Journal of Adolescence 36 (2013) 129–137

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Journal of Adolescence

journal homepage: www.elsevier .com/locate/ jado

A model to explain at-risk/problem gambling among male and femaleadolescents: Gender similarities and differences

Maria Anna Donati*, Francesca Chiesi, Caterina PrimiDepartment of Psychology, University of Florence, Via di San Salvi 12, Padiglione 26, 50135 Flirenze, Italy

Keywords:GamblingAt risk factorsAdolescentsMultiple logistic regressionGender differences

* Corresponding author. Tel.: þ39 (0) 55 6237846E-mail address: marianna_donati@yahoo.it (M.A

0140-1971/$ – see front matter � 2012 The Foundahttp://dx.doi.org/10.1016/j.adolescence.2012.10.001

a b s t r a c t

This study aimed at testing amodel inwhich cognitive, dispositional, and social factors wereintegrated into a single perspective as predictors of gambling behavior. We also aimed atproviding further evidence of gender differences related to adolescent gambling. Partici-pants were 994 Italian adolescents (64% Males; Mean age ¼ 16.57). Hierarchical logisticregressions attested the predictive power of the considered factors on at-risk/problemgambling - measured by administering the South Oaks Gambling Screen-Revised forAdolescents (SOGS-RA) - in both boys and girls. Sensation seeking and superstitiousthinking were consistent predictors across gender, while probabilistic reasoning ability, theperception of the economic profitability of gambling, and peer gambling behavior werefound to be predictors only among male adolescents, whereas parental gambling behaviorhad a predictive power in female adolescents. Findings are discussed referring to practicalimplications for preventive efforts toward adolescents’ gambling problems.� 2012 The Foundation for Professionals in Services for Adolescents. Published by Elsevier

Ltd. All rights reserved.

Large-scale international prevalence surveys have revealed high gambling participation among adolescents. A recentmeta-analytic study has suggested that 77–83% of adolescents were involved in some form of gambling (Blinn-Pike, Worthy,& Jonkman, 2010) and several cross-sectional studies have indicated that between 60 and 99% of young people aged 12–20years gambled over the past year (Splevins, Mireskandari, Clayton, & Blaszczynski, 2010). Moreover, adolescents representa particularly high-risk group in terms of developing gambling problems due to their high levels of risk-taking, their self-perceived invulnerability, and their presumed lack of recognition that gambling may lead to serious problems(Derevensky, Gupta, & Winters, 2003). Among the factors associated with adolescent problem gambling, cognitive factors(Derevensky, Gupta, & Baboushkin, 2007), dispositional factors (Vitaro & Wanner, 2011), and social factors (Chalmers &Willoughby, 2006) have been identified.

With regard to cognitive factors, much attention has been paid to adolescent problem gamblers’ knowledge of factualprobabilities and their susceptibility to biases related to gambling outcomes. Some cross-sectional studies revealed thatadolescent problem gamblers did not differ from non-problem gamblers in terms of their knowledge of objective odds orprobabilities, and did not necessarily have poorer knowledge of mathematical principles or gambling odds than non-problemgamblers. Nonetheless, they were more prone to mistaken views about randomness when compared with non-problemgamblers, and they held erroneous beliefs about their chance of winning (Delfabbro, Lahn, & Grabosky, 2006; Delfabbro,Lambos, King, & Puglies, 2009; Turner, Zangeneh, & Littman-Sharp, 2006). According to Turner, MacDonald, Bartoshuk,

; fax: þ39 (0) 55 6236047.. Donati).

tion for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

M.A. Donati et al. / Journal of Adolescence 36 (2013) 129–137130

and Zangeneh (2008), many of these erroneous beliefs center on the topic of independence of random events, such as thegambler’s fallacy (Tversky, 1974), a well-known bias in probabilistic reasoning stemming from the belief that the likelihood ofan event is related to preceding, independent outcomes. For instance, in roulette, after a long sequence of Red, Black is judgedto be more likely than Red on the next spin.

