text message to inform them that the study

was taking place and that NatCen Social Research would be in touch unless they told the operator

by a certain date that they did not want their details to be passed to NatCen. Overall, 902 people

opted out of participating and were removed from the final sample. This process also identified a

large number of cases with invalid contact details (n=18,801). The final issued sample size was

27,565.

Fieldwork was conducted between May and August 2014. Contact details available were either a

mobile telephone number or an email address, or both. All sampled cases with a valid email address

were contacted via email and invited to take part in a web survey. Email reminders were sent to

those who had not participated to date. Between May and August 2014, a total of five email

reminders were sent to each participant (unless they had already taken part in the survey). Those

with telephone numbers available were contacted by NatCen’s specialist Telephone Interviewing

Unit in an attempt to interview them over the phone. A minimum of seven calls were made, at

different times of day and night, to each phone number; the average number of calls made to each

number was 3.6 ranging between a minimum of 1 call and maximum of 21.

All data were collected using computed assisted interviewing methods. The first question asked

about use of loyalty cards to establish eligibility: this is because the names of card holders are,

typically, not recorded by gambling operators. Therefore, interviewers had to check they were talking

to the correct person and asked the potential participant if they held a loyalty card for one of the

three operators. If the participant said no, a further check question was asked to ascertain that they

were certain that they had never had a loyalty card. Participants who said no to both questions were

excluded from the study (see Appendix A for details). For those who were eligible, the questionnaire

covered the following topics:

5 This timeframe was chosen because this was the period covered in the original data provided by operators to the

research team. In other words, it was based on what information was available at the time.


engagement in a range of gambling activities in the past four weeks;

frequency of gambling participation for each activity;

use of loyalty cards;

problem screening questions;

attitudes to machines in bookmakers;

motivations for playing machines in bookmakers;

demographics;

data linkage.

The data linkage question was of primary importance. All participants were asked if they would give

permission for their survey responses to be linked to information from their loyalty card. Overall, 84%

of those interviewed agreed that their data could be linked together.

The questionnaire took 15 minutes to complete on average. All participants who completed the

questionnaire were sent a £5 Post Office voucher to thank them for their time. Ethical approval to

conduct the study was obtained from NatCen’s independent Research Ethics Committee.

Overall, 4727 people took part in the study. Taking into account those who were identified as

ineligible to participate during the interview process, the estimated response rate for this study was

between 17%-19%. This means that more people did not take part in the study than those who did.

This introduces the potential for non-response bias, as those who did take part may be different from

those who did not. All analysis was weighted to try to account for this bias and to adjust the survey

results to take into account the unequal probability of selection introduced by oversampling more

frequently used loyalty cards. However, few details about the profile of loyalty card holders were

available, meaning that it was difficult to develop a sophisticated weighting strategy that took into

account a fuller range of potential biases. Full details of the response rate calculations and weighting

strategy are given in Appendix A.

The following sections provide an overview of the issues that should be considered when reviewing

the survey results.


2.3 Profile of achieved sample The socio-demographic and economic profile of those interviewed is shown in Table 2.1. Where

possible, the profile of past year machine players from the BGPS 2010 is also shown.6 This provides

a benchmark against which to assess the ways in which the profile of the loyalty card survey (LCS)

participants varies.

Overall, 88% of LCS participants were men and 12% were women. This was similar to the profile of

machine players from the BGPS 2010. The age profile of LCS participants was younger than that of

the BGPS: 58% of LCS participants were aged 44 or under compared with 49% of BGPS machine

players. There were also some variations by region: LCS participants came disproportionately more

from London than BGPS machine players (19% vs 9%).7 A further noticeable difference was that a

higher proportion of LCS participants lived in areas of greater deprivation than the machine players

interviewed in the BGPS. For example, 36% of LCS participants lived in areas of greatest

deprivation in England compared with 22% of BGPS machine players. Finally, the profile of LCS

participants included a greater number from minority ethnic groups (18% vs 9%) and contained a

lower number of people in full time education (3% vs 18%).

6 Using the profile of people who played machines in a bookmaker’s in the past year is the nearest comparison that can

be made to nationally representative data. The loyalty card survey sampled people who had a loyalty card and used it at least once on a machine in a three-month period in 2013, meaning all were past year machine gamblers at the time of interview. The BGPS was chosen for comparisons rather than the more recent health surveys for England and Scotland as the BGPS includes more information about frequency of gambling and also includes data for the whole of Great Britain. 7 This is likely to be a reflection of the distribution of venues from the operators included in the study. For example, for

one operator, most of its venues are based in London and the South East.


Table 2.1

Profile of loyalty card survey participants and machine players

from the British Gambling Prevalence Survey 1

Socio-economic/demographic

characteristics

Survey

LCS BGPS

% %

Sex

Men 88 85

Women 12 15

Age

16-24 16 15

25-34 23 16

35-44 19 18

45-54 22 17

55-64 13 15

65-74 6 11

75+ 1 9

Government Office Region

North East 7 3

North West 11 13

Yorkshire and the Humber 8 9

East Midlands 6 6

West Midlands 7 10

East of England 7 11

London 19 9

South East 8 15

South West 6 6

Wales 4 4

Scotland 16 14

Index of multiple deprivation - England

Less deprived 64 78

Most deprived (80th centile or above) 36 22

Index of multiple deprivation - Wales

Less deprived 62 *

Most deprived (80th centile or above) 38 *

Index of multiple deprivation - Scotland

Less deprived 66 74

Most deprived (80th centile or above) 34 26

Ethnic Group

White/White British 82 91

Asian/Asian British 6 2

Black/Black British 7 3

Other ethnic group 5 4

Employment status

Paid employment 66 60

Unemployed 11 9

Looking after family/home 4 4

Student 3 18

Retired 9 1

Long term sick/disabled/other 7 8

Bases (unweighted) 4727 243

Bases (weighted) 4726 281

*Data not shown because of small base sizes


Table 2.2 shows the profile of LCS participants by a number of gambling characteristics. First, it is

clear that many participants had more than one loyalty card: 21% currently held more than one

loyalty card for different bookmakers and 31% said that they had more than one loyalty card for

different bookmakers previously. Second, nearly all had gambled in the past four weeks (96%) and

three out of four (74%) had gambled on machines in a bookmaker’s in the past four weeks. LCS

participants appeared to be highly engaged with gambling, with half (50%) having engaged in at

least five different forms of gambling in the past four weeks. Of those who had gambled on

machines, 79% had done so once a week, and around one in four (24%) had played these machines

at least four days a week.

The most comparable data to this is the number of activities undertaken on a monthly basis and

frequency of play among monthly machine gamblers from the BGPS 2010. This is not the same

timeframe as used in the LCS and so comparisons should be made with caution. However, past year

machine gamblers in the BGPS took part in fewer gambling activities on a monthly basis and

monthly machine gamblers reported playing them less frequently than their LCS counterparts (see

Figure 2.2).

Compared with nationally representative data about machine players, LCS participants appear to be

younger, to live in more deprived areas, have a greater proportion from non-White backgrounds and

are more engaged in gambling. Given that these people have signed up for and used a loyalty card

for a bookmaker’s, this is not surprising.

0

10

20

30

40

50

60

Every day/almost

every day

4-5 days per

week

2-3 days per

week

About once a

week

Less than once a

week

Frequency of participation

Perc

en

t

LCS

BGPS

Figure 2.2 Frequency of playing machines in a bookmakers among past

four week/monthly machine players, by survey


Table 2.2

Gambling behaviour among loyalty card survey participants

and machine players from the British Gambling

Prevalence Survey

Gambling characteristics Survey

LCS BGPS 2010

% %

Number of loyalty cards for a

bookmaker ever held

1 69

2 24

3 6

4 1

Don't know 1

Number of loyalty cards for a

bookmaker currently held

1 70

2 17

3 4

Don't know 9

Any gambling in past four

weeks/monthly** 96

Machines gambling in past four

weeks/monthly 74

Number of gambling activities in past

four weeks/monthly**

None 4 11

1-2 activities 17 26




9 or more activities 11 15

Frequency of playing machines

among past four weeks/monthly

machine players**

Every day/almost every day 13 5

4-5 days per week 10 6


About once a week 25 24

Less than once a week 21 50

Bases (unweighted) 4727 243

Bases (weighted) 4726 281

* The LCS asked people about participation in gambling activities in the past four weeks

whereas the BGPS asked firstly about the past year participation and then how often

people played each activity. From this, information about monthly gamblers was obtained.

Therefore, the two reference periods whilst being broadly similar are not identical, which

may influence results.


2.4 Use of loyalty cards The main aim of the broader research project was to match responses from this survey with data

generated by loyalty cards when used in bookmakers’ machines. However, data is only captured

and tracked to an individual if they put their card into the machine at the start of their play session

and remove it at the end. It is therefore important to understand whether people with loyalty cards

use them consistently or not, so that the gaps and limitations of the loyalty card data can be better

understood. This was attempted in two ways. First, focus groups with loyalty card customers were

held to explore their attitudes towards and use of loyalty cards. Second, findings from this process

were used to develop questions about loyalty cards usage included in the survey. Results from both

methods are discussed below.

Use of loyalty cards – findings from focus groups and in-depth interviews Based on data collected during focus groups and in-depth interviews, people who played machines

in a bookmaker’s were grouped into five main types. These were distributed along a continuum of

use ranging from nearly always using the card, to different types of infrequent use, to never using

one. (Full details of the methodology can be found in Appendix B). Four different types of loyalty

card users identified in this study are discussed below. A final fifth type constitutes an important

anomaly – potentially frequent card use based on multiple players' use of a single card.

Non-users

At the extreme end of the spectrum are betting shop customers who have never used a loyalty card

and do not intend to use one in the future. This group sees little benefit in owning a loyalty card.

They are sceptical about using a card, particularly as some think that betting shop operators use

loyalty card schemes to encourage customers to spend more, or feel that using a loyalty card would

mean intrusion and monitoring of a private activity. Therefore, this group felt that the (potential)

negative consequences outweighed any benefits.

Infrequent users

There were a group of gamblers who reported only using their loyalty card infrequently. This group

tended to be more suspicious about the negative influence of a loyalty card on gambling outcomes

(i.e., thinking that using a loyalty card would alter the way the machine played), and tended to make

decisions about using a card before each session of gambling or between games. It is evident that

player tracking data for infrequent users of loyalty cards will be incomplete, providing an inaccurate

picture of actual machine use.

Sporadic users

Sporadic users tend to use their loyalty card(s) infrequently and also described periods of not using

the card at all. This group includes those who lost a card and then signed up for a new one. This

group tended to have more infrequent or sporadic use of cards for practical reasons rather than

suspicion of operators. From a practical perspective for this research, if operators' gambler data

systems do not allow linkage of data from old and new cards, any emerging pattern of card use

based on available data would provide only partial insight into patterns of harmful machine gambling

behaviour.

Frequent users

These are gamblers who nearly always use their card and take advantage of the rewards and offers.

Their loyalty card data would provide an almost complete record of individual and multiple sessions


of gambling. This would be particularly true for those gamblers who prefer to gamble in betting shops

owned by one operator. However, this type of gambler tends to have multiple loyalty cards or to

engage in gambling machine play across a number of operators, meaning that unless data across

different loyalty cards could be linked we would be unlikely to see a full picture of an individual’s

machine use. That said, in some cases players owning multiple cards demonstrate a preference for

using one card only. Nonetheless, there are likely to be significant data gaps resulting from the

variability in frequent use for such players even though they tend to use their loyalty card quite

frequently.

Multiple users

Interestingly, there was also evidence of loyalty cards being used interchangeably between

gamblers. In this group were the intentional multiple gamblers who specifically asked a non-card

holding customer to use their card during a gambling session to accumulate points; those who gave

money to a card holding customer to gamble on their behalf with a view to sharing wins and rewards;

and potentially those who decided to stop gambling (on machines in betting shops) and gave their

card to another customer to use. An example of unintentional multi-player use was a lost loyalty

card, which is picked up by another person and used on a regular or infrequent basis. This suggests

that loyalty card data could combine the play behaviour of multiple customers, revealing little if

anything about individual play behaviour, levels of control and harmful play patterns.

This evidence demonstrates the range of ways in which loyalty card usage varies; some people use

their cards all the time, others have never used a loyalty card and never will. The focus groups and

in-depth interviews probed specifically around views of card usage and the reasons for not using

loyalty cards. These are discussed in more detail below.

Those who used their card frequently, for almost every session of play, felt that not using the card

would place them at a disadvantage as they would lose credits, points, and ‘free’ money. This was

viewed as a sensible and logical approach to card use. Using the card was believed to be

advantageous as it increased a player’s chances of winning by influencing machines to “open up”.

However, even those who used their loyalty card on a regular basis recalled occasions when they

did not use it. This was mainly because, on many of those occasions, they had been in a hurry and

had forgotten their card. The contingency plan adopted in one such instance had been to use a

temporary card to collect points which were later transferred to the registered, permanent loyalty

card.

For some participants, loyalty card use was more variable. One participant who owned two loyalty

cards and played regularly on gaming machines at betting shops run by both operators described

how he used one operator’s loyalty card and rarely utilised the second one, even though the rewards

offered were more attractive. The reason for this was familiarity with a betting shop which he had

frequented for more than 30 years and where he knew other customers and the staff. This familiarity

determined his use of a loyalty card and took precedence over loyalty card rewards.

For others, rushed visits to a betting shop (for example, a quick session of machine gambling on the

way home from work) were identified as one reason for not using a loyalty card. This was either

because it was felt that time would be wasted in taking the card out of a wallet or due to concern

about forgetting the card in a machine.

Whilst frequent users felt that using a card helped with “opening up” gambling machines, infrequent

users expressed the belief that using a loyalty card could negatively influence their chances of


winning. This was linked to awareness of player tracking by operators and a fear that their loyalty

card data might be used to influence the outcome of their play session:

“If I'm losing I don't really tend to use it [loyalty card]…sometimes I do and sometimes I don't

put it in. It's a bit superstitious of me, I know, but…obviously it's all computerised so they

know who's put their card in and they might just think, oh no, we're not gonna let him win

today.”

Participants also highlighted examples of cards being used interchangeably between players. For

example, a participant who did not have a loyalty card had agreed to use another customer’s loyalty

card:

“I've seen people, like when I've played the machine before, someone's actually come and

asked me… 'Oh, do you mind if I put my card in?' And they've actually come and put their

card in as I'm playing the machine and I'm thinking, 'What are they getting out of that?’”

In addition, there was evidence of players using their cards to play on gaming machines with money

which was not their own. One participant described how money given by friends was used to play

on gambling machines to accumulate points which were exchanged for free bets, which were shared

between the group.

There was variation in views over the perceived influence of the card on gambling machine

behaviour. One view was that loyalty cards influenced both the amount of money and time spent on

machines. Special offers and credits received via text message and the chance to accumulate points

and rewards was felt to encourage individuals to gamble on the machines, especially as gamblers

felt they were "getting something back for nothing".

“Yes, I can tell it [machine play] changed because every time I go there I knew that I would

get more points, so that means that I can use them, I can use them every time I bet it's going

to come back to me as a bonus credit. So that's why it's making me to play more.”

The tendency to gamble more in order to utilise offers before their expiration date was also

identified. Among those who wanted to limit the amount of time they gambled, the view was that

texts were a reminder to visit a betting shop; something they did not need when they were trying to

control their behaviour.

However, an alternative view was that owning a loyalty card helped players control betting behaviour

because keeping track of points accumulated helped increase awareness of money spent. This was

felt to help set individual limits on the machine play:

“It just gave me a bit more control; helped me, like, to keep control of my gambling, if that

makes sense…'Cause I'd only spend a certain amount until I've got that many points …and

then I'd stop…spending.”

Loyalty cards directly influenced the choice of betting shop, and some people preferred to visit ones

for which they had a loyalty card. However, loyalty card ownership worked alongside other factors

such as familiarity with a particular betting shop, or the levels of rewards offered to influence

gambling behaviour. Beliefs about how gambling machines work played a role in choice of betting

shop:


“The machines are all subtly different…like the difference between driving a Corsa, a Fiesta

or a four wheel drive…with the [name of operator] ones I've got an idea when it's on a paying

thing and when it's not and I can sort of adjust my gambling to it. I feel comfortable with

them.”

On the contrary, some felt that loyalty cards had no influence over gaming machine behaviour.

These participants believed that people played on the machine for a range of reasons and

accumulating loyalty card points was not necessarily a significant motivation to play longer or to

spend more money of gaming machines.

Overall, these discussions highlighted some important features of how and why people use loyalty

cards. First, it is clear that even the most regular of loyalty card users do not always use their card

when they gamble on machines. Some reasons are very practical, such as that the player is just

playing very short session and it is either not worth using their card or there is not enough time to

use it. Others reasons relate to how the player feels it may influence the machine and their chances

of winning. Some preferred not to use a card at all because they did not want their private behaviour

to be tracked and because they were concerned about how it might influence the machine. There

are clearly a number of superstitious and erroneous beliefs about the interaction of loyalty cards with

machines. Interestingly, there was also a range of views about how loyalty cards would affect the

behaviour of the individual which influenced whether the cards were used or not. Some people

clearly felt that the cards and marketing associated with them would prompt them to gamble more

than they would like, whereas others used the point tracking system to keep track of their gambling.

These qualitative insights are useful for understanding how and why loyalty card use varies, which

has important implications for this study (see Section 2.5). They were also particularly useful when

developing the survey questionnaire for loyalty card players. The themes identified were developed

into survey questions. The first question asked how often people used their loyalty card when

playing machines; the second question asked those who did not always use their loyalty card to

report why not, using a pre-coded list of reasons.

Use of loyalty cards – findings from the survey of loyalty card holders

As observed in the qualitative work, use of loyalty cards when using gambling machines existed

upon a spectrum. Overall, 32% of survey participants said that they always used their loyalty card

when using machines and a further 19% said they almost always did this. However, 21% said this

was something they did only sometimes with 14% each reporting that they rarely or never used their

loyalty card when using machines.

Rates of loyalty card use were broadly similar for men and women. However, there were notable

differences by age. Those aged 55 and over were much more likely than those aged 18-34 to use

their card always or most of the time when using machines (61% vs 49% respectively) (see Figure

2.3).


This means that for those aged 55 and over, loyalty card data is more likely to represent a fuller

picture of machine use. This has important implications for this study. Those aged 55 and over are,

typically, less likely to be problem gamblers and therefore less likely to experience gambling-related

harm. Conversely, those aged 18-34 are typically considered a key risk group for the experience of

gambling-related harm and it is this group for whom we are more likely to have more incomplete

data.

Those who did not ‘always’ use their loyalty card when playing machines were asked why not. The

main reasons were as follows:

I forget my card (50%);

I’ve lost my card (11%);

the card affects the way the machine plays (11%);

it’s not worth using the card for the stakes I place (10%);

I can’t be bothered (8%);

I don’t want my play tracked (4%).

A number of other reasons were also given such as, I think the card brings me bad luck; I’ve

destroyed the card; there are technical problems with it; or, you can only use it in one machine.

Endorsement of these reasons was low, with less than 2% of participants who did not always use

their card stating each reason.

As with the qualitative work, the range of reasons given is notable, varying from practical

considerations such as ‘I’ve lost my card’, to concerns about the effect the card has on how the

machine plays, to issues of privacy and some people clearly not wanting their play to be tracked and

monitored. This suggests that those who always use their card may be different in profile, and

potentially in behaviour, from those who use their card less frequently. Likewise, evidence from the

0

10

20

30

40

50

60

18-34 35-54 55+

Age group

Perc

en

t

Men WomenFigure 2.3

Always uses loyalty card when playing machines, by age and sexBase: All aged 18 and over


qualitative work suggests that those who have a loyalty card may also have different patterns of

behaviour to those who do not, though this requires further investigation.

2.5 Limitations Whilst Chapter 1 noted the unique contribution of this study, there are a number of limitations that

need to be taken into account. These are:

The response rate was low, and whilst weighting has attempted to adjust for potential non-

response biases, very little is known about the characteristics of loyalty card holders.

Therefore, it is difficult to assess the range and type of biases that may be evident in the

survey results. For example, those who provided valid contact details to operators may be

systematically different from those who did not. This is currently unknown, and therefore we

are uncertain as to how ‘representative’ these survey results are of all loyalty card holders.

Those who took part in the survey are heavily engaged in gambling. They have a younger

profile and live disproportionately in deprived areas. These are characteristics typically

associated with greater risk of gambling problems. These findings are not surprising, as this

is a survey of people who signed up for a loyalty card, therefore one would expect them to be

more heavily engaged in gambling. The findings from this survey, however, should not be

extrapolated to all machine players, as loyalty card customers represent only one segment of

the player base. Furthermore, it is estimated that only around one in ten bookmakers’

transactions are recorded via a loyalty card. Comparison of these data suggests that loyalty

card information misses shorter sessions of play (see Report 3).

Finally, not all people with a loyalty card use it consistently and some use it very infrequently.

Some participants have cards for more than one operator or more than one card for the

same operator. There appear to be some systematic biases around frequency of use of

loyalty cards, with younger people reporting less frequent use. This means that for certain

types of participants we are unlikely to have complete records of machine play when

analysing their loyalty card data. There may be some systematic biases between those who

always use their card and those who do not. This is an important limitation of using loyalty

card data to identify potentially harmful patterns of play, as it introduces a potential source of

error.


3 Gambling participation

3.1 Introduction All survey participants were asked to report whether they had engaged in one of 19 different forms of

gambling activity in the past four weeks (see Appendix C for the questionnaire). The activities

represented all forms of gambling legally available in Great Britain and mirrored those included in the

most recent surveys of adult gambling behaviour, the health surveys for England and Scotland.

Those who reported undertaking an activity in the past four weeks were asked how often they

engaged in that activity. The choice of a four-week reference period was deliberate to reduce

participant burden; loyalty card holders are highly engaged gamblers, and we did not want to over-

burden them with questions about gambling frequency and risk their not completing the following

problem gambling questions (see Chapter 5).

This chapter gives an overview of participation in different forms of gambling, including the number

of activities undertaken and the frequency of participation. Consideration is given to how these

behaviours vary by age, sex and a range of socio-economic factors.

3.2 Gambling participation by age and sex Table 3.1 shows participation in a range of gambling activities in the past four weeks. Overall,

gambling on machines in a bookmaker’s was the most popular gambling activity, with 74% of

participants reporting this. The next most prevalent gambling activities were the National Lottery

(51%) and betting on horse races (not online) (50%). Playing poker in pubs and clubs and using

betting exchanges were the least popular gambling activities (7% for both).

Men were generally more engaged in most gambling activities than women. They were particularly

more likely than women to bet online with a bookmaker (34% for men; 17% for women) and to bet

on sports events (49% for men; 23% for women).

Women, however, were more likely than men to play lotteries and related products. 56% of women

had bought scratchcards compared with 38% of men, while other lotteries were played by 29% of

women and 17% of men. Women were also more likely to have played bingo (19% for women; 7%

for men)

Notably, similar proportions of men and women had used machines in a bookmaker’s, used slot

machines or gambled online on casino, slots or bingo style content.


Past four weeks participation in gambling varied by age but the pattern was different for different

activities. Generally, participation in machine/gaming activities was higher among younger age

groups (see Table 3.2). In all but two machine/gaming activities participation decreased as age

increased. A particularly sharp decrease can be seen for football pools, where estimates fell from

27% of 18-24 year-olds to 10% for those aged 65 and over. Casino games show a similar pattern,

falling from 24% for 18-24 year olds to 6% for those aged 65 and over. Gambling on machines in

bookmakers, however, had the inverse relationship with age; participation steadily increased with

age, rising from 65% of those aged 18-24 to 80% of those aged 55-64 and 79% of those aged 65

and over.

With regard to betting activities, different age groups dominated different activities. Younger

participants tended to take part in online betting with a bookmaker (52% for 18-24 year olds; 8% for

those aged 65 and over) and private betting (25% for 18-24 year olds; 4% for participants aged 65

and over). Conversely, betting on horses increased with age (65% for those aged 65 and over; 39%

for 18-24 year olds). Activities such as dog races and sports events showed less linear patterns and

tended to be dominated by the middle age categories.

Participation in two out of three lottery-related activities increased with age. Prevalence of buying

tickets for the National Lottery Draw was higher among older participants than younger ones. Buying

Table 3.1

Past four weeks gambling prevalence, by sex All aged 18 and over

Gambling activities Sex Total

Men Women % % %

Lotteries and related products National Lottery 51 53 51 Scratchcards 38 56 40 Other lotteries 17 29 18

Machines/games

Football pools 19 9 18

Bingo (not online) 7 19 8

Machines in a bookmaker’s 75 72 74

Fruit machines 31 29 31

Casino table games (not online) 17 10 17

Poker played in pubs or clubs 7 4 7

Online gambling on slots, casino or bingo games 23 26 23

Betting activities

Online betting with a bookmaker 34 17 31

Betting exchange 7 3 7

Horse races (not online) 52 39 50

Dog races (not online) 32 17 30

Sports events (not online) 49 23 45

Other events or sports (not online) 13 6 12

Spread-betting 6 2 5

Private betting 16 7 15

Other gambling activity

Any other gambling 6 4 6

Bases*

Weighted 3862 526 4721

Unweighted 3897 520 4723

* Bases shown are for National Lottery Draw. Bases for other activities vary.


scratchcards, however, was more prevalent among the younger age groups and decreased with

age, falling from 53% for those aged 16-24 to 21% for those aged 65 and over.

