Heather Wardle a e, Alison Moody a, Mark Griffiths b, Jim Orford c & Rachel Volberg d a National Centre for Social Research, London, UK

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1 This article was downloaded by: [Mark Griffiths] On: 21 November 2011, At: 07:39 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK International Gambling Studies Publication details, including instructions for authors and subscription information: Defining the online gambler and patterns of behaviour integration: evidence from the British Gambling Prevalence Survey 2010 Heather Wardle a e, Alison Moody a, Mark Griffiths b, Jim Orford c & Rachel Volberg d a National Centre for Social Research, London, UK b International Gaming Research Unit, Psychology Division, Nottingham Trent University, Nottingham, UK c School of Psychology, University of Birmingham, Birmingham, UK d Gemini Research, MA, USA e University of Glasgow, Glasgow, UK Available online: 17 Nov 2011 To cite this article: Heather Wardle, Alison Moody, Mark Griffiths, Jim Orford & Rachel Volberg (2011): Defining the online gambler and patterns of behaviour integration: evidence from the British Gambling Prevalence Survey 2010, International Gambling Studies, 11:3, To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 International Gambling Studies Vol. 11, No. 3, December 2011, Defining the online gambler and patterns of behaviour integration: evidence from the British Gambling Prevalence Survey 2010 Heather Wardle a,e *, Alison Moody a, Mark Griffiths b, Jim Orford c and Rachel Volberg d a National Centre for Social Research, London, UK; b International Gaming Research Unit, Psychology Division, Nottingham Trent University, Nottingham, UK; c School of Psychology, University of Birmingham, Birmingham, UK; d Gemini Research, MA, USA; e University of Glasgow, Glasgow, UK This paper presents data from the British Gambling Prevalence Survey (BGPS) 2010, a large-scale random probability survey of adults (n ¼ 7756), to examine how people gamble and ways in which online and offline gambling are integrated. Fourteen per cent of respondents were past year Internet gamblers (7 if purchase of lottery tickets online is excluded). The majority of online gamblers were also offline gamblers and a broader taxonomy of gambling subgroups was evident. This included those who chose different mediums of access for different activities and those who gambled online and offline on the same activity (mixed mode gamblers). These mixed mode gamblers had the highest rates of gambling involvement and higher problem gambling prevalence rates. Direct comparisons between Internet and non-internet gamblers therefore ignore key questions of how people integrate online provisions with offline activities which may have important implications for our understanding of the relative risks associated with online gambling. Keywords: behaviour; empirical; Internet gambling; prevalence; prevention Introduction Online gambling is arguably one of the most important areas in the gambling studies field. It is an area that is subject to great debate, particularly about its impact, and a variety of approaches to legalization and regulation have been taken by different jurisdictions. Despite its increasing prominence, there has been relatively little empirical examination of these issues. This is partly to be expected with a relatively new and evolving form of gambling, since it is inevitable that there is lag between development and academic investigation. Furthermore, significant challenges face researchers seeking to examine online gambling behaviour, as online gamblers represent a hard-to-reach and sometimes hidden population. Therefore, research often relies on self-selecting samples and suffers from attendant problems. To date, around 40 studies of Internet gambling have been conducted, with most using self-selecting samples (e.g. International Gaming Research Unit, 2007; Ladd & Petry, 2002; Lloyd et al., 2010; Matthews, Farnsworth, & Griffiths, 2009; Wood, Williams, & Lawton 2007). Most of these studies have tended to conclude that problem gambling prevalence rates are higher among online gamblers when compared with those who have never gambled online. Other studies have focused on a single type of Internet gambling *Corresponding author. heather.wardle@natcen.ac.uk ISSN print/issn online q 2011 Taylor & Francis

