Measurement of Prevalence of Youth Gambling in Australia: Report on Review of Literature  

• 4.1 Implementation Methodology
• 4.2 Clarity, Ambiguity and Interpretation
• 4.3 Schools and Consent Procedures
• 4.4 Self-Awareness Feedback
• 4.5 Other Research Methods
• 4.6 Distinguishing Between Different Age Groups

Debate continues as to the appropriate theoretical underpinnings of gambling screens,the various models (medical, behavioural, social/environmental) and understanding of problem gambling behaviours that the screens are said to be designed to assess, and the broader understanding of problem gamblers (individual pathology through to the ‘continuum of problem gambling’). The medical, individual pathology/addiction model screens to test if the condition is either present or absent (dichotomous).Other theoretical understandings such as the problem gambling approach adopt a scaled or measure of ‘at risk’ approach to reflect the continuum of possibilities.

Studies into problem gambling using prevalence estimates rely principally on selfreporting techniques, which are frequently unsupported by other information which would improve the validity, reliability and accuracy of the estimates (i.e., known basic characteristics of respondents). The literature indicates that a variety of survey techniques are employed, many of which are not fully explained; conclusions and estimates of cut-off points are often highly subjective. ‘Goal post shifting’ is observed particularly in relation to the degree of gambling participation which is claimed to represent problem gambling. Clear examples of response bias can be observed in many surveys yet this often is overlooked or not commented upon at all.

Svensen (undated) examined the question of how should prevalence be measured in order to explain why Australians’ high per capita gambling expenditure does not appear to translate into high (or at least higher) apparent prevalence. Essentially, he concluded that the answer to this question is the result of a failure to measure consistently and accurately. One explanation for this is the replacement (or contesting) of the previous dominance of the medical model with its emphasis on pathological gambling, by other approaches including inter alia, the problem gambling model and harm minimisation. The difficulty of these approaches is that ‘estimates based on the problem gambling model are arbitrary as they depend upon the degree of problems judged necessary to meet cut-of criteria’ (Svensen p. 4).

Broader methodological questions regarding the conduct of prevalence studies, and particularly in regard to young people include:

• non-response bias;
• small sample size (particularly to test and validate screens);
• propensity of gamblers to lie about their behaviour;
• confusion and lack of understanding about the questions which can exacerbate the problem of false positives and false negatives;
• time scale of measurement: life-time prevalence rates, past 6 months, past 12 months;
• is the study to be used to predict future behaviours (i.e., be clear about the purpose for which the prevalence rate will be used including for estimating economic and social costs, risk diagnosis, therapeutic/treatment);
• is the study to be used to document current behaviour and how is this expected to change as young people grow through the ‘period of experimentation’;
• measuring the scale of harm as young people may have less at risk in that are unlikely to lose their job, house or other assets; and
• participation and gambling preferences differ by gender.

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4.1 Implementation Methodology:

Surveys Svensen (undated) notes that all surveys are subject to non-response biases and sampling errors and these create large problems for studies on problem gambling due to the low prevalence rate. As Shaughnessy et al (2000) notes there are three principal ways of obtaining survey data:

• mail surveys;
• personal interviews; and
• telephone interviews.

Each one of these methods has its advantages and disadvantages. Firstly, mail surveys are quick and easy to administer but there may be a response bias. Personal interviews are costly but allow the interviewer more control of the interview. A disadvantage is that the interviewer may influence the responses obtained. This may happen inadvertently. Lastly, telephone interviews, which are becoming increasingly popular, have the advantage of being much less costly than personal interviews and being simpler to oversee. Telephone interviewers have the disadvantages of missing a large group of respondents who do not have telephones that may influence results. Research has found that people take less time to form judgements on the telephone and have a larger difficulty remembering options.

Shaffer et al (1997) in their meta-analysis of problem gambling prevalence studies in the United States and Canada found a number of different factors influenced prevalence rates. Firstly, subject or population attributes accounted for more variance associated with prevalence estimates than any other single factor. In addition to individual respondent trait characteristics the research process also influenced estimates of disordered gambling prevalence. Process issues include: measurement instruments; geography or location; principal investigators and historical moment of study (although this only effects adults). Surprisingly, the quality of research methods exerts little influence on prevalence rates of problem gamblers.