Table 3.2

Past four weeks gambling prevalence, by age All aged 18 and over

Gambling activities Age group Total

18-24 25-34 35-44 45-54 55-64 65+ % % % % % % %

Lotteries and related products National Lottery Draw 31 44 53 61 64 69 51 Scratchcards 53 46 46 33 26 21 40 Other lotteries 9 16 21 22 22 26 18

Machines/games

Football pools 27 23 16 13 11 10 18

Bingo (not online) 4 9 12 7 8 9 8

Machines in a bookmaker’s 65 75 75 76 80 79 74

Fruit machines 33 40 31 28 24 21 31

Casino table games (not online) 24 18 16 14 12 6 17

Poker played in pubs or clubs 11 8 6 5 3 2 7

Online gambling on slots, casino or bingo games 30 31 26 18 11 7 23

Betting activities

Online betting with a bookmaker 52 38 32 24 18 8 31

Betting exchange 7 9 8 5 5 1 7

Horse races (not online) 39 39 47 62 63 65 50

Dog races (not online) 22 26 33 37 34 29 30

Sports events (not online) 50 44 48 50 40 31 45

Other events or sports (not online) 9 11 17 14 9 7 12

Spread-betting 6 7 7 4 3 0 5

Private betting 25 19 15 11 5 4 15

Other gambling activity

Any other gambling 5 7 8 6 5 3 6

Bases*

Weighted 715 1008 838 949 546 300 4721

Unweighted 491 822 764 1091 737 471 4723

3.3 Number of gambling activities, by age and sex On average, participants had taken part in 4.8 activities in the past four weeks. Men took part in

slightly more activities than women, with an average of 5 activities for men and 4.2 for women.

Around one in 10 men (11%) and one in 20 women (5%) had taken part in nine or more activities in

the past four weeks, demonstrating how engaged some loyalty card holders were with gambling

more generally.



Table 3.3 Number of gambling activities, grouped and mean, by sex All aged18 and over

Number of gambling activities Sex Total

Men Women % % %

None 3 5 4 1 to 2 16 22 17 3 to 4 30 32 30 5 to 6 25 25 25 7 to 8 14 11 14 9 or more 11 5 11 Mean 5.0 4.2 4.8 Standard error of the mean .07 .17 .06

Bases Weighted 3862 526 4726 Unweighted 3897 520 4727

Table 3.4 shows the number of activities undertaken by age. Typically, younger participants were

more likely to take part in a greater number of gambling activities. Those aged 18-54 took part in

around five activities on average, whereas those aged 55 and over took part in around four.

Likewise, those aged 18-24 were more likely to have taken part in nine or more activities in the past

four weeks and estimates fell with advancing age, falling from 14% for those aged 18-24 to 1% for

those aged 65 and over.

Table 3.4 Number of gambling activities, grouped and mean, by age All aged 18 and above

Number of gambling activities

Age group Total

18-24 25-34 35-44 45-54 55-64 65+

% % % % % % %

None 5 5 3 3 2 1 4 1 to 2 19 16 16 15 16 19 17 3 to 4 23 27 28 31 37 44 30 5 to 6 24 22 22 29 29 29 25 7 to 8 16 16 17 12 12 6 14 9 or more 14 13 13 10 4 1 11 Mean 5.0 5.1 5.2 4.9 4.4 4.0 4.8 Standard error of the mean .17 .15 .16 .12 .12 .11 .06

Bases Weighted 715 1008 838 949 546 300 4726 Unweighted 491 822 764 1091 737 471 4727


3.4 Gambling participation by socio-economic characteristics Table 3.5 shows gambling participation by income.8 Participation in the National Lottery Draw, online

gambling on casino, slot or bingo content, online betting, using betting exchanges and betting on

sports or other events (not online) varied by income. For these activities, participation was higher

among those with higher incomes, and lower among those with lower incomes. For betting on other

events (not online) estimates varied with no clear pattern. For all other activities, there was no

significant variation in participation by income, meaning that those with low incomes were just as

likely to engage as those with high incomes.

Table 3.5

Past four weeks gambling prevalence, by income quintile All aged 18 and over

Gambling activities Income quintile

Lowest (less

than £10,400) 2nd 3rd 4th

Highest (£32k or

more)

% % % % %

Lotteries and related products National Lottery Draw 42 52 54 56 57 Scratchcards 38 41 43 41 33 Other lotteries 18 19 22 20 15 Machines/games Football pools 18 15 20 20 14 Bingo (not online) 7 10 8 8 7 Machines in a bookmaker’s 73 73 73 74 79 Fruit machines 28 32 29 34 32 Casino table games (not online) 17 15 14 15 21 Poker played in pubs or clubs 6 7 9 5 8 Online gambling on slots, casino or bingo games 17 23 24 25 31 Betting activities Online betting with a bookmaker 23 30 33 36 47 Betting exchange 3 6 5 9 12 Horse races (not online) 49 48 52 54 56 Dog races (not online) 33 30 31 29 30 Sports events (not online) 43 41 50 49 51

Other events or sports (not online) 13 10 11 9 15 Spread-betting 4 5 5 5 8 Private betting 13 14 14 17 18 Other gambling activity Any other gambling 6 3 5 6 10

Bases* Weighted 940 797 566 924 652 Unweighted 961 816 554 899 638 * Bases shown are for National Lottery Draw. Bases for other activities vary.

There was a clear association between number of gambling activities undertaken and income. The

average number of activities undertaken increased as income increased (see Table 3.6).

Participants with the lowest income took part in an average of 4.5 activities whilst those with the

highest income took part in 5.4 activities on average.

8 Because of low bases sizes among women, estimates in this section are shown for all participants. Income was

collected by asking participants to report whether it was higher or lower than a certain threshold until an end amount

was obtained. This meant we could capture information about personal income without directly asking for the amount.


The next socio-economic characteristic considered was area deprivation. Differences in how area

deprivation is defined means these tables present information for participants who live in England

only. Participation rates varied by area deprivation for four activities: other lotteries and bingo, where

prevalence was higher among those living in the most deprived areas in England; and betting on

sports events (not online) and betting online, where participation was higher among less deprived

areas (see Table 3.7).

There were no significant differences in the number of activities undertaken by area deprivation,

meaning those living in the highest deprivation areas took part in as many forms of gambling as

those in less deprived areas (table not shown).

Table 3.6 Number of activities, grouped and mean, by income quintile All aged 18 and over

Number of gambling activities Income quintile

Lowest (less

than £10,400) 2nd 3rd 4th

Highest (£32k

or more)

% % % % %

None 6 3 3 3 2 1 to 2 18 17 14 14 13 3 to 4 32 35 29 28 27

5 to 6 22 23 28 26 28

7 to 8 13 11 15 17 15

9 or more 9 10 10 11 15

Mean 4.5 4.7 5.0 5.1 5.4 Standard error of the mean .14 .14 .16 .14 .16

Bases

Weighted 940 797 566 924 652 Unweighted 961 816 554 899 638


Finally, participation rates were examined by the economic activity of the individual. These results

are shown in Table 3.8. Participation in most activities varied by economic activity: playing the

football pools, betting on dogs, sports events or other events (not online) was most prevalent among

those who were unemployed, and least prevalent either among those who were retired or, in the

case of betting on dogs, those who were students. For example, 51% of unemployed participants

had bet on sports events in the past four weeks, compared with 33% for those who were retired.

There were some activities where prevalence was highest among students. These were: playing

table games in a casino (25%), betting online (49%), private betting (21%) and playing poker in a

pub or club (10%). For each activity, lowest prevalence estimates were observed among those who

were retired or, in the case of table games in a casino, those who were looking after the family or

home.

However, there were four activities where participation was highest among those who were looking

after the family or home. These were: scratchcards (50%), bingo (17%), gambling online on casino,

slot or bingo content (27%) and fruit machines (41%). This pattern may be associated with gender,

with women being more likely to buy scratchcards and to play bingo and more likely to be looking

after the family or home.

Table 3.7

Past four weeks gambling prevalence, by area deprivation (England only)

All aged 18 and over

Gambling activities Area deprivation

Not most deprived area Most deprived area

% %

Lotteries and related products National Lottery Draw 53 48 Scratchcards 43 38 Other lotteries 16 24 Machines/games Football pools 16 20 Bingo (not online) 8 12 Machines in a bookmaker’s 77 73 Fruit machines 32 30 Casino table games (not online) 16 18 Poker played in pubs or clubs 7 6 Online gambling on slots, casino or bingo games 24 23 Betting activities Online betting with a bookmaker 36 25 Betting exchange 7 7 Horse races (not online) 52 47 Dog races (not online) 29 33 Sports events (not online) 48 41 Other events or sports (not online) 11 11 Spread-betting 5 4 Private betting 14 16 Other gambling activity Any other gambling 5 8

Bases*

Weighted 1925 1098

Unweighted 1890 1111



There were also four activities where prevalence rates were higher among those who were retired

and lowest among students. These were tickets for the National Lottery Draw (68% and 27%

respectively), tickets for other lotteries (24% and 7% respectively), machines in a bookmaker’s (82%

and 53% respectively) and betting on horses (65% and 25% respectively).

Finally, there was only one activity where engagement was higher among those in paid employment.

This was using betting exchange. Estimates were 8% among those in paid employment and 2% for

those who were looking after the family or home.

Table 3.8

Past four weeks gambling prevalence, by economic activity and sex All aged 18 and over

Gambling activities Economic activity

Paid work Self-employed

Retired Student Looking after

family/home

Long-term sick/disabled

Unemployed

% % % % % % %

Lotteries and related products National Lottery Draw 52 54 68 27 52 39 45 Scratchcards 43 38 23 37 50 38 43 Other lotteries 19 15 24 7 17 24 14 Machines/games Football pools 19 15 10 23 12 15 23 Bingo (not online) 7 6 8 1 17 15 9 Machines in a bookmaker’s 74 78 82 53 74 77 73 Fruit machines 32 30 21 21 41 35 32 Casino table games (not online) 17 17 10 25 6 13 20 Poker played in pubs or clubs 7 5 3 10 7 4 9 Online gambling on slots, casino or bingo games 26 22 9 24 27 20 22 Betting activities Online betting with a bookmaker 39 27 13 49 22 18 25 Betting exchange 8 7 4 5 2 5 5 Horse races (not online) 49 51 65 29 42 55 49 Dog races (not online) 28 32 29 13 32 36 38 Sports events (not online) 48 46 33 49 37 42 51 Other events or sports (not online) 11 11 7 11 15 16 18 Spread-betting 5 7 2 4 4 4 6 Private betting 17 14 5 21 11 9 14 Other gambling activity Any other gambling 6 6 4 9 8 9 5

Bases*

Weighted 2257 626 403 123 156 306 500 Unweighted 2120 648 583 85 157 304 499 *Bases shown are for National Lottery Draw. Bases for other activities vary.

Finally, Table 3.9 shows the number of gambling activities undertaken by economic activity. Those

who were unemployed (5.0) or in paid employment (5.1) took part in more activities on average,

whereas those who were retired or students took part in fewer activities (4.2).


Table 3.9 Number of activities, grouped and mean, by economic activity All aged 18 and over

Number of gambling activities

Economic activity

Paid work

Self-

employed Retired Student

Looking after

family/home

Long-term

sick/disabled Unemployed

% % % % % % %

None 3 3 0 15 7 5 4 1 to 2 15 18 20 17 15 16 17

3 to 4 28 29 40 24 29 33 30

5 to 6 25 27 28 22 28 22 22

7 to 8 15 15 10 13 10 13 13

9 or more 12 9 2 9 11 11 14

Mean 5.1 4.8 4.2 4.2 4.8 4.7 5.0

Standard error of the mean .09 .15 .11 .38 .32 .25 .20

Bases Weighted 2257 626 403 123 156 306 500 Unweighted 2120 648 583 85 157 304 499

3.5 Frequency of gambling by age and sex

Most frequent activity All participants who had taken part in a particularly activity in the past four weeks were asked to

report how often they had gambled on that activity.9 Across responses to all the frequency

questions, the most frequent activity in which a participant engaged was identified.

Overall, 26% of participants engaged in their most frequent activity almost every day and 72%

engaged in their most frequent activity at least twice a week. Men had a higher frequency of

engagement than women; 27% of men and 17% of women engaged in their most frequent activity

almost every day (see Table 3.10). Older participants tended to gamble more frequently than

younger ones. Those aged 65 and over were most likely to engage in their most frequent activity

almost every day (38%) whereas only 21% of those aged 18-34 reported the same (see Table 3.11).

Frequency of gambling on machines in a bookmaker’s Given that the focus of this research is on people who gamble on machines in a bookmaker’s, the

frequency of gambling on these machines is also presented in Tables 3.10 and 3.11.

Overall, 40% of participants had played machines at least twice a week, with 10% playing machines

almost every day. The frequency of gambling on bookmaker’s machines was higher among men

(41% who played used them twice a week or more) than women (32% who played used them twice

a week or more).

9 A routing error in the questionnaire meant that a follow-up question was not asked for those who had played poker in

a pub or a club.


Older participants tended to use machines in bookmakers more frequently than younger participants.

18% of those aged 65 and over had used these machines almost every day: the equivalent estimate

for those aged 18-24 was 6%.

Table 3.10 Frequency of gambling on a) the most frequent activity

and b) machines in a bookmaker’s, by sex All aged 18 and over

Frequency of gambling Sex Total

Men Women

% % %

Most frequent activity Almost every day/every day 27 18 26

4-5 days per week 14 14 14


About once per week 15 22 15

Less than once per week 7 7 7

Did not gamble in past four weeks 3 5 4

Gambled – frequency unknown 0 1 2

Machines in a bookmaker’s

Almost every day/every day 10 6 10



About once per week 19 19 18

Less than once per week 15 21 16

Did not gamble in past four weeks 25 29 26

Bases

Weighted 3862 526 4718

Unweighted 3897 520 4719

*Bases are shown for most frequent activity


Table 3.11

Frequency of gambling on a) the most frequent activity and b) machines in a bookmaker’s, by age


Frequency of gambling Age group Total

18-24 25-34 35-44 45-54 55-64 65+

% % % % % % %

Most frequent activity Almost every day/every day 21 21 30 28 27 38 26 4-5 days per week 12 15 14 15 16 15 14 2-3 days per week 30 32 33 37 36 32 32

About once per week 20 21 13 12 14 10 15

Less than once per week 11 7 8 5 4 3 7

Did not gamble in past four weeks 5 5 3 3 2 1 4

Gambled – frequency unknown - - - - 0 1 2


Almost every day/every day 6 9 11 11 7 18 10

4-5 days per week 5 9 6 7 8 10 7

2-3 days per week 15 20 26 25 29 28 23




Bases (unweighted) 491 822 764 1091 737 471 4719

Bases (weighted) 715 1008 838 949 546 300 4718

3.6 Frequency of gambling by socio-economic characteristics Tables 3.12 to 3.14 show the frequency of gambling on the most frequent activity by income, area

deprivation and economic activity. For engagement in the most frequent activity, gambling frequency

was highest among those with lower incomes. For example, 32% of those from the lowest income

quintile gambled almost every day, compared with 24% of those from the highest income quintile.

The frequency of using machines in a bookmaker’s did not vary by income.


Table 3.12 Frequency of gambling on a) the most frequent activity and b) machines in a bookmaker’s, by income quintile All aged 18 and over

Frequency of gambling Income quintile

Lowest

(less than

£10,400) 2nd 3rd 4th

Highest

(£32k or

more)

% % % % %

Most frequent activity Almost every day/every day 32 28 21 23 24 4-5 days per week 11 16 15 15 17

2-3 days per week 26 32 35 39 35

About once per week 15 15 18 16 13

Less than once per week 8 6 7 4 8

Did not gamble in past four weeks 6 3 3 3 2


Almost every day/every day 11 10 7 11 8

4-5 days per week 7 9 5 6 8

2-3 days per week 24 21 25 24 22

About once per week 16 17 19 18 24

Less than once per week 14 17 16 15 17

Did not gamble in past four weeks 27 27 28 26 21

Bases

Weighted 940 797 566 924 652

Unweighted 961 816 554 899 638


Looking at area deprivation, the frequency of engagement in the most frequent activity and gambling

on machines in a bookmaker’s was higher among those living in the most deprived areas in

England. For example, 13% of those in the most deprived areas used machines almost every day,

compared with 9% for those in less deprived areas.


Table 3.13 Frequency of gambling on a) the most frequent activity and b)

machines in a bookmaker’s by area deprivation (England only)


Frequency of gambling Deprivation

Not most deprived Most deprived % %

Most frequent activity Almost every day/every day 24 30 4-5 days per week 15 14


About once per week 17 16

Less than once per week 7 7

Did not gamble in past four weeks 3 4


Almost every day/every day 9 13



About once per week 19 17

Less than once per week 17 14

Did not gamble in past four weeks 23 27

Bases

Weighted 1925 1098

Unweighted 1890 1111


Finally, looking at Table 3.14 shows that those who were retired or unable to work because of a

long-term disability or illness had the highest prevalence of gambling almost every day on their most

frequent activity (38% for both groups), whilst those who were students had the lowest (13%). The

same pattern was true when looking at the frequency of gambling on machines in a bookmaker’s.


Table 3.14 Frequency of gambling on a) the most frequency activity and b) machines in a bookmaker’s, by economic activity All aged 18 and over

Frequency of gambling Economic activity

Paid work

Self-

employed Retired Student

Looking after

family/home

Long-term

sick/disabled Unemployed

% % % % % %

Most frequent activity Everyday 21 27 38 13 28 38 33

4-5 days per week 14 19 14 9 11 16 12

2-3 days per week 36 32 31 31 30 22 31



Did no gamble in past four weeks 3 3 1 15 7 5 4


Everyday 7 12 14 3 12 16 13

4-5 days per week 6 9 9 1 3 12 8

2-3 days per week 22 25 30 9 27 23 25




Bases

Weighted 2257 626 403 123 156 306 500

Unweighted 2120 648 583 85 157 304 499

3.7 Summary This chapter shows that LCS participants were highly engaged in a range of gambling activity.

Nearly all had gambled in the past four weeks and, unsurprisingly, gambling on machines in a

bookmaker’s was the most popular activity. There were some marked variations, with younger

participants having a broader gambling repertoire as they took part in more activities on average.

Looking at the frequency of gambling and the frequency of playing machines in a bookmaker’s

showed a distinct social pattern, whereby those who gambled more frequently had more

economically constrained circumstances. Rates of gambling more frequently tended to be higher

among those from lower income groups, those living in more deprived areas or those who were

economically inactive.


4 Types of gamblers

4.1 Introduction As Chapter 3 has shown, loyalty card holders display a range of engagement in other gambling

activities. In this chapter, latent class analysis (LCA) was used to identify different types of loyalty

card holders based on their engagement in gambling activities10. Once these types were developed,

regression models were produced to identify the factors associated with membership of each group.

The LCA technique identifies how gambling behaviours cluster into homogeneous groups of

gamblers based on individual response patterns to the gambling participation questions. LCA has

advantages over traditional clustering methods as it allows membership of classes to be assigned on

the basis of statistical probabilities. The process of classification allows the identification of those

behaviours which cluster together, and the labelling of the classes in a manner which is meaningful

and interpretable.

A key question in exploratory LCA is how many classes the sample should be divided into. There is

no definitive method to determine the optimal number of classes. Because models with different

numbers of latent classes are not nested, this precludes the use of a difference likelihood-ratio test.

Therefore, we rely on measures of fit such as Akaike’s Information Criterion (AIC) and the Bayesian

Information Criterion (BIC) instead. When comparing different models with the same set of data,

models with lower values of these information criteria are preferred. The resulting classes also have

to be interpreted. For this report, interpretability had primary importance when deciding on the final

number of classes. The technical details behind the chosen LCA models are presented in

Appendix A.

4.2 Gambling types Participation in each of the 19 gambling activities in the past four weeks as well as the total number

of gambling activities undertaken (ranging from 0 to 19) was used to classify respondents into

mutually exclusive groups. This identified four classes or types of gambler. These were:

Class 1 – Lowest engagement gamblers

This group accounted for 21% of all loyalty card customers surveyed, and represented loyalty card

players who were less engaged in a range of gambling activities than others. One in five (21%) had

not gambled in the past four weeks and the rest had taken part in one or two activities only.

Compared with all adults in Great Britain this makes them regular gamblers, but compared with other

10

In this analysis, the number of gambling activities undertaken in the past four weeks was used as a proxy for gambling

engagement. Further examination of different measures of engagement could be undertaken but was not possible

within the reporting timescales for this study. However, other research has shown that using a combination of number

of activities and frequency of engagement gives interesting results, with behaviour existing on a spectrum of breadth

and depth of engagement. See Wardle, H. (2014) Female Gambling Behaviour: a case study of realist description.

University of Glasgow: PhD thesis.


loyalty card holders they are less engaged in gambling. Of the activities participated in, gambling on

machines in a bookmaker’s was the most popular – 38% had done this in the past four weeks. The

National Lottery Draw and scratchcards were the next most popular activities, rather than betting

activities in a bookmaker’s which might have been expected given that 38% had been to a

bookmaker’s in the past four weeks. Therefore, this group clearly includes some people who only

gambled on machines in a bookmaker’s and did not place bets whilst they were there.

Class 2 – Moderate engagement gamblers

This group represented 30% of LCS participants and had higher levels of engagement in gambling

than class 1. They had all taken part in three to four different gambling activities in the past four

weeks. The majority had gambled on machines in a bookmaker’s (74%) yet lower proportions had

bet at a bookmaker’s (39% betting on horses; 32% betting on other sports). Therefore, like class 1,

this group also contained some loyalty card customers who only gambled on machines and did not

use other products offered in the bookmaker’s premises.

Class 3 – Substantial engagement gamblers

This group represented 38% of loyalty card customers surveyed. They had a broader gambling

repertoire and engaged in many different forms of gambling. They had all taken part in at least five

different forms of gambling in the past four weeks and over a third (36%) had taken part in seven or

eight types of gambling. Gambling on machines in a bookmaker’s was the most popular form of

activity among this group (88%), followed by betting on horses with a bookmaker (69%) or betting on

sports or other events (63%) and then the National Lottery draw (62%). This is a group of gamblers

who were clearly more engaged in the range of gambling activities offered by bookmakers along with

other activities.

Class 4 – Heaviest engagement gamblers

This class represented participants who were most heavily engaged in gambling (11%). They had all

taken part in nine or more different activities in the past four weeks and nearly all (97%) had

gambled on machines in a bookmaker’s. The next most popular activities were betting on horses in a

bookmaker’s (86%), betting on sports events in a bookmaker’s (85%), betting online (78%) and the

National Lottery Draw (76%). This group had participation rates in spread-betting, betting exchanges

and playing poker in pubs or clubs that were five times higher than average for LCS participants,

demonstrating their depth of engagement in related betting activities and gambling more broadly.

As this shows, the different groups had varying patterns of gambling engagement and also varying

levels of interest in the range of products offered in bookmakers’ premises. This is shown in Figure

4.1. For example, around half of those in class 1 had not either placed a bet with a bookmaker or

played machines in a bookmaker’s in the past four week. Around one in three (30% and 31%

respectively) of those in classes 1 and 2 had only played machines in a bookmaker’s and had not

placed over the counter bets compared with around one in 10 (11%) for class 3 and one in 50 (2%)

for class 4.


Table 4.1 Gambling types, by engagement in gambling activities All aged 18 and over

Gambling participation Gambling type Total

Class 1 – Lowest

engagement

Class 2 – Moderate

engagement

Class 3 – Substantial

engagement

Class 4 – Heaviest

engagement

% % % % %

Number of gambling activities in past four weeks

None 21 0 0 0 4 1 to 2 79 0 0 0 17 3 to 4 0 100 0 0 30 5 to 6 0 0 64 0 25 7 to 8 0 0 36 0 14 9 or more 0 0 0 100 11

Type of engagement National Lottery Draw 19 51 62 76 51 Scratchcards 14 33 50 73 40 Other lotteries 4 13 24 36 18 Football pools 3 12 21 53 18 Bingo 1 4 10 28 8 Machines in bookmaker’s 38 74 88 97 74 Fruit machines 9 23 38 73 31 Table games in a casino 3 9 20 51 17 Poker in a pub/club 0 2 7 31 7 Gambled online on casino/slots/bingo 4 12 29 65 23 Betting online 8 19 40 78 31 Betting exchanges 1 2 7 32 7 Bet on horses (not online) 10 39 69 86 50 Bet on dogs (not online) 2 17 43 70 30 Bet on sports events (not online) 8 32 63 85 45 Bet on other events (not online) 1 2 14 53 12 Spread bet 0 1 5 27 5 Private betting or gambling 4 6 19 45 15 Other gambling 1 3 7 24 6

Bases (weighted) 1007 1400 1811 508 4726 Bases (unweighted) 1880 1498 914 435 4727


4.3 Factors associated with membership of each group Multivariate logistic regression models were used to examine the range of socio-demographic and

economic factors associated with membership of each gambling group. When using bivariate

analysis methods, like cross tabulation, it is possible that factors are associated with a characteristic

because of some other underlying factor. For example, being retired may be associated with class

membership. However, this may be because age is associated with membership. As retired people

are older, the association may be demonstrating a relationship with age rather than a relationship

with economic status. Regression models allow these potential relationships to be taken into account

and to assess what the association is with the variable of interest when other factors, like age, are

held constant.