3 340 H. Wardle et al. such as online poker (Griffiths, Parke, Wood, & Rigbye, 2010; Hopley & Nikki, 2010; Wood, Griffiths & Parke, 2007) which, whilst providing insight about a particular activity, does not view online gambling behaviour in a holistic way. A small number of online operators have recently allowed academic researchers access to their rich data of gambling consumer behaviour. This represents a significant step forward. However, this approach provides information about one area of gambling behaviour, recorded by a single operator (e.g. Broda et al., 2008; LaBrie, Kaplan, LaPlante, Nelson & Shaffer, 2008; LaBrie, LaPlante, Nelson, Schumann & Shaffer, 2007; LaPlante, Kleschinsky, LaBrie, Nelson & Shaffer, 2009; LaPlante, Schumann, LaBrie & Shaffer, 2008; Xuan & Shaffer, 2009). Because of these issues, much of the online gambling research conducted to date has had a rather narrow focus. A further complicating factor is the tendency for researchers, ourselves included, to simply compare online gamblers with non-online gamblers (Wardle & Griffiths, 2011). This approach makes an implicit assumption that these are mutually exclusive groups with little overlap in behaviour. Ladd and Petry (2002) were arguably the first to use this approach. They reported that 8.1 of their sample used the Internet to gamble and that rates of disordered gambling were higher among the Internet gambling group than among non-internet gamblers. This analytical technique has been replicated by almost all other researchers who have used survey data for their analysis. For example, examination of online gamblers identified in the 2007 British Gambling Prevalence Survey (BGPS) demonstrated that rates of problem gambling were higher among those who used the Internet to gamble (5) than those who did not (0.5) (Griffiths, Wardle, Orford, Sproston, & Erens, 2009). Similar results have been replicated in other jurisdictions (e.g. Wood & Williams, 2008; Olason et al., 2011). Whilst these researchers have rightly noted that it is not possible to examine causal pathways using these observations, these findings have fuelled concerns about the potential impact of online gambling and its association with gambling-related harm. However, this is a simplistic method of defining the online gambler (i.e. anyone who has used the Internet to gamble in a given period). Little consideration has been given to how online gambling behaviour may be integrated more broadly with offline behaviour (Wardle & Griffiths, 2011). For instance, Ladd and Petry (2002) noted that 89.2 of respondents had experience of playing the lottery, 81.7 had experience of playing slot machines and 78.7 had experience of buying scratchcards, whilst 8.1 had experience of using the Internet to gamble. It is likely, therefore, that a high proportion of the lifetime Internet gamblers in their sample also engaged in some form of offline activity. This is supported by evidence from the 2007 BGPS which showed that although the overall prevalence of online gambling was 5, the prevalence of using the Internet to gamble was 0.1 (Wardle & Griffiths, 2011), demonstrating that most online gamblers are also offline gamblers. Likewise, the California Problem Gambling Prevalence Survey also showed that most Internet gamblers also gambled offline and were more likely to be more engaged gamblers (Volberg, Nysse-Carris, & Gerstein, 2006). Therefore, using a simple dichotomy of online versus offline gamblers to examine the impact of online gambling within a given jurisdiction seems inadequate. This dichotomy does not recognize the full complexity of how people integrate online provision of certain activities within their patterns of gambling behaviour more generally. Understanding how people gamble is likely to be more nuanced than simply dividing the population into two homogenous groups. One may speculate that a broader taxonomy of online gamblers may exist, ranging from those who use the Internet to gamble, to those who gamble online and offline on the same activities, to those who engage in different activities in different

4 International Gambling Studies 341 environments (Wardle & Griffiths, 2011). This is supported somewhat by Wood and Williams (2008; 2011) who noted that online gamblers tended to be those who were already heavily engaged in gambling and that online gambling was likely added to respondents existing repertoire of gambling behaviour. They also noted some key differences between Canadian Internet and non-internet gamblers, which have also largely been observed in Great Britain: that Internet gamblers are more likely to be male; to be younger adults; to be employed; to have higher educational attainment; to consume tobacco and alcohol; to have good general health and to be single (Griffiths et al., 2009; Griffiths, Wardle, Orford, Sproston, & Erens, 2011; Wood & Williams, 2008; Wood & Williams, 2011). The main objective of this paper was to use the latest empirical evidence from the BPGS 2010 to explore whether there was evidence supporting this broader taxonomy of behaviour integration. Our research aims were to:. Examine how people reported gambling and, specifically, to examine the broader context of gambling behaviour among those who reported using the Internet to gamble.. Explore whether any there were any differences in profile between people who choose to gamble in certain modes and consider, briefly, whether gambling behaviour varied between these groups. Method Overview of the BGPS 2010 The BGPS 2010 was carried out by the National Centre for Social Research (NatCen). The sample was drawn at random from the Postcode Address File and stratified according to age, occupational status and ethnic group. A total of 9775 addresses were selected and interviews were carried out using Computer-Assisted Self Interview between November 2009 and May Ethical approval for the study was given by NatCen s Research Ethics Committee. Data Collection procedure Advance letters were sent to selected addresses and were followed by a visit from a NatCen interviewer. The interviewer first conducted a household interview with the Household Reference Person (HRP) or spouse/partner to collect basic information about household composition and demographic information about each resident. The HRP is defined as the householder (person in whose name the property is owned or rented) with the highest income. Every resident within the household aged 16 years and over was eligible to participate in the survey. Individual interviews were conducted using Computer-Assisted Self Interview (CASI) to protect the privacy of each individual respondent and to encourage honest reporting of a potentially sensitive behaviour. CASI methods involve interviewers asking each respondent to complete the questionnaire for themselves by reading the questions and entering answers direct into the interviewer s laptop. Once completed, the questionnaire is locked so that interviewer cannot view the respondent s responses. Where possible, individual interviews were conducted separately from other household members. However, interviewers were present in the household to answer any questions that the respondent might have. Multiple visits to the household were made, to attempt to interview all eligible residents. For some individuals where the interviewer could not make contact, interviews were conducted using NatCen s Telephone

