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
1.2
1.3
Unique contribution .................................................................................................. 14
Report conventions .................................................................................................. 15
Policy context ......................................................................................................................11
About machines in bookmakers ...........................................................................................12
The research process ...........................................................................................................12
Report structure ....................................................................................................................13
Measuring harm ....................................................................................................................14
2
Methods and research context .................................... 16
2.1
2.2
2.3
2.4
Loyalty card schemes............................................................................................... 16
Overview of methodological approach ..................................................................... 17
Profile of achieved sample ....................................................................................... 19
Use of loyalty cards .................................................................................................. 23
2.5
Limitations ............................................................................................................ 28
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
3
Gambling participation ................................................. 29
3.1
3.2
3.3
3.4
3.5
Introduction ............................................................................................................ 29
Gambling participation by age and sex .................................................................... 29
Number of gambling activities, by age and sex ....................................................... 31
Gambling participation by socio-economic characteristics ..................................... 33
Frequency of gambling by age and sex.................................................................... 37
3.6
3.7
Frequency of gambling by socio-economic characteristics ..................................... 39
Summary
............................................................................................................ 42
Most frequent activity ...........................................................................................................37
Frequency of gambling on machines in a bookmaker’s .......................................................37
4
Types of gamblers........................................................ 43
4.1
4.2
4.3
Introduction ............................................................................................................ 43
Gambling types......................................................................................................... 43
Factors associated with membership of each group ............................................... 46
4.4
Summary
............................................................................................................ 50
5
Problem and at-risk gambling ..................................... 51
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
Introduction ............................................................................................................ 51
Caveats
............................................................................................................ 52
Problem and at-risk gambling by age and sex ......................................................... 52
PGSI item endorsement by age and sex ................................................................... 54
Problem and at-risk gambling by gambler type, number of activities and sex ........ 57
Problem and at-risk gambling by income, area deprivation and economic activity 59
Factors associated with problem and at-risk gambling ........................................... 63
Problems with machine gambling by age and sex ................................................... 68
Problems with machine gambling by income, deprivation and economic activity .. 69
Factors associated with machine gambling problems ............................................. 72
Summary
............................................................................................................ 75
6
Motivations and attitudes............................................. 76
6.1
6.2
6.3
Attitudes towards machine gambling ...................................................................... 76
Motivations for machine gambling ........................................................................... 78
Summary
............................................................................................................ 85
7
Identifying problem gambling ...................................... 87
7.1
7.2
Introduction ............................................................................................................ 87
Profile of different player types by patterns of machine use ................................... 88
7.3
Differentiating between ‘problem’ and ‘non-problem’ gamblers ........................... 101
Metrics of machine use ........................................................................................................88
Types of gamblers and factor analysis of the PGSI ..............................................................89
Machine gambling behaviour ...............................................................................................91
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
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 1719%. 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
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7
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
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8
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.
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
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.
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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.
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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.
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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);
NatCen Social Research | Loyalty card survey
13


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.
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14
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.
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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
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16
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.
NatCen Social Research | Loyalty card survey
17

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.
NatCen Social Research | Loyalty card survey
18
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.
NatCen Social Research | Loyalty card survey
19
Table 2.1
Profile of loyalty card survey participants and machine players
from the British Gambling Prevalence Survey
1
Socio-economic/demographic
characteristics
Survey
Sex
Men
Women
Age
16-24
25-34
35-44
45-54
55-64
65-74
75+
Government Office Region
North East
North West
Yorkshire and the Humber
East Midlands
West Midlands
East of England
London
South East
South West
Wales
Scotland
Index of multiple deprivation - England
Less deprived
Most deprived (80th centile or above)
Index of multiple deprivation - Wales
Less deprived
Most deprived (80th centile or above)
Index of multiple deprivation - Scotland
Less deprived
Most deprived (80th centile or above)
Ethnic Group
White/White British
Asian/Asian British
Black/Black British
Other ethnic group
Employment status
Paid employment
Unemployed
Looking after family/home
Student
Retired
Long term sick/disabled/other
Bases (unweighted)
Bases (weighted)
*Data not shown because of small base sizes
NatCen Social Research | Loyalty card survey
LCS
%
BGPS
%
88
12
85
15
16
23
19
22
13
6
1
15
16
18
17
15
11
9
7
11
8
6
7
7
19
8
6
4
16
3
13
9
6
10
11
9
15
6
4
14
64
36
78
22
62
38
*
*
66
34
74
26
82
6
7
5
91
2
3
4
66
11
4
3
9
7
60
9
4
18
1
8
4727
4726
243
281
20
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).
Figure 2.2 Frequency of playing machines in a bookmakers among past
four week/monthly machine players, by survey
LCS
BGPS
60
50
Percent
40
30
20
10
0
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
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.
NatCen Social Research | Loyalty card survey
21
Table 2.2
Gambling behaviour among loyalty card survey participants
and machine players from the British Gambling
Prevalence Survey
Gambling characteristics
Survey
LCS
%
Number of loyalty cards for a
bookmaker ever held
1
2
3
4
Don't know
Number of loyalty cards for a
bookmaker currently held
1
2
3
Don't know
Any gambling in past four
weeks/monthly**
Machines gambling in past four
weeks/monthly
Number of gambling activities in past
four weeks/monthly**
None
1-2 activities
3-4 activities
5-6 activities
7-8 activities
9 or more activities
Frequency of playing machines
among past four weeks/monthly
machine players**
Every day/almost every day
4-5 days per week
2-3 days per week
About once a week
Less than once a week
Bases (unweighted)
Bases (weighted)
BGPS 2010
%
69
24
6
1
1
70
17
4
9
96
74
4
17
30
25
14
11
11
26
18
18
14
15
13
10
31
25
21
5
6
15
24
50
4727
4726
243
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.
NatCen Social Research | Loyalty card survey
22
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
NatCen Social Research | Loyalty card survey
23
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
NatCen Social Research | Loyalty card survey
24
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:
NatCen Social Research | Loyalty card survey
25
“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).
NatCen Social Research | Loyalty card survey
26
Figure 2.3
Always uses loyalty card when playing machines, by age and sex
Men
Women
B ase: A ll aged 18 and o ver
60
50
Percent
40
30
20
10
0
18-34
35-54
Age group
55+
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
NatCen Social Research | Loyalty card survey
27
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 nonresponse 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.
NatCen Social Research | Loyalty card survey
28
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 overburden 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.
NatCen Social Research | Loyalty card survey
29
Table 3.1
Past four weeks gambling prevalence, by sex
All aged 18 and over
Gambling activities
Sex
Lotteries and related products
National Lottery
Scratchcards
Other lotteries
Machines/games
Football pools
Bingo (not online)
Machines in a bookmaker’s
Fruit machines
Casino table games (not online)
Poker played in pubs or clubs
Online gambling on slots, casino or
bingo games
Betting activities
Online betting with a bookmaker
Betting exchange
Horse races (not online)
Dog races (not online)
Sports events (not online)
Other events or sports (not online)
Spread-betting
Private betting
Other gambling activity
Any other gambling
Bases*
Weighted
Unweighted
Total
Men
%
Women
%
%
51
38
17
53
56
29
51
40
18
19
7
75
31
17
7
9
19
72
29
10
4
18
8
74
31
17
7
23
26
23
34
7
52
32
49
13
6
16
17
3
39
17
23
6
2
7
31
7
50
30
45
12
5
15
6
4
6
3862
3897
526
520
4721
4723
* Bases shown are for National Lottery Draw. Bases for other activities vary.
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
NatCen Social Research | Loyalty card survey
30
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
Lotteries and related products
National Lottery Draw
Scratchcards
Other lotteries
Machines/games
Football pools
Bingo (not online)
Machines in a bookmaker’s
Fruit machines
Casino table games (not online)
Poker played in pubs or clubs
Online gambling on slots, casino
or bingo games
Betting activities
Online betting with a bookmaker
Betting exchange
Horse races (not online)
Dog races (not online)
Sports events (not online)
Other events or sports (not online)
Spread-betting
Private betting
Other gambling activity
Any other gambling
Bases*
Weighted
Unweighted
Age group
18-24
%
25-34
%
35-44
%
45-54
%
55-64
%
65+
%
Total
%
31
53
9
44
46
16
53
46
21
61
33
22
64
26
22
69
21
26
51
40
18
27
4
65
33
24
11
23
9
75
40
18
8
16
12
75
31
16
6
13
7
76
28
14
5
11
8
80
24
12
3
10
9
79
21
6
2
18
8
74
31
17
7
30
31
26
18
11
7
23
52
7
39
22
50
9
6
25
38
9
39
26
44
11
7
19
32
8
47
33
48
17
7
15
24
5
62
37
50
14
4
11
18
5
63
34
40
9
3
5
8
1
65
29
31
7
0
4
31
7
50
30
45
12
5
15
5
7
8
6
5
3
6
715
491
1008
822
838
764
949
1091
546
737
300
471
4721
4723
* Bases shown are for National Lottery Draw. Bases for other activities vary.
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.