Separate logistic regression models were developed to examine the range of factors associated with

membership of each cluster. Variables entered into the model were:

age

sex

economic activity

income quintile

ethnicity

household composition

area deprivation11

number of current loyalty cards held

The results are shown in Tables 4.2 to 4.5. Only variables significant in the final model are shown in

the tables. Odds ratios are shown for each category of the independent variable. These odds are

expressed relative to a reference category: an odds ratio of 1 or more indicates higher odds of

belong to each gambling class, whereas an odds ratio of less than 1 means lower odds of belonging

to each gambling class. Confidence intervals are also shown: if the confidence interval straddles 1,

then there is no difference in the odds of being this type of gambler than the reference category.

Looking first at class 1, the lowest engagement gambling group, sex, income and number of loyalty

cards held were significantly associated with membership (Table 4.2). Women had odds of being a

class 1 gambler that were 1.5 times higher than men. Odds of membership decreased as personal

income levels increased; those in the highest income quintile had odds of being a class 1 gambler

that were 0.60 times lower than those who had the lowest income. This means that those with higher

incomes were less likely to be a class 1, lower engagement gambler. Those who had two current

loyalty cards had odds of being a class 1 gambler that were 0.48 times lower than those with just

one. The odds for those with three cards did not vary significantly from the reference category of one

card. This makes intuitive sense if the number of currently held loyalty cards is taken as a proxy for

greater engagement with gambling at a bookmaker’s. Those with more cards and thus higher

engagement in gambling were less likely to be lower engagement gamblers. Finally, those from non-

White/White British ethnic groups tended to have higher odds of being a low engagement gambler,

11

As England, Scotland and Wales have different indices of deprivation that cannot be combined, the variable used in

the regression model was coded as follows: 1 = not most deprived area in England; 2 = most deprived area in England; 3

= lives in Wales or Scotland.


though only those from other ethnic groups had odds which varied significantly from those who were

White/White British.

Table 4.2

Estimated odds ratios for belonging to class 1 (Lowest engagement gamblers)


Socio-demographic and economic characteristics OR 95% CI

n Lower Upper

Sex (p<0.01)

Men 1 3897

Women 1.52 1.12 2.05 520

Unknown 6.89 1.97 24.03 310

Income quintile (p<0.01)

Lowest (less than £10,400 per year) 1 961

2nd 0.80 0.58 1.11 816

3rd 0.68 0.47 0.98 554

4th 0.66 0.48 0.91 899

Highest (more than £32,000 per year) 0.60 0.41 0.87 638

Unknown 1.15 0.82 1.61 859

Number of loyalty cards held currently (p<0.01)

1 1 3681

2 0.48 0.36 0.65 859

3 or more 0.74 0.43 1.29 187

Ethnic group (p<0.05)


Mixed 1.59 0.73 3.45 76

Asian/Asian British 1.38 0.93 2.05 270

Black/Black British 1.21 0.81 1.81 298

Other 1.78 1.05 3.02 170

Unknown 0.28 0.08 0.98 325

Looking at class 2, moderate engagement gamblers, age, ethnicity and number of loyalty cards held

were associated with membership (Table 4.3). Those who were older had higher odds of being a

class 2 gambler, the odds being 1.5 times higher among those aged 45-54 and rising to 2.7 times

higher among those aged 65 and over than those aged 18-24. Like class 1, the odds of membership

were lower among those with a greater number of current loyalty cards, the odds being 0.58 times

lower among those with three loyalty cards than those with just one. Again, this makes intuitive

sense as this group did not display a broad interest in gambling in bookmakers’ premises and

therefore may be less likely to obtain a number of loyalty cards. Finally, ethnicity was significantly

associated with class 2 membership but none of the groups varied from the reference category. This

may in part be due to small base sizes among these groups.


Table 4.3

Estimated odds ratios for belonging to class 2 (Moderate engagement gamblers) All aged 18 and over


n Lower Upper

Age group (p<0.01)

18-24 1 491

25-34 1.25 0.91 1.72 822

35-44 1.37 0.99 1.89 764

45-54 1.53 1.13 2.08 1091

55-64 2.02 1.46 2.81 737

65 and over 2.72 1.88 3.94 471

Unknown 2.71 1.14 6.44 351



Mixed 1.26 0.68 2.37 76



Other 0.63 0.38 1.04 170

Unknown 0.42 0.17 0.99 325


0 or 1 1 3681

2 0.82 0.65 1.04 859

3 or more 0.58 0.35 0.94 187

Income, ethnicity and number of loyalty cards currently held were associated with class 3 gambling

(Table 4.4). Firstly, the odds of being a class 3, substantial engagement gambler were higher among

those with higher incomes. Odds were typically around 1.3-1.4 times higher among those in the third

to fifth (i.e., highest) income groups than those with the lowest income. With regards to ethnicity,

odds of membership of this group were around 0.5 times lower among those who were of mixed

ethnic origin or those who were Asian/Asian British than those who were White/White British. For

other groups, odds did not vary significantly from the reference category. The pattern by ethnicity is

not surprising; class 3 gamblers were our second most engaged groups of gamblers overall and

previous research has shown that those from Asian/Asian British groups are less likely to gamble but

that those who do gamble are more likely to experience problems.12 Finally, as with class 1 and

class 2, number of loyalty cards currently held was significantly associated with membership. This

time the odds of membership were higher (1.3) among those who had two loyalty cards than those

who only had one. As this group displayed higher interest in both machine play in bookmakers and

betting with bookmakers, this seems logical.

12

Forrest, D., Wardle, H. (2011) Gambling in Asian communities in Great Britain. Asian Journal of Gambling Studies, 2(1):

2-16.


Table 4.4

Estimated odds ratios for belonging to class 3 (Substantial engagement gamblers) All aged 18 and over


n Lower Upper

Income quintile p<0.05)


2nd 0.99 0.75 1.29 816

3rd 1.42 1.07 1.90 554

4th 1.42 1.11 1.84 899

Highest (more than £32,000 per year) 1.36 1.03 1.80 638

Unknown 1.12 0.83 1.52 859



Mixed 0.52 0.28 0.99 76



Other 0.83 0.53 1.31 170

Unknown 0.59 0.40 0.89 325


0 or 1 1 3681

2 1.30 1.06 1.60 859

3 or more 0.93 0.62 1.41 187

Finally, the factors associated with class 4, heaviest engagement gambling, were sex, age and the

number of loyalty cards currently held (Table 4.5). Women were less likely to be class 4 gamblers,

the odds of membership being 0.4 times lower among women than men. Those who were older,

aged 55 and over, were also less likely to be class 4 gamblers, with odds being 0.25 times lower and

0.06 times lower among those aged 55-64 and 65 and over respectively than those aged 18-24.

Finally, those with two or three currently held loyalty cards had odds at least two times higher of

being a class 4 gambler than those with only one. This is likely to be associated with the breadth and

depth of gambling interest displayed by this group.


Table 4.5

Estimated odds ratios for belonging to class 4 (Heaviest engagement gamblers) All aged 18 and over


n Lower Upper

Sex (p<0.02)

Men 1 3897

Women 0.43 0.24 0.76 520

Unknown 5.67 1.62 19.85 310

Age group (p<0.01)

18-24 1 491

25-34 0.96 0.64 1.44 822

35-44 0.95 0.62 1.44 764

45-54 0.71 0.47 1.09 1091

55-64 0.25 0.13 0.47 737

65 and over 0.06 0.02 0.17 471

Unknown 0.13 0.04 0.47 351


0 or 1 1 3681

2 2.14 1.58 2.90 859

3 or more 3.65 2.13 6.24 187

4.4 Summary This analysis shows that even among LCS participants, who when compared with the general

population are highly engaged gamblers, there is a broad spectrum of gambling behaviour. This

ranges from class 1, who typically took part in fewer gambling activities and did not display much

interest in the range of other gambling products offered by bookmakers, to class 4 who were

extremely engaged gamblers and took part in nearly all forms of gambling.

The regression models showed that a range of different factors was associated with membership.

One of the most notable features was the relationship with the number of loyalty cards currently

held. This was significantly associated with membership of all groups. How this relationship operated

varied by gambler type and level of engagement in gambling. For classes 3 and 4, the more heavily

engaged gambling groups, having more than one loyalty card increased the odds of membership,

whereas for classes 1 and 2 it decreased the odds of membership. The number of loyalty cards held

may be operating as a proxy for gambling frequency. This was tested and frequency of engagement

in the most frequent gambling activity was included in the models. For class 4 gamblers, number of

loyalty cards held was significant even when gambling frequency was controlled for, suggesting

there may be some other latent reason explaining this association. The same pattern was observed

with class 1 gamblers, though for classes 2 and 3 number of loyalty cards held was simply replaced

in the model by gambling frequency (tables not shown).

More investigation is needed to explore this relationship. However, it raises an important

consideration for Report 3 of this series. To date, loyalty card data does not generally allow us to

identify people with multiple accounts because unique identifiers like name and address are not

recorded by operators. This raises the possibility we may be missing an important predictor of

gambling engagement because of this limitation.


5 Problem and at-risk gambling

5.1 Introduction ‘Problem gambling’ is typically defined as gambling to a degree that compromises, disrupts or

damages family, personal or recreational pursuits.13 The primary objective of this study was to

identify which loyalty card holders might be experiencing problems with their gambling. Therefore, all

participants were asked to answer nine questions measuring the extent of problems they experience

with their gambling behaviour. This included asking about a range of different difficulties such as

how often they had chased losses, had felt guilty about their gambling or felt that gambling had

caused health problems. Responses to each question ranged on a four-point scale from ‘always’ to

‘never’. Taken together, these questions are known as the Problem Gambling Severity Index

(PGSI)14 and responses from these nine questions are combined to produce a PGSI score (see

Appendix A for more details of the scoring method). A maximum score of 27 is possible (someone

who says they always experience each of the nine problems presented has a PGSI score of 27).

Those scoring 8 or more are categorised as problem gamblers, those with a score of 3-7 are

categorised as moderate risk gamblers and those with score of 1-2 are categorised as low risk

gamblers. Participants with a PGSI score of 0 are either non-gamblers or those who gamble without

any difficulties.15

A final question was asked of everyone who had played machines in the past year about how often

they felt they had had a problem with their gaming machine play. The same response scale as the

PGSI was used for this question (i.e., responses ranged from ‘always’ to ‘never’).

This chapter reports problem gambling and at-risk gambling prevalence rates among LCS

participants, across all forms of gambling. It examines how these rates vary by a range of different

characteristics. It also presents prevalence rates of problems with machine play specifically. This

distinction is important. Previous chapters have demonstrated that most people who play machines

in a bookmaker’s also engage in a range of other gambling activities. It is possible that some people

may have experienced problems with their gambling on other activities rather than their machine

play specifically.

13

Lesieur, H.R. & Rosenthal, M.D. (1991). Pathological gambling: A review of the literature (prepared for the American

Psychiatric Association Task Force on DSM-IV Committee on disorders of impulse control not elsewhere classified).

Journal of Gambling Studies, 7 (1), 5-40 14

Ferris, J. & Wynne, H. (2001). The Canadian problem gambling index. Ottawa, ON: Canadian Centre on Substance

Abuse. 15

Some researchers have recommended that different (lower) thresholds should be used when identifying problem

gamblers using the PGSI. However, these recommendations have not been universally accepted and are not currently

endorsed by the original developers of the PGSI instrument. Therefore, this chapter uses the thresholds and

categorisation recommended by the original developers and replicates the methods used in the BGPS, also allowing

comparisons to be made. See Currie, S. R., Hodgins, D. C. & Casey, D. M. (2013). Validity of the problem gambling

severity index interpretive categories. Journal of gambling studies, 29(2), 311-327.


5.2 Caveats There are a number of caveats which need to be considered when interpreting the problem gambling

estimates:

This is a survey of people who hold loyalty cards for bookmakers. These people are

heavily engaged with gambling and are not representative of all gamblers in the

population. People who are more engaged with gambling are more likely to be problem

gamblers. This should be taken into account when reviewing these results.

The LCS is a cross-sectional survey. Hence associations can be identified in the

analysis, but the direction of causality cannot be ascertained.

Some people may give ‘socially desirable’ (and potentially dishonest) answers to a

questionnaire and may underestimate the extent of their gambling behaviour. This is

likely to be particularly true where questions are interviewer administered, as was done in

this survey.

No screen for problem gambling is perfect. The best performing screens should try to

minimise both ‘false positives’ and ‘false negatives’. A false positive is where someone

without a gambling problem is classified as a problem gambler. A false negative is where

a person with a gambling problem is classified as someone without a gambling problem.

The number of false positives and false negatives is related to the thresholds used. The

threshold used for the PGSI follows the recommendation of the screen’s developers and

is the same as used in the BGPS 2007 and 2010.

The PGSI has been validated on a Canadian population. It has not been validated in

Great Britain. This may have implications for how accurate it is at identifying problem

gambling and related-harm in Great Britain.

Finally, a survey estimate is subject to sampling error and should be considered with

reference to the confidence intervals as well as the survey design and sample size.

Where possible, the survey methodology attempted to overcome some of these issues. For

example, the results were weighted to take into account non-response bias across a number of

domains and there was careful consideration of the choice of gambling screen and appropriate

thresholds for problem gambling. That said, it is not possible to account for all potential biases and

caveats. Therefore, this chapter presents an estimate of current problem gambling among LCS

participants.

5.3 Problem and at-risk gambling by age and sex Overall, 23% of participants were categorised as problem gamblers, according to the PGSI. A further

24% were moderate risk gamblers, 24% were low risk gamblers and 29% were non-problem

gamblers.16

16

The confidence intervals for these estimates were as follows: problem gambling 21-25%; moderate risk 22-26%; low

risk 22-26% and non-problem 27-31%. This means we are 95% confident that the true value lies within this range.


Men were more likely than women to be either problem or at-risk gamblers. Problem gambling

prevalence was 24% for men and 18% for women. Conversely, 41% of women and 27% of men

were non-problem gamblers.

For both men and women, at-risk and problem gambling varied by age. For problem gambling, rates

were typically highest among those aged 25-54 and lowest among the youngest and oldest age

groups. However, when looking at the patterns of non-problem and at-risk gambling a different

pattern was evident. This is shown in Figure 5.1, whereby those aged 18-24 were more likely than

their older counterparts to experience at-risk gambling behaviours (a PGSI score of 1-7). For

example, 57% of those aged 18-24 were at-risk gamblers compared with 47% of those aged 25-34.

0

10

20

30

40

50

60

18-24 25-34 35-44 45-54 55-64 65+

Age group

Perc

en

t

Figure 5.1

At-risk gambling prevalence, by ageBase: All aged 18 and over


Table 5.1

Problem gambling prevalence rates according to the PGSIa by sex and age All aged 18 and over

PGSI scores Age group Total

18-24 25-34 35-44 45-54 55-64 65+ % % % % % % % Men

Non-problem (score less than 1) 25 23 25 28 34 37 27 Low risk (score 1-2) 37 23 19 22 23 25 25 Moderate risk (score 3-7) 22 24 28 24 27 24 25 Problem gambler (score 8+) 17 30 28 27 16 14 24

Women Non-problem (score less than 1) * 40 34 41 38 52 41 Low risk (score 1-2) * 21 29 14 26 22 22 Moderate risk (score 3-7) * 20 13 26 27 8 19 Problem gambler (score 8+) * 20 23 18 9 18 18

All

Non-problem (score less than 1) 26 25 26 29 34 39 29 Low risk (score 1-2) 36 23 20 21 24 25 24 Moderate risk (score 3-7) 21 24 26 24 27 22 24 Problem gambler (score 8+) 17 29 28 25 15 15 23

Bases (weighted)

Men 675 885 726 829 463 256 3861 Women 40 122 112 121 82 43 526 All 715 1008 838 949 546 300 4465 Bases (unweighted)

Men 462 730 681 950 634 404 3894 Women 29 91 83 141 102 66 520 All 491 822 764 1091 737 470 4497 a

PGSI: Problem Gambling Severity Index. A score of 8 or more is indicative of problem gambling. A score of 1 or

more is indicative of at-risk gambling. * Estimates not shown because of small base sizes

5.4 PGSI item endorsement by age and sex For both men and women, chasing losses was the most commonly reported problem. 53% of men

and 45% of women had, at least sometimes, chased their losses. One in 10 men (10%) and around

one in 12 women (8%) said that they always chased their losses.

After loss-chasing, betting with more money than one could afford to lose was the next most highly

endorsed item, with 46% of men and 40% of women stating this was something they did at least

sometimes when they gambled. Feeling guilty about gambling was the next most frequent behaviour

(40% men and 32% women) followed by people criticising gambling behaviour among men (36%)

and needing to gamble with larger amounts of money to get the same excitement (26%) among

women.

This pattern of endorsement was broadly similar for all age groups. There was one exception: men

aged 65 and over were more likely to say that they had at least sometimes felt they had a problem

with their gambling or that gambling had caused a health problem, than that they felt guilty about

their gambling.


Table 5.2

Endorsement of each PGSI item, by age and sex All aged 18 and over

PGSI item Age group Total

18-24 25-34 35-44 45-54 55-64 65+ % % % % % % % Men

Bet more than could afford to lose Never 61 48 50 52 58 66 54 Sometimes 26 30 32 31 33 24 30 Most of the time 7 11 11 8 6 5 9 Always/almost always 5 11 8 9 4 5 8 Gambled with larger amounts of money

Never 69 63 60 68 72 71 66 Sometimes 25 23 27 22 21 20 23 Most of the time 2 10 8 5 4 6 6 Always/almost always 4 5 5 5 3 3 5 Chased losses Never 44 39 43 52 57 63 47 Sometimes 38 35 34 30 32 28 33 Most of the time 11 12 10 9 5 5 9 Always/almost always 7 14 13 9 5 4 10 Borrowed money to gamble Never 89 77 80 80 89 92 83 Sometimes 8 17 14 14 8 7 12 Most of the time 1 4 3 3 1 0 3 Always/almost always 2 2 3 2 3 1 2

Felt had problem with gambling Never 76 61 59 61 70 73 65 Sometimes 13 25 27 24 21 19 22 Most of the time 5 7 7 7 3 3 6 Always/almost always 6 8 8 8 6 5 7 Gambling caused health problems Never 84 72 66 69 80 80 74 Sometimes 10 14 21 19 16 15 16 Most of the time 2 6 5 4 2 4 4 Always/almost always 3 8 8 7 2 2 6 People criticised my gambling Never 62 58 63 61 72 75 64 Sometimes 26 26 23 27 20 18 24 Most of the time 5 7 7 5 4 5 6 Always/almost always 7 8 6 7 4 2 6 Gambling caused financial problems Never 81 70 64 69 77 81 72 Sometimes 13 18 24 18 17 12 18 Most of the time 2 5 4 4 3 4 4 Always/almost always 4 7 8 9 4 3 6 Felt guilty about gambling Never 72 70 52 69 63 81 60 Sometimes 20 18 32 18 27 12 26 Most of the time 2 5 7 4 4 4 5 Always/almost always 5 7 9 9 6 3 9


Table 5.2 continued

Endorsement of each PGSI item, by age and sex All aged 18 and over

PGSI item Age group Total

18-24 25-34 35-44 45-54 55-64 65+ % % % % % % % Women

Bet more than could afford to lose Never * 56 64 54 60 67 60 Sometimes * 29 25 32 34 18 28 Most of the time * 6 3 7 3 3 5 Always/almost always * 9 8 7 3 12 8 Gambled with larger amounts of money Never * 74 66 77 70 79 74 Sometimes * 18 25 16 22 19 19 Most of the time * 4 1 4 4 1 3 Always/almost always * 4 8 3 4 1 4 Chased losses Never * 52 51 56 55 70 55 Sometimes * 28 34 27 32 22 29 Most of the time * 10 11 10 8 2 8 Always/almost always * 11 4 7 5 6 8 Borrowed money to gamble Never * 82 80 88 91 91 86 Sometimes * 15 13 9 8 9 10 Most of the time * - 5 2 - - 2 Always/almost always * 4 2 1 0 - 2

Felt had problem with gambling Never * 74 70 71 78 78 75 Sometimes * 18 20 24 18 22 19 Most of the time * 4 3 0 1 - 2 Always/almost always * 5 6 4 3 - 4 Gambling caused health problems Never * 77 76 81 83 79 80 Sometimes * 14 15 13 11 20 14 Most of the time * 1 6 1 1 - 2 Always/almost always * 8 3 5 5 1 4 People criticised my gambling Never * 72 74 71 87 88 76 Sometimes * 19 19 24 4 11 17 Most of the time * 4 5 1 4 1 3 Always/almost always * 5 3 4 5 - 4 Gambling caused financial problems Never * 78 71 81 90 83 81 Sometimes * 9 16 13 4 15 11 Most of the time * 5 6 3 1 1 3 Always/almost always * 8 6 3 5 1 5 Felt guilty about gambling Never * 64 67 66 76 68 68 Sometimes * 25 17 24 18 25 22 Most of the time * 4 7 5 2 6 5 Always/almost always * 7 9 4 4 1 5

Bases

Weighted Men 675 885 726 829 463 257 3861 Women 40 122 112 121 82 43 526

Unweighted Men 462 730 681 950 634 405 3896 Women 29 91 83 141 102 66 520

* Estimates not shown because of small base sizes


5.5 Problem and at-risk gambling by gambler type, number of activities and sex

Chapter 4 discussed the four different types of gamblers evident in the survey. This ranged from

those who were less engaged in a range of gambling activities to those who were very heavily

engaged in gambling. Problem gambling and at-risk gambling rates were highest among those from

the heaviest engagement gambling group (class 4). Of this group, only 13% had not experienced

any problems with their gambling behaviour in the past 12 months; 48% were at-risk gamblers and

40% were classified as problem gamblers. Among those who were less engaged with gambling

(class 1), 16% were problem gamblers, 41% were at-risk gamblers and 44% had no problems with

their gambling behaviour. This is shown in Figure 5.2.

The same pattern was evident when looking at the number of gambling activities undertaken in the

past four weeks. Broadly speaking, the more activities people engaged in, the higher the rates of

problem and at-risk gambling. This was true both for men and women.

In terms of identifying groups at greater risk of problems, looking at the breadth of gambling

involvement is clearly important. However, there are some people who only engage in one or two

gambling activities and experience problems (see Table 5.4). For example, 17% of men and 12% of

women who took part in one or two activities in the past four weeks were problem gamblers (this

point is discussed further in Chapter 7). There were some participants who took part in no gambling

activities in the past four weeks and were also categorised as problem gamblers. This is likely to be

due to the different reference periods used in the questionnaire. Problem gambling behaviour was

measured over the past 12 months, whereas engagement in gambling was measured over the past

four weeks. It is therefore possible that some people had experienced problems in the past year and

had since abstained from gambling.


Table 5.3 PGSI score, by gambling group and sex


PGSI score Gambling group Total

Class 1: Lowest

engagement

Class 2: Moderate

engagement

Class 3: Substantial

engagement

Class 4: Heaviest

engagement

% % % % % Men

PGSI Non-problem (score less than 1) 23 30 39 14 27 Low risk (score 1-2) 24 25 26 22 25 Moderate risk (score 3-7) 27 25 17 26 25 Problem gambler (score 8+) 25 21 17 39 24 Women Non-problem (score less than 1) 35 43 53 5 41 Low risk (score 1-2) 21 26 18 25 22 Moderate risk (score 3-7) 22 17 16 25 19 Problem gambler (score 8+) 22 14 12 44 18 All Non-problem (score less than 1) 24 31 42 13 29 Low risk (score 1-2) 24 25 24 21 24 Moderate risk (score 3-7) 27 24 18 26 24 Problem gambler (score 8+) 25 20 16 40 23

Bases

Weighted 1537 1148 734 442 3861

Men 187 169 144 26 526

Women 1751 1332 904 478 4465

All

Unweighted

Men 1621 1235 661 377 3894

Women 185 190 122 23 520

All 1827 1441 817 412 4497


Table 5.4 PGSI score, by number of gambling activities and sex All aged 18 and over

PGSI score Number of gambling activities in past four weeks Total

0 1-2 3-4 5-6 7 or more % % % % % % Men

Non-problem (score less than 1) 51 36 30 27 15 27 Low risk (score 1-2) 21 28 25 24 24 25 Moderate risk (score 3-7) 7 19 25 26 28 25 Problem gambler (score 8+) 21 17 21 23 33 24 Women Non-problem (score less than 1) 71 49 43 34 26 41 Low risk (score 1-2) 16 19 26 22 20 22 Moderate risk (score 3-7) 1 20 17 25 19 19 Problem gambler (score 8+) 12 12 14 19 35 18 All Non-problem (score less than 1) 56 38 31 27 16 29 Low risk (score 1-2) 19 26 25 24 23 24 Moderate risk (score 3-7) 6 20 24 26 28 24 Problem gambler (score 8+) 18 16 20 23 33 23

Bases Weighted Men 132 602 1148 979 1000 3861 Women 27 117 169 130 83 526 All 169 735 1332 1122 1108 4465 Unweighted Men 88 573 1235 1047 951 3894 Women 15 107 190 128 80 520 All 116 701 1441 1186 1053 4497 * Estimates not shown because of small base sizes

5.6 Problem and at-risk gambling by income, area deprivation and economic activity

Table 5.5 shows PGSI scores by income. There was an inverse relationship between problem

gambling and income, with problem gambling rates falling as income increased. Overall, problem

gambling and at-risk gambling rates were highest among those with lower incomes. Around one in

three men (33%) and one in four women (24%) with personal incomes of less than £10,400 per year

were problem gamblers. Among those earning over £32,000 per year, estimates were 15% for men

and 3% for women.