5 342 H. Wardle et al. Unit; 5 of interviews were conducted using this method. HRP interviews were achieved at 55 of households and questionnaires were completed by 85 of eligible adults. The overall response rate was therefore 47, and data were collected from 7756 individuals. Further details are provided in the full survey report (Wardle et al., 2011). Questionnaire content All participants were asked to report whether they had taken part in any of 16 different gambling activities currently available in Britain. Of these 16 activities, nine could be undertaken both online and offline. These are shown in Table 1. For activities where different modes of participation are available, respondents were asked to report whether they had undertaken the activity in-person, online or both. The in-person category covers a range of methods of participation, from visiting a shop to purchase a lottery ticket to betting on horse races at a bookmaker s. All questions were cognitively tested prior to use in the survey and respondents reported that they correctly understood the term in-person to mean all forms of offline gambling. The term online also has some variation by activity type. For activities such as playing casino games or bingo, it meant taking part in these things using the Internet. These activities typically involve both spending time online and spending money during a session. For activities such as the National Lottery or betting, it meant purchasing a lottery ticket using the Internet or placing a bet using the Internet. Therefore, there is qualitative difference between activities and what online gambling means for each. A range of questions that collected socio-economic and demographic information was asked of all participants. This included obtaining data about:. Personal income levels. Economic activity. Educational attainment. Ethnicity. All participants were assigned to a National Statistics Socio-Economic Classification (NS-SEC) based on the current or former occupation of the Household Reference Person. This is similar to social class as measured by earlier scales such as the Registrar General s classification of occupations. Participants were also asked to report their general health status on a five-point scale ranging from very good to very bad, and were asked whether they smoked cigarettes currently and how many units of alcohol they had drunk on the heaviest drinking day in the past seven days. Everyone who reported gambling in the past year completed two problem gambling screens; one based on the DSM-IV, which included 10 criteria rated on a Table 1. Single mode and mixed mode gambling activities included in the BGPS Single mode activities Scratchcards Slot machines Fixed Odds Betting Terminals Poker in a pub/club Online gambling on slot machine/instant win games Spread betting Private betting Mixed mode activities National Lottery Other lotteries Bingo Casino games Betting on horses Betting on dog races Betting on sports event Betting on other events Football pools

6 International Gambling Studies 343 four-point scale ranging from very often to never, and the Problem Gambling Severity Index (PGSI). The findings reported in this paper use the DSM-IV for purposes of simplicity and clarity for the reader. However, the broad findings presented can also be replicated using the PGSI. DSM-IV scores range from zero to 10. The BGPS series has used the threshold of a score of three of more to represent problem gambling (see Wardle et al. [2007; 2011] for a more detailed discussion). This threshold is also used in this paper. However, to facilitate international comparisons a further threshold of a DSM-IV score of five or more is also presented, representing pathological gambling (American Psychiatric Association, 1994). All problem gambling estimates refer to problems experienced in the past year. Analysis The starting point for this analysis was the data presented in the main BGPS report that showed that 73 of participants had gambled in the past year and that the prevalence of online gambling on any activity was 14 (down to 7, if those who bought lottery tickets online are excluded). Of past year gamblers, 2 reported that they had gambled online. We conducted exploratory analysis of the past year gambling group to examine how they reported gambling in more detail. Based on their reported behaviour, we were able to subdivide past year gamblers into four clear and mutually exclusive groups: (1) Those who gambled in-person. This means they either (i) took part in single mode activities that could not be conducted online; (ii) they took part in multi-mode activities but did these activities in-person; or (iii) did a combination both (i) and (ii). (2) Those who gambled online. This group includes those who reported that their method of gambling in the past 12 months was via the Internet. Like in-person gamblers, this means they either participated in single mode activities, participated in mixed mode activities but online, or a combination of the two. (3) Mixed mode gamblers different activities. This includes those who reported gambling on a range of activities both in-person and online, but did not gamble online and in-person on the same activity. Therefore, by definition, this group participated in at least two types of gambling activity in the past year because they used different modes of access for at least two activities. (4) Mixed mode gamblers same activities. This includes those who, like Group 3, gambled both in-person and online on a range of activities but, unlike Group 3, reported that they gambled both online and in-person for at least one activity. A two-stage analytical procedure was employed. Firstly, univariate analysis examined the demographic profile, range of activities and general gambling behaviour of each group. Variables included in this analysis were chosen based on (i) prior associations evident in the international literature (e.g. age has repeatedly been shown to be associated with online gambling); and (ii) associations evident from prior analysis of BGPS data in both 2007 and 2010 (e.g. analysis of BGPS data has shown that cigarette smoking and health status is associated with online gambling). Based on this, it was reasonable to assume that subgroup membership may also vary by these factors and that this should be explored. Secondly, multivariate logistic regression was used to explore the factors associated with membership of each group. A two-step procedure was used. The following variables were entered simultaneously into a stepwise regression model: age, sex, marital status, ethnic group, personal income level, economic activity, educational qualifications, NS- SEC of Household Reference Person, general health status, cigarette smoking status,