Regarding dispositional factors, some studies have revealed a significant association between adolescent problemgambling and sensation seeking, showing that problem gamblers were higher sensation seekers than non-gamblers (e.g.,Gupta & Derevensky, 1998; Nower, Derevensky, & Gupta, 2004). Studies have also demonstrated that adolescent problemgamblers had superstitious beliefs about winning, i.e. they believed that they could control random events (Moore & Ohtsuka,1999), and that they believed in good luck (Chiu & Storm, 2010). Finally, in their cross-sectional study, Moore and Ohtsuka(1999) reported that adolescents who believed that gambling was a good way to obtain money were more likely togamble and experience gambling problems than adolescents who did not report these beliefs. In line with this, in their schoolsurvey, Delfabbro and Thrupp (2003) found that weekly gamblers rated gambling as significantly more profitable thaninfrequent and non-gamblers did, and problem gamblers tended to overestimate the potential economic benefits of gamblingactivities (Delfabbro et al., 2006; Delfabbro et al., 2009).

Concerning social factors, Vachon, Vitaro, Wanner, and Tremblay (2004), exploring the links between family risk factorsand adolescent gambling in a cross-sectional study, found that youth gambling frequency was related both to their parents’gambling frequency and the severity of the parents’ gambling problems. Winters, Stinchfield, Botzet, and Anderson (2002)conducted a prospective study examining how predictor variables measured during early adolescence were associatedwith gambling behavior in early adulthood. They found that participants who reported to have a parent with gamblingproblems during early adolescence were seven times more likely to develop gambling-related problems in early adulthood.A subsequent cross-sectional study by Wickwire, Whelan, Meyers, and Murray (2007) found that parental gambling wasassociated with adolescents being more likely to report at-risk or problem gambling. Several studies have also indicated thatadolescent problem gamblers tended to have friends who gambled (Delfabbro & Thrupp, 2003; Langhinrichsen-Rohling,Rohde, Seeley, & Rohling, 2004). Moreover, these friends also reported gambling problems (Hardoon, Gupta, & Derevensky,2004).

Finally, gambling is more common among male than female adolescents (e.g., Jackson, Dowling, Thomas, Bond, & Patton,2008; Spritzer et al., 2011), and boys are more at risk than girls to develop gambling problems (e.g., Hardoon et al., 2004;Jacobs, 2004). For these reasons, gender has been identified as a risk factor for adolescent problem gambling (e.g., Ellenbogen,Derevensky, & Gupta, 2007; Hardoon et al., 2004).

In Italy, gambling is an extensive phenomenon: Per capita expenditure on legalized gambling activities per annum is ninehundred and six euros, that is, three times higher than that reported in the United States, and gamblers are estimated to be38% of the entire national population. The phenomenon has grown since 2008, when Italians spent 48 billion euros ongambling, while current estimates suggest that the amount of national expenditure at the end of 2011 was about 80 billioneuros. Furthermore, in Italy gambling can be regularly practiced in casinos, managed by public holdings, and on the Internet.Although the Italian government does not allowminors (i.e., people younger than 18 years old) to gamble, the most commonforms of gambling activities, such as slot machines and bets, can be played in licensed venues which minors are entitled toenter. Thus, recently it has been found that 55% ofmaleminors and 35% of femaleminors gambled at least once (Bastiani et al.,2010), and that boys gambled more than girls among high school students (Vilella et al., 2010).

The purpose of the present study was threefold. First, we aimed to describe the phenomenon of gambling among a largesample of Italian adolescents. Second, we wanted to test a model in which cognitive, dispositional, and social factors wereintegrated into a single perspective, weighing the contribution of each factor. Third, we aimed to provide further insight intothe study of gender differences in relation to adolescent gambling behavior. We adopted an integrative perspective in order toattempt to overcome the limitation of previous studies which have investigated these factors separately. Furthermore, thisapproach is in line with the conceptualization of gambling behavior as a multidimensional phenomenon (Gupta &Derevensky, 2000), and with Ladouceur (2001) who stated that to understand adolescents’ gambling behavior, theirpersonally relevant perceptions, dispositions, and beliefs have to be taken into account.