Interestingly, this inverse relationship was not evident for at-risk gambling, with rates of moderate

risk gambling being higher among those with higher incomes. In some respects this may be an

artefact of the PGSI screen as many items relate to financial problems (for example, borrowing

money, gambling causing financial difficulty, etc.). Therefore, some items may be less appropriate to

those with higher incomes and meaning this group could be less likely to be classified as problem

gamblers and more likely to be classified as at risk if they are experiencing difficulties. This points to

a potential limitation of the PGSI instrument as it has a fairly narrow focus on the range of harms that

could result from gambling.

Problem gambling and at-risk gambling rates were also analysed by area deprivation. Data are

shown for England only, as Wales and Scotland have different deprivation indices that cannot be


combined. For both men and women, problem gambling prevalence was higher among those who

lived in the most deprived areas in England, though rates of at-risk and non-problem gambling were

broadly similar between areas.

Finally, problem and at-risk gambling varied by the participant’s economic activity. Problem gambling

rates were highest among those who were unemployed (39% for men; 27% for women) or those

who were economically inactive because of long term sickness or disability (33% for men; 25% for

women). Estimates tended to be lower among those who were retired or were full time students,

reflecting the older and younger age profile of these groups. (Estimates for women were lowest

among those in paid employment but base sizes are small and therefore caution should be taken

when interpreting these results).

Table 5.5 PGSI score, by income quintile and sex All aged 18 and over

PGSI score Income quintile Total

Lowest (less than

£10,400)

2nd 3rd 4th Highest (more than

£32,000)

% % % % % % Men

Non-problem (score less than 1) 22 23 23 31 33 27 Low risk (score 1-2) 22 22 27 25 24 25 Moderate risk (score 3-7) 23 27 26 23 28 25 Problem gambler (score 8+) 33 28 23 21 15 24 Women Non-problem (score less than 1) 34 41 [41] 43 [44] 41 Low risk (score 1-2) 23 19 [28] 18 [36] 22 Moderate risk (score 3-7) 19 19 [21] 19 [16] 19 Problem gambler (score 8+) 24 21 [10] 20 [3] 18 All Non-problem (score less than 1) 24 25 25 32 33 29 Low risk (score 1-2) 23 22 27 25 25 24 Moderate risk (score 3-7) 22 26 25 23 27 24 Problem gambler (score 8+) 31 27 22 21 15 23

Bases Weighted Men 802 684 505 840 622 3861 Women 138 113 61 84 30 526 All 940 797 566 924 652 4465 Unweighted Men 811 698 505 828 607 3894 Women 149 118 49 71 31 520 All 960 816 554 899 638 4497


Table 5.6 PGSI score, by area deprivation (England only) and sex


Gambling behaviour Area deprivation Total

Not most deprived area

in England

Most deprived areas in

England (80th

centile)

% % % Men

Non-problem (score less than 1) 27 25 27 Low risk (score 1-2) 25 22 25 Moderate risk (score 3-7) 25 24 25 Problem gambler (score 8+) 24 29 24 Women Non-problem (score less than 1) 39 43 41 Low risk (score 1-2) 20 19 22 Moderate risk (score 3-7) 23 15 19 Problem gambler (score 8+) 19 23 18 All Non-problem (score less than 1) 28 28 29 Low risk (score 1-2) 24 21 24 Moderate risk (score 3-7) 25 23 24 Problem gambler (score 8+) 23 28 23

Bases Weighted

Men 1703 946 3861

Women 222 151 526

All 1925 1098 4465

Unweighted

Men 1676 963 3894

Women 212 148 520

All 1889 1111 4497


Table 5.7

PGSI score, by economic activity and sex All aged 18 and over

PGSI score Economic activity Total

Paid employme

nt

Self-employed


family/home

Long-term sick

Unemployed

% % % % % % % %

Men Non-problem (score less than 1) 29 27 37 15 17 20 20 27 Low risk (score 1-2) 26 24 26 48 12 19 16 25 Moderate risk (score 3-7) 24 26 20 21 37 28 26 25 Problem gambler (score 8+) 21 23 17 16 34 33 39 24 Women Non-problem (score less than 1) 49 [18] 41 * 45 [33] 29 41 Low risk (score 1-2) 22 [29] 24 * 24 [15] 19 22 Moderate risk (score 3-7) 17 [32] 15 * 12 [27] 24 19 Problem gambler (score 8+) 12 [21] 20 * 19 [25] 27 18 All Non-problem (score less than 1) 31 26 38 17 31 22 21 29 Low risk (score 1-2) 26 24 26 44 18 19 16 24 Moderate risk (score 3-7) 24 26 19 21 24 28 25 24 Problem gambler (score 8+) 20 23 18 18 26 32 38 23

Bases Weighted

Men 2034 585 340 110 76 256 445 3861

Women 223 41 63 12 80 50 54 526

All 2257 626 403 123 156 306 500 4465

Unweighted

Men 1919 616 496 78 69 256 445 3894

Women 201 32 86 7 88 48 54 520

All 2120 648 582 85 157 304 499 4497 * Estimates not shown because of small base sizes


5.7 Factors associated with problem and at-risk gambling Some factors may be associated with problem gambling (such as being retired) because of an

underlying association with another characteristic (such as age). Multivariate regression models

allow these potential relationships to be taken into account and to assess what the association is

between the characteristic of interest when other factors, like age, are held constant.

Two separate logistic regression models were developed to a) examine the range of factors

associated with problem gambling, and b) examine the factors associated with at-risk gambling.

Models were run separately for men and women. This is the first time, in Great Britain at least, that

separate regression models for men and women have been presented. This is because in general

population surveys, the number of female problem gamblers identified has been too small to

analyse. It is widely acknowledged that men and women have different gambling preferences and

different motivations for gambling. Therefore, it is useful to explore whether a different range of

characteristics is associated with male and female problem and at-risk gambling.17

Eight different factors were entered into the regression models simultaneously. These were: age,

ethnicity, income, area deprivation, economic activity, household composition, number of loyalty

cards currently held and gambler type.

Table 5.8 shows the factors associated with both male and female problem gambling. Only factors

that were significant in the final model are presented in the table. Odds ratios are shown for each

category of the independent variable. These odds are expressed relative to a reference category: an

odds ratio of 1 or more indicates higher odds of being a problem or at-risk gambler and an odds ratio

of less than 1 means lower odds of being a problem or at-risk gambler. Confidence intervals are also

shown: if the confidence interval straddles 1, then there is no difference in the odds of being a

problem or at-risk gambler than the reference category.

Among men, age, income, ethnicity, economic activity and gambler type were associated with

problem gambling. The odds of being a male problem gambler were around 1.5-1.8 times higher

among those aged 25-54 than those aged 18-24. The odds were significantly higher among those

from non-White ethnic groups than White/White British ethnic groups. Odds were highest among

men who were Asian/Asian British, who had odds of being a problem gambler that were five times

higher than their White/White British counterparts. For both economic activity and income, the

relationship showed that those who were more economically disadvantaged were more likely to be

male problem gamblers. Odds were 1.8 times higher among those who were unemployed than those

who were in paid employment and were 0.4 times lower among those with an income of £32,000 per

year or more than those with an income of £10,200 per year or less.

Being a male problem gambler was associated with gambler types. Heaviest engagement gamblers

(class 4) had odds of being a male problem gambler that were over three times higher than lowest

engagement gamblers (class 1). Odds were also higher among those in classes 2 and 3, who also

had comparatively higher levels of gambling engagement than class 1.

Finally, currently having more than one loyalty card was associated with increased odds of being a

male problem gambler (1.37).

17

To allow this, the at-risk gambling model combines both moderate and low risk gambling to give large enough sample

sizes for women. (Further detail about the model development is given in Appendix A).


Among women, a lesser range of variables was associated with problem gambling status. This may

be because the base sizes for women were smaller and therefore it was more difficult to detect

differences, or because the range of factors associated with female problem gambling was different.

This requires further exploration. However, income, gambler type and number of loyalty cards

currently held were associated with female problem gambling. The odds of being a female problem

gambler were significantly lower among those with the highest levels of personal income. Odds were

0.1 times lower among those with an income of £32,000 per year or more than among those with an

income of £10,400 or less. Like men, gambler types were also associated with female problem

gambling, though only class 4 (the heaviest engagement gamblers) differed significantly from the

reference group (class 1). Finally, the number of loyalty cards current held was significantly

associated with female problem gambling; the odds of being a female problem gambler were 2.3

times higher for women who had two or more loyalty cards than those who had only had one.

Both the number of loyalty cards held and gambler type are measures of how engaged someone is

with gambling generally. It is therefore not surprising that these are associated with problem

gambling. What is of note is that these are both independently associated with problem gambling, so

that when high levels of gambling engagement are taken into account, the number of loyalty cards a

gambler had was still associated with problem gambling status.


Table 5.8

Estimated odds ratios for problem gambling, by associated risk factors and sex All aged 18 and over

Variable N Odds ratio

95% C.I.a Variable N Odds

ratio 95% C.I.

a

Men 3897 Lower Upper Women 520 Lower Upper

Gambling type (p<0.001)

Gambling type (p<0.05)

Cluster 1 – Lowest engagement 663 1

Cluster 1 – Lowest engagement 122 1

Class 2 – Moderate engagement 1236 1.43 1.01 2.01


Class 3 – Substantial engagement 1621 2.03 1.46 2.81


Class 4 – Heaviest engagement 377 3.23 2.12 4.91

Class 4 – Heaviest engagement 23 * * *





2nd 698 0.93 0.67 1.31 2nd 118 0.96 0.40 2.31 3rd 501 0.75 0.51 1.11 3rd 49 0.35 0.11 1.12 4th 828 0.54 0.37 0.78 4th 71 0.92 0.38 2.25 Highest (more than £32,000 per year) 607 0.38 0.25 0.58

Highest (more than £32,000 per year) 31 0.11 0.03 0.44

Unknown 447 0.48 0.31 0.73 Unknown 102 0.67 0.27 1.65



0 or 1 3019 1 0 or 1 411 1 2 or more 878 1.37 1.06 1.78 2 or more 109 2.33 1.18 4.63

Economic activity (p<0.01)

Paid employment 1919 1 Self-employment 616 1.33 0.96 1.83 Retired 497 1.59 0.89 2.82 Student 78 0.63 0.28 1.38 Looking after family/home 69 1.31 0.70 2.44 Long-term

illness/disability 256 1.66 1.05 2.64

Unemployed 445 1.86 1.33 2.61 Unknown 17 * * *

Ethnic group (p<0.05) White/White British 3130 1 Mixed 65 3.73 1.78 7.83 Asian/Asian British 259 5.20 3.61 7.50 Black/Black British 273 4.55 3.13 6.63 Other 155 3.82 2.27 6.42 Ethnic group unknown 15 * * *

Age (p<0.001) 18-24 462 1 25-34 730 1.78 1.22 2.59 35-44 681 1.53 1.03 2.27 45-54 950 1.59 1.09 2.33 55-64 634 0.91 0.58 1.44 65 and over 405 0.66 0.32 1.32 Age unknown 35 [0.99] [0.27] [3.65] a

Confidence interval *Estimates not shown due to small base sizes

Table 5.9 shows the factors associated with at-risk gambling. For men, these were ethnicity,

household composition, income, gambler type and number of loyalty cards held. Many of the

patterns observed were similar to those for problem gambling. For example, the odds of being an at-


risk gambler were higher among non-White ethnic groups, being around 2.6-3 times higher among

those from Black/Black British and Asian/Asian British ethnic groups. Likewise, those with higher

level of income were less likely to be an at-risk gambler, and the odds of being a male at-risk

gambler were higher among heaviest engagement gamblers; odds were 3.3 times higher for class 4

(heaviest engagement) than class 1 (lowest engagement).

The number of currently held loyalty cards and the type of household in which the participant lived

were both associated with male at-risk gambling. The odds of being a male at-risk gambler were 1.4

times higher among those with two current loyalty cards, further reinforcing this as a potentially

important predictor of behaviour. Compared with those living alone, the odds of being a male at-risk

gambler were lower among those who lived with a spouse or partner only and those who lived with a

child over the age of 16 only. This demonstrates that it is not just individual characteristics such as

income, but also broader contextual factors such as immediate social networks and relationships,

that are associated with at-risk gambling status.

Among women, only gambler type and ethnicity were associated with at-risk gambling, with the odds

operating in much the same way as for men. Those from non-White/White British ethnic groups were

more likely to be at-risk gamblers as were those from more engaged gambling groups; the odds

being four times higher among non-White women than women who were White/White British. The

odds of being an at-risk gambler were two times higher among those from class 3 (substantial

gamblers) than class 1 (odds for class 4 are not shown due to small base sizes).


Table 5.9

Estimated odds ratios for at-risk gambling, by associated risk factors and sex All aged 18 and over



ratio 95% C.I.

a

Men Base (weighted) 3897 Lower Upper Women Base (weighted) 520 Lower Upper

Gambling type (p<0.05) Gambling type Class 1 – Lowest

engagement 663 1 Class 1 – Lowest

engagement 122 1





Class 4 – Heaviest engagement 377 3.34 2.09 5.35

Class 4 – Heaviest engagement 23 * * *

Ethnic group (p<0.05) Ethnic group (p<0.05) White/White British 3145 1 White/White British 458 1 Mixed 65 2.08 0.77 5.64 Non-White 62 4.30 1.77 10.44 Asian/Asian British 259 3.03 1.69 5.45 Black/Black British 273 2.69 1.46 4.95 Other 155 1.80 0.80 4.07 Unknown 15 * * *

Income quintile (p<0.05) Lowest (less than £10,400 per year) 812

1

2nd 698 1.08 0.74 1.56 3rd 501 1.14 0.77 1.68 4th 828 0.72 0.51 1.01 Highest (more than £32,000 per year) 607 0.77 0.53 1.12

Unknown 447 0.70 0.47 1.04


0 or 1 3019 1 2 721 1.40 1.04 1.90 3 or more 157 1.84 0.84 4.06

Household composition (p<0.05)

Lives alone 1359 1 Lives with spouse/partner & one or more child under 16 535 0.71 0.51 1.00

Lives with spouse/partner only 778 0.66 0.49 0.89

Lives with spouse/partner & one or more other adult 286 0.74 0.48 1.13

Lives with child over 16 & one or more other adult 833 1.06 0.79 1.42

Lives with one or more child over 16 only 62 0.35 0.16 0.78

Lives with one or more child under 16 only 24 * * *

Lives with one or more child under 16 & other adult 20 * * *

a Confidence interval

*Estimates not shown due to small base sizes


5.8 Problems with machine gambling by age and sex All participants who had played any type of gambling machine in the past 12 months were asked

how often they felt they had had a problem with their gambling machine play. There was a strong

correlation between machine gambling problems and PGSI problem gambling status; the Pearson’s

correlation coefficient between the two measures was 0.66 indicating a strong positive relationship.

This means that someone who was more likely to have problems with their machine play was also

more likely to have a PGSI score categorising them as an at-risk or problem gambler. For example,

97% of those who said they always had problems with their machine play were categorised as

problem gamblers, whereas only 4% of those who said they never had any problems with their

machine play were categorised as problem gamblers (table not shown). This is logical; it means that

people who have problems with their machine play are likely to be problem gamblers but that not all

problem gamblers have problems with machines.

Overall, nearly two thirds (62%) of participants said that they had never had a problem with their

machine gambling. However, 15% of men and 11% of women said this was something that they

experienced most of the time when they played machines.

For both men and women, those aged 25-54 were most likely to state that they experienced

problems with their machine gambling most of the time they played, or more often. Among men,

estimates were highest among those aged 25-34 (20%) and among women they were highest

among those aged 35-44 (17%), see Table 5.10.


Table 5.10

Gambling problems with machines, by sex and age All aged 18 and over

Has problems with machine gambling

Age group Total

18-24 25-34 35-44 45-54 55-64 65+ % % % % % % % Men

Almost always 6 11 8 11 6 5 9 Most of the time 4 9 8 6 4 4 6 Some of the time 17 25 27 22 25 25 23 Never 73 55 57 61 64 66 62

Women Almost always * 9 12 8 5 1 7 Most of the time * 3 5 3 0 5 4 Some of the time * 20 24 16 13 18 18 Never * 67 59 73 82 76 71

All Almost always 6 11 8 11 6 4 8 Most of the time 5 8 7 6 3 4 6 Some of the time 16 25 27 22 23 24 23 Never 73 56 57 62 67 68 63

Bases (weighted)

Men 653 834 666 765 434 240 3615 Women 33 106 105 114 73 41 478 All 686 941 771 879 508 281 4144 Bases (unweighted)

Men 446 693 647 906 615 392 3730 Women 25 83 79 137 97 64 493 All 471 777 726 1043 713 456 4274


5.9 Problems with machine gambling by income, deprivation and economic activity

Problems with machine gambling showed similar patterns by income, area deprivation and economic

activity as problem gambling more generally. Typically those from more disadvantaged backgrounds

were more likely to have problems with machine play. Rates of experiencing this, at least most of the

time, were higher among those with lower income levels (18%), those living in the most deprived

areas (18%), and those who were unemployed (22%) or who were economically inactive because of

long-term sickness or disability (25%). Patterns were similar for both men and women.


Table 5.11

Gambling problems with machines, by income quintile and sex All aged 18 and over


Income quintile Total

Lowest (less than

£10,400)

2nd 3rd 4th Highest

(More than £32,000)

% % % % % % Men

Almost always 11 10 7 8 6 9 Most of the time 8 7 8 6 4 6 Some of the time 28 25 21 22 25 23 Never 53 59 64 64 66 62 Women Almost always 14 4 [1] 11 [1] 7 Most of the time 3 9 [1] 6 [2] 4 Some of the time 18 16 [20] 28 [3] 18 Never 65 71 [78] 55 [94] 71 All Almost always 11 9 7 9 6 8 Most of the time 7 7 7 6 4 6 Some of the time 27 23 21 22 24 23 Never 55 60 66 63 67 63

Bases Weighted Men 731 647 471 795 586 3615 Women 131 101 57 72 30 478 All 862 748 528 867 616 4144 Unweighted Men 763 669 485 799 588 3730 Women 144 112 47 64 31 493 All 907 781 532 863 619 4274


Table 5.12 Gambling problems with machines, by area deprivation (England only) and sex



Area deprivation Total

Not most deprived area

in England

Most deprived areas in

England (80th

centile)

% % % Men

Almost always 7 10 9 Most of the time 6 8 6 Some of the time 22 26 23 Never 65 56 62 Women Almost always 7 12 7 Most of the time 3 5 4 Some of the time 18 17 18 Never 72 66 71 All Almost always 7 11 8 Most of the time 6 7 6 Some of the time 22 24 23 Never 65 58 63

Bases Weighted Men 1633 872 3615 Women 199 144 478 All 1833 1015 4144 Unweighted Men 1626 915 3730 Women 199 144 493 All 1826 1059 4274


Table 5.13

Gambling problems with machines, by economic activity and sex All aged 18 and over

Has problem with machine gambling

Economic activity Total

Paid employme

nt

Self-employed


family/home

Long-term sick

Unemployed

% % % % % % % %

Men Almost always 7 9 7 2 9 16 14 9 Most of the time 6 7 5 1 4 10 8 6 Some of the time 23 23 21 22 32 27 23 23 Never 64 62 67 75 55 48 55 62 Women Almost always 3 [11] 2 * 12 [15] 14 7 Most of the time 2 [-] 8 * 4 [5] 7 4 Some of the time 17 [23] 20 * 14 [15] 24 18 Never 77 [66] 71 * 69 [65] 54 71 All Almost always 7 9 6 2 11 16 14 8 Most of the time 6 6 5 4 4 9 8 6 Some of the time 23 23 21 20 23 25 23 23 Never 65 62 67 74 63 51 55 63

Bases Weighted Men 1909 556 314 102 71 234 416 3615 Women 197 39 60 9 77 46 49 478 All 2107 595 374 111 148 280 464 4144 Unweighted Men 1842 596 477 71 66 240 423 3730 Women 188 31 84 5 85 46 51 493 All 2030 627 561 76 151 286 474 4274


5.10 Factors associated with machine gambling problems Multivariate logistic regression models were also run to examine the associations between machine

gambling problems and various characteristics. As with problem and at-risk gambling, models were

run separately for men and women and the same range of characteristics were included. As logistic

regression requires a binary outcome, the models show the factors associated with having problems

with machine gambling at least most of the time.

For men, age, ethnicity, economic activity, household composition and gambler type were

associated with gambling machine problems. Odds of having problems were significantly lower

among those aged 55 and over (0.5) than those aged 18-34. As with problem and at-risk gambling,

odds were higher among those from non-White/White British ethnic groups, being 2.8-3.8 times

higher among those from Asian/Asian British groups and Black/Black British groups. Odds were

between 1.6-2.4 times higher among those from economically inactive groups (i.e., unemployed or

those with long term disabilities) than those in paid employment. Interestingly, the odds of having

problems with machine gambling were 1.9 times higher among men who were full time students.

This was not observed in the problem gambling models, where student status did not differ

significantly from the reference group of paid employment. Likewise, those who were retired, even


after age was taken into account, were less likely to have problems with their machine play: odds

were 0.16 times lower among this group than among men in paid employment. Whilst household

composition was associated with machine gambling problems, only those who lived either with a

spouse and at least one child or a child over the age of 16 had odds significantly different to the

reference group of those who lived alone. In both cases, the odds of having machine gambling

problems were lower among these men than men who lived alone. Finally, odds of having machine

gambling problems were related to gambler type. Men from the heaviest engagement group (class

4) had odds of machine gambling problems that were 2.1 times higher than those from the lowest

engagement group (class 1).

Among women, only household composition and income were associated with machine gambling

problems. Like men, odds of having machine gambling problems were lower among women who

lived in households with a spouse or partner and at least one child (0.12) than those who lived

alone. Whilst income was significantly associated with female machine gambling problems, only

women with middle incomes (3rd income quintile) had odds significantly different from the reference

group. Here the odds of having machine gambling problems were 0.11 times lower among middle

income women than low income women.


Table 5.14

Estimated odds ratios for machine problem gambling, by associated risk factors and sex All aged 18 and over



ratio 95% C.I.

a

Men Base (unweighted) 3730 Lower Upper Women Base (unweighted) 493 Lower Upper

Gambling type (p<0.05) Income quintile (p<0.05) Class 1 – Lowest

engagement 571 1 Lowest (less than £10,400 per year) 144 1


2nd

112 0.70 0.25 1.96 Class 3 – Substantial

engagement 1599 1.25 0.86 1.83 3rd

47 [0.11] [0.03] [0.44] Class 4 – Heaviest

engagement 377 2.13 1.35 3.36 4th

64 1.13 0.39 3.29


Highest (more than £32,000 per year) 31 [0.19] [0.03] [1.00]

White/White British 3004 1 Unknown 95 0.38 0.12 1.19

Mixed 60 2.25 1.02 4.99 Asian/Asian British 245 3.84 2.52 5.84 Black/Black British 262 2.86 1.90 4.30

Other 145 2.42 1.36 4.32

Unknown 14 * * *

Economic activity (p<0.05)

Paid employment 1842 1 Self-employment 596 1.28 0.90 1.82 Retired 477 1.92 1.04 3.57 Student 71 0.16 0.05 0.51 Looking after family/home 66 0.82 0.33 2.02 Long-term

illness/disability 240 2.43 1.52 3.89

Unemployed 423 1.59 1.10 2.29 Unknown 15 * * *



Lives alone 1296 1 Lives alone 152 1 Lives with spouse/partner & one or more child under 16 503 0.81 0.55 1.20

Lives with spouse/partner & one or more child under 16

46 [0.83] [0.22] [3.11] Lives with spouse/partner only 754 0.74 0.51 1.07

Lives with spouse/partner only 110 0.55 0.18 1.68



Lives with child over 16 & one or more other adult 803 0.86 0.61 1.22

Lives with child over 16 & one or more other adult 48 [0.93] [0.29] [2.99]

Lives with one or more child over 16 only 59 0.09 0.02 0.30

Lives with one or more child over 16 only 26 * * *

Lives with one or more child under 16 only 22 * * *

Lives with one or more child under 16 only 33 [1.36] [0.32] [5.72]

Lives with one or more child under 16 & other adult 19 * * *

Lives with one or more child under 16 & other adult

23 * * *

Age (p<0.05) 18-34 1139 1 35-54 1553 0.86 0.64 1.16 55+ 1007 0.52 0.32 0.84

a Confidence interval

*Estimates not shown due to small base sizes


5.11 Summary This chapter examined problem gambling, at-risk gambling and problems with machine play. As

stated in the introduction, LCS participants are heavily engaged in gambling generally and therefore

these results are not representative of all machine gamblers.

Overall, LCS participants displayed a broad range of difficulties with their gambling behaviour. Only

around one in three participants (29%) had no problems with gambling; the rest were classified as

at-risk or problem gamblers. Around one in four participants (23%) were problem gamblers.

However, fewer participants experienced specific problems with machine play; 14% felt they had

problems with their machine gambling most of the time they played.

The BGPS has previously highlighted young men as a key risk group. In this study, young men did

not have problem gambling rates as high as other age groups but did have higher rates of being an

at-risk gambler. Problem gambling rates among the general population tend to be around four times

lower for women than men. Interestingly, whilst problem gambling rates in this study were lower

among women than men, the same disparity was not evident: estimates were 18% for women and

24% for men.

Problem gambling, at-risk gambling and machine problems were all associated with economic

disadvantage, measured either through low income or economic activity. This was particularly true

among men; those who either had the lowest levels of personal income or were unemployed or

unable to work because of long term disability/sickness were more likely to be either a problem

gambler or to have problems with their machine play. Similar patterns have been observed in the

BGPS series and this further highlights the relationship between economic disadvantage and the

experience of gambling problems.