7 344 H. Wardle et al. alcohol consumption, number of gambling activities engaged in and whether a DSM-IV problem gambler. These represent key demographic, socio-economic and gambling behaviour factors that our univariate analyses showed were associated with subgroup membership. Variables that were significantly associated with subgroup membership were then inputted to a final model using the enter method and Stata s survey analysis module. Only variables that were significantly associated with group membership in the final model are presented in this paper. The models presented in Table 4 show the factors predicting membership of each subgroup compared with the other past year gambling subgroups. In these regression models, the odds associated with the outcome variable (gambling mode subgroup) are presented for each category of the independent variable. For categorical variables, odds are expressed relative to a reference category, which is given a value of one. An odds ratio greater than one indicates higher odds of subgroup membership. An odds ratio less than one indicates lower odds. Ninety-five per cent confidence intervals are also shown for each odds ratio. If the interval does not include one, there is a significant difference between the odds ratio for the category and that of the reference category. All data were weighted for non-response. This takes into account response differentials and matched the achieved sample to the population profile of Great Britain with respect to age, sex and geographic location. The resulting weighted sample was a close match to the population profile of Great Britain across a range of domains (see Wardle et al. [2011] for further information). To take into account the stratified and clustered sample design, all analysis has been conducted using either the complex survey module in SPSS v18 or the survey module in Stata which produces Wald s F tests as the default test of significance (Rao & Scott, 1984; Rao & Scott, 1987). Results Online gambling subtypes: demographic profile Table 2 shows the past year gambling subgroups by a range of socio-demographic variables. The largest subgroup was in-person gamblers (80.5). The next largest group was mixed mode gamblers who gambled in-person and online on the same activity (10.6) followed by mixed mode gamblers who gamble in-person and online on different activities (6.8; hereafter referred to as mixed mode same activity and mixed mode different activity gamblers). Only 2.1 of past year gamblers were online gamblers. Among in-person gamblers there were roughly equal proportions of men and women, which broadly mirrors the population profile of Great Britain (Office for National Statistics [ONS], 2010). However, online gamblers were somewhat more likely to be female (53.8) than male (46.2). Among mixed mode groups, greater proportions were male; 56.6 of mixed mode same activity and 64.4 of mixed mode different activity gamblers were male. Notable differences were also observed by age group. Over 50 of those who were mixed mode different activity gamblers were aged years compared with 26.7 of those who were in-person gamblers. Mixed mode same activity gamblers also tended to be younger than their in-person or online counterparts. The profile of each group varied according to socio-economic status and key health variables. Those who were online or mixed mode gamblers were more likely to have higher levels of personal income. Over 50 of online and mixed mode same activity gamblers were in the highest personal income tertile compared with 36 of those who were in-person gamblers. Correspondingly, a higher proportion of online and mixed mode gamblers were in paid employment. Nearly three-quarters of mixed mode same

8 International Gambling Studies 345 Table 2. Past year gambling subgroups by socio-economic/demographic features and gambling behaviour. Past year gambling subgroup Sociodemographic/ economic characteristics In-person Online Mixed mode different activities Mixed mode same activities All past year gamblers P- value Sex Men Women Age group þ Personal income tertile Lowest income tertile 2nd Highest income tertile Main economic activity Paid work Unemployed Long term disability Looking after family/ home Retired Full time education Other General health Very good/ good Fair Bad/very bad Smoking status current cigarette smoker Bases (weighted ) Bases (unweighted ) activity gamblers (74.3) were in paid employment, compared with just over half of those who were in person gamblers (52.9). Online and mixed mode gamblers were also more likely to report that their general health was good or fair than their in-person counterparts. Finally, over one-third of mixed mode different activity gamblers were cigarette smokers whereas 16.8 of online gamblers were smokers. These findings may be associated with both age and gender, as online gamblers tended to be both older and female, among whom cigarette smoking prevalence tends to be lower.