In more detail, we examined the predictive power of a set of variables on gambling (including gender along with cognitive,dispositional, and social factors), weighing the specific contribution of each predictor. Specifically, we aimed to identify thefactors that predicted medium (at-risk) -to-high (problem) levels of gambling involvement. Our predictions were thefollowing. First, male adolescents would bemore likely to report gambling-related problems than female adolescents. Second,we expected that despite no difference between non-problem and at-risk/problem gamblers with regard to their knowledgeof mathematical and probabilistic rules, susceptibility to the gambler’s fallacy, as well as sensation seeking, superstitiousthinking, and positive attitude toward gambling as an economic activity would be predictive of at-risk/problem gamblingbehavior. Finally, we hypothesized that parental and peer gambling would predict at-risk/problem gambling.

The model described above was tested separately for boys and girls. In fact, while there is a growing body of researchexamining the risk factors, and the consequences of adolescent problematic gambling (see Derevensky & Gupta, 2004b), thereis only limited research examining the profile of adolescent female problem gamblers, due to low prevalence rates(Derevensky & Gupta, 2004a, 2004b). Given that it is not clear yet whether predictors of gambling involvement are similar formale and female adolescents (e.g., Chalmers &Willoughby, 2006; Jackson et al., 2008), we addressed this issue with a samplewhich included both boys and girls, who were characterized by different levels of gambling involvement (i.e., from noinvolvement to problem gambling).

M.A. Donati et al. / Journal of Adolescence 36 (2013) 129–137 131

Method

Participants

Participants were 994 students (64% boys,Mage¼ 16.57, SD¼ 1.63) attending four public high schools in a suburban area inItaly (Tuscany). High school in Italy consists of five years of education. The sample included 26% (n ¼ 253) first year students(63% boys;Mage¼ 14.72, SD¼ .55), 21% (n¼ 203) second year students (61% boys;Mage¼ 15.77, SD¼ .64), 20% (n¼ 198) thirdyear students (72% boys; Mage ¼ 16.90; SD ¼ .75), 16% (n ¼ 159) fourth year students (59% boys; Mage ¼ 17.78, SD ¼ .75), and17% (n ¼ 167) fifth year students (65% boys; Mage ¼ 18.87, SD ¼ .69).

From the available schools in the area, six schools were randomly selected. Subsequently, the schools’ principals werecontacted, apprised of the issue of adolescent problem gambling to generate support for the research, and they were pre-sented with the project. Once the schools agreed to participate (two declined to participate because they were alreadyinvolved in other projects), the detailed study protocol was approved by the institutional review boards at each school.Written informed consent was requested from students (or their parents, if they were minors), assuring them that the datawould be handled confidentially. The research was conducted during school time and all students invited to participateagreed to do so. Thus, sample bias due to denials during recruitment was avoided.

Measures and procedure

Gambling behavior was measured through the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA; Winters,Stinchfield, & Fulkerson, 1993; Italian version: Bastiani et al., 2010), one of the most widely used measure of adolescentgambling (Langhinrichsen-Rohling, Rohling, Rohde, & Seeley, 2004). Participants were initially asked to indicate thefrequency of gambling (Never, Less ThanMonthly, Monthly, Weekly, and Daily) in a list of gambling activities including: Cardsfor money, coin tosses for money, bets on games of personal skill, bets on sports teams, bets on horse or dog races, bingo, dicegames for money, slot machines, scratch-cards, lotteries and on-line games. Then, they were presented with 12 items relatedto the Diagnostic and Statistical Manual of Mental Disorders (3rd, rev.) criteria for pathological gambling (American PsychiatricAssociation,1987), fromwhich the total scorewas derived. An example is: “In the past 12 months, how often have you gone backanother day to try to win back money that you lost?”. Following Magoon and Ingersoll (2006), questions regarding parentalgambling behavior (“Do either of your parents play any games of chance for money?”) and peer gambling behavior (‘‘Do any ofyour friends play any games of chance for money?”) were also included.