Finally, this analysis highlights two new and interesting associations between problem gambling and

machine problems: the relationship with number of current loyalty cards held and who the gambler

lives with. Looking first to at-risk gambling and machine problems among men, living alone was

associated with both behaviours. Living alone was also associated with female machine problems,

with those who lived alone being more likely to experience problems.

The more loyalty cards held, the more likely male participants were to be at-risk gamblers, and both

men and women to be problem gamblers. As problem gambling rates among machine players

identified in nationally representative surveys like the BGPS are lower than among LCS participants,

it suggests that those who have a loyalty card may be more likely to be problem gamblers.

Furthermore, among those who do have loyalty cards, there is some evidence of a relationship

between a higher number of cards and some gambling problems.


6 Motivations and attitudes

6.1 Attitudes towards machine gambling To measure attitudes towards machine gambling, all participants were presented with two

statements. The first was a positive statement, ‘machine gaming is a harmless form of

entertainment’ and the second a negative statement, ‘machine gaming should be discouraged’.18

Participants were asked to rate their agreement with each statement on a five-point scale, ranging

from ‘strongly agree’, to ‘strongly disagree’.

Although 78% of survey participants had played machines in a bookmaker’s in the past four weeks,

participants were generally quite negative about machine gambling. This reflects findings from the

BGPS series which showed that overall, views of gambling tended to be more negative than

positive, despite gambling being an activity that the majority of adults participate in.19

Looking at the LCS, men were more negative than women, whilst older participants and those in

more deprived areas of England also tended to be more negative about machine gambling than

other groups.

More than half of the survey participants disagreed with the statement ‘machine gaming is a

harmless form of entertainment’ (30% disagreed and 24% strongly disagreed); 18% were ambivalent

(Table 6.1). Three in ten agreed with the statement (24% agreed and 5% strongly agreed). Men

were more likely than women to strongly disagree with the statement – 25% of men said they

strongly disagreed compared with 16% of women. Older participants were more likely to strongly

disagree than younger participants: 29% of those in the 45-54 years and the 65 years and older age

groups strongly disagreed with the statement, compared with 12% of 18-24 year olds. Those from

more deprived areas in England were more likely to disagree with this statement than those in less

deprived areas.

There were no differences in response patterns by income.

18

The phrasing of the statements was ‘machine gaming’ as advice from expert review suggested that some machine

‘gamblers’ do not view playing machine as ‘gambling’ and use of this term might bias results. The term ‘gamblers’ is

used through this chapter as this more accurately reflects the activity.

19 Wardle, H. et al. (2011) British Gambling Prevalence Survey 2010. London: TSO.


Table 6.1 Endorsement of the view ‘Machine gaming is a harmless form of entertainment’ All aged 18 and over

Item response

Strongly agree

Agree Neither agree nor disagree

Disagree Strongly disagree

Bases (unweighted)

Bases (weighted)

n n

Sex Men % 5 23 18 30 25 3866 3838 Women % 5 30 21 28 16 515 522 All % 5 24 18 30 24 4424 4403

Age 18-24 % 4 34 22 29 12 489 711 25-34 % 5 22 21 30 22 818 1003

35-44 % 6 22 17 30 26 759 832 45-54 % 5 19 16 30 29 1081 944 55-64 % 3 24 17 30 26 732 541 65+ % 4 22 14 31 29 466 299

Income quintile Highest (£32k or more) % 6 19 16 34 26 632 649 4th % 5 23 20 26 25 893 917

3rd % 4 23 23 28 22 552 564 2nd % 5 25 20 31 19 811 791 Lowest (less than £10,400) % 4 25 16 28 27 956 939

Area deprivation (England only) Most deprived areas in England % 5 22 17 32 24 1102 1093 Not the most deprived areas in

England % 5 24 20 28 23 1876 1913

Loyalty card holders’ responses to the statement ‘machine gaming should be discouraged’ were

more evenly distributed (Table 6.2): approximately two fifths of participants agreed with this

statement (19% strongly agreed and 22% agreed), around a fifth was ambivalent (22% neither

agreed nor disagreed) and nearly two fifths disagreed (30% disagreed, and 7% strongly disagreed).

Men were more likely than women to agree that machine gaming should be discouraged: 20% of

men strongly agreed with this statement compared with 9% of women. Older people were more

likely to agree with this statement than younger people: 25% of those aged 65 and over strongly

agreed whereas only 9% those aged 18-24 reported strong agreement. There were no significant

differences in this attitude by income or deprivation.


Table 6.2 Endorsement of the view ‘Machine gaming should be discouraged’ All aged 18 and over

Item response

Strongly agree

Agree Neither agree nor disagree

Disagree Strongly disagree

Bases (unweighted)

Bases (weighted)

n n

Sex Men % 20 22 21 29 7 3869 3842 Women % 9 23 26 36 7 509 513 All % 19 22 22 30 7 4417 4394

Age 18-24 % 9 22 24 37 7 488 711 25-34 % 19 22 25 27 7 818 999

35-44 % 22 25 17 30 5 759 831 45-54 % 23 19 24 27 7 1079 942 55-64 % 17 24 18 34 7 731 543 65+ % 25 22 17 29 6 467 299

Income quintile Highest (£32k or more) % 19 20 24 27 10 633 651 4th % 19 21 21 33 5 893 919

3rd % 17 25 21 32 5 548 556 2nd % 17 23 23 31 6 809 792 Lowest (less than £10,400) % 24 22 19 28 8 956 937

Area deprivation (England only) Most deprived areas in England % 19 23 21 30 7 1100 1088 Not the most deprived areas in

England % 18 22 22 32 6 1874 1909

6.2 Motivations for machine gambling All LCS participants who had played machines in a bookmaker’s in the past year were presented

with a series of statements regarding motivations for machine gambling. These statements were

‘playing machines…

o …to win money’ (Table 6.3)

o …because it is exciting’ (Table 6.4)

o …to escape boredom or fill your time’ (Table 6.5)

o …to make you feel better’ (Table 6.6)

o …to be around other people’ (Table 6.7)

Participants were asked to respond on a four-point scale ranging from almost always, most of the

time, sometimes or never.

The most common motivations for playing machines among LCS participants were ‘to win money’,

‘because it is exciting’ and ‘to escape boredom or fill your time’. This pattern was the same for both

men and women.


The majority of participants played machines in order to win money, with nearly half (49%) saying

they played ‘almost always’ to win money (Table 6.3). Men were more likely to play to win money

than women, with 50% of men and 36% of women saying they ‘almost always’ played for this

reason. Playing machines to win money did not vary by income or area deprivation.

Table 6.3 Motivation for playing machines in a bookmaker’s ‘to win money’ All who played machines in a bookmaker’s in the past year

Item response

Almost always Most of the time

Sometimes Never Bases (unweighted)

Bases (weighted)

n n

Sex Men % 50 14 25 11 3728 3612 Women % 36 13 36 15 490 474 All % 49 14 26 12 4229 4094

Age 18-24 % 42 17 29 12 471 686 25-34 % 51 13 26 11 778 941

35-44 % 50 15 22 13 726 771 45-54 % 52 10 26 12 1042 877 55-64 % 47 14 29 10 711 505 65+ % 50 14 21 15 456 281

Income quintile Highest (£32k or more) % 56 11 21 12 619 616 4th % 48 14 24 13 861 865

3rd % 47 13 26 14 532 528 2nd % 49 15 26 10 782 748 Lowest (less than £10,400) % 47 15 29 9 907 860

Area deprivation (England only) Most deprived areas in England % 48 15 25 12 1826 1832 Not the most deprived areas in

England % 51 12 28 10 1057 1012

Most participants also said they engaged in machine gambling because it was exciting; 47% said

that this was ‘sometimes’ a motivation for machine gambling; 17% reported this was a motivation

most of the time, and 14% said excitement was a motivation all of the time (Table 6.4). Women were

more likely than men to play machines ‘because it is exciting’: 54% of women and 47% of men

reported that they sometimes gambled on machines because it was exciting.

Younger participants were more likely than older participants to play machines for excitement; 21%

of those aged 18-24 and 13% of those aged 65 and over played ‘most of the time’ for this reason.

There were no differences by income or deprivation.


Table 6.4 Motivation for playing machines in a bookmaker’s ‘because it is exciting’ All who played machines in a bookmaker’s in the past year

Item response



Bases (weighted)

n n

Sex Men % 14 17 47 23 3720 3606

Women % 13 17 54 17 488 476 All % 14 17 47 22 4233 4105

Age 18-24 % 12 21 53 14 471 686 25-34 % 16 19 42 23 774 937 35-44 % 13 18 48 21 724 768 45-54 % 15 13 48 25 1041 878 55-64 % 12 15 48 25 711 507 65+ % 11 13 48 28 452 280

Income quintile Highest (£32k or more) % 13 22 44 21 618 616 4th % 14 14 48 24 858 863 3rd % 13 16 50 22 531 528

2nd % 16 15 45 24 779 747 Lowest (less than £10,400) % 14 20 47 20 908 862


England % 15 18 46 22 1820 1828

Two thirds of respondents said escaping boredom or filling their time was a motivation for their

machine gambling. More than two fifths of participants (42%) said they sometimes played machines

for this reason, 13% most of the time, and 11% almost always (Table 6.5). Younger participants

were more likely than older participants to play machines to escape boredom: 17% of those aged

25-34 played for this reason most of the time compared with 9% of those aged 55 and over. There

were no differences by income or deprivation in endorsement of this motivation for playing machines

in bookmakers.


Table 6.5 Motivation for playing machines in a bookmaker’s ‘to escape boredom or fill time’ All who played machines in a bookmaker’s in the past year

Item response

Almost always

Most of the time


Bases (weighted)

n n

Sex Men % 10 14 42 34 3723 3611

Women % 15 9 42 34 490 476 All % 11 13 42 34 4231 4101

Age 18-24 % 9 13 48 30 471 686 25-34 % 14 17 41 28 776 939 35-44 % 12 14 43 31 724 769

45-54 % 10 12 41 37 1043 879 55-64 % 8 9 42 41 712 508 65+ % 8 9 37 46 453 280

Income quintile Highest (£32k or more) % 11 12 43 34 617 616 4th % 1 2 15 39 35 860 866 3rd % 6 12 45 37 530 527

2nd % 11 14 43 32 780 747 Lowest (less than £10,400) % 13 13 44 30 908 862


England % 10 14 42 33 1825 1831

Gambling on machines to make oneself feel better and to be around other people were less

common motivations for machine gambling among LCS participants. Around a third of participants

played machines to feel better (Table 6.6) with 25% saying they sometimes played for this reason.

This motive was more common among women than men, with 42% of women and 33% of men

reporting they at least sometimes gambled on machines for this reason.

Gambling on machines to make oneself feel better was a less prevalent motivation among younger

participants, with 77% of those aged 18-24 stating that they never played machines for this reason.

The equivalent estimate among those aged 65 and over was 68%. Participants living in the most

deprived areas in England were more likely to be motivated to engage in machine gambling for

personal affect: 6% of those from the most deprived areas stated they ‘almost always’ played to feel

better, compared with 4% in other areas. There was no significant pattern by income.


Table 6.6 Motivation for playing machines in a bookmaker’s ‘to make you feel better’ All who played machines in a bookmaker’s in the past year 2014

Item response



Bases (weighted)

n n

Sex Men % 4 5 24 67 3712 3595 Women % 6 5 31 58 492 477 All % 4 5 25 66 4217 4082

Age 18-24 % 2 2 20 77 471 686 25-34 % 5 6 25 64 772 936

35-44 % 4 7 30 58 723 767 45-54 % 6 5 25 65 1039 873 55-64 % 4 4 25 66 712 506 65+ % 4 5 23 68 453 280




England % 4 6 26 64 1821 1826

Around a quarter of participants (26%) said that they played machines to be around other people

(Table 6.7). Younger people were more likely to play machines for company than those who were

older: 24% of those aged 18-24 played machines for this reason compared with 16% of those aged

65 or over. Those with a lower income (23%) were more likely to sometimes gamble on machines for

this reason than those with higher incomes (14%). There were no significant differences for this

motivation by gender or deprivation.


Table 6.7 Motivation for playing machines in a bookmaker’s ‘to be around other people’ All who played machines in a bookmaker’s in the past year

Item response

Almost always

Most of the time


Bases (weighted)

n n

Sex Men % 4 4 18 74 3721 3606 Women % 3 4 21 72 492 477 All % 3 4 18 74 4220 4088

Age 18-24 % 2 6 24 69 471 686 25-34 % 3 4 20 73 776 940

35-44 % 6 5 17 72 725 767 45-54 % 4 3 15 78 1044 879 55-64 % 3 3 17 77 712 508 65+ % 3 4 16 77 453 278




England % 3 4 17 75 1825 1831

The motivations for machine gambling were also examined by the latent class gambling types

described in Chapter 4. These were:

1. Class 1 – Lowest engagement gamblers, who engaged in a smaller range of gambling

activities than other classes. Of the gambling activities they did engage in, machines in

bookmakers were the most common form, followed by the National Lottery and

scratchcards.

2. Class 2 – Moderate engagement gamblers, who had taken part in a moderate number of

gambling activities within the past four weeks. Some of this group gambled on machines in

bookmakers and this was their most prevalent gambling activity. However, they did not

engage in other forms of gambling to the same extent.

3. Class 3 – Substantial engagement gamblers, who were engaged in a larger number of

gambling activities than classes 1 and 2. This group engaged in a range of gambling

activities, including gambling machines, betting on horses with a bookmaker, betting on

sports or other events and playing the National Lottery.

4. Class 4 – Heaviest engagement gamblers, who were engaged in the widest range of

gambling activities in the past four weeks of all the groups. Nearly all participants in this

group had played machines in bookmakers and also utilised other forms of gambling

available in a bookmaker’s, including betting on horses and other sports events. This group

also gambled online, and played the National Lottery.


Not all participants in each group had played machines in the past year and therefore this

analysis is restricted to those that had.

Examining the motivations to gamble by LCA group, class 4, the heaviest engagement

gamblers, demonstrated a different pattern of motivation to gamble on machines than other

groups. Class 4 gamblers were more likely to report that all of the different motivations

influenced them to play machines in bookmakers than the other groups (Table 6.8):

o 94% of participants in class 4 played machines in bookmakers to win money, at least some

of the time, compared with 84-89% of other groups;

o 32% of those in class 4 said they played machines because it is exciting most of the time,

compared with between 13-17% for other groups;

o 21% of the class 4 group said they played to escape boredom or fill time, compared with

10-15% of other groups;

o 8% said they played to make themselves feel better most of the time. Estimates ranged

between 3-5% for other groups;

o 9% said they played to be around other people most of the time compared with 2-4% for

other groups.


Table 6.8 Motivation for playing machines in a bookmaker’s by gambling types All who played machines in a bookmaker’s in the past year

Machine gambling Motivation

Item response

Almost always

Most of the time


Bases (weighted)

…to win money Class 1 – Lowest engagement % 41 12 31 16 677 730

Class 2 – Moderate engagement % 47 12 28 13 1365 1213 Class 3 – Substantial engagement % 51 15 23 11 1784 1683 Class 4 – Heaviest engagement % 57 15 21 6 403 469

…because it is exciting Class 1 – Lowest engagement % 12 13 49 26 681 734


…to escape boredom or fill your time

Class 1 – Lowest engagement % 9 10 41 40 680 733 Class 2 – Moderate engagement % 9 10 44 37 1363 1214

Class 3 – Substantial engagement % 11 15 43 32 1785 1683 Class 4 – Heaviest engagement % 16 21 38 25 403 471

…to be around other people Class 1 – Lowest engagement % 3 2 19 75 677 731 Class 2 – Moderate engagement % 3 3 19 75 1362 1212 Class 3 – Substantial engagement % 4 4 17 75 1780 1678

Class 4 – Heaviest engagement % 5 9 21 65 401 467

…to make you feel better Class 1 – Lowest engagement % 5 3 20 72 677 729


Attitudes towards machine gaming were also examined by LCA group, but no significant differences

were found. This demonstrates that attitudes to machine gambling were generally negative, even

among those who were engaged most heavily in gambling (class 4).

6.3 Summary Previous research has shown that whilst most people gamble, the majority of people hold less

favourable views of gambling activity. The same is true of LCS participants. Whilst the vast majority

had played machines in a bookmaker’s in the past four weeks or had played machines in the past

year, views of machine gambling were generally more negative than positive. Participants tended to

disagree that machine gambling was harmless though views were mixed about whether machine

gambling should be discouraged.

These results reflect attitudes to gambling among the general population. Evidence from the BGPS

2007 and 2010 showed that whilst people had generally more negative than positive views of

gambling, they felt that people had the right to gamble if they wanted to. This illustrates the complex

relationship between people’s attitudes and people’s behaviours. Furthermore, it is actually positive

that most participants recognised that machine gambling is an activity that involves risk. It is not a


‘harm-less’ activity per se, but one that may be ‘harm-ful’ for some people under some

circumstances.

Motivations for gambling on machines also mirrored motivations reported in the BGPS 2010, with

playing to win money and because of the excitement being primary reasons. Gambling to be around

others was of lesser importance among machine players than the general population, but this is to

be expected given the more solitary nature of machine play. That said, it is of interest that younger

participants were more likely to report this as a reason for machine gambling than their older

counterparts, which perhaps suggests some difference in gambling practice among this group.


7 Identifying problem gambling

7.1 Introduction The primary aim of the machines research programme was to examine whether industry data could

be used to distinguish between harmful and non-harmful gaming machine play use and if so, to

explore what measures might limit harmful use without impacting on those who do not exhibit

harmful behaviours.

So far, this report has analysed the broader gambling behaviour of LCS participants, all of whom

gambled on bookmakers’ machines between September and November 2013, using their responses

to the survey questions. This has been important to understand the context of participants’ machine

use, and previous sections have shown that:

a) this group of people gamble on many activities;

b) those more likely to be problem gamblers have more economically constrained

circumstances; and

c) those more highly engaged in a range of gambling activities are more likely to have

problems.

This was possible because the survey provides information about participants’ income, where they

live and who they live with. To date, gambling industry operators tend not to collect this level of detail

when signing people up to loyalty card schemes. Many do not collect basic demographic information

such as age or sex, let alone more detail about income or place of residence. Therefore, much of the

existing data held by industry operators is ‘context’ free. When people bet and use their loyalty

cards, we know what they do but very little about who is doing it.

Therefore, when attempting to use loyalty card data to distinguish between ‘harmful’ and ‘non-

harmful’ patterns of machine use, typically the only information available is transactional data.

Transactional data is the information collected by industry operators which tracks the money that is

put into a machine and the money that is paid out. For machines in a bookmaker’s every single

transaction (i.e., money bet) is recorded. When a loyalty card is used, this transaction is logged

against the loyalty card number. This study specifically aimed to link responses to the survey with

this transactional data to explore how behaviour varies between those who are and are not problem

gamblers. Report 3 of this series provides in-depth analysis of this linked data. However, some basic

analysis of this transactional data is presented in this chapter to provide a basic overview of some of

the main patterns, and to highlight key issues to be considered. Caveats that underpin the research

findings so far are also presented.


7.2 Profile of different player types by patterns of machine use

Metrics of machine use To examine how transactional data patterns vary for different types of gambler, key metrics

calculated from loyalty card data were merged onto the survey records. This was only undertaken for

those participants who agreed that their data could be linked in this way (4001 participants in total).

The metrics examined are:

number of machine gambling sessions per day;20

average length of individual machine gambling session (recorded in seconds);

average number of different games played per session;

average stake per bet;

average number of days elapsed between visit to use machines;

percentage of B2 games played in a session.

Total cash loaded into a machine per session.

These data have been collated and derived from the loyalty card records for each participant. These

metrics represent only a handful of all possible information available; the fuller breadth of the

transactional data is considered in Report 3. The metrics listed were chosen for inclusion in this

report as they represent areas of key policy and stakeholder interest (such as stake size) or are

metrics more likely to be associated with gambling-related problems (such as frequency of play

expressed as multiple sessions of machine gambling within one day). They are therefore useful to

examine to demonstrate both the potential and challenges of using industry data to identify ‘harmful’

patterns of machine play. The analysis and themes discussed in this chapter should be viewed as a

primer for the more detailed analysis presented in Report 3.

In addition to these metrics, the total number of gambling activities undertaken and the frequency of

engaging in the most frequent gambling activity reported in the survey are also considered. These

were chosen as it is useful to illustrate how behaviours vary when the full spectrum of gambling

behaviour, not just machine use, is taken into account.

20

A ‘session’ refers to a continuous session of play where money is loaded into the machine to start play and either

played until the extinction of funds or until the remaining funds are withdrawn.


Types of gamblers and factor analysis of the PGSI In previous chapters, the prevalence of problem gambling or at-risk gambling was reported. In this

section the profile of different types of gamblers and how their machine use varies is considered.

The groups examined are:

at-risk gamblers;

problem gamblers; and

those experiencing problems with their machine use.

In addition, exploratory factor analysis of the PGSI screen was conducted to explore the different

types of harms that people may be experiencing. As noted in the introduction to this report,

gambling-related harm is a much broader concept than problem gambling as it includes

‘the adverse financial, personal and social consequences to players, their families

and wider social networks that can be caused by uncontrolled gambling’.21

To date, there is no standardised or validated measure of gambling-related harm that can be used in

surveys.22 Therefore, this study used the PGSI with its measures of problem and at-risk gambling as

a proxy. We recognise that using the PGSI represents a more conservative measure of harm and

this means we are actually examining the extent to which industry data can be used to identify

patterns of machine play that are associated with problem and at-risk gambling. However, further

examination of the pattern of responses to the PGSI screen can highlight different sub-types of

problems, which may give more insight into how problematic patterns of machine play manifest for

different groups of gamblers. Identifying these different types of problems is the purpose of the factor

analysis.

Factor analysis is a technique used to identify underlying structures or characteristics of a construct,

in this case problem gambling. With an instrument like the PGSI, which consists of nine different

questionnaire items, the pattern of responses to questions and correlations between them can be

analysed to uncover different types of problems affecting gamblers. For example, analysis of

responses to a different problem gambling screen, based on the American Psychiatric Association’s

Diagnostic and Statistics Manual-IV, has shown that this measures two underlying constructs:

gambling-related harms and gambling-related consequences.23 The results from this can then be

used to give insight into the types of people who may experience greater gambling harms or greater

21

Responsible Gambling Strategy Board (2012) Strategy. Birmingham: Responsible Gambling Strat egy Board.

Available at: http://www.rgsb.org.uk/publications.html. 22

This research programme was commissioned, developed and implemented under time constrained circumstances.

There was not the time to develop a new set of questions aimed at measuring gambling-related harm for this study.

Therefore, it was agreed with the study commissioner and other stakeholders to use the PGSI screen instead. We

acknowledge that this changes the focus of the original research objective. 23

Orford, J., Sproston, K. & Erens, B. (2003). SOGS and DSM-IV in the British Gambling Prevalence Survey: Reliability and

factor structure. International Gambling Studies, 3 (1), 53-65. And Orford, J., Wardle, H., Griffiths, M., Sproston, K. &

Erens, B. (2010). PGSI and DSM-IV in the 2007 British Gambling Prevalence Survey: Reliability, item response, factor

structure and inter-scale agreement. International Gambling Studies, 10(1), 31-44.


gambling consequences or both. This is useful as it views problematic gambling as a range of

different behaviours and moves away from thinking simply about whether problems are experienced

or not.

In Great Britain, previous factor analysis of the PGSI screen has only highlighted one factor being

evident – that of problem gambling.24 This could be because previous survey datasets used

information collected from the general population, where the number of people endorsing each item

was low, potentially resulting in insufficient variation to identify different factors. This survey is a

study of highly engaged gamblers and therefore the number of participants endorsing each item was

higher, creating a greater opportunity to examine questionnaire response patterns.

Full details of the factor analysis approach are given in Appendix A. Results from this analysis

suggested that the PGSI screen consists of two distinct factors. These are identified by looking at

the ‘factor’ loadings, shown in Table 7.1. Essentially, the higher the number, the more strongly

correlated an item is with the other questionnaire items in that factor.

Table 7.1 Factor loadings for the PGSI


PGSI item

Factor 1 Factor 2

Bet more than could afford to lose .76 Gambled with larger amounts of money to get the same excitement

.84

Chased losses .75 Borrowed money to gamble .51 .51 Felt had a problem with gambling .48 .69 Gambling caused a health

problem .46 .70

People criticized my gambling .83 Gambling caused financial problems

.53 .69

Felt guilty about my gambling .67

Loadings less than 0.4 not shown

The factor loadings suggest that the two factors can be broadly distinguished as 1) potentially

harmful gambling actions, and 2) potentially harmful gambling consequences:

Factor 1 includes chasing losses, gambling with more money to get the same excitement and

betting more than one can afford to lose – all of which relate to actions that the individual

takes when gambling. All have factor loadings of 0.7 or higher.

Factor 2 deals more with consequences of gambling such as people criticising behaviour,

health impacts, financial difficulties or feeling guilty about what happens when the participant

gambles. All have factor loadings of 0.6 or higher.