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31
Table 3.3
Number of gambling activities, grouped and mean, by sex
All aged18 and over
Number of gambling activities
Sex
Total
Men
%
3
16
30
25
14
11
Women
%
5
22
32
25
11
5
%
4
17
30
25
14
11
Mean
5.0
4.2
4.8
Standard error of the mean
.07
.17
.06
3862
3897
526
520
4726
4727
None
1 to 2
3 to 4
5 to 6
7 to 8
9 or more
Bases
Weighted
Unweighted
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
18-24
Total
25-34
35-44
45-54
55-64
65+
%
%
%
%
%
%
%
None
1 to 2
3 to 4
5 to 6
7 to 8
9 or more
5
19
23
24
16
14
5
16
27
22
16
13
3
16
28
22
17
13
3
15
31
29
12
10
2
16
37
29
12
4
1
19
44
29
6
1
4
17
30
25
14
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
715
491
1008
822
838
764
949
1091
546
737
300
471
4726
4727
Bases
Weighted
Unweighted
NatCen Social Research | Loyalty card survey
32
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)
%
42
38
18
52
41
19
54
43
22
56
41
20
57
33
15
18
7
73
28
17
6
15
10
73
32
15
7
20
8
73
29
14
9
20
8
74
34
15
5
14
7
79
32
21
8
17
23
24
25
31
23
3
49
33
43
13
4
13
30
6
48
30
41
10
5
14
33
5
52
31
50
11
5
14
36
9
54
29
49
9
5
17
47
12
56
30
51
15
8
18
6
3
5
6
10
940
961
797
816
566
554
924
899
652
638
Lotteries and related products
National Lottery Draw
Scratchcards
Other lotteries
Machines/games
Football pools
Bingo (not online)
Machines in a bookmaker’s
Fruit machines
Casino table games (not online)
Poker played in pubs or clubs
Online gambling on slots, casino or
bingo games
Betting activities
Online betting with a bookmaker
Betting exchange
Horse races (not online)
Dog races (not online)
Sports events (not online)
Other events or sports (not online)
Spread-betting
Private betting
Other gambling activity
Any other gambling
Bases*
Weighted
Unweighted
* 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.
NatCen Social Research | Loyalty card survey
33
Table 3.6
Number of activities, grouped and mean, by income quintile
All aged 18 and over
Number of gambling activities
None
1 to 2
3 to 4
5 to 6
7 to 8
9 or more
Income quintile
Lowest (less
than £10,400)
%
6
18
32
22
13
9
2nd
%
3
17
35
23
11
10
3rd
%
3
14
29
28
15
10
4th
%
3
14
28
26
17
11
Highest (£32k
or more)
%
2
13
27
28
15
15
Mean
4.5
4.7
5.0
5.1
5.4
Standard error of the mean
.14
.14
.16
.14
.16
940
961
797
816
566
554
924
899
652
638
Bases
Weighted
Unweighted
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).
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34
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
%
53
43
16
48
38
24
16
8
77
32
16
7
20
12
73
30
18
6
24
23
36
7
52
29
48
11
5
14
25
7
47
33
41
11
4
16
5
8
1925
1890
1098
1111
Lotteries and related products
National Lottery Draw
Scratchcards
Other lotteries
Machines/games
Football pools
Bingo (not online)
Machines in a bookmaker’s
Fruit machines
Casino table games (not online)
Poker played in pubs or clubs
Online gambling on slots, casino
or bingo games
Betting activities
Online betting with a bookmaker
Betting exchange
Horse races (not online)
Dog races (not online)
Sports events (not online)
Other events or sports (not online)
Spread-betting
Private betting
Other gambling activity
Any other gambling
Bases*
Weighted
Unweighted
* Bases shown are for National Lottery Draw. Bases for other activities vary.
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.
NatCen Social Research | Loyalty card survey
35
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
Lotteries and related products
National Lottery Draw
Scratchcards
Other lotteries
Machines/games
Football pools
Bingo (not online)
Machines in a bookmaker’s
Fruit machines
Casino table games (not online)
Poker played in pubs or clubs
Online gambling on slots, casino
or bingo games
Betting activities
Online betting with a bookmaker
Betting exchange
Horse races (not online)
Dog races (not online)
Sports events (not online)
Other events or sports (not online)
Spread-betting
Private betting
Other gambling activity
Any other gambling
Bases*
Weighted
Unweighted
Economic activity
Paid work
Selfemployed
Retired
Student
%
%
%
Looking
Long-term Unemployed
after sick/disabled
family/home
%
%
%
%
52
43
19
54
38
15
68
23
24
27
37
7
52
50
17
39
38
24
45
43
14
19
7
74
32
17
7
15
6
78
30
17
5
10
8
82
21
10
3
23
1
53
21
25
10
12
17
74
41
6
7
15
15
77
35
13
4
23
9
73
32
20
9
26
22
9
24
27
20
22
39
8
49
28
48
11
5
17
27
7
51
32
46
11
7
14
13
4
65
29
33
7
2
5
49
5
29
13
49
11
4
21
22
2
42
32
37
15
4
11
18
5
55
36
42
16
4
9
25
5
49
38
51
18
6
14
6
6
4
9
8
9
5
2257
2120
626
648
403
583
123
85
156
157
306
304
500
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).
NatCen Social Research | Loyalty card survey
36
Table 3.9
Number of activities, grouped and mean, by economic activity
All aged 18 and over
Number of gambling
activities
Economic activity
SelfLooking after Long-term
Paid work employed Retired
Student
family/home sick/disabled Unemployed
%
%
%
%
%
%
%
None
1 to 2
3 to 4
5 to 6
7 to 8
9 or more
3
15
28
25
15
12
3
18
29
27
15
9
0
20
40
28
10
2
15
17
24
22
13
9
7
15
29
28
10
11
5
16
33
22
13
11
4
17
30
22
13
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
2257
2120
626
648
403
583
123
85
156
157
306
304
500
499
Bases
Weighted
Unweighted
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.
NatCen Social Research | Loyalty card survey
37
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
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four weeks
Gambled – frequency unknown
Machines in a bookmaker’s
27
14
33
15
7
3
0
18
14
34
22
7
5
1
26
14
32
15
7
4
2
Almost every day/every day
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four weeks
10
7
24
19
15
25
6
5
21
19
21
29
10
7
23
18
16
26
3862
3897
526
520
4718
4719
Bases
Weighted
Unweighted
*Bases are shown for most frequent activity
NatCen Social Research | Loyalty card survey
38
Table 3.11
Frequency of gambling on a) the most frequent activity and b) machines in a
bookmaker’s, by age
All aged 18 and over
Frequency of gambling
Most frequent activity
Almost every day/every day
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four weeks
Gambled – frequency unknown
Machines in a bookmaker’s
Almost every day/every day
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four weeks
Bases (unweighted)
Bases (weighted)
Age group
18-24
%
25-34
%
35-44
%
45-54
%
55-64
%
65+
%
Total
%
21
12
30
20
11
5
-
21
15
32
21
7
5
-
30
14
33
13
8
3
-
28
15
37
12
5
3
-
27
16
36
14
4
2
0
38
15
32
10
3
1
1
26
14
32
15
7
4
2
6
5
15
20
19
35
9
9
20
19
18
25
11
6
26
15
17
25
11
7
25
19
14
24
7
8
29
23
13
20
18
10
28
16
8
21
10
7
23
18
16
26
491
715
822
1008
764
838
1091
949
737
546
471
300
4719
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.
NatCen Social Research | Loyalty card survey
39
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
%
%
%
%
%
32
11
26
15
8
28
16
32
15
6
21
15
35
18
7
23
15
39
16
4
24
17
35
13
8
6
3
3
3
2
11
7
24
16
14
10
9
21
17
17
7
5
25
19
16
11
6
24
18
15
8
8
22
24
17
27
27
28
26
21
940
961
797
816
566
554
924
899
652
638
Most frequent activity
Almost every day/every day
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four
weeks
Machines in a bookmaker’s
Almost every day/every day
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four
weeks
Bases
Weighted
Unweighted
3rd
Highest
(£32k or
more)
4th
*Bases are shown for most frequent activity
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.
NatCen Social Research | Loyalty card survey
40
Table 3.13
Frequency of gambling on a) the most frequent activity and b)
machines in a bookmaker’s by area deprivation (England
only)
All aged 18 and over
Frequency of gambling
Deprivation
Not most deprived
%
Most deprived
%
24
15
34
17
7
3
30
14
29
16
7
4
9
7
25
19
17
23
13
8
22
17
14
27
1925
1890
1098
1111
Most frequent activity
Almost every day/every day
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four weeks
Machines in a bookmaker’s
Almost every day/every day
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four weeks
Bases
Weighted
Unweighted
*Bases are shown for most frequent activity
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.
NatCen Social Research | Loyalty card survey
41
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
Most frequent activity
Everyday
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did no gamble in past four
weeks
Machines in a bookmaker’s
Everyday
4-5 days per week
2-3 days per week
About once per week
Less than once per week
Did not gamble in past four
weeks
Bases
Weighted
Unweighted
Economic activity
SelfPaid work employed Retired
%
%
%
Looking after Long-term
family/home sick/disabled Unemployed
%
%
%
Student
21
14
36
18
7
27
19
32
15
5
38
14
31
13
3
13
9
31
16
15
28
11
30
19
5
38
16
22
10
8
33
12
31
13
7
3
3
1
15
7
5
4
7
6
22
21
18
12
9
25
19
12
14
9
30
17
12
3
1
9
23
17
12
3
27
15
17
16
12
23
11
15
13
8
25
15
13
26
22
18
47
26
23
27
2257
2120
626
648
403
583
123
85
156
157
306
304
500
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.