A classification of gambling problem severity into three categories (non-problem gambling, at-risk gambling and problemgambling) was made adopting the broad criterion, i.e. a combination of gambling frequency and the SOGS-RA score (Winterset al., 1993, modified by Poulin, 2000). Indeed, it has been found that gambling frequency is a predictor of the number ofproblem gambling symptoms (Chiu & Storm, 2010; Derevensky, Sklar, Gupta, & Messerlian, 2010) and, in line with Poulin(2002), we deemed it necessary to include gambling frequency in the analysis.

Past studies have shown that the SOGS-RA has adequate validity and good internal consistency (e.g., Poulin, 2002;Skoukaskas, Burba, & Freedman, 2009), and Item Response Theory-based evidence has attested that it accurately measuresmedium-to-high levels of gambling problems (Chiesi, Donati, Galli, & Primi, in press). In the present study, internal consis-tency of the SOGS-RA was satisfactory (a ¼ .73).

To measure probabilistic reasoning ability, the Gambler’s Fallacy Task (GFT, Primi & Chiesi, 2011) was used. It consists ofa marble bag game inwhich participants were asked which outcomewas more likely at the next draw after a sequence of fiveequal outcomes (five blue or five green marbles). In more detail, the task was composed of three different trials in which theproportion of Blue and Greenmarbles in the bag varied (first trial: 15B & 15G; second trial: 10B & 20G; third trial: 25B & 5G). Intotal, each participant answered six questions. Summing correct answers, we formed a probabilistic reasoning score rangingfrom 0 to 6, with higher scores corresponding to high ability to reason normatively, avoiding the gambler’s fallacy.

Before performing the task described above, participants were presented with four questions measuring knowledge ofbasic mathematical principles involved in probabilistic reasoning and the ability to compute simple probability, proportions,and percentages. An example of itemwas “Smokers are 35% of the population. 200 passengers board the train. Howmany of themwill be smokers?”. Students were given one point for each correct answer, thus, the total score ranged from 0 to 4, with highscores indicating a good basic mathematical ability.

Sensation seeking was measured through the Brief Sensation Seeking Scale (BSSS, Hoyle, Stephenson, Palmgreen, Lorch, &Donohew, 2002; Italian version: Primi, Narducci, Benedetti, Donati, & Chiesi, 2011). It contains eight Likert-type items usinga 5-point scale ranging from strongly disagree to strongly agree, yielding a maximum score of 40. Higher scores represent highlevels of sensation seeking. An example of an item is “I would love to have new and exciting experiences, even if they are illegal”.Past studies have shown that the BSSS has adequate reliability and validity (Hoyle et al., 2002; Primi et al., 2011). The scale hadgood internal coherence in the current sample (a ¼ .73).

To measure superstitious thinking, the Superstitious Thinking Scale (STS, Kokis, MacPherson, Toplak, West, & Stanovich,2002; Italian version: Chiesi, Donati, Papi, & Primi, 2010) was used. It is composed of eight Likert-type items using a 5-point scale ranging from totally false to totally true, yielding a maximum score of 40. Higher scores represent high levels ofsuperstitious thinking. An example of an item is ‘The number 13 is unlucky’’. The scale was found to have adequate validity andreliability (Chiesi et al., 2010). Coefficient alpha for the current sample was satisfactory (a ¼ .77).