One item, borrowing money to fund gambling, loaded equally onto both factors. From these results it

is possible to calculate factor scores. These scores represent a latent continuum of behaviour and

24

Factor analysis of the PGSI scale was attempted using the combined Health Survey for England 2012 and Scottish

Health Survey 2012 data. These were surveys of the general population and factor analysis showed that a single factor

solution was optimum. This replicates findings from Orford et al., 2010.


summarise the participant’s responses to each individual item within the factor. The scores for each

factor are standardised so that every factor has a mean of 0 and standard deviation of 1. A positive

factor score (i.e., greater than 0) indicates that these behaviours are endorsed more often than

average, whilst a negative factor score (i.e., less than 0) indicates that these behaviours are

endorsed less often than average.

For analysis in this chapter, scores were deciled and the 10% of participants with the highest

positive scores on each factor were identified.25 This produced two groups, those with the highest

(potentially) harmful gambling action scores and those with the highest (potentially) harmful

gambling consequences scores.26 From this, four distinct groupings of loyalty card holders were

identified:

1) those with non-high harmful gambling actions and consequences scores (82.5% of loyalty

card holders);27

2) those with a high harmful gambling consequences score only (7.5% of loyalty card holders);

3) those with a high harmful gambling action score only (7.8% of loyalty card holders);

4) those with high harmful gambling actions and consequences scores (2.3% of loyalty card

holders).

The profile of these groups, in addition to the profile of problem gamblers, at-risk gamblers and those

with machine-related problems, are examined in the sections that follow.

Machine gambling behaviour Table 7.2 shows how machine gambling behaviour varies among non-problem, at-risk and problem

gamblers. Of the different machine-data variables examined, only the average number of sessions

per day, average number of days between visits, average stake sized and total cash deposited into

the machine varied by PGSI status.

Problem gamblers, on average, had 2.2 machine sessions per day28 whereas non-problem gamblers

had 1.8. This means that on the days when at-risk and problem gamblers used machines, they on

average had two distinct sessions of play, whereas non-problem gamblers on average engaged less

than this.

25

This is a somewhat arbitrary threshold of ‘high’. The minimum factors scores for this 90th percentile were 1.42 for

factor 1 and 1.40 for factor 2. This threshold was chosen to illustrate the potential of this approach; other ways to define

‘high’ may give different results. 26

We recognise that high scores on each factor does not necessarily mean that a participant is experiencing greater

harm, but rather that this is indicative that they could potentially be experiencing greater harmful actions or

consequences. For clarity for the reader, we simply refer to high harmful gambling actions or consequences.

27 That is, the participants whose gambling actions and gambling consequences scores were lower than the scores for

90th percentile for each.

28 As noted earlier, a session is a discrete time of machine use. People can have many different ‘sessions’ of play in the

same day.


Average stake size varied between problem, at-risk and non-problem gamblers. On average,

problem gamblers staked £7.43 per bet, at-risk gamblers between £5 and £6 per bet and non-

problem gamblers £4.27 per bet. Median values ranged from £2.23 per bet for non-problem

gamblers to £3.51 for problem gamblers. The difference between the average (the mean) and the

median values highlights how diverse these data are, indicating that there are some gamblers in

each group for whom their staking level is much greater than the median, giving higher average

values (looking at the 90th percentile values shows that the highest staking 10% of problem

gamblers had an average stake of £20 per bet, whereas equivalent values for non-problem gamblers

were £10 per bet). In short, this highlights that for all groups there is a broad range of staking

patterns, but that on average stake per bet is higher among problem gamblers.

Problem gamblers also deposited significantly higher amounts of cash into machines in their

gambling sessions than non-problem gamblers (£41 on average vs £23).29

One question related to this is whether there is some fundamental difference in the ‘purchasing’

power of people who are problem gamblers. Differences in stake size and deposit amounts need to

be contexualised by differences in income. Figure 7.1 shows problem gambling status by low income

and demonstrates that the income levels of problem gamblers in this survey are significantly lower

than that of non-problem gamblers. Around one in three (31%) of problem gamblers were low

income earners compared with one in four non-problem gamblers (24%). Likewise only 15% of

problem gamblers were highest income earners, compared with 33% of non-problem gamblers.

Finally, frequency of engagement in the most frequent gambling activity and number of other

gambling activities engaged in also varied by PGSI status. Patterns showed that a greater proportion

of problem gamblers engaged in seven or more activities than non-problem gamblers. The results

displayed a linear association: as level of risk increased, the proportion engaging in seven or more

activities increased (rising from 13% for non-problem gamblers to 36% for problem gamblers). This

linear gradient was also observed with frequency of gambling: 16% of non-problem gamblers had

gambled almost every day, rising to 41% for problem gamblers.

29

It should be recognised that some of this may be reinvested winnings.


Table 7.2

Gambling behaviour, by PGSI status All aged 18 and over

Gambling behaviour PGSI status Total

Non-problem gambler

Low risk gambler

Moderate risk gambler

Problem gambler

Number of gambling activities undertaken in past four weeks

None 10 3 1 3 4 1-2 22 18 14 11 17 3-4 32 31 30 26 30 5-6 24 25 27 25 25 7 or more 13 24 28 36 25

Frequency of participation in most frequent activity Almost every day/everyday 16 18 31 41 26 4-5 days per week 9 14 17 19 14 2-3 days per week 36 36 34 26 32 About once a week 20 20 14 7 15 Less than once a week 10 9 4 4 7 Did not gamble 9 3 1 3 4 Gambled, frequency unknown - - 0 0 2

Average number of machine sessions per day Mean 1.8 1.8 2.0 2.2 2.0 Standard error of the mean .04 .05 .04 .07 .03

Median 1.5 1.5 1.8 1.8 1.6 10

th centile 1.0 1.0 1.0 1.0 1.0

90th

centile 3.0 3.0 3.3 4.0 3.2

Average session length (seconds) Mean 1046.3 1010.2 1238.4 1156.2 1109.2 Standard error of the mean 130.82 129.25 154.22 100.54 65.45

Median 453.0 497.0 596.0 642.0 540.0 10

th centile 127.0 148.0 154.0 173.0 148.0

90th

centile 1644.0 1721.0 2267.0 2163.0 1979.0

Average number of different games per session Mean 1.2 1.3 1.3 1.3 1.2 Standard error of the mean .04 .05 .07 .05 .03

Median 1.0 1.0 1.0 1.0 1.0 10

th centile .0 .0 .0 .0 .0

90th

centile 2.0 2.0 2.0 2.0 2.0

Average days between visits Mean 32.3 30.9 29.9 25.6 29.8 Standard error of the mean 1.97 2.37 2.26 1.85 1.06

Median 16.5 15.1 13.6 13.3 14.4 10

th centile 3.0 3.0 2.7 2.6 2.9

90th

centile 81.0 84.3 74.0 57.5 74.5

Average stake size (pence) Mean 427.2 504.7 589.7 743.1 557.9 Standard error of the mean 22.31 30.86 35.86 47.65 17.22

Median 223.0 253.0 293.0 351.0 264.0 10

th centile 44.0 58.0 57.0 64.0 53.0

90th

centile 1010.0 1169.0 1355.0 2091.0 1340.0


Table 7.2 continued

Gambling behaviour, by PGSI status All aged 18 and over

Gambling behaviour PGSI status Total

Non-problem gambler

Low risk gambler

Moderate risk gambler

Problem gambler

Percentage of B2 games played per session Mean 60.3 61.4 59.4 59.6 60.2 Standard error of the mean 1.63 1.76 1.71 1.76 0.84

Median 72.5 77.1 69.2 66.4 71.7 10

th centile 0.4 0.7 1.2 1.8 0.9

90th

centile 100 100 100 100 100

Average total deposit per session (pence) Mean 2276.5 2865.3 3964.4 4127.5 3252.1 Standard error of the mean 104.17 164.47 293.78 217.28 101.27 Median 1316.0 1654.0 2135.0 2570.0 1844.0 10

th centile 191.0 280.0 363.0 350.0 281.0

90th

centile 5375.0 6464.0 8350.0 9391.0 7370.0

Bases Weighted 1143 968 961 919 3992 Unweighted 1089 923 1025 951 3988

*Bases shown are for average number of sessions per day. Bases for other gambling behaviour characteristics vary.

Table 7.3 shows how well these metrics differentiate between those who had problems with their

machine use and those who did not. As observed with PGSI status, the average number of sessions

per day, stake size, number of gambling activities undertaken, total money deposited into a machine

per session and gambling frequency were all significantly higher among those who had more

frequent problems with their machine use. The patterns operated in much the same ways as

observed for PGSI status. For example, the average stake size among those who always felt they

had problems with their machine gambling was £8.33 compared with £5.05 for those who never had

problems.

In addition to these metrics, the average number of days between visits to a bookmaker’s to gamble

on machines varied by machine problem status. Those experiencing more frequent problems with

their machine use had fewer days between visits to a bookmaker’s to use machines than non-

problem gamblers. The average number of days between visits for participants who ‘almost always’

had problems with their machine use was 24.6 days whereas it was 31.7 days for those who never

had problems with machines. This is just the average behaviour which can be skewed by extreme

values (i.e., it includes some people who perhaps only played machines once or twice between

September 2013 to June 2014). The median (i.e., the middle value for all participants) for those who

‘almost always’ had problems was 11.3 days between visits to a bookmaker’s to use machines

whereas for those with no problems with machine use it was 15.1.

Chapter 2 of this report outlined that people do not always use their loyalty card when playing

machines. This metric in particular may be affected by this as people may have made many more

visits to bookmakers where they either did not use their card or visited the premises of a different

operator. As there is no way of knowing this, we caution readers against viewing these findings as

definitive patterns of machine use. Furthermore, the average number of days between visits is

somewhat misleading as it averages this across the full data period. This obscures variations in


gambling behaviour, for example where a gambler may have made many visits in one month and

none in another; these patterns are explored further in Report 3.

Table 7.3

Gambling behaviour, by machine gambling problem All aged 18 and over

Gambling behaviour Had problems with machine use Total

Never Some of the time

Most of the time

Almost always


None 3 2 1 4 4 1-2 17 12 7 16 17 3-4 31 28 31 23 30 5-6 27 25 22 25 25 7 or more 22 33 39 32 25

Frequency of participation in most frequent activity Almost every day/everyday 22 30 40 46 26 4-5 days per week 13 18 20 17 14 2-3 days per week 35 35 27 23 32 About once a week 19 12 9 6 15 Less than once a week 8 3 3 4 7 Did not gamble 3 2 1 4 4 Gambled, frequency unknown 0 0 - - 2

Average number of machine sessions per day Mean 1.9 2.0 2.2 2.4 2.0 Standard error of the mean .03 .06 .10 .14 .03

Median 1.6 1.7 1.9 2.0 1.6 10

th centile 1.0 1.0 1.0 1.0 1.0

90th

centile 3.0 3.3 4.1 4.6 3.2


Median 506.0 624.0 671.0 661.0 540.0 10

th centile 144.0 158.0 183.0 206.0 148.0

90th

centile 1856 2127 2168 2477 1979

Average number of different games per session Mean 1.2 1.3 1.3 1.3 1.2 Standard error of the mean .04 .05 .08 .07 .03

Median 1.0 1.0 1.0 1.0 1.0 10

th centile .0 .0 1.0 .0 .0

90th

centile 2.0 2.0 2.0 2.0 2.0


Median 15.1 12.7 12.7 11.3 14.4 10

th centile 2.9 2.9 2.8 2.2 2.9

90th

centile 84.7 57.0 42.5 51.6 74.5

Average stake size Mean 504.7 612.8 610.5 832.9 557.9 Standard error of the mean 19.87 43.49 64.14 81.39 17.22

Median 246.0 291.0 326.0 443.0 264.0 10

th centile 50.0 59.0 59.0 71.0 53.0

90th

centile 1227.0 1434.0 1762.0 2394.0 1340.0


Table 7.3 continued

Gambling behaviour, by machine gambling problem All aged 18 and over

Gambling behaviour Had problems with machine use Total

Never Some of the time

Most of the time

Almost always


Median 74.3 67.7 48.2 65.0 71.7 10

th centile 0.6 1.1 2.2 1.1 0.9

90th

centile 100 100 100 100 100 Average total deposit per session Mean 2834.4 4303.0 3980.5 4165.3 3252.1 Standard error of the mean 110.05 323.74 370.38 278.83 101.27

Median 1608.0 2366.0 2687.0 3105.0 1844.0

10th

centile 282.0 400.0 521.0 400.0 281.0

90th

centile 6366.0 9377.0 9490.0 9000.0 7370.0



Table 7.4 shows how these metrics vary according to factor group status. For metrics like number of

gambling activities undertaken and frequency of gambling, the pattern showed that engagement was

lower among those who did not have a high score to either factor. For example, 63% of those with

high scores to both factors had gambled nearly every day compared with 22% who did not have high

scores to both factors.

The difference in mean stake sizes was particularly acute: the average stake of those with high

gambling action and consequence scores was over two times higher than those with non-high

scores to both factors (£11.61 per bet vs £5.26 per bet). Interestingly, those who had a high harmful

gambling consequence score had similar stake sizes to those who did not have a high score to both

factors (see Figure 7.2) and median values were of a similar magnitude.

A similar pattern was observed for total monetary deposits per session. The total amount deposited

was highest among those with high harmful gambling action and consequence scores (£48.77) and

the rates among those with high harmful action scores only were similar to this (£45.56). However,

those with non-high scores to both factors or high harmful consequence scores deposited over £10

less per session on average.


This suggests that some metrics perform differently depending on what type of ‘harm’ is being

looked at. If one were wanting to identify those at the most extreme end of the spectrum, that is

those with high harmful gambling actions and consequences scores, then looking for people with

higher staking levels may be one way to do this. However, if one wanted to intercede with people

with high harmful gambling consequences scores only, then stake sizes would be unlikely to

discrimate effectively between groups. These issues are discussed further in Section 7.3.

Finally, this point is further reinforced by the findings for average session length. This too varied by

factor score group, but not in the way that might be expected. Those with high harmful gambling

action and consequence scores had shorter session lengths, on average, than others: their average

session length was around 13 minutes compared with around 18 minutes for other groups. However,

examination of median values shows that the averages for those who did not have high scores to

both factors were likely to be affected by some extreme (in this case longer sessions) as median

sessions lengths were similar. (See Figure 7.3.)


Table 7.4

Gambling behaviour, by factor score group All aged 18 and over

Gambling behaviour Factor score group Total

Non high harmful

gambling actions or

consequence scores

High harmful gambling

actions only


consequence only


actions and consequence

scores


None 5 2 3 6 4 1-2 18 10 10 12 17 3-4 30 30 24 23 30 5-6 25 25 27 12 25 7 or more 22 33 37 48 25

Frequency of participation in most frequent activity Almost every day/everyday 22 42 46 63 26 4-5 days per week 13 21 17 13 14 2-3 days per week 36 24 21 12 32 About once a week 18 6 7 5 15 Less than once a week 7 4 5 1 7 Did not gamble 5 2 3 6 4 Gambled, frequency unknown 0 - 1 - 2

Average number of machine use sessions per day Mean 1.9 2.0 2.4 2.2 2.0 Standard error of the mean .03 .08 .17 .17 .03

Median 1.6 1.8 1.6 1.8 1.6 10

th centile 1.0 1.0 1.0 1.0 1.0

90th

centile 3.1 3.0 5.0 4.1 3.2


Median 522.0 624.0 701.0 586.0 540.0 10

th centile 149.0 116.0 117.0 158.0 148.0

90th

centile 1951.0 2356.0 2100.0 1532.0 1979.0

Average number of different games per session Mean 1.2 1.4 1.3 1.1 1.2 Standard error of the mean .03 .13 .09 .08 .03 Median 1.0 1.0 1.0 1.0 1.0 10

th centile .0 .0 1.0 .0 .0

90th

centile 2.0 2.0 2.0 2.0 2.0


Median 15.0 12.3 12.5 15.4 14.5 10

th centile 2.9 2.3 3.3 2.6 2.9

90th

centile 78.5 57.0 69.5 77.3 74.5

Average stake size (pence) Mean 526.0 558.9 712.6 1160.6 557.9 Standard error of the mean 18.23 57.81 60.29 218.52 17.22

Median 250.0 266.0 400.0 732.0 264.0 10

th centile 52.0 57.0 81.0 109.0 53.0

90th

centile 1256.0 1173.0 1762.0 2609.0 1340.0


Table 7.4 continued

Gambling behaviour, by factor score group All aged 18 and over

Gambling behaviour Factor score group Total

Non-high harmful

gambling actions or

consequence scores


actions only


consequence only


actions and consequence

scores


Median 74.2 53.2 65.9 79.4 71.7 10

th centile .8 2.2 5.7 .1 .9

90th

centile 100.0 100.0 100.0 100.0 100.0

Average total deposit per session Mean 3068.7 3420.6 4556.1 4877.2 3252.1 Standard error of the mean 109.18 339.00 436.57 732.34 101.27 Median 1758.0 2000.0 2669.0 3670.0 1844.0 10

th centile 277.0 220.0 400.0 935.0 281.0

90th

centile 6800.0 8203.0 10834.0 9211.0 7370.0




7.3 Differentiating between ‘problem’ and ‘non-problem’ gamblers

Sensitivity and specificity: an illustration The purpose of this research programme is to examine whether industry data can be used to identify

harmful patterns of machine play. Section 7.2 showed how patterns of machine gambling (as

recorded by industry data) varied by different measures of gambling problems. That some

differentiation between gamblers is evident is encouraging. However, consideration needs to be

given to how well these patterns of play differentiate between different types of gamblers.

The ultimate purpose of this research is to see if patterns of harmful play can be identified and to use

these patterns to trigger responsible gambling interventions. This logic is already being used by

bookmakers. In March 2014, the Association of British Bookmakers (ABB) launched its new

voluntary code of responsible gambling. This code includes mandatory breaks in machine play if a

machine gambler had used the machine continuously for 30 minutes or had put £250 or more into

the machine, among other things.30 This is an example of a potentially harmful pattern of play being

identified and intervention occurring when someone breaches these levels. A critical question,

however, is how well these thresholds differentiate problem gamblers from non-problem gamblers. In

an ideal world, one would want a threshold to be set that includes all problem gamblers and

excludes all non-problem gamblers.

Sensitivity and specificity analysis can be used to quantify this. Estimates of sensitivity show the

proportion of positive cases that a measure or intervention (like those set by the ABB code) is

correctly capturing. In other words, they show the proportion of problem gamblers that a measure is

correctly identifying. Specificity measures the proportion of negative cases that a measure excludes,

meaning the proportion of non-problem gamblers that are excluded from the intervention. These

estimates then start to give us some idea of how well a pattern or measure performs in intervening

with problem gamblers. Estimates are presented as proportions ranging between 0 and 1.00.

Sensitivity measures of 1.00 means that all problem gamblers have been identified, whereas a

measure of 0 means none have. Likewise, a specificity estimate of 1.00 means that all non-problem

gamblers have been excluded, whereas an estimate of 0 means that no non-problem gamblers have

been excluded. In short, the closer sensitivity and specificity estimates are to 1.00 the better.

Tables 7.5 to 7.8 provide examples of sensitivity and specificity estimates for a range of different

thresholds. This shows that there are a number of trade-offs to be considered and that the

thresholds used need to be chosen with care. In Tables 7.5 to 7.8, specificity and sensitivity

estimates are produced for whether someone is a problem gambler or not (based on their responses

30

For more details see: Association of British Bookmakers (2013) The ABB’s code for responsible gambling and player

protection in licensed betting offices in Great Britain. Available at: http://bylb.iceni.co/wp-

content/uploads/2013/10/ABB-code-for-responsible-gambling.pdf. Accessed 21 June 2014.

http://bylb.iceni.co/wp-content/uploads/2013/10/ABB-code-for-responsible-gambling.pdf

http://bylb.iceni.co/wp-content/uploads/2013/10/ABB-code-for-responsible-gambling.pdf


to the PGSI screen). Similar estimates were produced for problems with machine use and factor

score status which performed in much the same way to the data presented here (tables not shown).

Table 7.5 shows a range of sensitivity and specificity analyses for number of gambling activities

undertaken. This shows that if we are attempting to identify problem gamblers, looking at those who

have taken part in seven or more activities in the past four weeks would correctly identify 35% of

problem gamblers and correctly exclude 79% of non-problem gamblers. This means this threshold

has reasonable specificity but it is not very sensitive. Changing the threshold to be five or more

activities in the past four weeks improves sensitivity, meaning that 60% of problem gamblers would

be correctly identified but only 54% of non-problem gamblers correctly excluded.

Table 7.5

Sensitivity and specificity analysis for number of gambling

activities undertaken

Threshold Sensitivity and specificity

Sensitivity Specificity

Seven or more gambling activities in the

past four weeks

0.35 0.79

Five or more gambling activities in the

past four weeks

0.60 0.54

Table 7.6 shows similar analysis for number of machine sessions per day, using a threshold of two

or more machine play sessions per day. This threshold would correctly identify 45% of problem

gamblers, and correctly exclude 66% of non-problem gamblers. Increasing the threshold beyond two

sessions per day simply reduces sensitivity whilst increasing specificity (not shown).

Table 7.6

Sensitivity and specificity analysis for average number of

machine sessions per day



Two or more sessions per day 0.45 0.66

Table 7.7 shows sensitivity and specificity analysis for a range of different staking levels. At £3.51 or

higher, 50% of problem gamblers would be correctly identified and 60% of non-problem gamblers

correctly excluded. At £10 per stake, 21% of problem gamblers would be correctly identified and

86% of non-problem gamblers excluded, meaning this would have good specificity but poor

sensitivity.


Table 7.7

Sensitivity and specificity analysis for average stake per

bet



£3.51 or higher* 0.50 0.60

£2.23 or higher** 0.61 0.48

£10 or higher 0.21 0.86

*This was the median stake among problem gamblers.

**This was the median stake among non-problem gamblers.

Finally, Table 7.8 shows sensitivity and specificity estimates which were produced for frequency of

gambling, based on the survey data. A threshold of gambling every day/almost every day would

correctly identify 41% of problem gamblers and exclude 79% of non-problem gamblers. Lowering the

threshold to four or more gambling days per week significantly improves sensitivity, as this would

correctly identify 60% of problem gamblers but at the cost of specificity – it would only exclude 66%

of non-problem gamblers. However, this represents the best results in terms of highest values for

both sensitivity and specificity of all the analysis presented.

Table 7.8

Sensitivity and specificity analysis for frequency of

gambling on most frequent activity



Gambles almost every day 0.41 0.79

Gambles four or more days per week 0.60 0.66

Summary The specificity and sensitivity analysis presented in the previous section shows the difficulty of using

a single metric to correctly identify all problem gamblers whilst excluding all non-problem gamblers.

With the examples shown, there are clear trade-offs that need to be made. This is because the

behaviour of problem gamblers and non-problem gamblers overlaps and is not mutually exclusive.

This point is illustrated in Figure 7.4 using stake size as an example.


Figure 7.4 shows that although there is some variation in the proportion of problem gamblers at each

staking level, problem gamblers have a range of staking behaviour. For example, nearly one in five

of those with the lowest average stake per bet (53p) were problem gamblers and two in five were

non-problem gamblers. The rest were at-risk gamblers. Even at the highest level of stakes (the 10th

decile in Figure 7.4, representing an average stake of £13.40 per bet or more), nearly one in five

people (18%) were non-problem gamblers. Because of this overlap it is unlikely that stake size alone

would sufficiently discriminate between problem and non-problem gamblers.

Gambling is a complex behaviour and varies for different people under different circumstances. Few

of these contextual circumstances are known or are evident in industry data. Therefore, when

attempting to identify patterns of behaviour that might indicate someone is experiencing harm, a

probabilistic approach is needed. This means looking at patterns of behaviour and thinking whether,

on the balance of probability, someone is more or less likely to be experiencing problems. For

example, if the highest staking level as shown in Figure 7.4 were set as a threshold for a gambling

intervention, four out of five of the people affected would be those with some level of difficulty with

their gambling behaviour (i.e., a PGSI score of 1 or more) and one in five would not have any

problems (a PGSI score of 0). However, as also seen from Figure 7.4, this would exclude many

people who are problem gamblers and do not stake to this level.

The main questions are whether policy makers, industry and regulators are willing to accept these

trade-offs, and what level of error they are willing to accept for different policy approaches. The

answers to these questions are likely to vary based on the type of intervention. If the intervention is

non-intrusive, a pop-up message for example, then it may be acceptable that this is something

experienced by both non-problem and problem gamblers alike. If it is more intrusive, such as a

mandatory break in play, then attempting to exclude as many non-problem gamblers as possible

may be preferred whilst recognising that this means one is likely to miss some people with problems.

Underpinning all of this should be consideration of the impact of any intervention; ensuring that it

does not have unintended consequences and that it is reaching the anticipated group of people.

Report 3 in this series extends this analysis to consider how a broader range of machine play

patterns interact to predict problem gambling. Report 3 looks at many more patterns than presented

here. It also examines how different behaviours interact with one another. For example, there may


be some important patterns or associations between length of session and staking level: some

people may gamble for a long time at low stakes and this may not be particularly problematic,

whereas others may have much shorter sessions at much higher stakes which may be problematic –

this is as yet unknown).

The preliminary investigation presented here has highlighted some important themes: problem

gambling behaviour can, according to some patterns of play, be differentiated from that of non-

problem gamblers, but only if policy makers, stakeholders and industry agree a range of acceptable

trade-offs between sensitivity and specificity and the attendant degrees of error this brings.


8 Conclusions This report focuses on the broader gambling behaviour and experiences of LCS participants, with

the aim of better understanding the context of their machine play. The data recorded by gambling

industry operators tend not to include details of who people are or how their circumstances vary, so

these findings provide an important contribution to the broader aim of this research programme – to

see if industry data can be used to distinguish between harmful and non-harmful patterns of play on

machines in bookmakers.

