J Gambl Stud DOI 10.1007/s10899-014-9501-2 ORIGINAL PAPER

Problem Gambling and the Youth-to-Adulthood Transition: Assessing Problem Gambling Severity Trajectories in a Sample of Young Adults Jason D. Edgerton • Timothy S. Melnyk • Lance W. Roberts

 Springer Science+Business Media New York 2014

Abstract In this study, using four wave longitudinal data, we examined problem gambling severity trajectories in a sample of young adults. Using latent growth curve modeling, we examined how initial level of problem gambling severity and the rate of change were affected by 11 time-invariant predictors: gender, age of onset of gambling, experiencing a big win early in gambling career, experiencing a big loss early in gambling career, alcohol dependence, drug dependence, anxiety, depression, perceived social support, illusion of control, and impulsiveness. Five of the eleven predictors affected initial levels of problem gambling severity; however only impulsiveness affected the rate of change across time. The mean trajectory was negative (lessening of problem gambling risk severity across time), but there was significant inter-individual variation in trajectories and initial levels of problem gambling severity. The main finding of problem gambling risk diminishing over time challenges the conventional picture of problem gambling as an inevitable ‘‘downward spiral,’’ at least among young adults, and suggests that targeted prevention campaigns may be a cost-effective alternative for reaching treatment resistant youth. Keywords Problem gambling
Risk levels
Risk/protective factors
Young adults
Latent growth curve modeling

Introduction Opportunities for legal gambling have grown greatly in recent decades, as have public concerns over the prevalence and consequence of gambling and problem gambling

J. D. Edgerton (&)
L. W. Roberts Department of Sociology, University of Manitoba, 318 Isbister Building, Winnipeg, MB R3T 2N2, Canada e-mail: [email protected] T. S. Melnyk Department of Sociology, University of Nevada, Las Vegas, NV, USA

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(Campbell and Lester 1999; KPMG 2003). Research has demonstrated that Canadian youth aged 15–24 are roughly 1.5 times more likely to experience gambling problems than adults aged 25 and over (Huang and Boyer 2007). Gambling among young people is a growing, yet under-recognized, social problem with many significant short and long-term consequences for individuals and society (Messerlian et al. 2005). A growing body of research points to gambling and problem gambling as a public health concern (Korn et al. 2003; Shaffer and Korn 2002) associated with various personal, health, and social problems (e.g. Crockford and el-Guebaly 1998; Eber and Shaffer 2000; Petry et al. 2005; Rush et al. 2008) and there is evidence to suggest that adolescent and young adult onset gambling is associated with problem gambling later in life (Burge et al. 2004; Lynch et al. 2004; Shaffer and Hall 2001). However, most of the research on gambling and problem gambling generally relies on cross-sectional samples and retrospective accounts (Abbott and Clarke 2007). Although this research has resulted in the identification of a range of risk factors and correlates of problem gambling, it is limited in its capacity to assess change in gambling behavior over time and to determine causal connections. The current study begins to address this gap by analyzing longitudinal data from the Manitoba Longitudinal Study of Young Adults (MLSYA). The following section will review the literature on the stability and course of problem gambling as well as evidence regarding various important risk and/or protective factors for problem gambling. Then, based on the reviewed literature, we will posit a number of hypotheses regarding the effects of these covariates on the mean level and rate of change in problem gambling severity/risk over time. Next, we describe our data, measures and analytical procedures before presenting the results. Finally, we discuss the limitations and implications of our findings and how future research can build upon these. Stability and Course of Gambling Behavior and Problem Gambling Stability of problem gambling behavior can be understood as the ‘‘tendency for individuals to stay at one diagnostic level as opposed to moving to another improved or worsened level’’ (LaPlante et al. 2008: 52). Conventional wisdom tends to view problem gambling as a chronic and progressive condition. For example, Gamblers Anonymous states on its website, ‘‘we are convinced that gamblers of our type are in the grip of a progressive illness. Over any considerable period of time we get worse, never better’’ (Gamblers Anonymous n.d.). Research into the lived experiences of problem gamblers has revealed a contrasting perspective of the gambling career characterized by periods of greater and lesser harm, attempts at treatment seeking or abstinence, and a ‘downward spiral’ of activity and consequences for some and the resolution of problems for others (Lesieur 1977; Tepperman 2009). Although aggregate rates of problem gambling appear relatively consistent in crosssectional studies, longitudinal research reveals a more complex picture. At the individual level of analysis, problem gambling emerges as unstable and multidirectional in its course (LaPlante et al. 2008). In their review of five longitudinal studies of gambling behavior among non-treatment samples, LaPlante et al. (2008: 58) conclude that the evidence indicates ‘‘considerable movement in and out of severe and less severe levels of gambling disorder’’, and that people with gambling problems may improve on their own, although, ‘‘improvement is not a certainty and the rates of worsening are still substantial’’. In one of the first longitudinal studies to assess gambling behavior over the late adolescence to early adult transition period, Winters et al. (2005) found evidence of four basic pathways of

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gambling severity over time: (1) Resistors (60 % of sample) who reported no problem gambling across three waves of the study; (2) Desistors (13 %) who moved from at-risk or problem gambling to non-problem gambling by wave 2 or 3; (3) Persistors (4 %) who reported at-risk or problem gambling in all waves; (4) New Incidences (21 %) who revealed at-risk or problem behavior beginning in late adolescence or early adulthood. Additionally, eight individuals (2 %) were in a fifth ‘other’ category characterized by new incidence at wave 2 and desistance at wave 3. Slutske et al. (2003) used individual-level longitudinal data (4 waves spanning 11 years) to examine the extent to which adolescent problem gambling resolved prior to adulthood and the incidence rate of problem gambling in early adulthood. At the aggregate level they found the prevalence of problem gambling to remain relatively stable at 2–3 % across all four waves. However, generally it was different individuals reporting problems at different time points keeping aggregate rates stable while indicating ‘‘high problem transience’’. In addition, most individuals reported a problem at only one time point in the study, and of the minority who reported a problem more than once, it was almost always across two consecutive waves. Thus, ‘‘at the individual level problem gambling appeared to be more transitory and episodic than enduring and chronic’’ suggesting ‘‘that a downward spiral is not the inevitable outcome of gambling-related problems and that many cases, perhaps the majority, of subclinical gambling-related problems resolve naturally’’ (Slutske et al. 2003: 271). In perhaps the longest running longitudinal time-series analysis of youth and adolescent gambling behavior, Stinchfield (2011) demonstrated an increase in frequent gambling among a Minnesota sample, especially for boys, from 1992 to 2004 which corroborates the findings of multiple studies during that same time period (e.g. Jacobs 2000; Shaffer et al. 1999; Dickson et al. 2002). However, Stinchfield (2011) also found a sharp decline in frequent youth gamblers between 2004 and 2007. This suggests a widespread cohort phenomenon where interest in gambling is declining despite the proliferation of opportunities to gamble. Though a clear explanation for this finding is not apparent, Stinchfield speculates that perhaps other activities such as web-surfing, cell phones, or video games have taken priority over gambling, and it is worth noting that televised gambling (e.g. poker) has faded in popularity since the early 2000’s. Moreover, Stinchfield (2000, 2004) argues that youth gambling tends to be only part of a larger syndrome of deviance and maladjustment during key formative years which means it could be replaced by other risky behaviors, especially considering the current state of gambling as a ubiquitous and normalized activity. Risk and Protective Factors for Problem Gambling Gender Studies have consistently shown that young males are more likely than females to gamble and experience harm from their gambling (Desai et al. 2005; Burge et al. 2006; Stinchfield et al. 2006; Currie et al. 2011). Moreover, there are a number of salient differences between males and females who gamble. Males tend to begin gambling at a younger age, are more likely to exhibit problems with impulse control and preoccupation with gambling, and are more likely to gamble with the intent of winning money (Ellenbogen et al. 2007). Female gamblers, on the other hand, report higher rates of depression, anxiety, and loneliness, and therefore gamble to ‘escape’ these negative mood states (also Ledgerwood and Petry 2006).