9 346 H. Wardle et al. Online gambling sub-types: gambling behaviour Whilst Table 2 provides some key insights into the broad demographic profile of each subgroup, it is also important to understand how gambling behaviour varies between groups and how this may be associated with some of the patterns evident in Table 2. Looking at Table 3, it is clear that the online subgroup is overwhelmingly comprised of those who used the Internet to purchase National Lottery tickets online (84.3). This also means that there was a smaller subgroup of online gamblers who participated in some other activity online, either taking part in this activity alone or in combination with the National Lottery. However, there were 33 respondents within this smaller subgroup and therefore sample sizes were not sufficient to analyse them separately. This pattern was not evident among mixed mode gamblers. Whilst lottery participation was the most prevalent form of gambling, a number of other activities were also popular, with between a quarter to a third of people in each mixed mode group participating in activities like casino games, betting on horse races, buying scratchcards or playing slot machines. Obviously, there were no lottery gamblers in the mixed mode different activity group and just 86 lottery participants (14) in the mixed mode same activity group (this base size precludes examination of this group separately. We have checked the impact of including these gamblers with the mixed mode same activity group upon results and it does not alter any of the patterns observed). Those in the mixed mode activities subgroups had the highest levels of gambling involvement. The vast majority were regular gamblers, meaning that they reported gambling at least once a month on one or more activities (82.5 for the different activities group and 88.8 for the same activities group). This was significantly higher than the proportion of in-person regular gamblers (70.5) and online regular gamblers (75.0). Both mixed mode groups also reported taking part in a higher number of activities in the past year than those in the other subgroups. The mean number of activities participated in was 4.3 for both, 2.3 for in-person and 1.2 for online gamblers. Mixed mode same activity gamblers took part in a greater number of online gambling activities: the mean number of online activities they participated in was 1.8 compared with 1.4 among mixed mode different activity gamblers and 1.2 for online gamblers. In short, mixed mode gamblers seem to have the highest levels of gambling involvement across single and mixed mode activities and tend to gamble more regularly than their single mode counterparts. They are also more likely to be younger, to be male and to have higher levels of educational attainment and higher income levels than their inperson counterparts. DSM-IV scores also varied by past year gambler subgroup. No participants within the online group were classified as problem gamblers according to the DSM-IV. Substantive importance should not be attached to this finding, given the small base size of this group and the relatively low prevalence rates of problem gambling identified in the British population as a whole. However, problem gambling prevalence (i.e. a DSM-IV score of three or more) was significantly higher among mixed mode gamblers than among in-person gamblers. Rates were 0.9 for in-person gamblers, 2.4 for mixed mode same activity gamblers and 4.3 for mixed mode different activity gamblers. Rates of pathological gambling (i.e. a DSM-IV score of 5 or more) were significantly higher also among the latter group, being 3.4. Among mixed mode same activity gamblers and inperson gamblers, rates were 0.8 and 0.4 respectively. Likewise, mean DSM-IV scores were highest among mixed mode different activity gamblers (0.4) and lowest among online and in-person gamblers (0.0 and 0.1 respectively).

10 International Gambling Studies 347 Table 3. Past year gambling sub-groups by gambling activities. Past year gamblers Type of gambling activity Mixed mode activities National Lottery in person National Lottery online National Lottery both in person and online Past year gambling sub-group In-person Online Mixed mode different activities Mixed mode same activities All past year gamblers Other Lotteries in person Other lotteries online Other lotteries both in person and online Bingo in person Bingo online Bingo both in person and online Football pools in person Football pools online Football pools both in person and online Betting on horse races in person Betting on horse races online Betting on horse races in person and online Betting on dog races in person Betting on dogs races online Betting on dog races both in person and online Sports betting in person Sports betting online Sports betting both in person and online Betting on other events in person Betting on other events online

11 348 H. Wardle et al. Table 3 continued Past year gamblers Type of gambling activity Betting on other events both in person and online Past year gambling sub-group In-person Online Mixed mode different activities Mixed mode same activities All past year gamblers Casino games in person Casino games online Casino games both in person and online Single mode activities Scratchcards Slot machines Fixed Odds Betting Terminal Poker in a pub/club Online gambling Spread betting Private betting Number of activities undertaken in past year Mean number of activities* Standard error of the mean Number of online activities undertaken in past year Mean number of online activities* Standard error of the mean Frequency of gambling regular gamblers* DSM-IV score 3 þ (problem gambler)* þ (pathological gambler)* Mean DSM-IV score* Standard error of the mean Bases (weighted) Bases (unweighted) *p, Factors predicting membership of each gambling subgroup Multivariate logistic regression models were produced to examine the factors associated with subgroup membership. Variables associated with being an in-person gambler were age, marital status, level of educational attainment, social class (as measured by