NatCen Social Research | Loyalty card survey
42
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.
NatCen Social Research | Loyalty card survey
43
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.
NatCen Social Research | Loyalty card survey
44
Table 4.1
Gambling types, by engagement in gambling activities
All aged 18 and over
Gambling participation
Gambling type
Class 1 –
Lowest
engagement
%
Class 2 –
Moderate
engagement
%
Class 3 –
Substantial
engagement
%
Class 4 –
Heaviest
engagement
%
%
21
79
0
0
0
0
0
0
100
0
0
0
0
0
0
64
36
0
0
0
0
0
0
100
4
17
30
25
14
11
19
14
4
3
1
38
9
3
0
51
33
13
12
4
74
23
9
2
62
50
24
21
10
88
38
20
7
76
73
36
53
28
97
73
51
31
51
40
18
18
8
74
31
17
7
4
8
1
10
2
8
1
0
4
1
12
19
2
39
17
32
2
1
6
3
29
40
7
69
43
63
14
5
19
7
65
78
32
86
70
85
53
27
45
24
23
31
7
50
30
45
12
5
15
6
1007
1880
1400
1498
1811
914
508
435
4726
4727
Number of gambling activities in
past four weeks
None
1 to 2
3 to 4
5 to 6
7 to 8
9 or more
Type of engagement
National Lottery Draw
Scratchcards
Other lotteries
Football pools
Bingo
Machines in bookmaker’s
Fruit machines
Table games in a casino
Poker in a pub/club
Gambled online on
casino/slots/bingo
Betting online
Betting exchanges
Bet on horses (not online)
Bet on dogs (not online)
Bet on sports events (not online)
Bet on other events (not online)
Spread bet
Private betting or gambling
Other gambling
Bases (weighted)
Bases (unweighted)
NatCen Social Research | Loyalty card survey
Total
45
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 nonWhite/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.
NatCen Social Research | Loyalty card survey
46
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)
All aged 18 and over
Socio-demographic and economic characteristics
Sex (p<0.01)
Men
Women
Unknown
Income quintile (p<0.01)
Lowest (less than £10,400 per year)
2nd
3rd
4th
Highest (more than £32,000 per year)
Unknown
Number of loyalty cards held currently (p<0.01)
1
2
3 or more
Ethnic group (p<0.05)
White/White British
Mixed
Asian/Asian British
Black/Black British
Other
Unknown
OR
95% CI
n
Lower
Upper
1.52
6.89
1.12
1.97
2.05
24.03
3897
520
310
1
0.80
0.68
0.58
0.47
1.11
0.98
961
816
554
0.66
0.60
0.48
0.41
0.91
0.87
899
638
1.15
0.82
1.61
859
0.48
0.74
0.36
0.43
0.65
1.29
3681
859
187
1
1.59
1.38
1.21
1.78
0.28
0.73
0.93
0.81
1.05
0.08
3.45
2.05
1.81
3.02
0.98
3588
76
270
298
170
325
1
1
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.
NatCen Social Research | Loyalty card survey
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Table 4.3
Estimated odds ratios for belonging to class 2 (Moderate engagement
gamblers)
All aged 18 and over
Socio-demographic and economic characteristics
OR
95% CI
n
Lower
Upper
1.25
1.37
1.53
2.02
2.72
2.71
0.91
0.99
1.13
1.46
1.88
1.14
1.72
1.89
2.08
2.81
3.94
6.44
491
822
764
1091
737
471
351
1
1.26
0.68
2.37
3588
76
Asian/Asian British
Black/Black British
1.29
0.68
0.90
0.46
1.85
1.01
270
298
Other
Unknown
Number of loyalty cards held currently (p<0.05)
0 or 1
2
3 or more
0.63
0.42
0.38
0.17
1.04
0.99
170
325
0.65
0.35
1.04
0.94
3681
859
187
Age group (p<0.01)
18-24
25-34
35-44
45-54
55-64
65 and over
Unknown
Ethnic group (p<0.05)
White/White British
Mixed
1
1
0.82
0.58
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.
NatCen Social Research | Loyalty card survey
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Table 4.4
Estimated odds ratios for belonging to class 3 (Substantial engagement
gamblers)
All aged 18 and over
Socio-demographic and economic characteristics
Income quintile p<0.05)
Lowest (less than £10,400 per year)
2nd
3rd
4th
Highest (more than £32,000 per year)
Unknown
Ethnic group (p<0.05)
White/White British
Mixed
Asian/Asian British
Black/Black British
Other
Unknown
Number of loyalty cards held currently (p<0.05)
0 or 1
2
3 or more
OR
95% CI
n
Lower
Upper
1
0.99
1.42
1.42
1.36
1.12
0.75
1.07
1.11
1.03
0.83
1.29
1.90
1.84
1.80
1.52
961
816
554
899
638
859
1
0.52
0.57
0.98
0.83
0.59
0.28
0.39
0.71
0.53
0.40
0.99
0.83
1.36
1.31
0.89
3588
76
270
298
170
325
1
1.30
0.93
1.06
0.62
1.60
1.41
3681
859
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.
NatCen Social Research | Loyalty card survey
49
Table 4.5
Estimated odds ratios for belonging to class 4 (Heaviest engagement
gamblers)
All aged 18 and over
Socio-demographic and economic characteristics
Sex (p<0.02)
Men
Women
Unknown
Age group (p<0.01)
18-24
25-34
35-44
45-54
55-64
65 and over
Unknown
Number of loyalty cards held currently (p<0.01)
0 or 1
2
3 or more
OR
95% CI
Upper
0.24
1.62
0.76
19.85
3897
520
310
0.64
0.62
0.47
0.13
0.02
0.04
1.44
1.44
1.09
0.47
0.17
0.47
491
822
764
1091
737
471
351
1.58
2.13
2.90
6.24
3681
859
187
1
0.43
5.67
1
0.96
0.95
0.71
0.25
0.06
0.13
1
2.14
3.65
n
Lower
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.
NatCen Social Research | Loyalty card survey
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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.
NatCen Social Research | Loyalty card survey
51
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.
NatCen Social Research | Loyalty card survey
52
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.
Figure 5.1
At-risk gambling prevalence, by age
B ase: A ll aged 18 and o ver
60
Percent
50
40
30
20
10
0
18-24
25-34
35-44
Age group
NatCen Social Research | Loyalty card survey
45-54
55-64
65+
53
Table 5.1
Problem gambling prevalence rates according to the PGSIa by sex and age
All aged 18 and over
PGSI scores
Age group
18-24
%
Total
25-34
%
35-44
%
45-54
%
55-64
%
65+
%
%
Men
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
25
37
22
17
23
23
24
30
25
19
28
28
28
22
24
27
34
23
27
16
37
25
24
14
27
25
25
24
Women
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
*
*
*
*
40
21
20
20
34
29
13
23
41
14
26
18
38
26
27
9
52
22
8
18
41
22
19
18
All
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
26
36
21
17
25
23
24
29
26
20
26
28
29
21
24
25
34
24
27
15
39
25
22
15
29
24
24
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.