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Early Onset of Gambling Behavior Early exposure to gambling has been consistently reported as a significant correlate of later gambling behavior. Specifically, having a parent who regularly gambled tends to predict higher levels of adolescent and young adult gambling (Stinchfield 2000; Stinchfield et al. 2006; Winters et al. 2005) and there is evidence that early onset of gambling among children predicts gambling problems later in life. Stinchfield (2000) reports that children exposed to gambling as young as 10 years old exhibited higher rates of gambling in middle and high school. Burge et al. (2006) found in their sample of treatment-seeking adults that those who first gambled before age 15 had roughly double the gambling debt, began gambling regularly at an earlier age, and were more likely to report comorbid substance abuse, psychological disorder, and suicide ideation than later onset gamblers. These findings highlight the importance of early adolescence as a developmental period and suggest it to be a key area of focus for gambling etiology research. One notable exception comes from the previously mentioned findings of Slutske et al. (2003), which suggest that adolescent onset gambling does not necessarily precede adult problem gambling but instead may be a less severe final stage in itself. Based on their interpretation, gambling in adolescence can be a relatively mild pathology, or generally causes no harm at all before the behavior is fully resolved and ceases to continue into adulthood. Early Big Win/Loss Evidence suggests that a memorable big win, especially if it occurs early in one’s gambling career, may predict future problematic gambling since it potentially solidifies a distorted cognitive link between gambling and winning money (Weatherly et al. 2004). In this scenario, the individual cannot accept the inevitability of losing money in the long run due to the compelling experience of winning a large sum in the short run. This idea was first proposed by Robert Custer who observed that gamblers who win a large amount of money early on, perhaps the amount of one’s annual salary or more, predisposes the individual to take greater risks, and to fantasize about spectacular gambling success in the future (Lesieur and Rosenthal 1991). A study by Turner et al. (2006) found that a sample of pathological gamblers were more likely to report winning the first time they gambled in a ‘serious’ venue (e.g. casino, slot parlor, etc.). Though few empirical studies denote this finding, there is some evidence that problem gamblers are more likely to report an early significant loss from gambling (Wiebe et al. 2001: 4). It is unclear, however, why this association might exist. One possibility, from an ethnographic account of professional poker players, is that a losing gambler might persist simply to remain in ‘action’ (Avery 2009). The notion is similar to an old gambling adage: ‘The greatest thing in the world is gambling and winning. Second best is gambling and losing’. To continue gambling despite heavy losses suggests that money has ceased to be the focus, and has instead been replaced by the emotional or physiological response to the activity. In other words, money facilitates the gambling experience, though the amount won or lost is of little importance. Comorbidities with Mental Health and Substance Use Disorders Data from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) in the US demonstrated that gambling is comorbid with a range of psychiatric conditions including 73 % of pathological gamblers with an alcohol use disorder and 38 %

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with a drug disorder (Petry et al. 2005). Compared to the general population, pathological gamblers are six times more likely to have an alcohol use disorder and 4.4 times more likely to have a drug use disorder. Furthermore, the association between substance abuse and gambling is generally well-documented (e.g. Barnes et al. 2005; Crockford and el-Guebaly 1998; Hayatbakhsh et al. 2012; MacCallum and Blaszczynski 2002). The NESARC data also revealed that lifetime pathological gamblers were over 3 times more likely to have experienced a major depressive episode or generalized anxiety. The connection between gambling and depression (e.g. Feigelman et al. 2006; Gupta, Derevensky, and Margaret 2004; Langhinrichsen-Rohling 2007; Lynch et al. 2004) and between gambling and anxiety disorders (Black and Moyer 1998; Bland et al. 1993; Blaszczynski and McConaghy 1997; Cocco et al. 1995; Ste-marie et al. 2002) are both also well-established in the literature. Social Support Social support is a factor associated with many addictive behaviors, and there is evidence to suggest it plays a role at multiple stages of gambling and problem gambling development. Gupta and Derevensky (1997) reference Social Learning Theory as a pathway for children and adolescents to learn deviant behavior, such as gambling or alcohol use, from parents or other significant role models. Results from their study show that roughly 80 % of young adolescents report gambling with family members and the most common place to gamble among that age group is at home or a friend’s home. Moreover, Hardoon et al. (2004) suggest that familial support is a likely protective factor while the negative influence of peer groups presents a potential risk for problem gambling among adolescents. In addition, the children of problem gamblers tend to report decreased physical and emotional support, issues of trust, security, and safety, and greater engagement in a variety of deviant behaviors. Illusion of Control Illusion of control can be defined as an ‘‘expectancy of a personal success probability inappropriately higher than the objective probability would warrant’’ (Johansson et al. 2009: 83). Typically, this distorted cognition manifests as overconfidence in situations where an individual estimates an unknown parameter (e.g. ‘What is the population of California?’) or as an overestimation of the true odds of some random event (e.g. ‘What will appear on the next dice roll?’). Both of these cognitive processes are thought to impact gambling behavior, perhaps more so in regards to problem gambling. Pathological gamblers have been found to report more cognitive distortions than casual gamblers (Myrseth et al. 2010), a finding which holds for illusion of control but not necessarily for other distorted cognitions such as superstitious thinking or irrational beliefs about randomness (Kallmen et al. 2008). Impulsivity There is mixed evidence on the relationship between impulsivity and problem gambling. According to Blaszczynski et al. (1997), only a subtype of gambler will demonstrate elevated levels of impulsivity. These are the pathological gamblers categorized as Antisocial Impulsivist (Blaszczynski and Nower 2002) who tend to show concomitant features of Attention Deficit Hyperactivity Disorder, psychopathy, antisocial behavior, and substance abuse. This position is bolstered by findings from Petry (2001) who showed higher indicators of impulsiveness among substance abusers and problem gamblers. Moreover, the most impulsive group in that study included those who were both problematic gamblers and