M.A. Donati et al. / Journal of Adolescence 36 (2013) 129–137132

For the perception of the economic profitability of gambling, the Gambling Attitude Scale (GAS, Delfabbro & Thrupp, 2003;Italian version: Primi, Donati, Bellini, Busdraghi, & Chiesi, in press) was used. It contains nine Likert-type items, using a 5-point scale ranging from strongly agree to strongly disagree, yielding a maximum score of 45. An example of an item is“You can make a living from gambling”. Total scores on the scale were calculated so that high scores corresponded to anoptimistic perception of gambling (Delfabbro et al., 2006; Delfabbro et al., 2009). The scale has been found to have goodvalidity and reliability (Delfabbro & Thrupp, 2003; Primi et al., in press). Internal consistency for this scalewas adequate in oursample (a ¼ .80).

The above-described scales were administered in the classrooms and students were required to work individually. Theorder of presentation was the following: SOGS-RA, GFT, BSSS, STS, and GAS. Teachers were not present during the admin-istration of the scales. Administration of the instruments required approximately 60 min.

Results

Prior to conducting the analyses, we looked at missing values in the data. Starting from the assumption thatmissing valuesfor the SOGS-RA variable - the outcome variable in the regression model - could not be replaced by a missing data treatment,a listwise deletion was conducted excluding cases for which the SOGS-RA score was missing, i.e. those participants who didnot respond to one or more items. Only 3% (n¼ 32) of participants (68% boys;Mage¼ 16.42, SD¼ 1.68) were excluded. For theremaining cases (n ¼ 962), before computing total scores, mean imputation was used, replacing missing data with thearithmetic mean of each item. In order to avoid the excessive shrinking of variances by this procedure, missing data were notallowed to exceed 10% of the total cases in the sample (Kline, 1998). When missing data exceeded 10% we decided to excludethe case. Thus, another 19 cases were excluded (72% boys;Mage¼ 16.98, SD¼ 1.47). After all these preliminary treatments, thecomplete data set included 943 cases.

Gambling habits and activities

The results indicated that 91% of the respondents have gambled at least once in the past 12 months. Among them, 54%were infrequent gamblers (those who gambled monthly or less often) and 46% were frequent gamblers (those who gambledweekly or daily). Gender differences were found in gambling frequency (c2 (1, N ¼ 858) ¼ 53.23, p < .001, OR ¼ .34, 95% CI[.25–.46]). Among males, 46% were infrequent gamblers, and 55% were frequent gamblers, while 71% of female respondentswere infrequent gamblers, and 29% were frequent gamblers.

As shown in Table 1, the most common activities were scratch-tickets (75%), cards for money (74%), and lotteries (57%),while the least practiced were bets on horse or dog races (7%), online games (16%) and dice games for money (25%).

Among boys, the most common activities were cards for money (80%), instant scratch-cards (76%), and lotteries (61%),while bets on animals (9%), on-line games (20%), and dice games for money (22%) were practiced less. Among girls, the mostcommon activity was instant scratch-cards (73%), followed by cards for money (63%), and lotteries (51%), while bets onanimals (4%), on-line games (8%), and sports-bets (16%) were less common.

Regarding gender, a considerable percentage of boys (78%) and girls (81%) gambled at least once during the previous year.Male and female adolescents showed different levels of gambling involvement in several activities. Boys gambled more thangirls on cards formoney (Boys: 80%, Girls: 63%; p< .001, OR¼ .43, 95% CI [.32–.59]), bets on sports teams (Boys: 57%; Girls: 16%;p< .001, OR¼ .14, 95% CI¼ [.10–.20]), bets on horse and dog races (Boys: 9%, Girls: 4%; p¼ .004, OR¼ .40, 95% CI [.21–.75]), slotmachines (Boys: 41%, Girls: 20%; p< .001, OR¼ .36, 95% CI [.26–.49]), lotteries (Boys: 61%, Girls: 51%; p¼ .007, OR¼ .68, 95% CI[.52–.90]), and on-line games (Boys: 20%, Girls: 8%; p< .001, OR¼ .35, 95% CI [.22–.55]). Girls gambledmore than boys on cointosses (Boys: 29%; Girls: 39%; p¼ .002, OR ¼ 1.57, 95% CI [1.17–2.09]), bingo (Boys: 31%; Girls: 48%; p< .001, OR¼ 2.02, 95% CI[1.52–2.69], and dice games (Boys: 22%, Girls: 31%; p¼ .005, OR¼ 1.56, 95% CI [1.14–2.13]). No differences were found for betson games of personal skill (Boys: 37%, Girls: 31%; p ¼ .114) and instant scratch cards (Boys: 76%, Girls: 73%; p ¼ .272).