The results in this report are not representative of all machine players and may not be representative

of all loyalty card holders, given low response rates to this study. Nevertheless, this study highlights

a number of key themes.

LCS participants have a very distinct profile, compared with other machine players. They are

heavily engaged in gambling and appear to have more economically constrained

circumstances.

Loyalty card holders do not consistently use their loyalty cards when gambling on machines

in bookmakers, which means that, for most loyalty card holders, the data recorded by

gambling operators is unlikely to show their full patterns of gambling.

There appear to be some systematic differences in patterns of loyalty card use. For example,

younger survey participants use their cards less often than older participants. This could

create difficulties when attempting to use industry data to identify problem gamblers, as the

behaviours of different at-risk groups may not be recorded in a similar way.

Loyalty card survey participants have high rates of problem gambling and at-risk gambling.

Equivalent estimates from nationally representative studies show significantly lower rates of

problem gambling among those who gambled on machines in a bookmaker’s than LCS

participants. Estimates of problem gambling among LCS participants were 23%, yet

equivalent estimates were 9% from the BGPS and 7% from the health surveys for England

and Scotland. As registering for a loyalty card is a self-selecting action, this raises the

possibility that simply having a loyalty card, under the current schemes, is an indicator of

increased level of risk of gambling problems; meaning that those more likely to experience

problems may be more likely to have a loyalty card. This requires further investigation.

The number of loyalty cards held is an important predictor of both different types of loyalty

card gamblers and of gambling problems. This may simply reflect that these people are much

more engaged with gambling generally.

Even though LCS participants appear to come from more economically constrained

backgrounds than machine players as a whole, there is a distinct social gradient evident

within this group. LCS customers who have low incomes, live in deprived areas or are

economically inactive gamble on machines in bookmakers more frequently and are more

likely to experience gambling problems.

Linking the survey information with data recorded by gambling operators on loyalty cards

shows that patterns of play among problem gamblers and non-problem gamblers overlap

significantly. This may cause difficulties in identifying patterns of play that are characteristic

of problem gamblers, given that some of their patterns of play appear to be similar to those of

non-problem gamblers. This problem is likely to be especially acute if only one or two play

characteristics (such as stake size or session length) are considered.


From these key themes there are a number of implications to be drawn.

The primary aim of this research programme is to see if industry data can be used to distinguish

between harmful and non-harmful patterns of machine gambling. The companion report to this

study, Report 3, explores this in detail. However, evidence from this study has shown that ‘predictive’

models may be incomplete, given that we are uncertain whether we have full information about a

person’s pattern of machine play. It is therefore important to investigate whether these models vary

according to how often someone reported using their loyalty card. This would allow us to understand

whether and how much this matters.

Also, gambling behaviour is dynamic and may vary a lot over time. Some people may experience

very episodic patterns of gambling. If they use their loyalty card consistently, this will be recorded in

their data. However, the problem gambling screens and questions used in this study focus on

collecting information that is generalized over a longer timeframe, in this case 12 months (that is, we

ask on average how often have you experienced x,y or z?). This raises the possibility that we might

not be able to distinguish between harmful and non-harmful patterns of play as well as we might like

simply because we are not comparing similar data. Much greater consideration of what harmful

patterns of play look like and how to define and measure them is needed.

The quality of the loyalty card data, and their use for research purposes, could be improved by

obtaining more information about users at the point of registration and implementing more rigorous

quality control checks on the details provided. It may be that these improvements have been

implemented since this study was designed. One operator, for example, has recently launched a

new loyalty card that is linked to the ‘know your customer’ initiative. This is a process used by

businesses to verify the identification of their customers. From a research perspective, this

significantly improves the accuracy of these data as more contact details and demographic

information about potential research participants is available.

Given the low response rate, it is not certain whether this study is representative of all loyalty card

customers or whether those who gave correct contact details are systematically different in some

way to those who did not. Improvements in the quality of contact details recorded by operators would

allow similar studies to be conducted to explore this further.

This research has some implications for marketing and promotional activity to loyalty card

customers. Findings from this study suggest that those who have loyalty cards may be at higher risk

of problems. It suggests that operators should think carefully about the level and type of promotions

offered to these customers, or at least consider balancing these promotions with responsible

gambling messages.

Finally, there is a clear set of implications for policy makers, regulators and industry stakeholders

wishing to develop interventions that target individuals based on certain patterns of play. Because

patterns of gambling seem to overlap significantly between problem gamblers and non-problem

gamblers, decisions will need to be made about what level of ‘error’ stakeholders are willing to

accept when promoting responsible gambling interventions aimed at those most at risk of problems.

On the one hand, an intervention may have unintended consequences because it affects too many

non-problem gamblers (i.e., it is not very specific). On the other hand, some interventions, no matter

how well intentioned, may not have the desired impact because they are simply not effective at

capturing all problem gamblers (i.e., they are not very sensitive). This highlights the need for any


new policies to be thoroughly tested and evaluated with evaluation built into the policy development

and design process from the very start.


Appendix A. Technical appendix This appendix provides further detail on the methodological approach and the main analysis

techniques used.

Survey processes

Sample design A listing of loyalty card numbers which had been used in machines between September–November

2013, and which also had a mobile telephone number or email address available, was obtained from

Ladbrokes, William Hill and Paddy Power. In total, there were 180,542 cards of which 131,275 had

some form of contact detail available.

For each card, the following information was provided (these were calculated from the raw

transactional data by our collaborators, Featurespace):

how long the loyalty card had been active for;

how many machine play sessions per day between September–November were recorded

against the card;

how many consecutive days of machine play between September–November were recorded

against the card;

total loss on machines between September–November recorded against the card;

total number of minutes of machine play between September–November recorded against

the card;

longest machine playing session (in minutes) recorded against the card.

These variables were used to identify and oversample cards which represented heavier engagement in machine gambling.31 A primary aim of this study was to identify sufficient numbers of problem gamblers so that their machine play characteristics could be compared with non-problem gamblers. Therefore, it was necessary to boost the potential number of cards with machine play characteristics more likely to be associated with problem gambling. Based on inspection of the data, the following thresholds were set for sample selection.

Any card where there had been more than one machine play session per day and the

session had lasted for 30 minutes or more was selected (4504 cases).

Any card where there had been more than three consecutive days of machine play and the

session lasted for 30 minutes or more was selected (19,130 cases).

Finally, a simple random sample of 23,634 cases was selected from the remaining list,

stratifying the sample by:

o operator;

31

Given that the purpose of this research programme is to attempt to identify patterns of machine gambling that

indicate that someone is experiencing problems, there was little prior evidence to help guide this process. Therefore,

these metrics were arbitrarily chosen based on what might be most likely to indicate that someone was more engaged

in gambling and, therefore, potentially more likely to experience problems.


o average number of sessions per day;

o maximum number of consecutive days of play;

o longest playing session;

o player loss.

A total sample of 47,268 cases was selected.

902 cases were removed after the opt-out exercise.

A further 18,801 cases were identified as having invalid contact details.32

The final sample issued by NatCen was 27,565 cases.

Table A.1 shows the breakdown of the final sample by available contact details.

Table A.1

Final issued sample, by contact method

Contact details Number of cases

%

Mobile phone only 18771 68

Email only 4278 16

Mobile phone and email 4516 16

Total 27565 100

Opt-out process To ensure compliance with the UK Data Protection Act 1998, operators first had to contact all

selected participants to inform them that their contact details would be passed to NatCen unless they

stated they did not want this to happen. Operators sent all sampled participants text messages to

inform them about the study and the fact that NatCen would attempt to contact them unless the

participant refused. The text also included details of a project-specific website where participants

could find out more information about the study and contact the researchers direct. Participants were

given up to three weeks to respond to the text message before contact details were shared with

NatCen. Overall, 902 participants opted out of the study. Any participants who subsequently

contacted operators to ask to be removed from the study were removed from the NatCen sample on

the same day and no further attempts to contact them were made.

Fieldwork As can be seen from Table A.1, 68% of sampled cases had a mobile telephone number as their only

available contact method. A further 16% had only an email address, while 16% of the sample had

both email and telephone details. Therefore, a multimode survey instrument was designed. This

allowed for completion over the telephone with one of NatCen’s trained interviewers but also gave all

participants a unique web access code if they preferred to complete the questionnaire online. For

mobile-only participants, individuals were encouraged to complete the questionnaire then and there

32

These cases were identified through a process called ‘pinging’ which sends a message to the telephone number to

establish if it is working or not. Operators also advised of telephone numbers that were identified as invalid during the

opt-out period.


while the interviewer had contact with them. The offer of web completion was only made if the

interviewer felt that the potential participant was reluctant to take part. Where people did say they

would complete online, this was monitored and if after one week they still had not done so, the

Telephone Interviewing Unit placed a courtesy telephone call to them to remind them to do so. For

email only participants, an email invitation to participate and up to five reminders were sent

throughout the fieldwork period.

All fieldwork was conducted between 15th May 2014 to 13th August 2014.

All telephone interviewers attended a project-specific training session before working on the project,

where all project protocols, including the importance of explaining and gaining consent for data

linkage, were covered.

Response rates Table A.2 shows the total number of achieved interviews by mode of completion.

Table A.2

Achieved interviews, by mode of completion

Mode Number of cases

%

Telephone interview 4210 89

Web survey 517 11

Total 4727 100

Overall, interviews were obtained from 4727 people: 89% of interviews were conducted via

computer-assisted telephone interviewing and 11% by web survey completion.

Calculating response rates for this study is complex. There are a number of technical criteria to be

taken into account. For example, although 47,268 cases were selected as having valid contact

details, when checked by operators and a ‘pinging’ process 18,801 cases did not actually have a

correct telephone number or email address. Furthermore, NatCen telephone interviewers identified a

further 5021 cases where the telephone number given was not valid. This highlights the difficulty of

using operator records as a sampling frame for a survey: it appears that contact details are not

routinely checked and verified, meaning that the accuracy of contact information is unknown. This

creates challenges when attempting to calculate response rates for this study, as it is not clear what

the denominator should be.

Table A.3 gives an overview of the outcomes for the selected sample: 2% of the selected sample

opted out of the study, and were therefore not included in the final sample issued by NatCen. A

further 39% of the selected sample was removed because of insufficient contact details. Of the

27,565 cases issued by NatCen, a further 3% were identified as ineligible as participants stated they

did not have a loyalty card (it may be that they were unwilling to admit this, or a genuine mistake with

the contact details, this is unknown). 17% were interviewed, 21% refused, 2% were categorised as

other unproductive (i.e., the participant was ill or away) and no contact was made with 58% of the

issued sample. This last figure may seem large; however, this includes 3761 cases where only email

addresses were available and the participants did not respond to our repeated invitations to


participate. This category also includes 5021 cases where the given telephone number was

unobtainable.

Table A.3

Final outcome for all selected sample

N % %

Selected sample 47268 100

Opted out of survey 902 2

Ineligible cases (no valid

contact details) 18801 39

Total number of issued

cases 27565 58 100

Ineligible: screened out

by interviewer 729 3

Interviewed 4727 17

No contact 15912 58

Refused 5755 21

Other unproductive –

contact made 442 2

Estimated further

ineligible* 3410

* 5456 people agreed to take part in the survey. Of these, 729 or 13% were excluded

as they did not have a loyalty card. The estimated further ineligible number is

calculated assuming that the same proportion of unproductive cases would also be

ineligible.

As Table A.3 demonstrates, there are considerable quality issues with this sample, making

calculating response rates difficult. There are three main ways response could be calculated. These

are shown in Table A.4 below.

Table A.4

Response rate options

Option Method Calculation Response rate

1 Use total selected as

denominator

(4727/47268)*100

10%

2 Exclude ineligible cases from

denominator

(4727/(47268 – 18801 –

729))*100

17%

3 Exclude ineligible cases and

estimated further ineligible

cases from denominator

(4727/(47268 – 18801 – 729

– 3410))*100

19%

The first option uses the total selected sample as the denominator and this gives a response rate of

10%. However, this is a very conservative calculation and does not take into account the ineligible

cases identified (i.e., those who said they did not have a loyalty card). Option 2 takes this into

account and gives an estimated response rate of 17%. Finally, option 3 follows procedures used on

national surveys such as the Health Survey for England to obtain an estimate of what proportion of

unproductive cases would also have been screened out as ineligible and calculated response rates

with these cases removed. This gives a response rate of 19%. Options 2 and 3 are less


conservative. It seems appropriate to base response rates on those for whom valid contact details

were available, therefore the final response for this study can be said to be in the range of 17-19%.

Weighting Two weights were computed to adjust the survey estimates to take into account non-response: one

for all participants to the survey and the other for those who agreed to link their responses to other

records. These weights were generated using a two-step process. First, a selection weight was

calculated as the probability of selection differed across card holders. Second, calibration weighting

was calculated to weight participants (or those who agreed to data linkage) for non-response. These

weights ensure that the sample matches the population for key characteristics, thereby minimising

the risk of non-response bias. Here the ‘population’ is all 181,581 loyalty cards which was our total

sampling population. Only anonymized data for these 181,581 loyalty cards were available to

NatCen and people who sign up to loyalty cards for operators agree to their using these data for a

variety of purposes in the terms and conditions.

Selection weights The selection weights are related to the sample design and are equal to the inverse of the probability

of selection. At the sampling stage, available information about playing habits was used to identify

the card holders more likely to be at risk of gambling problems. All cases at risk of gambling

problems were included in the sample so, for this group, the selection weight was equal to one. A

systematic random sample was then drawn among those who were not at risk of gambling problems.

The selection weight for this group was equal to the ratio of the number of cases identified as not at

risk of gambling problems to the number of sampled members within this group.

Calibration weights Calibration weighting was used to weight the participants (and those who agreed to data linkage)

back to the population of card holders using four relevant variables available at the population level:

operator or the bookmaker where the card was held;

member days or number of days holding the card of the operator;

player loss which indicates the money won or lost between September and November;

playing habits which is a combination of three variables: the longest session played

(less than 30 minutes; 30 minutes or more); the maximum number of consecutive days

they played (less than three days; three or more days); and the average of sessions per

day (less than one; one or more). This variable has six categories to measure card

holders’ engagement, from low (1) to high engagement (6): See Table A.5.


-

Table A.5

Playing Habits Category Longest Session Max Consecutive Days

33 Average of sessions

per day

1 - Low engagement

Less than 30 minutes Less than 3 days Less than 1

2 Less than 30 minutes 3 or more days Less than 1

3 Less than 30 minutes Less than 3 days AND 3 or more days

1 or more

4 30 minutes or more Less than 3 days Less than 1

5 30 minutes or more 3 or more days Less than 1

6 - High engagement

30 minutes or more Less than 3 days AND 3 or days

1 or more

Table A.6 shows the performance of the final weights on the main variables involved in the weighting

process.

33

Notice that the categories of the max consecutive days were merged for Playing Habits=3 and Playing Habits=6 in

order to avoid cells with small frequencies since they could be problematic for the weighting.


Table A.6

Final weighting on the main variables Variable Survey

Responses Agreed to Data Linkage

Population Unweighted

Sample Weighted

Sample Population Unweighted

Sample Weighted

Sample

% % % % % %

Operator

Ladbrokes 53 52.1 53 53 52.6 53

Paddy Power 9.9 10 9.9 9.9 9.3 9.9

William Hill 37.1 37.9 37.1 37.1 38.1 37.1

Member Days

Less than 6 months

9.3 5.2 9.3 9.3 4.9 9.3

6-9 months 19.4 12.5 19.4 19.4 12.2 19.4

9 months or more

18.2 30.3 18.2 18.2 30.3 18.2

Missing 53 52.1 53 53 52.6 53

Total player loss

> 50,000 11 25.7 11 11 25.5 11

50,000 to 10,000 18.9 25.1 18.9 18.9 25.4 18.9

10,000 to 2,000 18.8 13.7 18.8 18.8 13.3 18.8

2,000 to -2,000 34.5 17.4 34.5 34.5 17.7 34.5

Under -2,000 16.7 18.1 16.7 16.7 18.1 16.7

Playing Habits

1 - Low engagement

52 24.9 52 52 25.1 52

2 4 3.1 4 4 3.2 4

3 0.2 0.1 0.2 0.2 0.1 0.2

4 25.4 13.2 25.4 25.4 12.9 25.4

5 14.8 44.4 14.8 14.8 44 14.8

6- High engagement

3.6 14.2 3.6 3.6 14.6 3.6


Analysis

Scoring the problem gambling screening instrument This section explains how the Problem Gambling Severity Index (PGSI) instrument was scored and

the thresholds used to classify a problem gambler. The PGSI criteria are shown in Table A.7.

Table A.7 PGSI items

Bet more than can afford to lose

A need to gamble with increasing amounts of money

Chasing losses

Borrowed money or sold items to get money to gamble

Felt had a problem with gambling

Gambling causing health problems including stress and anxiety

People criticising gambling behaviour

Gambling causing financial problems for you or your household

Felt guilty about way that you gamble or what happens when you

gamble

All nine PGSI items have the following response codes: never, sometimes, most of the time, almost

always. The response codes for each item are scored in the following way:

score 0 for each response of ‘never’;

score 1 for each response of ‘sometimes’;

score 2 for each ‘most of the time’;

score 3 for each ‘almost always’.

This means a PSGI score of between 0 and 27 points is possible. There are four classifications

categories for PGSI scores. Their description and scored cut-off points are shown in Table A.8.

Table A.8 PGSI categories

PGSI classification category PGSI score

Non-problem gambler 0

Low risk gambler 1-2

Moderate risk gambler 3-7

Problem gambler 8+

The threshold for ‘problem gambling’ was 8 or over, in line with previous research. Cases were

excluded from the problem gambling analysis if more than half the PGSI items were missing (and

the score was <8). A total of four cases were excluded for this reason (these are the same four

cases that were excluded from the DSM-IV analysis).


Latent Class Analysis A key question in exploratory Latent Class Analysis (LCA) is how many classes the sample should

be divided into. However, there is no definitive method of determining the optimal number of

classes. Because models with different numbers of latent classes are not nested, this precludes the

use of a difference likelihood-ratio test.

For LCA (for men and women), we produced seven solutions (ranging from two to eight clusters)

and used the following five ways to check these and decide on the optimal solution:

(a) Looking at measures of fit such as Akaike’s Information Criterion (AIC and AIC3) and the

Bayesian Information Criterion (BIC). In comparing different models with the same set of

data, models with lower values of these information criteria are preferred.

(b) Looking at the misclassification rate. The expected misclassification error for a cluster

solution is computed by cross-classifying the modal classes by the actual probabilistic

classes. The sum of cases in the diagonal of this cross-classification corresponds to the

number of correct classifications achieved by the modal assignment of cluster probabilities.

The following formula is then applied: error=100*correct classifications/all cases. Models

with lower misclassification rates are preferred.

(c) Looking at the percentage of cases in each cluster with a low probability of cluster

membership. The vast majority of cases in a cluster should exhibit a high probability of

belonging to the cluster, typically above 0.6.

(d) The resulting classes should be stable. For example, when moving from a six- to a seven-

cluster solution, one of the clusters from the six-cluster solution should split to form two

clusters in the seven-cluster option with the other clusters remaining largely unchanged.

Cluster stability is investigated by cross-classifying successive cluster solutions.

(e) The resulting classes have to be interpreted. For the purposes of this analysis the main

importance in deciding the number of classes was placed on interpretability.

The following tables and figures show checks (a) to (d) for each LCA.


Figure A1

Measures of fit

Figure A2

Measures of fit (AIC3)


Table A.9

Misclassification error (%)

2-cluster 3-cluster 4-cluster 5-cluster 6-cluster 7-cluster 8-cluster

0.0 0.0 0.1 8.4 7.7 7.8 7.5

Table A.10

% of cases with cluster membership probability less

than 0.6 (four-cluster solution)

Cluster A Cluster B Cluster C Cluster D

% <0.01 <0.01 <0.01 <0.01

n 1880 1498 914 435

Table A.11

Stability of clusters (four-cluster solution)

Cluster A Cluster B Cluster C Cluster D Cluster E All

Cluster A 1056 210 614 0 0 1880

Cluster B 0 798 700 0 0 1498

Cluster C 0 0 0 914 0 914

Cluster D 0 0 0 0 435 435

All 1056 1008 1314 914 435 4727

Rationale for choice of final model Based on the information above, a four-cluster solution was chosen as the final model. This was

because the resulting model had very low classification error and gave a stable result, both in terms

of how it split groups when successive clusters were added and in terms of being replicable when

the model was reproduced from scratch. The BIC and AIC values are not lowest for the four-cluster

solution but do start to flatten somewhat from cluster 4 onward. Finally, the four-cluster solution

was easily interpretable, giving four meaningful classes for analysis. Solutions with more than four

classes were more complex to interpret and were not easy to distinguish from one another. Taking

all of the above together, a four-cluster model was the preferred solution.

Logistic regression procedure for all models For all models presented in this report, stepwise logistic regression was used to identify significant

predictors of different gambling behaviours (i.e., predicting LCA class membership, problem gambling

status, etc). For the LCA regressions 14 models were considered (seven for men and seven for

women, one per cluster) and in each one, class membership was the binary dependent variable (1:

belonging to the cluster, 0: not belonging to the cluster). For the problem gambling regressions, one


model was produced where problem gambling according to the PGSI was a binary variable (1:

problem gambler, 0: non-problem gambler).

Missing values were recoded to the mode for each variable, except of income and sex, where they

were included as a separate category.

All analyses were performed in STATA (a statistical analysis package) within the survey module

(svy) which takes into account the weighting of the survey.

Because stepwise regression is not available in STATA’s survey module, the stepwise procedure

for each model considered was simulated using the following steps:

A. A forward stepwise logistic regression with all independent variables was initially run

outside the svy module (i.e. using the ‘sw’ command).

B. The variables identified as significant (at the 95% significance level) were then included in

an ‘svy logit’ regression to test whether they remained significant.

C. If one variable was found to be not significant (if its p-value was greater than 0.05), it was

removed from the model, and the model with the remaining variables was re-run and re-

checked.

D. If more than one variable were found to be not significant, the one with the largest p-value was

removed and the model with the remaining variables was re-run and re-checked.

E. When no more variables could be removed (because their p-value was less than 0.05), all

other variables not in the model were added one by one (i.e., separate ‘svy logit’ models

were run – as many as the remaining variables – with the existing variables plus one of the

remaining ones at a time).

F. If none of the additional variables were significant, the procedure stopped and the initial

model from step E was the final model.

G. If one of the additional variables was significant, then the variables already in the model

were checked for removal. Variables were removed one at a time (the variable with the

largest p-value was removed first), until no more variables could be removed.

H. If more than one additional variable was significant, the one with the smallest p-value

entered the model and the remaining variables were checked for removal in the same way

as in step G. The remaining significant variables were then entered, one at a time, based on

their p-value (variables with the smallest p-value taking precedent) and after each entry the

model was re-checked for variable removals.

I. If at this step the current model was different from the one at step E, the algorithm continued

and steps E to H were repeated. The procedure stopped when there were no changes to the

model (in terms of the significant variables included) between iterations.


Factor analysis Chapter 7 presents the results of exploratory factor analysis of the PGSI. This section provides more

detail on this factor analysis and how the final factor solution was chosen.

Scoring the data and missing values The PGSI consists of nine items. Responses to each item were: never, sometimes, often, always.

Each item was scored in the following way:

0 for ‘never’; 1 for ‘sometimes’; 2 for ‘often’; 3 for ‘always’.

For each respondent, the number of valid responses across the 15 items was calculated. Overall,

184 respondents failed to provide a valid answer to all nine PGSI items. As this number was low,

these cases were excluded from the factor analysis.

Items included in the factor analysis Pearson correlations between all pairs of the nine items were examined. Most items displayed some

degree of correlation with other items, though at varying degrees of strength. These are shown in

Table A.12.


Table A.12

Correlation coefficients Bet more

than could afford to lose

Gambled with more money

Chased losses

Borrowed money to gamble

Felt had a gambling problem

Gambling caused health problems

People criticized gambling

Gambling caused financial problems

Felt guilty about gambling

Bet more than could afford to lose

1.00

Gambled with more money

0.61 1.00

Chased losses

0.63 0.59 1.00

Borrowed money to gamble

0.52 0.48 0.50 1.00

Felt had a gambling problem

0.61 0.54 0.57 0.52 1.00

Gambling caused health problems

0.57 0.51 0.55 0.56 0.66 1.00

People criticized gambling

0.44 0.39 0.45 0.43 0.55 0.50 1.00

Gambling caused financial problems

0.65 0.54 0.59 0.61 0.70 0.72 0.52 1.00

Felt guilty about gambling

0.58 0.51 0.58 0.50 0.66 0.66 0.51 0.70 1.00

.

Final factor solution The final factor solution presented in Chapter 7 was the end product of a number of exploratory

phases. To decide which solution best fit the data, a number of criteria were used:

1) All factors with eigenvalues greater than 1 were retained. This produced a 1 factor solution, which was then rotated using varimax rotation.8

2) A scree plot was examined to see if other factors were evident. This suggested the presence of a second factor which had an eigenvalue just below 1 (0.7) and so was retained (and rotated as previously). See Figure A3 below.

3) A two and three rotated factor solution was examined to assess which solution was easiest to interpret.


Figure A3

Scree plot for factor analysis

The two-factor solution described in Chapter 7 gave the clearest pattern of item loadings onto each

factor and the most interpretable factors. The proportion of variance explained by this solution was

acceptable at 69%.