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substance users. Accordingly, impulsive gamblers are likely to have an array of associated addictions and disorders, and may be the most severe group of problem gamblers and the least likely to respond positively to treatment (Blaszczynski and Nower 2002). Adolescents are thought to be particularly susceptible to problems related to impulse control as the inability to inhibit decision-making is a normal feature of the developing brain (Chambers and Potenza 2003). Late adolescence is an especially important developmental stage for substance use disorders, and the shared characteristics of substance use and gambling disorders suggests an epigenetic link between biology and environmental factors (Verdejo-Garcia et al. 2008). The Gambling Landscape in Manitoba The four waves of the MLSYA took place from 2007 to 2011 and provide a representative sample of Manitoba’s urban centers (i.e. Winnipeg and Brandon) as well as its rural population. Legal opportunities to gamble are plentiful throughout the province—there are two government run Las Vegas style themed casinos in Winnipeg, a racetrack and gaming lounge also in Winnipeg, and three First Nation casinos in rural Manitoba, including one 45 minutes north of Winnipeg. Moreover, Video Lottery Terminals1 (VLT) are accessible throughout Manitoba with over 5,200 machines present in a variety of bars, hotels, and restaurants making it the highest density of machines per capita in Canada at 5.8 per thousand adults (Smitheringale 2003). By law, no gambling devices may be accessible or even visible to minors,2 therefore they are kept hidden from view (located in a separate room/section) in any establishment where children may be present. Though the machines themselves are conspicuously kept out of plain sight, the Manitoba Lotteries Corporation (MLC), responsible for owning and operating all casino and VLT activities in Manitoba, is highly visible to the public. In 2008, MLC sponsored3 a wide range of cultural and sporting events including Ballet in the Park, the Rainbow Stage theater company, National Aboriginal Day, and the Manitoba Marathon in addition to humanitarian efforts such as Habitat for Humanity. Manitoba also serves as an industry-wide leader in its efforts to promote responsible gambling. The Casinos of Winnipeg were the first in Canada, and perhaps the world, to provide access to experts from a problem gambling treatment facility4 directly on the gaming floor. Moreover, information on cost of play, randomness, and the internal workings of slot machines are also made readily available to all patrons at either of Winnipeg’s casinos. This same information can be accessed online through the Addictions Foundation of Manitoba website, and they also offer educational materials geared at informing high school and college students via the ‘‘Lucky Day5’’ program. In short, though Manitoba has a high saturation of gambling venues relative to other Canadian provinces, the MLC also adheres to its commitment to provide a safe and sustainable gaming industry. In this study we conduct a longitudinal analysis of problem gambling severity in a sample of young adults using latent growth curve modeling (LGCM). In the present analysis, LGCM is used to assess changes across time in the (1) mean level of problem gambling severity score; (2) in individual trajectories of change in problem gambling severity scores; and (3) in variation across individuals. Additionally, based on the 1

VLTs are ticket-in ticket-out machines that offer a variety of games including slots and video poker, generally at 1-, 5-, and 25-cent stakes.

2

18 years of age is the legal age to gamble or consume alcohol in Manitoba.

3

Information accessed at: www.manitobalotteries.com/news/year:2008.

4

Experts were counselors employed by the Addictions Foundation of Manitoba.

5

Can be accessed at luckyday.ca.

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preceding review of the literature, the following hypotheses will be tested regarding the effects of the specified predictors on mean problem gambling risk/severity and trajectories of change. (a) Gender—Males will have higher initial levels of problem gambling risk/ severity and slower rates of change over time than females; (b) age of onset of gambling will have a negative effect on initial level of gambling (the younger the age of onset the higher the initial level of problem gambling risk/severity at time 1) and a positive effect on rate of change (the younger the age of onset the steeper the rate of decline in problem gambling risk over time); (c) having experienced a big win or big loss early in gambling career will predict higher initial level of problem gambling risk/severity; (d) comorbidity—alcohol dependence, drug dependence, anxiety, and depression will all have positive effects on initial level of problem gambling risk/severity, and a positive effect on rate of change (either an increase in problem gambling over time or a slower decline); (e) level of perceived social support will have a negative effect on initial level of problem gambling risk/severity (higher social support, lower initial level of problem gambling risk) and a negative effect on rate of change (the higher the social support the greater rate of decline in level of problem gambling risk/severity over time). Finally, both (f) illusion of control and (g) impulsiveness will have positive effects on initial level of problem gambling risk/ severity (the greater the illusion of control and the greater impulsiveness, the higher the initial level of problem gambling risk/severity), and rate of change (either an increase in problem gambling risk/severity or a slower rate of decline). Method Dataset The Manitoba Longitudinal Study of Young Adults (MLSYA) was conducted in four cycles from December, 2007 through December, 2011. The MLSYA panel began with a sample of 679 Manitobans aged 18–20 and concluded with 517 respondents which gives a retention rate of approximately 90 % between cycles. Of the original sample of 679 young adults, 51.8 % were female and 48.2 % male. By age, 35.6 % were 18 years old in cycle 1, 36.8 % were 19 and 27.5 % were 20 years. About 80 % of respondents resided in Winnipeg and 20 % lived elsewhere in Manitoba. About two-thirds of the sample (66 %) described themselves as ‘‘single—never married’’, while 32 % said they were ‘‘in a relationship’’ and only 2 % were married or living common-law. Just over half (56 %) were currently pursuing some form of post-secondary education, 33 % had completed high school and were not continuing as students, 9 % had yet to finish high school, and 2 % had already completed a college diploma or degree. Measures Problem Gambling—measured6 by the Problem Gambling Severity Index (PGSI), a 9-item subscale contained within the Canadian Problem Gambling Index (CPGI) (Ferris and Wynne 2001). Possible scores on the PGSI range from 0 to 27, with higher scores indicating greater severity of problem gambling. In the current study, reliability statistics range from a = .79 in wave 1 to a = .85 in wave 4 and are indicative of good internal consistency. The raw score version of the PGSI variable was skewed and extremely kurtosed 6

More detail on selected scales is presented in Appendix 1.