Table 1Adolescent gamblers engaging in each activity at each frequency.

Gambling activities Never Less than monthly Monthly Weekly Daily

n % N % N % N % n %

Cards for money 236 27 281 31 158 18 165 18 55 6Coin tosses for money 602 67 213 24 53 6 21 2 5 1Bets on games of personal skill 586 66 179 20 84 9 36 4 9 1Bets on sports teams 518 58 145 16 88 10 133 15 10 1Bets on horse or dog races 830 92 43 5 12 1 5 1 4 1Bingo 562 63 269 30 48 5 12 1 3 1Dice games for money 668 75 147 16 57 6 19 2 3 1Slot Machines 596 67 181 20 68 8 30 3 19 2Gratta & Vinci/win for Life 225 25 356 40 195 22 96 11 22 2Lotteries 381 43 264 29 150 17 84 9 15 2On-line games 749 84 43 5 26 3 46 5 30 3

M.A. Donati et al. / Journal of Adolescence 36 (2013) 129–137 133

On the basis of the broad criterion of the SOGS-RA’s problem gambling classification (Poulin, 2000), participants weredivided into non-problem (65%), at-risk (18%), and problem gamblers (17%). There was a significant difference in thepercentage distribution of the three categories of gamblers between boys and girls (c2(2, N ¼ 943) ¼ 59.04, p < .001). Moregirls than boys were non-problem gamblers (respectively 56% and 81%), while boys showed higher rates of at-risk (22%) andproblem gambling (22%) than girls did (12% and 8%, respectively).

Gambling modeling

To identify risk factors associated with at-risk and problem gambling, we performed a series of hierarchical logisticregressions using gender, normative probabilistic reasoning, sensation seeking, superstitious thinking, perception of theeconomic profitability of gambling, and parental and peer gambling behavior as independent variables. Gambling problemseverity was used as a dependent variable. In linewith Potenza et al. (2011) andWickwire et al. (2007), who have adopted thiscombination as dependent variable in logistic regressions, we compared non-problem gamblers (NPGs) to at-risk/problemgamblers (ARPGs).

As a preliminary step,weverified thatNPGs andARPGs showed the samebasicmathematical ability (MMaths¼3.09, SD¼ .91andMMaths ¼ 3.01; SD ¼ .93; t (924) ¼ 1.24,p ¼ .214, respectively), and we excluded this variable from the regression models.

In the first regression analysis (Table 2), the starting point was amodel inwhich only gender was included as a predictor ofgambling behavior (Model 1). This model was able to correctly classify 65.8% of respondents. Normative probabilisticreasoning was entered in the second step into the analysis (Model 2). Comparing the two models (Model 1 and Model 2), thesignificant Chi-Square difference justified the introduction of this variable into the analyses. Model 2 was able to correctlyclassify 66.8% of respondents. Then, the remaining variables in the study were individually added in subsequent steps, eachtime verifying the significance of Chi-Square difference between the tested models. Results showed that the percentage ofcorrectly classified respondents grew progressively from 65.8% of Model 1 to 71.3% of Model 7. Model 7 predictors weregender, normative probabilistic reasoning, sensation seeking, superstitious thinking, perception of gambling’s profitability,parental gambling, and peer gambling.