Data analysis and reporting

Presentation of results In general, the commentary highlights differences that are statistically significant at the 95% level.

This means that there is a 5 in 100 chance that the variation seen is simply due to random chance. It

should be noted that statistical significance is not intended to imply substantive importance.

Statistical packages and computing confidence intervals All survey data are estimates of the true proportion of the population sampled. With random

sampling, it is possible to estimate the margin of error either side of each percentage, indicating a

range within which the true value will fall.

These margins of error vary according to different features of a survey, including the percentage of

the estimate for the sampled population, the number of people included in the sample, and the

sample design.

Survey data are typically characterised by two principal design features: unequal probability of

selection requiring sample weights, and sampling within clusters. Both of these features have been

considered when presenting the combined survey results. Firstly, weighting was used to minimise

response bias and ensure that the achieved sample was representative of the general population

living in private households. Secondly, results have been analysed using the complex survey module


in PASW v18 and the survey module in STATA, which can account for the variability introduced

through the use of a complex clustered survey design.

The survey module in STATA is designed to handle clustered sample designs and account for

sample-to-sample variability when estimating standard errors, confidence intervals and performing

significance testing. Given the relatively low prevalence of problem gambling estimates, the tabulate

command was used to compute 95% confidence intervals for these estimates. The distinctive

feature of the tabulate command is that confidence intervals for proportions are constructed using a

logit transformation so that their end point always lies between 0 and 1. (The standard errors are

exactly the same as those produced by the mean command).


Appendix B. Focus group and in-depth interviews

methodology

Research aims and objectives Qualitative work was conducted prior to the survey to provide a) contextual understanding to support

and explain interpretations of the data, and b) information about players’ use of loyalty cards. The

specific aims of this qualitative stage were to:

understand machine players’ attitudes to betting shop loyalty cards;

explore players’ views on operators’ motivations to offer a loyalty card scheme;

find out how and why people use or do not use betting shop loyalty cards; and

explore the influence of betting shop loyalty cards on machine play.

Methodology Research was conducted in two case study areas, one in Greater London and the other in a small

town outside the London commuter belt. Participants took part in either a focus group or in-depth

interview.

Topic guides covering a range of themes relating to gambling on machines in a bookmaker’s and the

use (or non-use) of loyalty cards were used in the focus group and interviews. This helped to ensure

a consistent approach across the encounters and between members of the research team.

Researchers used the guides flexibly so they could respond to the nature and content of each

discussion. They also used open non-leading questions and answers were fully probed. The key

themes from the topic guides used in the loyalty card and non-loyalty card encounters are provided

below.

Topic guide for loyalty card holders

1. Introduction to research and what participation involves

2. Participant background

3. Overview of betting behaviour

4. Betting shop loyalty card use

5. Views on betting shop loyalty cards

6. Concluding comments

Topic guide for non-loyalty card holders

1. Introduction

2. Participant background

3. Overview of gambling behaviour

4. General awareness of betting shop loyalty cards and attitudes

5. Non-use of loyalty cards

6. Views on betting shop loyalty cards

7. Concluding comments


The focus group discussion lasted just over an hour and interviews lasted between about 20 minutes

to an hour. To facilitate analysis, all data collection encounters were digitally recorded and

transcribed verbatim.

Recruitment and sample A purposive sampling1 strategy was used to ensure that the study captured a diverse range of views

and experiences. Across the two geographical areas, the main sampling criterion was the

ownership of a betting shop loyalty card from either of the two betting shop operators who supported

this study.

Loyalty card holders were recruited through player databases held by betting shop operators. An

opt-out stage conducted by the operator was followed by a screening process to identify loyalty card

holders who regularly used their card when gambling on machines in betting shops. Non-loyalty card

holders were recruited on site across six different betting shops in the two areas. Because of poor

initial recruitment, this was expanded to a further two betting shops in a third area. A screening

exercise identical to that used for loyalty card holders was carried out. In addition to the verification

of loyalty card ownership (or non-ownership), screening criteria included:

frequency of machine gambling in betting shops;

frequency of loyalty card usage;

age.

Gender was not specified as a recruitment criterion as betting shop machine gamblers are more

likely to be male and therefore women are more difficult to recruit. This is an acknowledged

limitation.

In total 26 players participated in the research during March and April 2014. Eight individuals took

part in a focus group and 18 individuals took part in a face to face2 or telephone interview. An

overview of the achieved sample can be found below in Table B.1.

Table B.1 Achieved sample

Table B.1 Sample characteristics

Loyalty card holder

Yes 16

No 10

Age

18-29 9

1 Purposive sampling is a standard technique used to select a study population for qualitative studies. It involves

identifying a set of characteristics and criteria relevant to the study objectives. The level of priority associated with each criterion determines how sampling will progress and the setting of quotas to ensure that the key sampling characteristics are included in the study. 2 Two people took part in a paired depth interview (a face to face interview with two participants).


Table B.1 Sample characteristics

30-39 5

40+ 12

Current working status

In paid work 21

Not in paid work 3

Retired 2

Children aged under 16 years in the household

Yes 6

No 20

Total 26

Ethical protocol Ethical approval was sought from NatCen’s Research Ethics Committee which complies with the

requirements of the Economic and Social Research Council3 and Government Social Research Unit

Research Ethics Frameworks.

At the recruitment stage, individuals were given an information leaflet explaining the research and

describing what participation would entail. A full explanation was also given to recruited participants

both in writing and verbally prior to a group discussion or an interview. This information included an

overview of the topic areas likely to be discussed, and an explanation of the voluntary nature of

participation, and that participants could withdraw from the research at any time. Participants were

also reassured about the confidential nature of taking part, and focus group participants were asked

to respect the confidentiality of the group. Because participants were recruited across a small

number of betting shops, they were asked not to share any content of the discussion with friends,

family or other betting shop customers. Consent to take part in the research was sought prior to the

start of each data collection encounter. At the end of the encounter all participants were provided

with a leaflet listing the contact details of a range of support services, and offered a high street

shopping voucher as a token of appreciation for their time.

Analysis A Framework approach to data management was used. Framework, developed by NatCen, is a

matrix approach to managing and charting qualitative data by individual case and across all themes

captured during data encounters. The charted data are analysed to extract the range of experiences

and views and to identify similarities and differences across cases. Further interrogation of the data

identifies and explains emergent patterns and themes.4 The advantage of this approach is that it

facilitates the analysis of different aspects of an individual’s experiences and the connections

3 The Economic and Social Research Council (2005) Research Ethics Framework. Swindon: ESRC

4 Ritchie, J., Lewis, J., McNaughton Nicholls, C. and Ormston, R., (2013) Qualitative Research Practice, 2

nd Edition,

London: Sage.


between them as well as enabling analysis of particular themes across different cases. Participants’

verbatim quotations are used to illustrate themes and findings where appropriate.

Analysis of qualitative data involves scrutiny of the range and diversity of views and experiences of

research participants on any given subject. Its purpose is not to estimate the prevalence of particular

views and experiences.

Research challenges Recruitment of non-loyalty card holders took place on site initially across six betting shops in two

areas. Loyalty card holders were selected from loyalty card membership databases held by two

operators and recruited with the support of these operators. All eligible participants were invited to

take part in a group discussion. Recruitment was largely successful as a sufficient number of

individuals agreed to take part. However, many participants failed to attend the group discussions.

There were a range of reasons for this which included childcare and work commitments, and that

people had simply forgotten (despite reminders). To ensure the study also included non-loyalty card

holders, additional recruitment for a group discussion was carried out in a third area and eight

people attended this group.

Loyalty card holders who failed to attend the group discussion, or who were unable to attend it but

wanted to take part in the study, were invited to take part in an individual interview instead. 18 in-

depth interviews were conducted in total.

Consistency in the data collected was maintained through the use of the same topic guide across all

data collection methods.


Appendix C Questionnaire

Final survey questionnaire for survey of loyalty card holders

29.04.2014 NOTE: All questions are single code unless otherwise specified

Introduction and eligibility {Ask all} Intro {Telephone interview wording} Thank you for agreeing to take part in this survey. We're interested in speaking to people who have loyalty cards for a bookmaker so, for example, the Ladbrokes 'Odds On' card, William Hill 'Bonus Club' card or the Paddy Power 'VIP' card. {Web survey wording} Welcome to the gaming and betting study. We're looking to speak to people who have loyalty cards for a bookmaker so, for example, the Ladbrokes 'Odds On' card, William Hill 'Bonus Club' card or the Paddy Power 'VIP' card {Ask all} {CODE ALL THAT APPLY} Loyalty Have you ever had any of the following cards?

1. Ladbrokes Odds On card 2. William Hill Bonus Club card 3. Paddy Power VIP card 4. Other 5. None of these

{Ask if loyalty = other} OtherCard What other cards have you had? STRING [50] {Ask if loyalty = none of these} Loyalchk {Telephone interview wording} Can I please double check that you have never signed up for a card with any bookmaker, even if you have never used it? You could have done this online or in person at a shop. {Web survey wording} Just to check, you have never signed up for a card with any bookmaker, even if you have never used it? You could have done this online or in person at a shop.

1. Yes, I think I’ve signed up for a bookmaker’s card 2. No, I have never signed up for a bookmaker’s card


{Ask if loyalchk = no} Exit Thank you. We're only looking to interview people who have a loyalty card for a bookmaker. Therefore we don't have any further questions for you today. If we wanted to talk to you again in the future may we contact you to see if you would be willing to take part in future research?

1. Yes 2. No

{Ask if Exit = Yes} Address We’ll need to take your contact details… : Continue {Ask if Exit = Yes} Forename Please enter your first name : String[50 characters] {Ask if Exit = Yes} Surname Please enter your first name : String[50 characters] {Ask if Exit = Yes} Address1 First line of address : String[100 characters] {Ask if Exit = Yes} Address2 Second line of address : String[100 characters] {Ask if Exit = Yes} Address3 Town or city :String[100 characters] {Ask if Exit = Yes} Postcode Postcode :String[100 characters] {Ask if Exit = Yes, No, Don’t Know or Refused} Exit2 That’s the end of the questionnaire, thank you for your time. {Ask if loyalty = Ladbrokes,William Hill or Paddy Power} {CODE ALL THAT APPLY} CurrentCard Which of the following cards do you currently have?

1. Ladbrokes Odds On card 2. William Hill Bonus Club card


3. Paddy Power VIP card 4. None of these

Gambling Participation { Ask if loyalty = Ladbrokes,William Hill or Paddy Power or Don’t Know or Refused OR Loyalchk = Yes, Don’t Know or Refused} {CODE ALL THAT APPLY} Activity {Tel wording} I'm going to read out a list of activities. Please tell me whether you have spent any money on each one in the last 4 weeks? In the last 4 weeks, that is since {DATE FOUR WEEKS PRIOR TO INTERVIEW}, have you spent any money on…

1. Tickets for the National Lottery Draw (including Thunderball and Euromillions and tickets bought online)

2. Scratchcards (not online, newspaper or magazine scratchcards) 3. Tickets for any other lottery, including charity lotteries 4. The football pools 5. Bingo cards or tickets, including playing at a bingo hall (not online) 6. Gaming machines in a bookmaker’s to bet on roulette, poker, blackjack or other games 7. Fruit or slot machines somewhere else 8. Table games (roulette, cards or dice) in a casino 9. Playing poker in a pub tournament/ league or at a club 10. Online gambling like playing poker, bingo, instant win/scratchcard games, slot machine style

games or casino games for money 11. Online betting with a bookmaker on any event or sport 12. Online betting exchange (This is where you lay or back bets against other people using a

betting exchange. There is no bookmaker to determine the odds. This is sometimes called 'peer to peer' betting)

13. Betting on horse races in a bookmaker, by phone or at the track 14. Betting on dog races in a bookmaker, by phone or at the track 15. Betting on sports events in a bookmaker, by phone or at the venue 16. Betting on other events in a bookmaker, by phone or at the venue 17. Spreadbetting (In spread-betting you bet that the outcome of an event will be higher or lower

than the bookmaker's prediction. The amount you win or lose depends on how right or wrong you are)

18. Private betting or gambling for money with friends, family or colleagues 19. Another form of gambling in the last 4 weeks

{Ask if activity does not include machines in a bookmaker’s} Machines12 Have you spent money on machines in a bookmaker’s in the past 12 months?

1. Yes 2. No

{Ask if have not played any gambling activities in the past 4 weeks (activity) and machine12 = No or Don’t know or Refused} Gam12 Have you spent money on any gambling activity in the past 12 months?

1. Yes 2. No


Frequency of participation: all activities undertaken in the last 4 weeks

{Ask if activity = National Lottery} NLFREQ In the past 4 weeks, how often have you bought tickets for the National Lottery Draw (including Thunderball, Euromillions)? This can be from a shop or online.

1. Every day/almost every day 2. 4-5 days per week 3. 2-3 days per week 4. About once a week 5. Less than once a week

{Ask if activity = scratchcards} scFREQ In the past 4 weeks, how often have you bought scratchcards? Please do not include anything bought online or from a newspaper or magazine.


{Ask if activity = other lottery} olotFREQ In the past 4 weeks, how often have you bought tickets for any other lottery, including charity lotteries?


{Ask if activity = football pools} poolsFREQ In the past 4 weeks, how often have you spent money on the football pools?


{Ask if activity = bingo} bingoFREQ In the past 4 weeks, how often have you spent money on bingo cards or tickets (please do not include online bingo)?


{Ask if activity = machines in a bookmaker’s} bkmachineFREQ In the past 4 weeks, how often have you spent money on gaming machines in a bookmaker’s?



{Ask if activity = fruit machines} fruitFREQ In the past 4 weeks, how often have you spent money on fruit or slot machines?


{Ask if activity = table games in casino} casinoFREQ In the past 4 weeks, how often have you spent money on table games (roulette, cards or dice) in a casino? Please do not include online casinos.


{Ask if activity = online gambling} onlineFREQ In the past 4 weeks, how often have you spent money gambling online on poker, bingo, instant win/scratchcard games, slot machine style games or casino games?


{Ask if activity = online betting} onbetFREQ In the past 4 weeks, how often have you spent money betting online with a bookmaker on any event or sport?


{Ask if activity = betting exchange} betexFREQ In the past 4 weeks, how often have you spent money betting online on betting exchanges?



{Ask if activity = horse races} horseFREQ In the past 4 weeks, how often have you spent money betting on horse races in a bookmaker’s, by phone or at the track? Please do not include bets made online.


{Ask if activity = dog races} dogFREQ In the past 4 weeks, how often have you spent money betting on dog races in a bookmaker’s, by phone or at the track? Please do not include bets made online.


{Ask if activity = sports} sportsFREQ In the past 4 weeks, how often have you spent money betting on sports events in a bookmaker’s, by phone or at the track? Please do not include bets made online.


{Ask if activity = other betting} othbetFREQ In the past 4 weeks, how often have you spent money betting on other events in a bookmaker’s, by phone or at the track? Please do not include bets made online.


{Ask if activity = spread betting} spreadFREQ In the past 4 weeks, how often have you spent money spreadbetting?


{Ask if activity = private betting} privFREQ In the past 4 weeks, how often have you bet or gambled privately for money with friends, family or colleagues?

1. Every day/almost every day 2. 4-5 days per week


3. 2-3 days per week 4. About once a week 5. Less than once a week

{Ask if activity = other} othFREQ In the past 4 weeks, how often have you spent money on other forms of gambling?


{Ask all} bookvisit When you visit a bookmaker's, what's usually the main reason for your visit?

1. To bet on the horses 2. To bet on other events/activities 3. To play on the gaming machines 4. To bet on the Irish lottery, 49's or Keno 5. For something to do 6. To watch races/matches/games etc 7. To socialise with others 8. Something else

{Ask if BOOKVISIT = SOMETHING ELSE} Othvisit Please tell us your main reason for visiting a bookmaker's" : string [100 characters] {Ask if activity = bookmachines, horse, dog, sports, OR other betting} VisitUsu Some people’s betting behaviour can change around the time of major events, like the FIFA World Cup, for example. Thinking about how often you’ve visited a bookmaker’s in the last 4 weeks, would you say you’ve visited a bookmaker’s…

1. More often than usual 2. Less often than usual 3. About the same as usual

{Ask if Activity includes Gambling machines in bookmakers OR Machines12 = Yes} loyfreq When playing machines at a bookmaker's, how often do you use your loyalty card?

1. Always 2. Most of the time 3. Some of the time 4. Rarely 5. Never

{Ask if (Activity includes Gambling machines in bookmakers OR Machines12 = Yes) AND loyfreq is NOT always} {CODE ALL THAT APPLY} whylessfr


Why don't you always use your card when playing machines? 1. I forget my card 2. It's not worth it for the stake 3. I don't have time 4. I can't always be bothered 5. I forget that I can use the card 6. I've lent it to someone else 7. You can only use one card in one machine at a time 8. My card is damaged 9. I've lost my card 10. The card affects the way the machine plays 11. I don't like being tracked 12. Some other reason

{Ask if whylessfr = other} whylesso Please tell us why you don't always use your card : string [100] {Ask if Activity includes Gambling machines in bookmakers OR Machines12 = Yes} cardnum How many different loyalty cards for bookmaker's do you have?

1. One 2. Two 3. Three 4. More than three

Gambling behaviours {Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} IntroPGSI {Telephone interview wording} I am now going to ask you a set of questions about gambling, please indicate the extent to which each one has applied to you in the past 12 months {Web survey wording} For the next set of questions about gambling, please indicate the extent to which each one has applied to you in the past 12 months {Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} pgsi1 In the past 12 months, how often have you bet more than you could afford to lose?

1. Almost always 2. Most of the time 3. Sometimes 4. Never

{Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} pgsi2 In the past 12 months, how often have you needed to gamble with larger amounts of money to get the same excitement?

1. Almost always 2. Most of the time 3. Sometimes


4. Never {Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} pgsi3 In the past 12 months, how often have you gone back to try to win back the money you'd lost?


{Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} pgsi4 In the past 12 months, how often have you borrowed money or sold anything to get money to gamble?


{Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} pgsi5 In the past 12 months, how often have you felt that you might have a problem with gambling?


{Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} pgsi6 In the past 12 months, how often have you felt that gambling has caused you any health problems, including stress or anxiety?


{Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} pgsi7 In the past 12 months, how often have people criticised your betting, or told you that you have a gambling problem, whether or not you thought it is true?


{Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes} pgsi8 In the past 12 months, how often have you felt your gambling has caused financial problems for you or your household?


{Ask if Activity includes any valid response OR Machines12 = Yes OR Gambling12 = Yes}


pgsi9 In the past 12 months, how often have you felt guilty about the way you gamble or what happens when you gamble?


{Ask if activity includes machines in a bookmaker’s OR machine12 = yes} machprob In the past 12 months, how often have you felt that you might have a problem with your gaming machine play?


{Ask All} machatintro The following are things that some people have said about machine gaming. Please tell us how much you agree or disagree with each statement" TContinue {Ask All} machatt1 Machine gaming is a harmless form of entertainment

1. Strongly agree 2. Agree 3. Neither agree nor disagree 4. Disagree 5. Strongly disagree

{Ask All} machmot2 Machine gaming should be discouraged

1. Strongly agree 2. Agree 3. Neither agree nor disagree 4. Disagree 5. Strongly disagree

{Ask if activity includes machines in a bookmaker’s OR machine12 = yes} machmointro The following are reasons that some people have given about why they play gaming machines in a bookmaker's. Please state how much each applies to you. TContinue {Ask if activity includes machines in a bookmaker’s OR machine12 = yes} machmo3 How often do you play machines in a bookmaker's because it's exciting?



{Ask if activity includes machines in a bookmaker’s OR machine12 = yes} machmo1 How often do you play machines in a bookmaker's to escape boredom or fill your time?


{Ask if activity includes machines in a bookmaker’s OR machine12 = yes} machmo2 How often do you play machines in a bookmaker's to make you feel better?


{Ask if activity includes machines in a bookmaker’s OR machine12 = yes} machmo4 How often do you play machines in a bookmaker's to win money?


{Ask if activity includes machines in a bookmaker’s OR machine12 = yes} machmo5 How often do you play machines in a bookmaker's to be around other people?


Demographics Demintro {Telephone interview wording} I am now going to ask you a few questions about yourself {Web survey wording} The next few questions are all about you... {Ask All} Age What is your age? RANGE: 18...100 {Ask All} Sex Are you male or female?

1. Male 2. Female

{Ask All}


Ethnic What is your ethnic group?

1. White/White British 2. Mixed/multiple ethnic groups 3. Asian/Asian British 4. Black/Black British 5. Chinese 6. Arab 7. Other ethnic group

{Ask All} Econact In the last 7 days were you mainly:

1. Working as an employee (or temporarily away) 2. On a government sponsored training scheme 3. Self-employed or freelance 4. Doing other paid work 5. Retired 6. A student 7. Looking after the home or family 8. Long-term sick or disabled 9. None of these

{Ask All} WIntro {Telephone interview wording} I am now going to ask you some questions about your household income. {Web survey wording} The following questions are about your household income : continue {Ask All} WIncBW Thinking of your own personal income from all sources, before any deductions for income tax, National Insurance, and so on, is it £26,000 per year or more?

1. Yes 2. No

{Ask if WIncBW=Yes} WIncUp And is it £40,000 per year or more?

1. Yes 2. No

{Ask if WIncUp=Yes} WincUp1 And is it…

1. …between £40,000 and £46,799 2. …between £46,800 and £51,999 3. …£52,000 or more

{Ask if WIncUp=No} WIncUp2 And is it…

1. …between £26,000 and £31,199 2. …between £31,200 and £36,399


3. …between £36,400 and £39,999 {Ask if WIncBW=No} WIncDw Is it less than £10,400 per year?

1. Yes 2. No

{Ask if WIncDw=Yes} WincDw1 And is it…

1. …up to £2,599 2. …between £2,600 and £5,199 3. …between £5,200 and £10,399

{Ask if WIncDw=No} WIncDw2 And is it…

1. …between £10,400 and £15,599 2. …between £15,600 and £20,799 3. …between £20,800 and £25,999

{Ask All} hhold Do you live with other people?

1. Yes 2. No

{Ask if hhold = yes} {CODE ALL THAT APPLY} Hhold2 Who else do you live with?

1. Spouse or partner 2. Your own children under the age of 16 3. Your own children over the age of 16 4. Other children under the age of 16 5. Other adult family members 6. Other adults - non family members

{Ask if who = childu16} howmanyC16 How many of your own children under the age of 16 do you live with? RANGE: 1...15 {Ask if who = childO16} howmanyO16 How many of your own children over the age of 16 do you live with? RANGE: 1...15 {Ask if who = childOth} howmanyOC How many other children do you live with? RANGE: 1...15 {Ask if who = Other adult family members}


howmanyOa How many other adult family members do you live with? RANGE: 1...15 {Ask if who = Other adults – non family members} howmanyOn How many other adults do you live with? RANGE: 1...15

Data linking and final questions {Ask All} Link Thanks for all the information you've given us so far. {If Loyalty = don’t know or refused OR Loyalchk = Don’t know or refused add additional sentence see italics} Our records show that you may at some point, have signed up for a loyalty card with a bookmaker. In order to make your survey responses even more useful, we'd like to link your survey answers to information from the bookmaker's loyalty card records. This is so that we can see how play varies for different types of people. We will only use this for research purposes; your personal details will be kept completely confidential. All information will be treated in line with the Data Protection Act Are you happy for us to link your survey answers with loyalty card records? Telephone interview version only: IF ASKED: Our records suggest you have a loyalty card for {Name of operator from sample} Telephone interview version only: IF NECESSARY: What data do we mean? The information we are talking about is information recorded by the machine about the amount staked, the length of time spent playing, games played, amount won etc. Each machine records all of this data for each transaction - this is completely anonymous. Telephone interview version only IF NECESSARY: Why are we doing this? The machine gives us more accurate information than asking people can. For example if we asked you how much time you spent playing gaming machines in the past 6 months, it is likely that you will not accurately remember, whereas the machine records the exact amount of time. Telephone interview version only: IF NECESSARY: What will we do with the data once we've linked it? We will use the data to look at your survey answers about your machine play and other types of gambling activity etc, and compare this with the machine data on your length of play, type of games played, amount spent etc. This will give us an accurate overall picture of machine play for one person - we will then do the same with lots of other people to build up an overall picture of different types of machine play.

1. Yes 2. No

{Ask All} Address2


{Telephone interview wording} That's the end of the survey. We'd like to send you a £5 voucher to thank you for your time. To do this, I need your name and address {Web survey wording} “That's the end of the survey. We'd like to send you a £5 voucher to thank you for your time. To do this, we need your name and address” : continue {Ask All} Forename2 Please enter your first name :String [50 characters] {Ask All} Surname2 Please enter your first name : String [50 characters] {Ask All} Address1a First line of address : String [100 characters] {Ask All} Address2a Second line of address : String [100 characters] {Ask All} Address3a Town or city : String [100 characters] {Ask All} Postcode2 Postcode : String [100 characters] {Ask All} IF any (Address1-Postcode) is empty, don’t know or refused THEN AddCheck {Web survey only} Without your full address details, we won’t be able to send your £5 thank you voucher to you. Please press PREVIOUS to enter your details. Home Is this your home address?

1. Yes 2. No

{Ask All} Recontact If at some future date we wanted to talk to you further, may we contact you to see if you are willing to help us again?"

1. Yes 2. No


{Ask All} END That's the end of the questionnaire, thank you for your time.

gambling machines research programme...gambling machines research programme report 2: identifying...

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