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(values over 50), consequently even robust estimation methods that adjusted for violations of normality could not achieve adequate fit to the data. Bentler and Chou (1987) argued that categorical variables that have at least 4 categories and are approximately normally distributed can be treated as continuous with little concern. Similarly, Green et al. (1997) found that the relative improvement of the v2 statistic with increases in the number of categories is greatest in the change from 2 to 4 category response formats, after which improvements diminish. Accordingly, given its near normal distribution (see Table 1), the 5-category form of the PGSI with Currie et al.’s (2013) cut-offs was used: 0 = ‘nongambler’, 1 = ‘non-problem gamblers’ (PGSI = 0), 2 = ‘low-risk’ (PGSI = 1–4), 3 = ‘moderate-risk’ (PGSI = 5–7), and 4 = ‘problem-gambler’ (PGSI [ 7). This decision is also justified conceptually in that ‘‘…the PGSI is intended to be a continuous measure of problem gambling severity hence the non-problem, low-risk and moderate-risk gambler categories should represent a meaningful progression in level of risk for all gamblers who are below the threshold of problem gambling’’ (Currie et al. 2013: 312). Predictors Age of Onset of Gambling, Big Loss, and Big Win—each is measured by a single item from the CPGI (Ferris and Wynne 2001). ‘‘How old were you the very first time you gambled for money or something else of value?’’ ‘‘Do you remember a big LOSS when you first started gambling?’’ ‘‘Do you remember a big WIN when you first started gambling?’’ Alcohol Dependence—the Alcohol Dependence Scale was adapted with some modification from the Canadian Community Health Survey, Cycle 2.1 (Statistics Canada 2003). Possible scores range from 0 to 9 where higher scores correspond with a higher likelihood of alcohol dependence. Cronbach’s alpha for Time 1 was .72 indicating moderately good internal consistency. Drug Dependence— level of drug use was measured using the same instrument included in the Canadian Community Health Survey, Cycle 2.1 (Statistics Canada 2003). This is a dichotomous variable, coded 1 = regular drug use (1–3 times per month or more) over the past 12 months, 0 = infrequent or non-use. Depression—section A of the Composite International Diagnostic Interview Short Form (Walters et al. 2002) determines probability of caseness for diagnosis of Major Depression based on the full CIDI instrument. Scores range from 0 to 7, with higher scores indicating greater probability of caseness for major depression. Anxiety—section B of the Composite International Diagnostic Interview Short Form (Walters, et al. 2002) was used as a measure of Generalized Anxiety Disorder (GAD). This is a dichotomous variable, coded 1 = meets symptom requirements for GAD diagnosis, 0 = does not meet requirements. Social Support—the Multidimensional Scale of Perceived Social Support (Zimet et al. 1988) is a 12-item self-report measure of subjectively assessed social support. The overall score was used in the present study. Internal reliability for Time 1 was very good at a = .91. Impulsiveness— the Barratt Impulsiveness Scale-11 (Patton, Stanford and Barratt 1995) is a 30-item instrument used to measure the known dimensions of impulsiveness. The alpha coefficient for the overall BIS-11 scale used in the present study was good at .84. Illusion of Control—the Drake Beliefs about Chance Inventory (Wood and Clapham 2005) is a 22-item instrument assesses two major cognitive errors that tend to be associated with gambling behavior: Illusion of control and superstition. In the current study only the illusion of control scale (a = .90) was used. Data Analysis Latent growth curve modeling (LGCM) was conducted with PGSI risk categories treated as a continuous outcome in order to assess changes across time in the (1) mean level of

123

.05**

.10

11. Anxiety

12. Depression

1.53

.0

Skew

Kurtosis

% Missing

8.1

3.90

1.31

.67

1.17

.22***

.21***

-.11**

.07

.09*

.15***

.26***

-.02

-.01

-.10*

.16***

.57***

.68***

–

2

.19***

.70***

–

14.9

5.34

1.49

.64

1.15

.16***

.21***

-.11**

.07

.02

.09*

.18***

-.03

-.10*

-.06

3

* p B .05 level; ** p B .01 level; *** p B .001 (2-tailed)

.76

.78

Standard deviation

1.27

.16***

15. Impulsiveness

Mean

.24***

14. Illusion of control

-.15***

.09*

10. Drug dependence

13. Social support

.22***

9. Alcohol dependence

-.08*

5. Gender (male)

8. Early big loss

4. PGSI (Wave 4)

-.05

.19***

3. PGSI (Wave 3)

7. Early big win

.50***

2. PGSI (Wave 2)

-.07

.55***

1. PGSI (Wave 1)

6. Age of gambling onset

–

.60***

1

Variable

.03

.15**

–

21.9

7.05

1.64

.57

1.09

.11*

.15***

-.06

.07

.01

.09*

.21***

-.05

-.13**

4

–

.07

.5

.48

.07

.13***

-.23***

-.07

-.04

.11**

.02

-.02

-.03

-.12**

.0

-2.0

5

–

12.2

6.09

-2.32

2.51

16.6

-.09*

-.10*

.04

-.12**

-.09*

-.19***

-.10*

.01

-.02

6

.45

.49

.39

.00

-.02

-.05

.04

.00

.04

-.3

.33***

–

3.7

-1.81

7

Table 1 Descriptive statistics for the observed variables used in the latent growth curve model

3.7

-.17

1.35

.42

.22

-.01

-.03

.03

.02

-.03

.04

-.03

–

8

.31*** .14***

.11**

.0

2.73

1.76

1.8

1.15

.23***

.14***

-.08*

–

9

.20***

.09*

2.4

.43

1.56

.40

.19

.18***

.07

-.13**

–

10

.28***

.0

27.58

5.43

.17

.03

.01

.03

-.01

–

11

.0

2.53

2.03

1.81

.79

.05

.03

-.16***

–

12

.4

.1

-.72

.42 .16

8.12

1.40

21.13

.09*

–

14

-.74

5.52

-.14**

-.11**

–

13

11.0

-.09

.14

10.35

63.59

–

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problem gambling severity score; (2) in individual trajectories of change in problem gambling severity scores; and (3) in variation across individuals, as well as the hypothesized effects of the specified time 1 covariates. This analysis will allow us to assess stability in gambling behavior even at the sub-clinical or non-problem levels. A continuous variable approach assumes that the gambling risk levels actually represent different points on an ‘‘underlying continuum of liability.’’ Accordingly, as Slutske (2007: 138) points out, there are a number of compelling reasons to ‘‘…focus on continuous measures of gambling pathology and on subclinical problem gambling,’’ most notably, ‘‘…a gain in statistical power and provision of a more sensitive metric of intraindividual change.’’ Missing data is a concern in all empirical research but particularly so in longitudinal analyses. The greatest concern with missing data is that the pattern, or mechanism, of missingness is systematic and thus exerts a systematic or biasing effect on estimates. Rubin’s (1976) taxonomy of mechanisms of missing data distinguishes three basic forms of missingness: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In brief, missing data is MCAR if missingness on variable X is unrelated to the values of other observed variables as well as the values of X itself, missing data is MAR if missingness of variable X is not related to the values of X itself, and data is MNAR when missingness on variable X is dependent on the values of X itself. When missing data is MCAR or MAR it is ‘‘ignorable,’’ and the most straightforward method of estimating parameters is full information maximum likelihood (FIML). The FIML approach fits a likelihood to each individual case in the sample using all available data, and then these likelihoods are weighted and summed across all cases to produce final parameter estimates and standard errors. In comparison to other approaches to dealing with missing data, FIML has demonstrated lower incidence of convergence failure, higher efficiency, lower bias, and more accurate rates of model rejection (Enders and Bandalos 2001). The Mplus 7 software package used in the current study offers FIML estimation. In longitudinal research, panel attrition—the tendency for a proportion of participants to drop out of the study at each successive measurement occasion—represents a prime source of missing data. Previous research has shown that participants with more severe symptoms of a disorder are more likely to drop out of longitudinal research (Desmond et al. 1998; Mirowsky and Reynolds 2000). Such a pattern represents a MNAR mechanism of missingness and introduces bias into estimates. For example, cases with missing responses on a variable may be systematically different from cases with valid responses for that variable, and thus estimates that rely only on valid responses will be systematically different from what they would have been had all cases had valid responses. Such systematic attrition is a particular concern for longitudinal studies of problem gambling. If problem gamblers are dropping out of the study at a faster rate than non-problem gamblers then conclusions about the course of problem gambling are based on individuals who do not have, and may never develop, pathological gambling behaviors (Wohl and Sztainert 2011). Such systematic attrition can bias estimates of relationships between variables of interest and estimates of prevalence rates in the population. Given the nature of the problem we cannot completely eliminate the potential biasing effects of attrition on estimates, but we can attempt to mitigate.