NatCen Social Research | Loyalty card survey
54
Table 5.2
Endorsement of each PGSI item, by age and sex
All aged 18 and over
PGSI item
Men
Bet more than could afford to lose
Never
Sometimes
Most of the time
Always/almost always
Gambled with larger amounts of
money
Never
Sometimes
Most of the time
Always/almost always
Chased losses
Never
Sometimes
Most of the time
Always/almost always
Borrowed money to gamble
Never
Sometimes
Most of the time
Always/almost always
Felt had problem with gambling
Never
Sometimes
Most of the time
Always/almost always
Gambling caused health problems
Never
Sometimes
Most of the time
Always/almost always
People criticised my gambling
Never
Sometimes
Most of the time
Always/almost always
Gambling caused financial
problems
Never
Sometimes
Most of the time
Always/almost always
Felt guilty about gambling
Never
Sometimes
Most of the time
Always/almost always
Age group
18-24
%
Total
25-34
%
35-44
%
45-54
%
55-64
%
65+
%
%
61
26
7
5
48
30
11
11
50
32
11
8
52
31
8
9
58
33
6
4
66
24
5
5
54
30
9
8
69
25
2
4
63
23
10
5
60
27
8
5
68
22
5
5
72
21
4
3
71
20
6
3
66
23
6
5
44
38
11
7
39
35
12
14
43
34
10
13
52
30
9
9
57
32
5
5
63
28
5
4
47
33
9
10
89
8
1
2
77
17
4
2
80
14
3
3
80
14
3
2
89
8
1
3
92
7
0
1
83
12
3
2
76
13
5
6
61
25
7
8
59
27
7
8
61
24
7
8
70
21
3
6
73
19
3
5
65
22
6
7
84
10
2
3
72
14
6
8
66
21
5
8
69
19
4
7
80
16
2
2
80
15
4
2
74
16
4
6
62
26
5
7
58
26
7
8
63
23
7
6
61
27
5
7
72
20
4
4
75
18
5
2
64
24
6
6
81
13
2
4
70
18
5
7
64
24
4
8
69
18
4
9
77
17
3
4
81
12
4
3
72
18
4
6
72
20
2
5
70
18
5
7
52
32
7
9
69
18
4
9
63
27
4
6
81
12
4
3
60
26
5
9
NatCen Social Research | Loyalty card survey
55
Table 5.2 continued
Endorsement of each PGSI item, by age and sex
All aged 18 and over
PGSI item
Age group
18-24
%
Women
Bet more than could afford to lose
Never
Sometimes
Most of the time
Always/almost always
Gambled with larger amounts of
money
Never
Sometimes
Most of the time
Always/almost always
Chased losses
Never
Sometimes
Most of the time
Always/almost always
Borrowed money to gamble
Never
Sometimes
Most of the time
Always/almost always
Felt had problem with gambling
Never
Sometimes
Most of the time
Always/almost always
Gambling caused health problems
Never
Sometimes
Most of the time
Always/almost always
People criticised my gambling
Never
Sometimes
Most of the time
Always/almost always
Gambling caused financial
problems
Never
Sometimes
Most of the time
Always/almost always
Felt guilty about gambling
Never
Sometimes
Most of the time
Always/almost always
35-44
%
45-54
%
55-64
%
65+
%
%
*
*
*
*
56
29
6
9
64
25
3
8
54
32
7
7
60
34
3
3
67
18
3
12
60
28
5
8
*
*
*
*
74
18
4
4
66
25
1
8
77
16
4
3
70
22
4
4
79
19
1
1
74
19
3
4
*
*
*
*
52
28
10
11
51
34
11
4
56
27
10
7
55
32
8
5
70
22
2
6
55
29
8
8
*
*
*
*
82
15
4
80
13
5
2
88
9
2
1
91
8
0
91
9
-
86
10
2
2
*
*
*
*
74
18
4
5
70
20
3
6
71
24
0
4
78
18
1
3
78
22
-
75
19
2
4
*
*
*
*
77
14
1
8
76
15
6
3
81
13
1
5
83
11
1
5
79
20
1
80
14
2
4
*
*
*
*
72
19
4
5
74
19
5
3
71
24
1
4
87
4
4
5
88
11
1
-
76
17
3
4
*
*
*
*
78
9
5
8
71
16
6
6
81
13
3
3
90
4
1
5
83
15
1
1
81
11
3
5
*
*
*
*
64
25
4
7
67
17
7
9
66
24
5
4
76
18
2
4
68
25
6
1
68
22
5
5
885
122
726
112
829
121
463
82
257
43
3861
526
730
91
681
83
950
141
634
102
405
66
3896
520
Bases
Weighted
Men
675
Women
40
Unweighted
Men
462
Women
29
* Estimates not shown because of small base sizes
NatCen Social Research | Loyalty card survey
Total
25-34
%
56
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.
NatCen Social Research | Loyalty card survey
57
Table 5.3
PGSI score, by gambling group and sex
All aged 18 and over
PGSI score
Gambling group
Class 1:
Class 2:
Lowest
Moderate
engagement
engagement
%
%
Total
Class 3:
Substantial
engagement
%
Class 4:
Heaviest
engagement
%
%
Men
PGSI
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
23
24
27
25
30
25
25
21
39
26
17
17
14
22
26
39
27
25
25
24
Women
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
35
21
22
22
43
26
17
14
53
18
16
12
5
25
25
44
41
22
19
18
All
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
24
24
27
25
31
25
24
20
42
24
18
16
13
21
26
40
29
24
24
23
1537
187
1751
1148
169
1332
734
144
904
442
26
478
3861
526
4465
1621
185
1827
1235
190
1441
661
122
817
377
23
412
3894
520
4497
Bases
Weighted
Men
Women
All
Unweighted
Men
Women
All
NatCen Social Research | Loyalty card survey
58
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
0
1-2
3-4
5-6
%
%
%
%
Total
7 or more
%
%
Men
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
51
21
7
21
36
28
19
17
30
25
25
21
27
24
26
23
15
24
28
33
27
25
25
24
Women
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
71
16
1
12
49
19
20
12
43
26
17
14
34
22
25
19
26
20
19
35
41
22
19
18
All
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
56
19
6
18
38
26
20
16
31
25
24
20
27
24
26
23
16
23
28
33
29
24
24
23
602
117
735
1148
169
1332
979
130
1122
1000
83
1108
3861
526
4465
573
107
701
1235
190
1441
1047
128
1186
951
80
1053
3894
520
4497
Bases
Weighted
Men
132
Women
27
All
169
Unweighted
Men
88
Women
15
All
116
* 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
NatCen Social Research | Loyalty card survey
59
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
Lowest (less
than
£10,400)
%
Total
2nd
3rd
4th
%
Highest
(more than
£32,000)
%
%
%
%
Men
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
22
22
23
33
23
22
27
28
23
27
26
23
31
25
23
21
33
24
28
15
27
25
25
24
Women
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
34
23
19
24
41
19
19
21
[41]
[28]
[21]
[10]
43
18
19
20
[44]
[36]
[16]
[3]
41
22
19
18
All
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
24
23
22
31
25
22
26
27
25
27
25
22
32
25
23
21
33
25
27
15
29
24
24
23
802
138
940
684
113
797
505
61
566
840
84
924
622
30
652
3861
526
4465
811
149
960
698
118
816
505
49
554
828
71
899
607
31
638
3894
520
4497
Bases
Weighted
Men
Women
All
Unweighted
Men
Women
All
NatCen Social Research | Loyalty card survey
60
Table 5.6
PGSI score, by area deprivation (England only) and sex
All aged 18 and over
Gambling behaviour
Area deprivation
Not most
Most deprived
deprived area
areas in
in England
England (80th
centile)
%
%
Total
%
Men
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
27
25
25
24
25
22
24
29
27
25
25
24
Women
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
39
20
23
19
43
19
15
23
41
22
19
18
All
Non-problem (score less than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
28
24
25
23
28
21
23
28
29
24
24
23
1703
222
1925
946
151
1098
3861
526
4465
1676
212
1889
963
148
1111
3894
520
4497
Bases
Weighted
Men
Women
All
Unweighted
Men
Women
All
NatCen Social Research | Loyalty card survey
61
Table 5.7
PGSI score, by economic activity and sex
All aged 18 and over
PGSI score
Economic activity
Paid
Selfemployme employed
nt
Total
Retired
Student
Looking Long-term Unemploy
after
sick
ed
family/ho
me
%
%
%
%
%
%
%
Men
Non-problem (score less
than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
29
26
24
21
27
24
26
23
37
26
20
17
15
48
21
16
17
12
37
34
20
19
28
33
20
16
26
39
27
25
25
24
Women
Non-problem (score less
than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
49
22
17
12
[18]
[29]
[32]
[21]
41
24
15
20
*
*
*
*
45
24
12
19
[33]
[15]
[27]
[25]
29
19
24
27
41
22
19
18
All
Non-problem (score less
than 1)
Low risk (score 1-2)
Moderate risk (score 3-7)
Problem gambler (score 8+)
31
26
24
20
26
24
26
23
38
26
19
18
17
44
21
18
31
18
24
26
22
19
28
32
21
16
25
38
29
24
24
23
340
63
403
110
12
123
76
80
156
256
50
306
445
54
500
3861
526
4465
496
86
582
78
7
85
69
88
157
256
48
304
445
54
499
3894
520
4497
Bases
Weighted
2034
585
Men
223
41
Women
2257
626
All
Unweighted
1919
616
Men
201
32
Women
2120
648
All
* Estimates not shown because of small base sizes
NatCen Social Research | Loyalty card survey
62
%
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).
NatCen Social Research | Loyalty card survey
63
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.
NatCen Social Research | Loyalty card survey
64
Table 5.8
Estimated odds ratios for problem gambling, by associated risk factors and sex
All aged 18 and over
Variable
a
N Odds 95% C.I.
ratio
3897
Lower Upper
Men
Gambling type
(p<0.001)
Cluster 1 – Lowest
663
engagement
1
Class 2 – Moderate
1236 1.43
engagement
1.01 2.01
Class 3 – Substantial
1621 2.03
engagement
1.46 2.81
Class 4 – Heaviest
377 3.23
engagement
2.12 4.91
Income quintile
p<0.05)
Lowest (less than
812
£10,400 per year)
1
698 0.93
2nd
0.67 1.31
501 0.75
3rd
0.51 1.11
828 0.54
4th
0.37 0.78
Highest (more than
607 0.38
£32,000 per year)
0.25 0.58
447 0.48
Unknown
0.31 0.73
Number of loyalty cards
held currently
(p<0.05)
3019
0 or 1
1
878 1.37
2 or more
1.06 1.78
Economic activity
(p<0.01)
1919
Paid employment
1
616 1.33
Self-employment
0.96 1.83
497 1.59
Retired
0.89 2.82
78 0.63
Student
0.28 1.38
69 1.31
Looking after family/home
0.70 2.44
Long-term
256 1.66
illness/disability
1.05 2.64
445 1.86
Unemployed
1.33 2.61
17
Unknown
*
*
*
Ethnic group (p<0.05)
3130
White/White British
1
65 3.73
Mixed
1.78 7.83
259 5.20
Asian/Asian British
3.61 7.50
273 4.55
Black/Black British
3.13 6.63
155 3.82
Other
2.27 6.42
15
Ethnic group unknown
*
*
*
Age (p<0.001)
462
18-24
1
730 1.78
25-34
1.22 2.59
681 1.53
35-44
1.03 2.27
950 1.59
45-54
1.09 2.33
634 0.91
55-64
0.58 1.44
405 0.66
65 and over
0.32 1.32
35 [0.99] [0.27] [3.65]
Age unknown
a
Confidence interval
*Estimates not shown due to small base sizes
Variable
Women
Gambling type
(p<0.05)
Cluster 1 – Lowest
engagement
Class 2 – Moderate
engagement
Class 3 – Substantial
engagement
Class 4 – Heaviest
engagement
Income quintile
p<0.05)
Lowest (less than
£10,400 per year)
2nd
3rd
4th
Highest (more than
£32,000 per year)
Unknown
Number of loyalty
cards held currently
(p<0.05)
0 or 1
2 or more
a
N Odds
95% C.I.
ratio
520
Lower Upper
122
1
190
1.13
0.45
2.85
185
2.10
0.88
5.02
23
*
*
*
149
118
49
71
1
0.96
0.35
0.92
0.40
0.11
0.38
2.31
1.12
2.25
31
102
0.11
0.67
0.03
0.27
0.44
1.65
411
109
1
2.33
1.18
4.63
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-
NatCen Social Research | Loyalty card survey
65
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).