An important issue for this review is the difficulty in surveying young people who are neither at school or attending university. There is some concern that adolescent surveys completed only in the school context will obviously exclude non-school attendees who may have significantly different characteristics than school students. Udry and Chantala (2003) explored the question of excluding school dropouts from surveys and what impact this had in biasing risk estimates. Respondents details were taken from school rosters and the surveys were completed a year or two years after with the student at their home, irrespective of their enrolment status. The behaviours collected by the survey they used for their analysis included: having sex, substance abuse, witnessing or experiencing violence, emotional distress, and exercise and diet. They found that responses from adolescents that had left high school before graduating were different. However, basing sample surveys on schools rather than on homes will not significantly bias population estimates for adolescents due to omitting those who have left before graduating or have left due to graduation. Udry and Chantala (2003) state that the reason for this was that at the time of the study in the United States drop out rates were so low, at a national level, that the absence of adolescents who have not completed school did not bias estimates for the total population. However, the study does make the oversight of categorising adolescents planning to go back to school as enrolled; this raises some concerns on their findings.

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4.2 Clarity, Ambiguity and Interpretation

We have cited the research work, particularly of Ladouceur (2000) on both understanding items and survey methodologies (telephone, face to face) that may lead to the problem of ‘false positives’ (see discussion in Sections 2.2 and 2.3 with adults and adolescents).

Ambiguity and lack of clarity of questions, definitions and instructions is also of concern.

Blaszczynski (1997) examined respondents interpretation and answers to the question ‘how much do you spend gambling’ because the validity of questions in any screen or survey depend on the lack of ambiguity in interpretation. He recommended that more ‘attention be paid in prevalence and clinical studies to providing subjects with clear instructions’ to the expenditure question under study, but to other items more generally. The point here is that both authors referred to above have demonstrated that lack of clear definitions or instructions can have an impact on reported prevalence rates. Survey techniques such as use of the telephone can lead to impulsive responses.

4.3 Schools and Consent Procedures

McPhee and Canham (2002) argue that active consent procedures, used to protect students, can cause problems for the validity of study results. The process of consent procedures may result in low parental response rates, low participation rates and a distinct subpopulation of youth that threaten the external validity of studies. Their review of the literature found that youth that do not receive parental permission are quite different compared to do those who do. Studies have found the following characteristics are more likely have been given for students not receiving parental permission: rated by peers and teachers as being less popular, less academically competent, more socially withdrawn, more aggressive, high in risk-taker, have lower self-esteem and tend to engage in substance use and other problem behaviours. For these reasons, McPhee and Canhan (2002) argue for a passive consent procedure.

Although, McPhee and Canham (2002) argue for a passive consent procedure, in practice, due to many existing school policies, they were required to undertake a survey under active consent procedures. They made a number of recommendations to increase participation rates by both students and individual schools if this regime is chosen.

Firstly, they suggest forming a multidisciplinary committee to guide the research project (their team included a former principal to fulfil a role as educational consultant). Second, ensure minimal disruption to the school, staff and students in the following ways: ensure that the survey is as short as possible, researchers should work around the school timetable and make sure demands on teachers and staff are minimal. Third, they suggest educating school administrators and staff about the research and the research procedure. McPhee and Canham (2002) gave presentations to school staff which made teachers more comfortable with the research. These face–to-face sessions provided the added benefit that staff were able to be made more aware of youth gambling problems and issues. Finally, they maximised student participation in the survey by increasing parental consent. They did this by using several communication methods such as school newsletters, parent council meeting and student council meetings and local newspapers, among others, to inform and educate the school community. They also developed a package for parents that included the consent form, a brief description of the study, and contact names and phone numbers for addressing concerns. Parents could also indicate that they wanted a copy of results. Parents who did not return a consent form by the specified date were sent a reminder notice and then sent an additional package if a response was still not sent. To encourage co-operation from the school the researchers even provided a youth gambling related activity to students who did not have parental consent to complete while others were responding to the survey. At the conclusion of the study, the researchers recommended providing thorough and clear results to the schools in different formats, including inter alia, summaries and presentations.