The specific weight of each predictor in Model 7 are reported in Table 3. Gender showed a significant negative relationshipwith the dependent variable: A female adolescent was .30 times less likely to report at-risk or problem gambling than a maleadolescent. Moreover, probabilistic reasoning displayed a significant negative relationship with gambling behavior, indicatingthat for each one-point increase in the probabilistic reasoning task, the respondent was .89 times less likely to be ARPG.Dispositional factors showed significant positive relationshipwith the dependent variable. For every one-point increase at thesensation seeking and superstitious thinking scales, the respondent was 1.06 times more likely to be classified as ARPG; forevery one-point increase at the perception of gambling profitability scale, the respondent was 1.04 more likely to be ARPG. Asfor social factors, if his/her parents gambled, the adolescent was 1.54 times more likely to be ARPG and if his/her friendsgamble, he/she was 1.64 more likely to be ARPG.

In order to investigate if the final model was able to explain at risk/problem gambling in each gender group and to identifygender-specific factors related to at-risk/problem gambling, logistic regressions were conducted separately for male andfemale adolescents. Results showed that the regression model was significant for boys (p < .001), with predictors thatclassified correctly 67.8% of male respondents. As shown in Table 4, only parental gambling behavior had no predictive poweron at-risk/problem gambling. Probabilistic reasoning displayed a significant negative relationship with gambling status,indicating that for each one-point increase in the probabilistic reasoning task, a male adolescent was .87 times less likely to beARPG. Dispositional factors displayed significant positive relationships with gambling behavior. For every one-point increaseat the sensation seeking and superstitious thinking scales, the respondent was 1.05 times more likely to be classified as ARPG,and for every one-point increase at the perception of gambling profitability scale, he was 1.04 times more likely to be ARPG.

As for female adolescents, results showed that the regression model was significant (p < .001) with predictors thatclassified correctly 81.4% of female respondents. As shown in Table 5, factors with a significant predictive power on at-risk/problem gambling were sensation seeking, superstitious thinking, and parental gambling behavior. Each of these factorsdisplayed significant positive relationships with gambling behavior. For every one-point increase at the sensation seekingscale, a female adolescent was 1.11 times more likely to be ARPG; for every one-point increase at the superstitious thinkingscale, she was 1.07 times more likely to be classified as ARPG. If her parents gambled, she was 2.94 times more likely to beARPG.

Table 2Hierarchical logistic regression analyses with gambling behavior as dependent variable (no problem/at-risk and problem) in the total sample.

Model �2log (df) Correct classification (%) Model comparison D � 2log (Ddf) p

Model 1: gender 1059.94 (1) 65.8 – – –

Model 2: Model 1 þ probabilistic reasoning 1048.36 (2) 66.8 Model 2 – Model 1 11.58 (1) <.01Model 3: Model 2 þ sensation seeking 1015.01 (3) 69.0 Model 3 – Model 2 33.26 (1) <.001Model 4: Model 3 þ superstitious thinking 991.33 (4) 69.8 Model 4 – Model 3 23.77 (1) <.001Model 5: Model 4 þ economic perception of gambling 979.54 (5) 70.9 Model 5 – Model 4 11.79 (1) <.01Model 6: Model 5 þ parental gambling behavior 971.50 (6) 70.4 Model 6 – Model 5 8.04 (1) <.01Model 7: Model 6 þ peer gambling behavior 965.96 (6) 71.3 Model 7 – Model 6 5.53 (1) <.05

Table 3Logistic regressionwith gambling behavior as dependent variable (no problem/at-risk and problem) in the total sample: statistics for each predictor of Model7.

Predictorsa B S.E. Wald df p O. R.