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We cannot test the MAR assumption and so cannot completely rule out MNAR, but there are ways to strengthen the plausibility of the MAR assumption.7 One way to help address this concern is to add missing data correlates (or auxiliary variables) to the multiple imputation model, such correlates may help better account for missingness and thus render the MAR assumption more plausible. Similarly, the plausibility of the MAR assumption can also be improved with maximum likelihood estimation by adding missing data correlates (Collins, et al. 2001; Graham 2003; Enders 2010). The missing data correlates are not part of the growth model, but are allowed to correlate with the outcome and due to ‘‘such potential correlation these variables can be used to reduce the uncertainty caused by the missing data and thereby improve the precision of the estimation. In addition if these variables are related to the missing data mechanism including these variables in the analysis could reduce or eliminate parameter estimates biases that are due to the missing data when the missing data is not missing at random (MNAR).’’ (Asparouhov and Muthe´n 2008: 2). In the present study the LGCM was estimated using Mplus’ robust maximum likelihood estimator8 with three correlates of missingness incorporated as auxiliary variables. The rates of missingness for the variables in the model are presented in Table 1. Previous research has shown that regular use of electronic gaming machines or casino table games are more common among moderate risk and problem gamblers than low-risk or non-problem gamblers (e.g. Currie et al. 2013). Thus regular participation in these gambling forms is indicative of higher risk for problem gambling. Accordingly, a binary variable was created to distinguish those respondents who spent ‘‘money on slot machines, VLTs, or casino table games once a month or more’’ (coded 1) from those who did not (coded 0). Another measure of gambling intensity that has been demonstrated to be a correlated with problem gambling severity is level of monetary expenditures on gambling. Following Williams and Volberg (2009, 2010) we set $300 per year as the threshold for annual losses indicative of probable moderate/high risk gambling (more likely to spend over $300). To do so we created a binary variable from responses to the item ‘‘what is the largest amount you lost on gambling in a year’ (0 = $299 or less; 1 = $300 or more). Finally, those with moderate and high severity gambling risk are also more likely to have greater levels of substance use (e.g. el-Guebaly et al. 2006). Responses to the question ‘‘how often used drugs/alcohol while gambling’’ were used as a continuous measure of this attribute. Latent growth curve modeling combines features of repeated measures MANOVA, confirmatory factor analysis, and structural equation modeling to assess changes in a construct over time. LGCMs analyze both the between-wave covariance matrix and the observed means of measures over time. It is possible to analyze both variation in individual growth trajectories over time and to identify factors that account for this variation. LGCMs treat the repeated measures as multiple indicators of an underlying latent growth factor. The first step in a LGCM analysis is to specify a basic unconditional LGCM (Fig. 1) to test 7

Techniques that assume MNAR, such as pattern-mixture models and selection models, are also becoming more common, but these models rest on a set of unverifiable assumptions (e.g. distributional normality, specifiable starting values for inestimable parameters) the violation of which can actually result in worse, not better, estimates (Enders 2010). Given the stringent conditions underlying MNAR models, ‘‘…a wellexecuted MAR analysis may be preferable to an MNAR analysis, even if there is reason to believe the missingness is systematically related to the outcome variable,’’ in other words, ‘‘a good MAR model may be better than a bad MNAR model’’ (Enders 2010: 344). A conclusion echoed by Schafer (2003) who recommends the use of auxiliary variables to account for missingness under MAR assumptions.

8

This robust maximum likelihood estimator (MLR), not only takes missingness into account, but produces estimates that are relatively robust to violations of normality (Muthe´n and Muthe´n 2010).

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for the presence of change in the construct of interest over time. The basic LGCM is made up of two latent factors with repeated measures of the construct over time as indicators. The first latent factor describes the intercept of the growth curve with all the factor loadings across t times set to 1.0 (represented by vector k0t). This represents the starting point for the growth curve at Time 1. The second latent factor describes the slope of the growth curve and represents the trajectory of the curve over time. With 4 waves as in the MLSYA, the linear coding for time on the slope factor used in the present study was is 0, 1, 2, 3 (see Fig. 1). The means of the intercept and slope factors represent the overall intercept and slope for a all subjects in the sample, or the average level of the construct at time 1 and the average rate of change in that construct across the repeated measures. The variances of the latent factors reflect the variation of individuals around the group average growth parameters, that is, inter-individual differences in problem gambling severity at initial measurement, and inter-individual differences in rate of change in problem gambling severity across subsequent measurements. In addition to the unconditional model, we also tested a second conditional model with the hypothesized time 1 covariates (i.e. timeinvariant predictors) added to assess their effects on mean level of problem gambling at time 1 and on the trajectory of change in problem gambling over time.