NatCen Social Research | Loyalty card survey
66
Table 5.9
Estimated odds ratios for at-risk gambling, by associated risk factors and sex
All aged 18 and over
Variable
a
N Odds 95% C.I.
ratio
3897
Lower Upper
Men Base (weighted)
Gambling type (p<0.05)
Class 1 – Lowest
1
663
engagement
Class 2 – Moderate
1236 1.60
engagement
1.20
Class 3 – Substantial
1621 2.23
engagement
1.66
Class 4 – Heaviest
377 3.34
engagement
2.09
Ethnic group (p<0.05)
3145
White/White British
1
65 2.08
Mixed
0.77
259 3.03
Asian/Asian British
1.69
273 2.69
Black/Black British
1.46
155 1.80
Other
0.80
15
Unknown
*
*
Income quintile (p<0.05)
Lowest (less than
1
812
£10,400 per year)
698 1.08
2nd
0.74
501 1.14
3rd
0.77
828 0.72
4th
0.51
Highest (more than
607 0.77
£32,000 per year)
0.53
447 0.70
Unknown
0.47
Number of loyalty cards
held currently (p<0.05)
3019
0 or 1
1
721 1.40
2
1.04
157 1.84
3 or more
0.84
Household composition
(p<0.05)
1359
Lives alone
1
Lives with spouse/partner
& one or more child
535 0.71
under 16
0.51
Lives with spouse/partner
778 0.66
only
0.49
Lives with spouse/partner
286 0.74
& one or more other adult
0.48
Lives with child over 16
833 1.06
& one or more other adult
0.79
Lives with one or more
62 0.35
child over 16 only
0.16
Lives with one or more
24
child under 16 only
*
*
Lives with one or more
child under 16 & other
20
adult
*
*
a
Confidence interval
*Estimates not shown due to small base sizes
NatCen Social Research | Loyalty card survey
2.15
2.98
5.35
5.64
5.45
4.95
4.07
*
Variable
Women Base (weighted)
Gambling type
Class 1 – Lowest
engagement
Class 2 – Moderate
engagement
Class 3 – Substantial
engagement
Class 4 – Heaviest
engagement
Ethnic group (p<0.05)
White/White British
Non-White
a
N Odds
95% C.I.
ratio
520
Lower Upper
1
122
190
1.63
0.83
3.20
185
2.08
1.04
4.14
23
*
*
*
458
62
1
4.30
1.77
10.44
1.56
1.68
1.01
1.12
1.04
1.90
4.06
1.00
0.89
1.13
1.42
0.78
*
*
67
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.
NatCen Social Research | Loyalty card survey
68
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
Most of the time
Some of the time
Never
6
4
17
73
11
9
25
55
8
8
27
57
11
6
22
61
6
4
25
64
5
4
25
66
9
6
23
62
Women
Almost always
Most of the time
Some of the time
Never
*
*
*
*
9
3
20
67
12
5
24
59
8
3
16
73
5
0
13
82
1
5
18
76
7
4
18
71
All
Almost always
Most of the time
Some of the time
Never
6
5
16
73
11
8
25
56
8
7
27
57
11
6
22
62
6
3
23
67
4
4
24
68
8
6
23
63
834
106
941
666
105
771
765
114
879
434
73
508
240
41
281
3615
478
4144
693
83
777
647
79
726
906
137
1043
615
97
713
392
64
456
3730
493
4274
Bases (weighted)
Men
653
Women
33
All
686
Bases (unweighted)
Men
446
Women
25
All
471
* Estimates not shown because of small base sizes
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.
NatCen Social Research | Loyalty card survey
69
Table 5.11
Gambling problems with machines, by income quintile and sex
All aged 18 and over
Has problems with machine
gambling
Income quintile
Total
Lowest (less
than
£10,400)
%
2nd
3rd
4th
%
%
Men
Almost always
Most of the time
Some of the time
Never
11
8
28
53
10
7
25
59
Women
Almost always
Most of the time
Some of the time
Never
14
3
18
65
All
Almost always
Most of the time
Some of the time
Never
Bases
Weighted
Men
Women
All
Unweighted
Men
Women
All
%
Highest
(More than
£32,000)
%
%
7
8
21
64
8
6
22
64
6
4
25
66
9
6
23
62
4
9
16
71
[1]
[1]
[20]
[78]
11
6
28
55
[1]
[2]
[3]
[94]
7
4
18
71
11
7
27
55
9
7
23
60
7
7
21
66
9
6
22
63
6
4
24
67
8
6
23
63
731
131
862
647
101
748
471
57
528
795
72
867
586
30
616
3615
478
4144
763
144
907
669
112
781
485
47
532
799
64
863
588
31
619
3730
493
4274
NatCen Social Research | Loyalty card survey
70
Table 5.12
Gambling problems with machines, by area deprivation
(England only) and sex
All aged 18 and over
Has problems with machine
gambling
Area deprivation
Not most
Most deprived
deprived area
areas in
in England
England (80th
centile)
%
%
Total
%
Men
Almost always
Most of the time
Some of the time
Never
7
6
22
65
10
8
26
56
9
6
23
62
Women
Almost always
Most of the time
Some of the time
Never
7
3
18
72
12
5
17
66
7
4
18
71
All
Almost always
Most of the time
Some of the time
Never
7
6
22
65
11
7
24
58
8
6
23
63
1633
199
1833
872
144
1015
3615
478
4144
1626
199
1826
915
144
1059
3730
493
4274
Bases
Weighted
Men
Women
All
Unweighted
Men
Women
All
NatCen Social Research | Loyalty card survey
71
Table 5.13
Gambling problems with machines, by economic activity and sex
All aged 18 and over
Has problem with machine Economic activity
gambling
Paid
Selfemployme employed
nt
Total
Retired
Student
Looking Long-term Unemploy
after
sick
ed
family/ho
me
%
%
%
%
%
%
%
Men
Almost always
Most of the time
Some of the time
Never
7
6
23
64
9
7
23
62
7
5
21
67
2
1
22
75
9
4
32
55
16
10
27
48
14
8
23
55
9
6
23
62
Women
Almost always
Most of the time
Some of the time
Never
3
2
17
77
[11]
[-]
[23]
[66]
2
8
20
71
*
*
*
*
12
4
14
69
[15]
[5]
[15]
[65]
14
7
24
54
7
4
18
71
All
Almost always
Most of the time
Some of the time
Never
7
6
23
65
9
6
23
62
6
5
21
67
2
4
20
74
11
4
23
63
16
9
25
51
14
8
23
55
8
6
23
63
314
60
374
102
9
111
71
77
148
234
46
280
416
49
464
3615
478
4144
477
84
561
71
5
76
66
85
151
240
46
286
423
51
474
3730
493
4274
Bases
Weighted
Men
1909
556
Women
197
39
All
2107
595
Unweighted
Men
1842
596
Women
188
31
All
2030
627
* Estimates not shown because of small base sizes
%
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
NatCen Social Research | Loyalty card survey
72
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.