12 International Gambling Studies 349 Table 4. Odds of being classified an in-person, online or mixed mode gambler. Past year gamblers Model 1: In person gambler Odds ratio 95 CI Age group (p < 0.001) (1.12, 1.60) 55þ 2.21 (1.64, 2.98) Marital status (p < 0.001) Married/living as married 1 Separated, divorced, widowed 1.31 (1.02, 1.68) Single, never married 1.47 (1.20, 1.80) Ethnic group (p < 0.05) White 1 Asian or Asian British 0.97 (0.61, 1.56) Black or Black British 1.09 (0.61, 1.92) Other 0.39 (0.22, 0.69) Economic activity of individual (p < 0.001) Paid work 1 Unemployed 1.15 (0.74, 1.80) Long-term disability 2.08 (1.14, 3.79) Looking after family/home 1.11 (0.84, 1.48) Retired 1.90 (1.37, 2.63) Full time education 1.44 (1.01, 2.05) NS-SEC of Household Reference Person (p < 0.001) Managerial & professional occupations 1 Intermediate occupations 1.09 (0.81, 1.47) Small employers & own account workers 1.36 (1.03, 1.81) Lower supervisory & technical occupations 1.17 (0.87, 1.58) Semi-routine & routine occupations 1.68 (1.35, 2.08) Highest educational qualification (p < 0.01) Professional qualification or above 1 GCSEs/O levels or A levels 1.24 (1.04, 1.49) or equivalent None/other 1.52 (1.19, 1.95) Number of past year gambling activities 0.73 (0.70, 0.76) Model 2: Online gamblers Odds ratio CI Age group (p < 0.001) (0.46, 1.19) 55þ 0.32 (0.18, 0.59) Marital status (p < 0.05) Married/living as married 1 Separated, divorced, widowed 0.89 (0.54, 1.46) Single, never married 0.45 (0.25, 0.82) Highest educational qualification (p < 0.05) Professional qualification or above 1 GCSEs/O levels or A levels 0.89 (0.58, 1.36) or equivalent None/other 0.43 (0.23, 0.81) Number of past year gambling activities 0.21 (0.13, 0.33)

13 350 H. Wardle et al. Table 4 continued Past year gamblers Model 3: Mixed mode different activity gamblers Odds ratio 95 CI Age group (p < 0.001) (0.49, 0.89) 55þ 0.30 (0.20, 0.45) Sex (p < 0.001) Male 1 Female 0.74 (0.60, 0.92) Marital status (p < 0.05) Married/living as married 1 Separated, divorced, widowed 0.67 (0.43, 1.03) Single, never married 0.71 (0.52, 0.98) Number of past year gambling activities 1.26 (1.21, 1.32) Model 4: Mixed mode same activity gamblers Odds ratio 95 CI Age group (p < 0.001) (0.68, 1.08) 55þ 0.42 (0.30, 0.57) Sex (p < 0.01) Male 1 Female 1.27 (1.05, 1.53) Marital status (p < 0.01) Married/living as married 1 Separated, divorced, widowed 0.73 (0.52, 1.02) Single, never married 0.73 (0.57, 0.93) Highest educational qualification (p < 0.05) Professional qualification or above 1 GCSEs/O levels or A levels 0.83 (0.66, 1.03) or equivalent None/other 0.65 (0.47, 0.89) Cigarette smoking status (p < 0.01) Not current cigarette smoker 1 Current cigarette smoker 0.71 (0.56, 0.89) Personal Income tertile (p < 0.001) Lowest 1 2nd 1.10 (0.83, 1.47) Highest 1.80 (1.37, 2.35) Income not known 1.21 (0.85, 1.71) Number of past year gambling activities 1.38 (1.31, 1.44) NS-SEC), ethnic group, economic activity and number of past year gambling activities. The odds of being an in-person gambler were significantly higher among those aged 35 years and over compared with the reference category of those aged years. The odds were also higher among those who were widowed, separated or divorced (1.31) or single (1.47), compared with those who were married. There was a general pattern by which those with the lowest socio-economic indicators were more likely to be in-person gamblers. Those with lower educational attainment had higher odds of being an in-person gambler, as did those from the routine and manual households (1.68) compared with those in managerial and professional households. This may be partly related to the distribution of

14 International Gambling Studies 351 Internet access observed in the UK. Figures from the Office of National Statistics (ONS) show that in 2010, 17 of British adults had never used the Internet. However, this proportion was largest among those with no educational qualifications (55) and lowest among those educated to degree level or higher (3) (ONS, 2010). Furthermore, those who received a long-term disability allowance were 2.08 times more likely to be in-person gamblers than those in paid employment, though the odds were also significantly higher among those who were retired and in full-time education. Interestingly, personal income was not associated with being an in-person gambler and, whilst ethnic group was significantly associated with in-person gambling, those from other ethnic backgrounds varied from the reference category of White/White British. The number of gambling activities was associated with in-person gambling, the odds being 0.74 times lower for each additional activity undertaken, reflecting lower levels of gambling involvement among this group. A lesser range of variables was associated with online gambling. These were age, marital status, educational qualifications and number of past year gambling activities. The odds of being an online gambler were 0.32 times lower among those aged 55 years and over than among those aged years. Odds were also 0.45 times lower among those who were single compared with those who were married, and were 0.43 times lower among those with no educational qualifications compared with those who had a degree or higher. The number of activities undertaken in the past year was a strong predictor of online gambling, the odds being 0.21 times lower for each additional activity undertaken. Like online- gamblers, the odds of being a mixed mode different activity gambler were lower among those who were older and those who were single. Odds were also 0.74 times lower among women than men. The number of past year gambling activities was positively associated with mixed mode different activity gambling. The odds increased by 1.26 as the number of activities participated in increased. Finally, a broader range of variables were associated with mixed mode same activity gambling. These were age, sex, personal income level, marital status, educational attainment and cigarette smoking status. The odds operated in much the same way as for the other online or mixed mode group, being lower among older adults, lower among those who were single and lower among those with no academic qualifications. The odds of being a mixed mode same activity gambler were 1.80 times higher among those with the highest income level than among those with the lowest income. Interestingly, the odds of being a mixed mode same activity gambler were 1.27 times higher among women than among men. This is the past year gambling subgroup where this pattern was observed. The odds were 0.79 times lower among current cigarette smokers. As with mixed mode different activity gambling, the number of gambling activities undertaken was positively associated with mixed mode same activity gambling. Odds increased by 1.38 as the number of activities undertaken increased. Discussion The development and popularity of the Internet and the provision of gambling using this medium has given rise to much debate about the impact of online gambling and concern that online gamblers may experience greater risk of harm. However, much of this debate has taken place with little consideration of how online gambling may be integrated within a broader spectrum of gambling behaviour and how different patterns of integration may be associated with the experience of gambling-related harm. The evidence presented in this paper is not intended as a definitive definition of gambler subtypes but seeks to