Results Model 1, the unconditional LGCM, was fit without any predictors in order to assess the mean level of PGSI scores at time 1, the mean rate of change in scores across time, the interaction between initial score and rate of change, and inter-individual differences in initial scores and rate of change. According to conventional criteria,9 the model fit indices indicate adequate fit of the model to the data (v2 = 16.597, df = 5, p = .005; RMSEA = .06; CFI = .99; TLI = .98; SRMR = .04). Estimates (see Table 2) indicate that the mean initial latent level of problem gambling severity is significantly different from zero, Mintercept = 1.248 (.027), z = 45.663, p \ .001. There is also significant inter-individual variability in the initial latent problem gambling severity scores, Varintercept = .351 (.038), z = 9.241, p = \ .001). In terms of the rate of change of problem gambling risk/severity, our results indicate that, on average, there is significant linear decline in latent problem gambling severity scores over time Mslope = -.048 (.009), z = -5.310, p \ .001), and significant inter-individual variability in the rate of decline over time Varslope = .017 (.005), z = 3.689, p = \ .001). Also a significant correlation between the intercept and slope factors (ris = -.540 (.078), z = -6.960, p = \ .001) indicates that that rate of decline is somewhat dependent on initial level of problem gambling risk/severity. That is, those with higher initial levels of problem gambling risk/severity at time 1 experienced slightly steeper declines over time. In model 2, the conditional LGCM, the hypothesized time 1 predictors—gender (male), age of onset of gambling, having experienced a big win when first began gambling, having experienced a big loss when first began gambling, alcohol dependence, drug dependence, depression, anxiety, perceived social support, illusion of control, and impulsiveness—were added to the model to assess if they affect the initial mean level of gambling and the mean rate of change in gambling over time. Model fit indices also indicate adequate fit of the model to the data (v2 = 52.085, df = 27, p = .003; CFI = .96; TLI = .93; 9

Conventional criteria for establishing good model fit are: RMSEA B .06, CFI C .95, TLI C .95, SRMR B .05 (Byrne 2012).

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Fig. 1 The unconditional growth model for problem gambling severity/risk

Table 2 Means and variances for latent growth curve model of problem gambling severity Intercept

Slope

Mi

z

Vari

z

Ms

z

Vars

z

1.248***

45.663

.351***

9.241

-.048***

-5.310

.017***

3.689

*** p B .001

RMSEA = .04; SRMR = .02). Combined, time 1 variables accounted for 26 % of the variance in the intercept and 13 % of the variance in the slope. Model 2 (see Table 3) indicates that as expected males, on average, have significantly higher initial levels of problem gambling risk/severity than females, but there is no gender difference in the rate of decline. Level of alcohol dependence at time 1 was positively associated with problem gambling, but did not affect the trajectory of change in gambling severity over time. Probability of major depression caseness at time 1 was positively associated with gambling severity, that is, increased probability of depression is associated with increased initial problem gambling severity scores, but had no effect on the rate of change in problem gambling severity. Illusion of control at time 1 had a positive effect on initial level of problem gambling risk/severity, that is, increased illusion of control is associated with increased level of problem gambling risk/severity at time 1, but had no effect on the rate of change over time. Impulsiveness also had a positive effect on initial level of gambling risk/severity—higher impulsiveness predicts higher level of problem gambling risk/severity at time 1. Interestingly, impulsiveness also affected the rate of decline in level of problem gambling risk/severity over time but not in the expected direction; higher initial levels of impulsiveness predict slightly faster rates of decline in level of problem gambling risk/severity over time.

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J Gambl Stud Table 3 Predictor effects for latent growth curve model of problem gambling severity Time 1 predictors

Intercept Effect (SE)

Slope a

b

Effect (SE)

Gender (male)

.182 (.050)***

.188***

Age of onset

.003 (.009)

.014

.001 (.003)

.006

Early big win

-.041 (.051)

-.040

-.021 (.020)

-.077

Early big loss

-.066 (.054)

-.056

Alcohol dependence

.053 (.016)***

.198***

-.010 (.020)

ba -.039

.029 (.022)

.089

-.001 (.006)

-.010

Drug dependence

-.123 (.069)

-.100

.052 (.030)

.157

Anxiety

-.087 (.108)

-.033

.021 (.054)

.03

Depression Perceived social support

.035 (.016)* -.044 (.024)

.134* -.094

-.009 (.006)

-.124

.016 (.009)

.121

Illusion of control

.011 (.003)***

.178***

-.001 (.001)

-.068

Impulsiveness

.011 (.003)***

.230***

-.003 (.001)***

-.237***

* p B .05; *** p B .001 a

Standardized coefficient

None of the other predictors—age of onset of gambling, having experienced a big loss or a big win when first began gambling, drug dependence, anxiety, perceived social support—had any significant effect on initial mean level of gambling or trajectory of change. Finally, a significant correlation between the intercept and slope factors (ris = -.384 (.119), z = -3.220, p = .001) remains—higher initial levels of problem gambling risk/ severity at time 1 are associated with slightly steeper declines over time. So the results indicate that there is significant inter-individual variation in the initial mean level of problem gambling severity, and this mean level varies across repeated measures, with the average slope being negative. Although the mean slope describes a general pattern of diminution in problem gambling severity over the time period sampled, there is also significant inter-individual variation in the rate of change over time. There was also a significant negative correlation between initial level of problem gambling severity and rate of change over time, that is, higher initial levels of gambling severity predict a slight increase in the rate of decline in problem gambling severity over time. Turning to the hypothesized effects of modeled predictors, the results indicate the following: (a) Gender—males did indeed have higher mean initial levels of problem gambling severity than females, but there was no significant gender difference in the rate of decline in problem gambling over time; (b) age of onset had no significant effect on initial mean level of problem gambling nor on its slope; (c) neither having experienced a big win or a big loss had a significant effect on initial mean level of problem gambling or its slope; (d) Comorbidity—neither anxiety nor drug dependence had any significant effects, while alcohol dependence and depression both had positive effects on the initial level of problem gambling (greater alcohol dependence and depression, greater initial problem gambling severity) but no effect on its trajectory; (e) level of perceived social support had no significant effects on initial level of problem gambling nor its trajectory; (f) illusion of control had a significant positive effect on initial level of problem gambling (the greater the illusion of control, the higher the initial level of problem gambling risk/severity), but not on its trajectory; while (g) impulsiveness had a significant positive effect on initial level of problem gambling severity (greater impulsiveness, greater problem gambling), and a

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significant effect on the rate of decline, but not in the expected direction, instead higher impulsivity predicted a slight acceleration in the rate of decline in problem gambling severity over time.