NatCen Social Research | Loyalty card survey
73
Table 5.14
Estimated odds ratios for machine problem gambling, by associated risk factors and sex
All aged 18 and over
Variable
Men Base (unweighted)
Gambling type (p<0.05)
Class 1 – Lowest
engagement
Class 2 – Moderate
engagement
Class 3 – Substantial
engagement
Class 4 – Heaviest
engagement
a
N Odds 95% C.I.
ratio
3730
Lower Upper
571
1
1183
1.10
0.74
1.65
1599
1.25
0.86
1.83
377
2.13
1.35
3.36
Unknown
Economic activity
(p<0.05)
Paid employment
Self-employment
Retired
Student
Looking after family/home
Long-term
illness/disability
Unemployed
Unknown
Household composition
(p<0.05)
Lives alone
Lives with spouse/partner
& one or more child
under 16
Lives with spouse/partner
only
Lives with spouse/partner
& one or more other adult
Lives with child over 16
& one or more other adult
Lives with one or more
child over 16 only
Lives with one or more
child under 16 only
Lives with one or more
child under 16 & other
adult
Age (p<0.05)
18-34
35-54
55+
3004
60
245
262
1
2.25
3.84
2.86
1.02
2.52
1.90
4.99
5.84
4.30
145
2.42
1.36
4.32
14
*
*
*
1842
596
477
71
66
1
1.28
1.92
0.16
0.82
0.90
1.04
0.05
0.33
1.82
3.57
0.51
2.02
240
423
15
2.43
1.59
*
1.52
1.10
*
3.89
2.29
*
1296
1
503
0.81
Women Base (unweighted)
Income quintile (p<0.05)
Lowest (less than £10,400
per year)
2nd
3rd
4th
Highest (more than £32,000
per year)
Unknown
Ethnic group (p<0.05)
White/White British
Mixed
Asian/Asian British
Black/Black British
Other
Variable
Household composition
(p<0.05)
Lives alone
Lives with spouse/partner &
one or more child under 16
0.55
0.74
0.51
1.07
274
0.49
0.28
0.87
803
0.86
0.61
1.22
59
0.09
0.02
0.30
22
*
*
*
19
*
*
*
1139
1553
1
0.86
0.64
1.16
1007
0.52
0.32
0.84
144
1
112
0.70
0.25
1.96
47 [0.11]
[0.03]
[0.44]
1.13
0.39
3.29
31 [0.19]
95 0.38
[0.03]
0.12
[1.00]
1.19
[0.22]
[3.11]
64
152
1
46 [0.83]
1.20
754
a
N Odds
95% C.I.
ratio
493
Lower Upper
Lives with spouse/partner
only
Lives with spouse/partner &
one or more other adult
Lives with child over 16 &
one or more other adult
Lives with one or more child
over 16 only
Lives with one or more child
under 16 only
Lives with one or more child
under 16 & other adult
110
0.55
0.18
1.68
55
0.12
0.03
0.42
48 [0.93]
[0.29]
[2.99]
*
*
*
33 [1.36]
[0.32]
[5.72]
*
*
26
23
*
a
Confidence interval
*Estimates not shown due to small base sizes
NatCen Social Research | Loyalty card survey
74
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.
NatCen Social Research | Loyalty card survey
75
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.
NatCen Social Research | Loyalty card survey
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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
Bases
Strongly
disagree (unweighted)
Bases
(weighted)
n
n
Sex
Men
Women
All
%
%
%
5
5
5
23
30
24
18
21
18
30
28
30
25
16
24
3866
515
4424
3838
522
4403
Age
18-24
25-34
35-44
45-54
55-64
65+
%
%
%
%
%
%
4
5
6
5
3
4
34
22
22
19
24
22
22
21
17
16
17
14
29
30
30
30
30
31
12
22
26
29
26
29
489
818
759
1081
732
466
711
1003
832
944
541
299
Income quintile
Highest (£32k or more)
4th
3rd
2nd
Lowest (less than £10,400)
%
%
%
%
%
6
5
4
5
4
19
23
23
25
25
16
20
23
20
16
34
26
28
31
28
26
25
22
19
27
632
893
552
811
956
649
917
564
791
939
%
%
5
5
22
24
17
20
32
28
24
23
1102
1876
1093
1913
Area deprivation (England only)
Most deprived areas in England
Not the most deprived areas in
England
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.
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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
Bases
Strongly
disagree (unweighted)
Bases
(weighted)
n
n
Sex
Men
Women
All
%
%
%
20
9
19
22
23
22
21
26
22
29
36
30
7
7
7
3869
509
4417
3842
513
4394
Age
18-24
25-34
35-44
45-54
55-64
65+
%
%
%
%
%
%
9
19
22
23
17
25
22
22
25
19
24
22
24
25
17
24
18
17
37
27
30
27
34
29
7
7
5
7
7
6
488
818
759
1079
731
467
711
999
831
942
543
299
Income quintile
Highest (£32k or more)
4th
3rd
2nd
Lowest (less than £10,400)
%
%
%
%
%
19
19
17
17
24
20
21
25
23
22
24
21
21
23
19
27
33
32
31
28
10
5
5
6
8
633
893
548
809
956
651
919
556
792
937
%
%
19
18
23
22
21
22
30
32
7
6
1100
1874
1088
1909
Area deprivation (England only)
Most deprived areas in England
Not the most deprived areas in
England
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.
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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)
n
Bases
(weighted)
n
Sex
Men
Women
All
%
%
%
50
36
49
14
13
14
25
36
26
11
15
12
3728
490
4229
3612
474
4094
Age
18-24
25-34
35-44
45-54
55-64
65+
%
%
%
%
%
%
42
51
50
52
47
50
17
13
15
10
14
14
29
26
22
26
29
21
12
11
13
12
10
15
471
778
726
1042
711
456
686
941
771
877
505
281
Income quintile
Highest (£32k or more)
4th
3rd
2nd
Lowest (less than £10,400)
%
%
%
%
%
56
48
47
49
47
11
14
13
15
15
21
24
26
26
29
12
13
14
10
9
619
861
532
782
907
616
865
528
748
860
%
%
48
51
15
12
25
28
12
10
1826
1057
1832
1012
Area deprivation (England only)
Most deprived areas in England
Not the most deprived areas in
England
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.
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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
Almost always
Most of the
time
Sometimes
Never
Bases
(unweighted)
n
Bases
(weighted)
n
Sex
Men
Women
All
%
%
%
14
13
14
17
17
17
47
54
47
23
17
22
3720
488
4233
3606
476
4105
Age
18-24
25-34
35-44
45-54
55-64
65+
%
%
%
%
%
%
12
16
13
15
12
11
21
19
18
13
15
13
53
42
48
48
48
48
14
23
21
25
25
28
471
774
724
1041
711
452
686
937
768
878
507
280
Income quintile
Highest (£32k or more)
4th
3rd
2nd
Lowest (less than £10,400)
%
%
%
%
%
13
14
13
16
14
22
14
16
15
20
44
48
50
45
47
21
24
22
24
20
618
858
531
779
908
616
863
528
747
862
%
%
16
15
16
18
46
46
23
22
1058
1820
1015
1828
Area deprivation (England only)
Most deprived areas in England
Not the most deprived areas in
England
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.
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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
Most of the
always
time
Sometimes
Never
Bases
(unweighted)
n
Bases
(weighted)
n
Sex
Men
Women
All
%
%
%
10
15
11
14
9
13
42
42
42
34
34
34
3723
490
4231
3611
476
4101
Age
18-24
25-34
35-44
45-54
55-64
65+
%
%
%
%
%
%
9
14
12
10
8
8
13
17
14
12
9
9
48
41
43
41
42
37
30
28
31
37
41
46
471
776
724
1043
712
453
686
939
769
879
508
280
Income quintile
Highest (£32k or more)
4th
3rd
2nd
Lowest (less than £10,400)
%
%
%
%
%
11
12
6
11
13
12
15
12
14
13
43
39
45
43
44
34
35
37
32
30
617
860
530
780
908
616
866
527
747
862
%
%
13
10
13
14
41
42
33
33
1056
1825
1014
1831
Area deprivation (England only)
Most deprived areas in England
Not the most deprived areas in
England
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.
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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
Item response
Almost always
Most of the
time
2014
Sometimes
Never
Bases
(unweighted)
n
Bases
(weighted)
n
Sex
Men
Women
All
%
%
%
4
6
4
5
5
5
24
31
25
67
58
66
3712
492
4217
3595
477
4082
Age
18-24
25-34
35-44
45-54
55-64
65+
%
%
%
%
%
%
2
5
4
6
4
4
2
6
7
5
4
5
20
25
30
25
25
23
77
64
58
65
66
68
471
772
723
1039
712
453
686
936
767
873
506
280
Income quintile
Highest (£32k or more)
4th
3rd
2nd
Lowest (less than £10,400)
%
%
%
%
%
3
5
1
5
5
3
6
4
4
7
26
21
27
27
28
68
68
68
64
60
615
859
529
780
904
610
863
527
747
857
%
%
6
4
5
6
28
26
61
64
1054
1821
1010
1826
Area deprivation (England only)
Most deprived areas in England
Not the most deprived areas in
England
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.
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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
Most of the
always
time
Sometimes
Never
Bases
(unweighted)
n
Bases
(weighted)
n
Sex
Men
Women
All
%
%
%
4
3
3
4
4
4
18
21
18
74
72
74
3721
492
4220
3606
477
4088
Age
18-24
25-34
35-44
45-54
55-64
65+
%
%
%
%
%
%
2
3
6
4
3
3
6
4
5
3
3
4
24
20
17
15
17
16
69
73
72
78
77
77
471
776
725
1044
712
453
686
940
767
879
508
278
Income quintile
Highest (£32k or more)
4th
3rd
2nd
Lowest (less than £10,400)
%
%
%
%
%
3
4
2
3
5
1
3
5
5
6
14
15
16
21
23
82
78
77
72
66
619
861
529
781
908
616
866
526
747
862
%
%
4
3
5
4
21
17
70
75
1057
1825
1013
1831
Area deprivation (England only)
Most deprived areas in England
Not the most deprived areas in
England
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.
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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.