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4.4 Self-Awareness Feedback

Intervention following the findings of juvenile prevalence studies is problematic for privacy and other reasons. Jacobs (2000) states that ‘all too familiar is the paradox of an individual obtaining high SOGS scores in company with a denial that a problem with gambling had ever existed’ (p. 144). It should be considered as part of the methodology that feedback to those who record high scores is offered following the survey. Another methodological approach could involve providing a scoring method for self-test surveys so that the individual could assess their level of risk at the conclusion of the survey.

Another methodological approach could involve providing a scoring method for self-test surveys so that the individual could assess their level of risk at the conclusion of the survey. They could also be informed where to find further information. While protecting privacy and anonymity is important some form of ‘self-awareness feedback’ (Jacob, 2000) should be built into juvenile prevalence studies.

4.5 Other Research Methods

It is clear from longitudinal studies that gambling preferences change with age, from informal to legalised activities and from games of skill and sports betting to games involving continuous play hosted in licensed venues. The gambling behaviours of 15 and 16 year olds are different to those of a 24 year old and the social context in which preferences change is important. This suggests a role for a longitudinal study alongside any national prevalence study.

There may also be a role for other qualitative methods, including inter alia, interviews, focus groups and case studies to understand the sociological factors that influence gambling behaviour. Peer pressure, familial patterns, the association between school performance, alcohol and drug use, attitudes to gambling, exposure to gambling and access issues can be explored in detail using such techniques. Certainly it is the case that the role of gambling in youth culture is not well understood; gender issues are important and the role of ethnicity is also unclear. No single prevalence study is likely to satisfactorily address ‘existing puzzles’.

4.6 Distinguishing Between Different Age Groups

ToR:3 considered the need to distinguish between different age groups including 15-18 year olds and 19-24 year olds. In the discussion of various screens in Section 2 we have drawn attention to screens that have been developed for adolescents and in Section 3 provided commentary on selected studies with a focus on youth gambling.

In this brief review it is not possible to cover the ever expanding number of articles and studies into adolescent gambling. Suffice to say, primary school students, secondary and college students from 15 to 24 have been the subject of many studies - in school based surveys, telephone sample surveys, by grade level, through general health and substance use surveys. A variety of methodologies were used; sample and whole population studies; random and non-random selection; longitudinal and point estimates; using adolescent and general screens. The objectives of the many studies are equally varied including, inter alia, to report on prevalence rates, to discover risk factors, to assist with education and interventions, and to identify types of gambling causing the most significant difficulties.

One of the most interesting findings is the general conclusion that age has not been found to be a predictor of problem gambling among adolescents (Poulin 2002, Winter et al, 2000, Wiebe 1999). A parent who has/had a gambling problem is more likely to be a predictor of problem adolescent gamblers. Prevalence rates for males are higher than for females (rate varies between 3 and 8 times). The age at which a respondent is involved in a prevalence study appears to influence their response to ‘first gambling activities’.

A significant number of studies report higher rates of gambling for adolescents than for adults. An example of this is the study of Gambling Prevalence Among Adolescents in Florida comparing adolescents aged 13-17 years with all adults:

• at risk gamblers (youth 8.2 per cent vs. adult 4.0 per cent);
• problem gamblers (2.7 per cent vs. 0.5 per cent); and
• probable pathological gamblers (1.1 per cent vs. 0.3 per cent).

While many authors/researchers comment on similar findings no satisfactory explanations are provided as to why the rates decline. Table 4.1 and Appendix B provide a summary of youth prevalence studies and adult prevalence studies in Canada. The studies are not comparable because of different definitions, use of different screening instruments, survey methodologies and age ranges of youth. Very few of the studies we have cited report on the accuracy, validity or reliability of their results, a comment supported by Poulin (2000) when she states

‘In the absence of such information it is difficult to know if observed differences in estimates are a reflection of real differences in the rates of at-risk and problem gambling in the underlying populations, or of different methods, or of various threats to validity and reliability’, (p. 74).

In terms of this review, we can say that there are numerous studies covering the age range 12 to 17/18 years and class levels 8-12 and primary school level. Young people 18- 24 years are almost always included in adult prevalence studies. Canadian Province or USA state commissioned studies on youth prevalence most often are restricted to adolescents aged 12 to 17 years (e.g., Shapira 2002).