Discussion The LGCM results indicate a general pattern of lessening, not worsening, problem gambling severity across the time period covered. Additionally, there was a negative correlation between initial level of problem gambling severity and rate of lessening; higher initial levels of problem gambling were associated with a slightly more rapid decline in problem gambling severity over time. These findings present a challenge to the conventional picture of problem gambling as an inevitable ‘‘downward spiral’’, at least among young adults, and are more consistent with findings that suggest problem gambling among young adults may ‘‘be more transitory and episodic than enduring and chronic’’ and, for many, will ‘‘resolve naturally’’ at less severe subclinical levels of severity (Slutske et al. 2003: 271). This general pattern of lessening also makes sense in light of Stinchfield’s (2000, 2011) observations that interest in gambling may be in general decline in recent years among youth cohorts. His view is that youth gambling is only one aspect of a larger syndrome of deviance and maladjustment that, for many, characterizes the transition from youth to adulthood, replaced in time by other ‘‘risky’’ activities and/or fading in salience for most in the course of natural maturational processes. We suggest that the ‘‘downward spiral’’ may remain an appropriate metaphor to understand the experiences of the most severe problem gamblers. However, harm experienced by the typical gambler is moderate, transitory, in many cases short-term, and is either resolved or endured without dramatic intervention. Another plausible interpretation of the data is that it demonstrates exposure and adaptation effects (Shaffer and Martin 2011) of gambling on this population of young adults. To wit, when a population is first exposed to legal gambling, as was the case in this sample, a higher level of participation and concomitant gambling problems are expected. Indeed, this pattern is observed in wave 1 of the MLSYA data when a large portion of the sample reached adulthood.10 By the final wave, there is evidence of adaptation as gambling spending and participation declined substantially, as did incidence rates of problem gambling. Our results may suggest that over time, many individuals in the sample managed to ‘‘adapt to the risks and hazards of exposure to new gambling opportunities’’ (Shaffer and Martin 2011: 493) as has been revealed in other longitudinal studies. Unfortunately, with only 4 waves of data we cannot track the longer term trajectories of this cohort as they proceed further into adulthood—we cannot, for example, assess the degree to which suggested adaptational and maturational effects persist, or the incidence of early adulthood problem gambling reappearing later on. The results also revealed significant inter-individual variation in initial level of problem gambling severity and in rate of change over time. Future work with MLSYA could examine the possibility of multiple distinct patterns or trajectories of change in gambling severity over time, similar to the studies by Winters et al. (2002), Vitaro et al. (2004), and Blaszczynski and Nower’s (2002) pathways model. As expected, gender was a significant predictor of initial level of problem gambling severity with males having significantly higher average problem gambling severity at time 10

Wave 1 is the only wave to include 18 year olds; the legal age to gamble in Manitoba.

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1. This is consistent with a considerable body of previous research indicating higher likelihood of problem gambling among males. Contrary to expectations, there was no evidence of significant gender differences in the rate of change in gambling severity over time. Even so, it may still be informative in future to test a multiple group LGCM in which the same model is run on male and female subsamples to assess whether any of the other predictor effects on problem gambling differ significantly by gender. Contrary to expectation, the results indicate that age of onset has no significant effect on initial level of problem gambling or rate of change. This contradicts those studies that link early age onset gambling with increased likelihood of gambling problems later in adulthood (e.g. Stinchfield 2000; Burge et al. 2006), but is in line with Slutske et al.’s (2003) findings that suggest this connection is less certain, with many youths’ problem gambling behavior remaining relatively mild and not persisting into adulthood. Although a connection between early big wins and losses on future gambling behavior has been theorized (e.g. Avery 2009; Lesieur and Rosenthal 1991; Rachlin 1990), and there are a few studies suggesting that a significant proportion of problem gamblers report having experienced a big win or big loss early in their gambling careers (e.g. Turner et al. 2006; Wiebe et al. 2001), our results provide no support for such a link, as neither having experienced a big win or a big loss early in one’s gambling career had a significant effect on initial mean level of problem gambling or its rate of change. It is important to acknowledge the limitations of the measures of big win, big loss, and age of onset of gambling utilized in the current study. The item used to measure ‘age of onset of gambling’ does not adequately separate formal gambling (e.g. in a casino) from informal (e.g. incidental and occasional peer-related challenges). Additionally, both the ‘big win’ and ‘big loss’ items generate purely subjective responses and so are difficult to interpret. What amount of money constitutes a big win or big loss will vary across individuals and thus the effects on excitement and motivation will differ, for example, between a youth from a modest background with little income and one from an affluent family with high income. While there is substantial evidence in the literature highlighting the co-occurrence of problem gambling with other mental health disorders such as anxiety, depression, and drug and alcohol dependence (e.g. Barnes et al. 2005; Crockford and el-Guebaly 1998; Hayatbakhsh et al. 2012; MacCallum and Blaszczynski 2002; Petry et al. 2005), our results were mixed on this account. Neither anxiety nor drug dependence had any significant effects, while alcohol dependence and depression both had a positive effect on the initial level of problem gambling (the greater alcohol dependence and the more probable depression, the greater initial problem gambling severity), but no effect on its trajectory. In the present study, we used time 1 measures of anxiety, depression, and alcohol and drug dependence as time-invariant predictors of level of problem gambling severity and rate of change in severity across repeated measures. However, this does not take into account how change over time in these co-occurring conditions might affect problem gambling, nor how changes in problem gambling may affect these comorbid disorders. Possible future approaches to addressing these questions could include incorporating co-occurring disorders as time-varying predictors and/or some form of parallel process modelling (Curran and Hussong 2003; Preacher et al. 2008). Although there is some suggestion in the problem gambling literature that social support has a protective effect (e.g. Hardoon et al. 2004) and may also help foster recovery (Hodgins and el-Guebaly 2000; Petry and Weiss 2009), our analysis found no significant effects for perceived level of social support on either the initial level of problem gambling or its trajectory. In contrast, we did find partial evidence for the illusion of control effect on

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problem gambling—on one hand, the greater the illusion of control, the higher the initial level of problem gambling severity, on the other hand, illusion of control showed no effect on the rate of change. For policymakers, our findings suggest that prevention efforts would be most efficiently directed at young adults who have recently come of age to gamble legally. The subset of young male gamblers who also frequently consume alcohol are of special concern. The transition to adulthood represents the first exposure to formal opportunities to gamble in a legal, regulated setting (see also Goudriaan et al. 2009). As indicators of problem gambling appear to taper off naturally in this sample (i.e. without the need for treatment), the time period closest to initial exposure is likely the best opportunity to manage harmful patterns of cognition and behavior. Alternatively, if, as our findings and others (e.g. Slutske et al. 2003) seem to indicate, a substantial proportion of adolescent gamblers do not require formal treatment interventions but will recover naturally—this suggests that prevention campaigns may in fact be more cost-effective than establishing services for adolescents resistant to seeking counseling. The significance of this implication is further underscored by estimates indicating that nearly 90 % of problem gamblers do not seek treatment (Hodgins 2014). Finally, impulsiveness had a significant positive effect on initial level of problem gambling severity (greater impulsiveness, greater problem gambling), but had an unexpected negative association with problem gambling over time. That is to say, there was some decline in problem gambling longitudinally despite higher levels of impulsivity. This is a puzzling finding considering that studies have consistently shown a positive correlation between impulsivity and more severe problem gambling among samples of adults and youth (Nower et al. 2004). A plausible interpretation of our result rests on a limitation of the data, namely, that impulsivity is measured at only two time points and so was modeled as a time-invariant predictor. Our measure of impulsivity was taken early in the study when a substantial proportion of the sample was 18 or 19 years old. Since impulsivity and impulse regulation may be particular vulnerabilities in late adolescence (Chambers and Potenza 2003), what the results might be showing is not an authentic negative relationship between impulsivity and problem gambling trajectory, but the effect that transitioning from adolescence has on problem gambling. Future research could explore impulsivity as a timevarying predictor which would give a more precise indication of the true relationship. This would require having repeated measures of impulsivity across multiple waves, a feature not available with the MLYSA data. This possibility is also in line with the earlier discussion of maturational effects and the benefits of having more waves of data that track gambling behaviors further into the adult years. This study examined a rich data set using sophisticated analyses to test a range of hypotheses about youth gambling. Despite the interesting and important results, an important limitation of this investigation deserves noting. The MLSYA data used in this study come from a single cohort design, and so are susceptible to confounding amongst age, period, and cohort effects. A cohort-sequential design would have provided greater ability to disentangle such confounding effects. Although we have employed state-of-theart methods of dealing with missing data (maximum likelihood estimation with auxiliary variables), the issue of selective attrition (non-random missingness) causing biased estimates cannot be ruled out definitively, and so must be kept in mind when considering the results. In sum, LGCM results indicate a general pattern of lessening in problem gambling severity across the time period covered, even so, there was significant inter-individual variation in initial level of problem gambling severity and in rate of change over this time