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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
Item response
Motivation
Almost
Most of the
always
time
…to win money
Class 1 – Lowest engagement
Class 2 – Moderate engagement
Class 3 – Substantial engagement
Class 4 – Heaviest engagement
Sometimes
Never
Bases
(unweighted)
Bases
(weighted)
%
%
%
%
41
47
51
57
12
12
15
15
31
28
23
21
16
13
11
6
677
1365
1784
403
730
1213
1683
469
Class 1 – Lowest engagement
Class 2 – Moderate engagement
Class 3 – Substantial engagement
Class 4 – Heaviest engagement
%
%
%
%
12
11
16
15
13
14
17
32
49
51
47
37
26
24
21
16
681
1362
1784
406
734
1212
1684
474
…to escape boredom or fill your
time
Class 1 – Lowest engagement
Class 2 – Moderate engagement
Class 3 – Substantial engagement
Class 4 – Heaviest engagement
%
%
%
%
9
9
11
16
10
10
15
21
41
44
43
38
40
37
32
25
680
1363
1785
403
733
1214
1683
471
…to be around other people
Class 1 – Lowest engagement
Class 2 – Moderate engagement
Class 3 – Substantial engagement
Class 4 – Heaviest engagement
%
%
%
%
3
3
4
5
2
3
4
9
19
19
17
21
75
75
75
65
677
1362
1780
401
731
1212
1678
467
…to make you feel better
Class 1 – Lowest engagement
Class 2 – Moderate engagement
Class 3 – Substantial engagement
Class 4 – Heaviest engagement
%
%
%
%
5
3
3
7
3
4
5
8
20
25
26
30
72
67
65
55
677
1358
1779
403
729
1208
1676
469
…because it is exciting
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
NatCen Social Research | Loyalty card survey
85
‘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.
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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 ‘nonharmful’ 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.





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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.
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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.
NatCen Social Research | Loyalty card survey
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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
All aged 18 and over
PGSI item
Factor 1
Bet more than could afford to lose
Gambled with larger amounts of
money to get the same excitement
Chased losses
Borrowed money to gamble
Felt had a problem with gambling
Gambling caused a health
problem
People criticized my gambling
Gambling caused financial
problems
Felt guilty about my gambling
Factor 2
.76
.84
.75
.51
.48
.46
.51
.69
.70
.53
.83
.69
.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.
NatCen Social Research | Loyalty card survey
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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.
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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 nonproblem 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.
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Table 7.2
Gambling behaviour, by PGSI status
All aged 18 and over
Gambling behaviour
Number of gambling activities
undertaken in past four weeks
None
1-2
3-4
5-6
7 or more
Frequency of participation in
most frequent activity
Almost every day/everyday
4-5 days per week
2-3 days per week
About once a week
Less than once a week
Did not gamble
Gambled, frequency unknown
Average number of machine
sessions per day
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
Average session length
(seconds)
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
Average number of different
games per session
Mean
PGSI status
Nonproblem
gambler
Total
Low risk
Moderate
gambler risk gambler
Problem
gambler
10
22
32
24
13
3
18
31
25
24
1
14
30
27
28
3
11
26
25
36
4
17
30
25
25
16
9
36
20
10
9
-
18
14
36
20
9
3
-
31
17
34
14
4
1
0
41
19
26
7
4
3
0
26
14
32
15
7
4
2
1.8
1.8
2.0
2.2
2.0
.04
.05
.04
.07
.03
1.5
1.0
3.0
1.5
1.0
3.0
1.8
1.0
3.3
1.8
1.0
4.0
1.6
1.0
3.2
1046.3
1010.2
1238.4
1156.2
1109.2
130.82
129.25
154.22
100.54
65.45
453.0
127.0
1644.0
497.0
148.0
1721.0
596.0
154.0
2267.0
642.0
173.0
2163.0
540.0
148.0
1979.0
1.2
1.3
1.3
1.3
1.2
Standard error of the mean
.04
.05
.07
.05
.03
Median
th
10 centile
th
90 centile
Average days between visits
Mean
1.0
.0
2.0
1.0
.0
2.0
1.0
.0
2.0
1.0
.0
2.0
1.0
.0
2.0
32.3
30.9
29.9
25.6
29.8
1.97
2.37
2.26
1.85
1.06
16.5
3.0
81.0
15.1
3.0
84.3
13.6
2.7
74.0
13.3
2.6
57.5
14.4
2.9
74.5
427.2
504.7
589.7
743.1
557.9
22.31
30.86
35.86
47.65
17.22
223.0
44.0
1010.0
253.0
58.0
1169.0
293.0
57.0
1355.0
351.0
64.0
2091.0
264.0
53.0
1340.0
Standard error of the mean
Median
th
10 centile
th
90 centile
Average stake size (pence)
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
NatCen Social Research | Loyalty card survey
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Table 7.2 continued
Gambling behaviour, by PGSI status
All aged 18 and over
Gambling behaviour
Percentage of B2 games played
per session
Mean
PGSI status
Nonproblem
gambler
Total
Low risk
Moderate
gambler risk gambler
Problem
gambler
60.3
61.4
59.4
59.6
60.2
1.63
1.76
1.71
1.76
0.84
72.5
0.4
100
77.1
0.7
100
69.2
1.2
100
66.4
1.8
100
71.7
0.9
100
Standard error of the mean
2276.5
104.17
2865.3
164.47
3964.4
293.78
4127.5
217.28
3252.1
101.27
Median
th
10 centile
th
90 centile
1316.0
191.0
5375.0
1654.0
280.0
6464.0
2135.0
363.0
8350.0
2570.0
350.0
9391.0
1844.0
281.0
7370.0
Standard error of the mean
Median
th
10 centile
th
90 centile
Average total deposit per
session (pence)
Mean
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 nonproblem 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
NatCen Social Research | Loyalty card survey
94
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
Number of gambling activities
undertaken in past four weeks
None
1-2
3-4
5-6
7 or more
Frequency of participation in
most frequent activity
Almost every day/everyday
4-5 days per week
2-3 days per week
About once a week
Less than once a week
Did not gamble
Gambled, frequency unknown
Average number of machine
sessions per day
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
Average session length
(seconds)
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
Average number of different
games per session
Mean
Had problems with machine use
Never Some of the Most of the
time
time
Total
Almost
always
3
17
31
27
22
2
12
28
25
33
1
7
31
22
39
4
16
23
25
32
4
17
30
25
25
22
13
35
19
8
3
0
30
18
35
12
3
2
0-
40
20
27
9
3
1
46
17
23
6
4
4
26
14
32
15
7
4
2
1.9
2.0
2.2
2.4
2.0
.03
.06
.10
.14
.03
1.6
1.0
3.0
1.7
1.0
3.3
1.9
1.0
4.1
2.0
1.0
4.6
1.6
1.0
3.2
1022.3
1209.4
1022.9
1191.2
1109.2
77.74
137.51
99.47
162.74
65.45
506.0
144.0
1856
624.0
158.0
2127
671.0
183.0
2168
661.0
206.0
2477
540.0
148.0
1979
-
1.2
1.3
1.3
1.3
1.2
Standard error of the mean
.04
.05
.08
.07
.03
Median
th
10 centile
th
90 centile
Average days between visits
Mean
1.0
.0
2.0
1.0
.0
2.0
1.0
1.0
2.0
1.0
.0
2.0
1.0
.0
2.0
31.7
25.2
18.6
24.6
29.8
1.43
1.97
1.70
3.37
1.06
15.1
2.9
84.7
12.7
2.9
57.0
12.7
2.8
42.5
11.3
2.2
51.6
14.4
2.9
74.5
504.7
612.8
610.5
832.9
557.9
19.87
43.49
64.14
81.39
17.22
246.0
50.0
1227.0
291.0
59.0
1434.0
326.0
59.0
1762.0
443.0
71.0
2394.0
264.0
53.0
1340.0
Standard error of the mean
Median
th
10 centile
th
90 centile
Average stake size
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
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Table 7.3 continued
Gambling behaviour, by machine gambling problem
All aged 18 and over
Gambling behaviour
Percentage of B2 games played
per session
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
Average total deposit per
session
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
Had problems with machine use
Never Some of the Most of the
time
time
Total
Almost
always
60.4
58.6
52.4
58.8
60.2
1.12
1.84
3.48
2.94
0.84
74.3
0.6
100
67.7
1.1
100
48.2
2.2
100
65.0
1.1
100
71.7
0.9
100
2834.4
4303.0
3980.5
4165.3
3252.1
110.05
323.74
370.38
278.83
101.27
1608.0
282.0
6366.0
2366.0
400.0
9377.0
2687.0
521.0
9490.0
3105.0
400.0
9000.0
1844.0
281.0
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 7.4 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.
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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.)