15 352 H. Wardle et al. demonstrate that in a diverse and developed gambling market, like Great Britain, where many forms of gambling are legally available, drawing a blunt distinction between online and offline gamblers is increasingly problematic. This is because, by and large, people who gamble online also gamble offline and tend to regularly take part in a range of activities. As observed in Table 2, a mere 2.1 of past year gamblers gambled online and these people displayed quite distinct patterns of behaviour. The majority of online gamblers were people who simply used the Internet to purchase their National Lottery tickets online. This appears to be a choice of convenience and, as these results show, the demographic profile, gambling involvement and problem gambling prevalence rates were quite different among this group when compared with in-person or mixed mode gamblers. This reinforces the point that there may be a qualitative difference between people who purchase access to certain types of gambling activity online (such as lotteries, football pools and, potentially, even betting with a bookmaker) and those who spend greater amounts of time online playing casino games, bingo or slot-machine-type games. The existence of a further group of online gamblers who were more engaged in these types of games was also noted, though the numbers present in the dataset did not allow consideration of them separately. Exploring the potential existence of further subgroups and their profiles requires further empirical investigation. Results from this paper show that the bulk of online gamblers who also gamble offline can be broadly categorized into two distinct groups: (1) those who gamble online on activities which are different to their offline participation; and (2) those who gamble online and offline on the same activities. As the BGPS series is based on cross-sectional data, information is not available about sequence of mode choices (i.e. whether the online or offline behaviour came first). However, the former is clearly a group of people who choose to take part in different activities in different environments, whereas the latter group mix and match their mode of participation and take part in certain activities utilizing both online and offline mediums. What is particularly interesting is that the mixed mode groups displayed the highest levels of gambling involvement. They were more likely to be regular gamblers and the number of activities undertaken was a strong positive predictor of membership of these groups. This, perhaps, is not surprising: the more engaged a person is with gambling in general, the more likely they are to utilize different modes of access. Unsurprisingly, those who used the Internet to gamble, be it online or mixed mode, fared better on a range of socio-economic indicators than those who were in-person gamblers. They typically had better levels of education, higher levels of personal income and were more likely to be in paid employment than their in-person counterparts. This may be related to varying levels of access and familiarity with the Internet but also may be a reflection of the different age profile between groups (or an interaction of the two), as online or mixed mode gamblers tended to be younger than in-person gamblers. This requires further examination but potentially points to the existence of different cohorts of gamblers: those who are younger and more likely to use online methods of access and those who are older and less likely to do so. A key finding from this study is the relationship between online gambling and levels of gambling involvement. Those who display a single mode preference are typically less involved with gambling than their mixed mode counterparts (measured here by the proportion of regular gamblers within each subgroup and the number of activities undertaken). It was also observed that mixed mode gamblers display higher problem gambling prevalence rates. Previous work by LaPlante et al. (2009) suggested that gambling involvement is an important predictor of gambling problems. Therefore, it is possible that the higher levels of involvement observed among mixed mode gamblers may be driven by