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period. Additionally, there was a negative correlation between initial level of problem gambling severity and rate of lessening, that is, higher initial levels of problem gambling were associated with a slightly more rapid decline in problem gambling severity over time. When predictors are added to the model—gender, alcohol dependence, depression, level of perceived social support, illusion of control, and impulsiveness affect initial level problem gambling severity—males had, on average, greater problem gambling severity than females, greater social support predicts lessened problem gambling severity, while greater depression, alcohol dependence, illusion of control, and impulsiveness all predict increased problem gambling severity. The only predictor to significantly affect the rate of change in gambling severity over time was impulsivity, with greater impulsivity predicting slightly faster decline in gambling severity over time. Of particular note, our main finding—overall lessening in problem gambling severity across time—further challenges the assumed progressive nature of gambling disorders (the inevitable ‘downward spiral’) and suggests that a substantial proportion of youth gamblers will recover naturally. The implication for policymakers is that targeted prevention campaigns may actually be a more cost-effective approach—compared to establishing new/more treatment services—for reaching at-risk and/or problem gambler youth who are reluctant to seek help. Acknowledgments Manitoba Gaming Control Commission, Manitoba Lotteries and the Addictions Foundation of Manitoba graciously permitted the researchers access to the Manitoba Longitudinal Study of Young Adults (MLSYA) dataset. This research was funded by the Manitoba Gambling Research Program of Manitoba Liquor & Lotteries; however, the findings and conclusions of this paper are those solely of the author(s) and do not necessarily represent the views of Manitoba Liquor & Lotteries.

Appendix 1 PGSI The score is derived from Likert scale items where the respondent is asked whether a series of questions applied ‘never’, ‘sometimes’, ‘most of the time’, or ‘almost always’ over the past 12 months. A previous study by Wynne (2003) produced a higher alpha coefficient (a = .84) for the CPGI than typically seen in other widely used problem gambling prevalence instruments, namely the South Oaks Gambling Screen and the DSM-IV. A principal component analysis revealed that all nine items load strongly on a single factor ([ .46) accounting for 40 % of the total variance explained. The original risk level categories proposed by the PGSI’s authors (Ferris and Wynne 2001) were: ‘non-problem gamblers’ (PGSI = 0), ‘low-risk’ (PGSI = 1–2),‘moderate-risk’ (PGSI = 3–7), or ‘problem-gambler’ (PGSI [ 7). Currie et al. (2013) improved the validity of these categories by recalibrating the cut-offs to: ‘non-problem gamblers’ (PGSI = 0), ‘low-risk’ (PGSI = 1–4),‘moderate-risk’ (PGSI = 5–7), or ‘problem-gambler’ (PGSI 8?). The Currie et al. (2013) cut-offs were used in the current study, with addition of a non-gambler category resulting in a 5 category outcome variable with values of: 0 = ‘non-gambler’, 1 = ‘nonproblem gamblers’ (PGSI = 0), 2 = ‘low-risk’ (PGSI = 1–2), 3 = ‘moderate-risk’ (PGSI = 3–7), and 4 = ‘problem-gambler’ (PGSI [ 7). Alcohol Dependence Respondents in all four waves of the MLSYA were first asked the stem question ‘‘How often in the past 12 months have you had five or more drinks on one occasion?’’ Those who answered at least ‘once a month’ were then asked a series of nine questions designed to

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assess alcohol dependence during the past 12 months. An example question includes: ‘In the past 12 months, did you ever find that you had to drink more alcohol than usual to get the same effect or that the same amount of alcohol had less effect on you than usual?’ Depression Probability of caseness for major depression is calculated based on the sum of positive responses to each of seven symptom questions (e.g. feeling tired, trouble concentrating, loss of interest, etc.). Social Support In addition to providing an overall measure of perceived social support, the MSPSS contains three psychometrically validated subscales measuring support from: family (e.g. ‘‘I get the emotional help and support I need from my family’’), friends (e.g. ‘‘I can count on my friends when things go wrong’’), and significant others (e.g. ‘‘There is a special person who is around when I am in need’’). All participants in all four waves of the Manitoba Longitudinal Study of Young Adults were asked to complete this section of the survey. All 12 questions are scored on a 7-point Likert scale ranging from 1 (‘‘Very Strongly Disagree’’) to 7 (‘‘Very Strongly Agree’’). Overall scores for each scale were derived by summing the total of the individual questions and calculating the mean average (see Zimet 1998). In the interests of data preservation, participants who gave no answer to one or two items had their missing responses replaced by the mean of remaining valid responses. Participants who failed to answer three or more questions were excluded from analysis. Impulsiveness Through a principal component analysis, Patton and his colleagues found six distinct firstorder factors and three second-order factors. However, their analysis showed many of the instrument items overlap in terms of their principal component loadings, making interpretation of the factors somewhat unclear. This ambiguity notwithstanding, the alpha coefficient for the overall BIS-11 scale was calculated at between .79 and .82 in four separate samples. In the current study, the BIS-11 was asked of respondents in waves 2 and 4, and was calculated as a single, 30-item scale. All questions are formulated as 4-point Likert scales with valid responses of: (1) Rarely/Never, (2) Occasionally, (3) Often, and (4) Almost Always/Always. Negatively worded questions were reverse-coded so that all items were conceptually aligned. A straight summation of scores gives a potential range from 30 to 120. The BIS-11 measures the more personality trait-like aspects of impulsiveness that occur ‘‘over extended periods of time’’ rather than the more ‘‘state-dependent aspects of personality’’ assessed by behavioral measures (Stanford et al. 2009). As the BIS-11 was not administered in wave 1 of the MLSYA, and given the relatively stable trait-like nature of impulsiveness as measured by the BIS-11, BIS-11 scores from wave 2 were substituted as measures of initial levels of impulsiveness. Illusion of Control Erroneous and illogical thought patterns have been identified by the authors as a plausible explanation for excessive gambling behavior where the gambler is at a mathematical

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