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Table 7.4
Gambling behaviour, by factor score group
All aged 18 and over
Gambling behaviour
Factor score group
Total
Non high High harmful High harmful High harmful
harmful
gambling
gambling
gambling
gambling actions only consequence actions and
actions or
only consequence
consequence
scores
scores
Number of gambling activities
undertaken in past four weeks
None
1-2
3-4
5-6
7 or more
Frequency of participation in
most frequent activity
Almost every day/everyday
4-5 days per week
2-3 days per week
About once a week
Less than once a week
Did not gamble
Gambled, frequency unknown
Average number of machine use
sessions per day
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
Average session length
(seconds)
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
Average number of different
games per session
Mean
5
18
30
25
22
2
10
30
25
33
3
10
24
27
37
6
12
23
12
48
4
17
30
25
25
22
13
36
18
7
5
0
42
21
24
6
4
2
-
46
17
21
7
5
3
1
63
13
12
5
1
6
-
26
14
32
15
7
4
2
1.9
2.0
2.4
2.2
2.0
.03
.08
.17
.17
.03
1.6
1.0
3.1
1.8
1.0
3.0
1.6
1.0
5.0
1.8
1.0
4.1
1.6
1.0
3.2
1100.6
1229.7
1179.7
797.2
1109.2
76.22
187.72
153.47
92.92
65.45
522.0
149.0
1951.0
624.0
116.0
2356.0
701.0
117.0
2100.0
586.0
158.0
1532.0
540.0
148.0
1979.0
1.2
1.4
1.3
1.1
1.2
Standard error of the mean
.03
.13
.09
.08
.03
Median
th
10 centile
th
90 centile
Average days between visits
Mean
1.0
.0
2.0
1.0
.0
2.0
1.0
1.0
2.0
1.0
.0
2.0
1.0
.0
2.0
30.8
23.5
26.1
29.0
29.8
1.20
3.04
3.48
4.97
1.06
15.0
2.9
78.5
12.3
2.3
57.0
12.5
3.3
69.5
15.4
2.6
77.3
14.5
2.9
74.5
526.0
558.9
712.6
1160.6
557.9
18.23
57.81
60.29
218.52
17.22
250.0
52.0
1256.0
266.0
57.0
1173.0
400.0
81.0
1762.0
732.0
109.0
2609.0
264.0
53.0
1340.0
Standard error of the mean
Median
th
10 centile
th
90 centile
Average stake size (pence)
Mean
Standard error of the mean
Median
th
10 centile
th
90 centile
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Table 7.4 continued
Gambling behaviour, by factor score group
All aged 18 and over
Gambling behaviour
Factor score group
Total
Non-high High harmful High harmful High harmful
harmful
gambling
gambling
gambling
gambling actions only consequence actions and
actions or
only consequence
consequence
scores
scores
Percentage of B2 games played
per session
Mean
60.7
53.9
61.4
60.5
60.2
0.93
3.08
2.90
5.92
0.84
74.2
.8
100.0
53.2
2.2
100.0
65.9
5.7
100.0
79.4
.1
100.0
71.7
.9
100.0
Standard error of the mean
3068.7
109.18
3420.6
339.00
4556.1
436.57
4877.2
732.34
3252.1
101.27
Median
th
10 centile
th
90 centile
1758.0
277.0
6800.0
2000.0
220.0
8203.0
2669.0
400.0
10834.0
3670.0
935.0
9211.0
1844.0
281.0
7370.0
Standard error of the mean
Median
th
10 centile
th
90 centile
Average total deposit per
session
Mean
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.
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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/wpcontent/uploads/2013/10/ABB-code-for-responsible-gambling.pdf. Accessed 21 June 2014.
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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
Seven or more gambling activities in the
past four weeks
Five or more gambling activities in the
past four weeks
Sensitivity
0.35
Specificity
0.79
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
Threshold
Sensitivity and specificity
Two or more sessions per day
Sensitivity
0.45
Specificity
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.
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Table 7.7
Sensitivity and specificity analysis for average stake per
bet
Threshold
Sensitivity and specificity
Sensitivity
0.50
0.61
0.21
£3.51 or higher*
£2.23 or higher**
£10 or higher
Specificity
0.60
0.48
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
Threshold
Sensitivity and specificity
Gambles almost every day
Gambles four or more days per week
Sensitivity
0.41
0.60
Specificity
0.79
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.
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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
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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 nonproblem 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.
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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.
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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
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new policies to be thoroughly tested and evaluated with evaluation built into the policy development
and design process from the very start.
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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.
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



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
Email only
Mobile phone and email
Total
18771
4278
4516
27565
%
68
16
16
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.
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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
Web survey
Total
4210
517
4727
%
89
11
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
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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
Opted out of survey
Ineligible cases (no valid
contact details)
Total number of issued
cases
Ineligible: screened out
by interviewer
Interviewed
No contact
Refused
Other unproductive –
contact made
Estimated further
ineligible*
%
47268
902
100
2
18801
39
27565
58
%
100
729
4727
15912
5755
3
17
58
21
442
2
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
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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.
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Table A.5
Playing Habits
33
Category
Longest Session
Max Consecutive Days
1 - Low
engagement
2
Less than 30 minutes
Less than 3 days
Average of sessions
per day
Less than 1
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 .
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Table A.6
Final weighting on the main variables
Variable
Survey
Responses
Population
Unweighted
Sample
Weighted
Sample
%
%
%
Ladbrokes
53
52.1
Paddy Power
9.9
10
37.1
37.9
9.3
Agreed to
Data
Linkage
Population
Unweighted
Sample
Weighted
Sample
%
%
%
53
53
52.6
53
9.9
9.9
9.3
9.9
37.1
37.1
38.1
37.1
5.2
9.3
9.3
4.9
9.3
19.4
12.5
19.4
19.4
12.2
19.4
18.2
30.3
18.2
18.2
30.3
18.2
53
52.1
53
53
52.6
53
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
2
52
24.9
52
52
25.1
52
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
6- High
engagement
14.8
44.4
14.8
14.8
44
14.8
3.6
14.2
3.6
3.6
14.6
3.6
Operator
William Hill
Member Days
Less than 6
months
6-9 months
9 months or
more
Missing
Total player
loss
> 50,000
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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).
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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 sevencluster 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.
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Figure A1
Measures of fit
Figure A2
Measures of fit (AIC3)
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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
1056
1008
1314
914
435
4727
All
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
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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 rechecked.
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.
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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.
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Table A.12
Correlation coefficients
Bet more
than could Gambled
afford to with more Chased
lose
money
losses
Bet more than
could afford to
lose
Gambled with
more money
Gambling
Borrowed Felt had a caused
People
money to gambling health
criticized
gamble
problem problems gambling
Gambling
caused
Felt guilty
financial about
problems gambling
1.00
0.61
1.00
0.63
0.59
1.00
0.52
0.48
0.50
1.00
0.61
0.54
0.57
0.52
1.00
0.57
0.51
0.55
0.56
0.66
1.00
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
Chased losses
Borrowed money
to gamble
Felt had a
gambling
problem
Gambling
caused health
problems
People criticized
gambling
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.
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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
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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).
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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
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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).
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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
nd
Ritchie, J., Lewis, J., McNaughton Nicholls, C. and Ormston, R., (2013) Qualitative Research Practice, 2 Edition,
London: Sage.
4
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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 indepth 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.
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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
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{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
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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
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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.
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 lottery}
olotFREQ
In the past 4 weeks, how often have you bought tickets for any other lottery, including charity
lotteries?
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 = football pools}
poolsFREQ
In the past 4 weeks, how often have you spent money on the football pools?
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 = bingo}
bingoFREQ
In the past 4 weeks, how often have you spent money on bingo cards or tickets (please do not
include online bingo)?
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 = machines in a bookmaker’s}
bkmachineFREQ
In the past 4 weeks, how often have you spent money on gaming machines in a bookmaker’s?
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1.
2.
3.
4.
5.
Every day/almost every day
4-5 days per week
2-3 days per week
About once a week
Less than once a week
{Ask if activity = fruit machines}
fruitFREQ
In the past 4 weeks, how often have you spent money on fruit or slot machines?
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 = 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.
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 = 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?
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 = online betting}
onbetFREQ
In the past 4 weeks, how often have you spent money betting online with a bookmaker on any event
or sport?
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 = betting exchange}
betexFREQ
In the past 4 weeks, how often have you spent money betting online on betting exchanges?
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
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{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.
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 = 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.
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 = 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.
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 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.
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 = spread betting}
spreadFREQ
In the past 4 weeks, how often have you spent money spreadbetting?
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 = 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
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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?
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 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
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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
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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?
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}
pgsi4
In the past 12 months, how often have you borrowed money or sold anything to get money to
gamble?
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}
pgsi5
In the past 12 months, how often have you felt that you might have a problem with gambling?
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}
pgsi6
In the past 12 months, how often have you felt that gambling has caused you any health problems,
including stress or anxiety?
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}
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?
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}
pgsi8
In the past 12 months, how often have you felt your gambling has caused financial problems for you
or your household?
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}
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pgsi9
In the past 12 months, how often have you felt guilty about the way you gamble or what happens
when you gamble?
1. Almost always
2. Most of the time
3. Sometimes
4. Never
{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?
1. Almost always
2. Most of the time
3. Sometimes
4. Never
{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?
1. Almost always
2. Most of the time
3. Sometimes
4. Never
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{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?
1. Almost always
2. Most of the time
3. Sometimes
4. Never
{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?
1. Almost always
2. Most of the time
3. Sometimes
4. Never
{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?
1. Almost always
2. Most of the time
3. Sometimes
4. Never
{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?
1. Almost always
2. Most of the time
3. Sometimes
4. Never
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}
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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
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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}
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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
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{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
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{Ask All}
END
That's the end of the questionnaire, thank you for your time.
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