16 International Gambling Studies 353 this, and/or other associations. This requires further investigation but is an important observation. Previous research, the authors included, has suggested that online gamblers are more likely to experience problems (e.g. Griffiths et al., 2009; 2011). The findings presented in this paper demonstrate that simply comparing online and offline gamblers and prevalence rates among them does not adequately take into account an individual s full spectrum of behaviour and ignores potentially important associations which may be driving this association, such as patterns of mode integration and levels of gambling involvement. Previous reliance in examining this issue using a dichotomized approach has, in the authors opinion, prevented researchers in the gambling studies field from considering the full complexities of how, why and under what circumstances people use the Internet to gamble, which should also include focus on cultural and environmental contexts. To move this agenda forward, research into the broad impact of Internet gambling should shift focus, with the key question being not What do people do? but rather How do people do it and how is this integrated with other gambling behaviour? Focus on the How questions provides a more holistic perspective that takes into account the full spectrum of individual gambling behaviour and explores how online activities are integrated within broader patterns of participation. In a jurisdiction like Great Britain, that has a long gambling history and broad availability of a range of products, gamblers are not a homogeneous group. This point should be explicitly recognized by all stakeholders working in the gambling studies field (including researchers). At a minimum, different jurisdictions should consider their own broader gambling landscape, particularly the legal availability and popularity of other gambling activities, and then consider how online gambling is, or is likely to be, integrated by the general public within existing provisions. This would give a more nuanced understanding of the likely or actual impact of online gambling. It could be speculated that the impact of online gambling will vary between jurisdictions based on these features. For example, in a developed market, where there are a plethora of gambling options available, online gambling, for some, may simply represent an additional medium of access. For jurisdictions with more prohibitive regimes, online gambling may represent a new form of activity not otherwise available. These different contexts are likely to translate into different impacts, yet little research has been conducted to explore this in detail. Based on findings from this study, some highly practical recommendations can be made in relation to the promotion of responsible gambling strategies. The majority of Internet gamblers tend to be those who have the highest levels of gambling involvement, who are also more likely to experience problems with their gambling behaviour. This means there is a significant opportunity for Internet gambling companies to interact with a hard-to-reach group to promote responsible gambling messages. The development of a joined-up and proactive strategy between online operators, along with different levels of interventions based on individual behaviour, has the potential to intercede with a group that is most at risk of developing gambling problems. Online operators are in a unique position to assess individual levels of risk based on the examination of player data and to develop a variety of strategies and interventions based on the level of risk predicted. This is an advantage over many offline operators and, in an ideal world, online operators should coordinate and develop joint strategies as this is likely to have maximum reach and impact. This would be a holistic and utilitarian approach to the promotion of responsible gambling strategies, by which the industry best placed and most able to proactively interact with a key risk group assumes a lead responsibility in the promotion of socially responsible practice. There are, of course, a number of limitations that should be taken into account when interpreting the findings presented here. Firstly, as with any survey, there are a range of potential sources of bias that could influence results. The overall response rate to this study

17 354 H. Wardle et al. was 47, meaning that more people did not take part than those who did. This provides the potential for non-response bias to influence results. However, a number of actions were taken to minimize this potential, such as weighting adjustments to account for response biases. The final weighted sample was compared with various national population profile estimates and judged to be an accurate reflection of the British population. Secondly, as with all survey data, information is based on self-report and subject to attendant problems of this mode of collection. Thirdly, although our overall sample size is large, the base sizes for some of the past year gambling subgroups are small. Notably, 125 respondents were online gamblers. Therefore, the subgroups presented are not definitive; it is plausible that further subgroups exist and other analytical techniques could perhaps be used to examine this. However, the groups presented here have been developed based on clear groupings evident from the data and based on responses to specific survey questions asking how participants engaged in gambling. In conclusion, the purpose of this paper was to explore how online gambling in Great Britain is integrated with other gambling behaviour and to suggest that a more holistic perspective be used when thinking about these issues. These findings have been generated from a jurisdiction that is widely considered to have one of the most accessible gambling markets in the world. Therefore, we acknowledge that these findings may not be transferable to other jurisdictions with less diverse or developed gambling markets. However, we believe that these issues should be considered on a jurisdiction-by-jurisdiction basis and taken into account when examining the impact of online gambling. A key question should be: how is online gambling adding to and/or complementing the existing gambling offer? We believe that this is likely to have differential impact and to influence how and why people choose online gambling and how they integrate this with other available activities. We would encourage researchers to consider these issues in their jurisdictions and we would welcome a full and open debate regarding this (Orford, 2011). Acknowledgements We wish to acknowledge the support of the Gambling Commission who funded the British Gambling Prevalence Survey, of which the work reported in this paper was part. Notes on contributors Heather Wardle is a Research Director at the National Centre for Social Research and a PhD candidate in Sociology with the University of Glasgow. Her research interests focus on the social context of gambling and understanding the interaction of multiple-risk factors for health and wellbeing. She was Project Director of the British Gambling Prevalence Survey Alison Moody is a Researcher at the Department of Epidemiology and Public Health, University College London. She contributed to this research in her previous role as a Senior Researcher with the National Centre for Social Research where her research focused on health and wellbeing, with particular emphasis on substance use and misuse and gambling studies. Dr Mark Griffiths is a Chartered Psychologist and Director of the International Gaming Research Unit. He has published over 250 refereed research papers, three books, and over 65 book chapters. He has won ten national and international awards for his work including the John Rosecrance Prize (1994), Joseph Lister Prize (2004) and the US National Council on Problem Gambling Research Award (2009) Jim Orford is Emeritus Professor of Clinical and Community Psychology, University of Birmingham. He is the author of several books on Community Psychology and addictions and his

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