Gender �1.22 .19 39.61 1 <.001 .30Probabilistic reasoning �.12 .04 7.84 1 <.01 .89Sensation seeking .06 .02 16.99 1 <.001 1.06Superstitious thinking .05 .01 17.16 1 <.001 1.06Economic perception of gambling .04 .01 7.60 1 <.01 1.04Parental gambling behavior .43 .17 6.54 1 <.01 1.54Peer gambling behavior .49 .21 5.34 1 <.05 1.64

Goodness-of-fit test: Hosmer & Lemeshow: c2 ¼ 2.74, df ¼ 8, p ¼ .95.Cox and Snell R2 ¼ .16; Nagelkerke R2 ¼ .22.Correct classification: 71.3%.

a Overall model evaluation: Likelihood ratio test: c2 ¼ 145.35, df ¼ 7, p < .001.

M.A. Donati et al. / Journal of Adolescence 36 (2013) 129–137134

Discussion

In Italy during the past ten years the political approach toward gambling showed a trend toward liberalization, makinggambling available and easily accessible almost everywhere. Close to 400,000 slot-machines are spread in Italian bars, bettingcorners are available in many kiosks, scratch cards up to 20 euros are sold at supermarkets, post offices, and even throughvending machines at the metro and rail stations. Additionally, hundreds of authorized websites offer legalized onlinegambling, including poker, casino, and sports betting (Bastiani et al., 2011). As a consequence, it is not surprising that in thecurrent study most adolescents reported to have gambled at least once in the past year, and the activities in which theyparticipated the most regularly were card games, sports bets, and instant scratch tickets. These results are in line withinternational cross-sectional research (e.g., Blinn-Pike at al., 2010; Splevins et al., 2010), together with the fact that boysgambled more frequently and with higher severity than girls (e.g., Hardoon et al., 2004; Jacobs, 2004).

The present study showed that a model inwhich cognitive, dispositional, and social factors were all considered together issuitable to explain gambling behavior among adolescents. Thus, adolescent gambling is a multidimensional phenomenonthat can be explained taking into account several distinct factors, such as probabilistic reasoning, the economic perception ofgambling, the tendency to seek for intense experiences, dispositional thinking styles, and exposition to social proximalmodels. Our findings confirmed previous results reported in the literature regarding the risk factors implicated in thedevelopment of adolescent problematic gambling behavior. Considering each factor separately, past studies have highlightedthat gambling was associated with mistaken views of randomness (Delfabbro et al., 2006; Delfabbro et al., 2009), high levelsof sensation seeking (e.g., Nower et al., 2004; Powell, Hardoon, Derevensky, & Gupta, 1999), superstitious beliefs (Chiu &Storm, 2010; Moore & Ohtsuka, 1999), optimistic attitudes toward the profitability of gambling (e.g., Delfabbro et al., 2006;Delfabbro et al., 2009; Delfabbro & Thrupp, 2003), and parental and peer involvement in gambling (e.g., Langhinrichsen-Rohling, Rohde, et al., 2004; Wickwire et al., 2007). Considering all these factors in an integrated way, our study providesempirical evidence of the complexity of adolescent gambling behavior.

Inside this integrated perspective, gender related similarities and differences were also investigated, advancing existingknowledge regarding this issue (e.g., Chalmers &Willoughby, 2006; Jackson et al., 2008). As for gender similarities, sensationseeking and superstitious thinking were significant predictors of at-risk/problem gambling in both gender groups. Theseresults are in accordance with past studies which have shown that the desire for intense sensory experiences was predictiveof problem gambling behavior in male and female youth (Gupta & Derevensky, 1998; Nower et al., 2004), and that irrationalbeliefs were strong predictors of problematic gambling in both gender groups (Moore & Ohtsuka, 1999). As for genderdifferences, it has been found that the susceptibility to the gambler’s fallacy, the economic perception of gambling, and peergambling behavior were predictors of at-risk/problem gambling among male adolescents, while parental gambling behaviorhad a predictive power in female adolescents. We can explain differences related to social factors referring to the fact thatgambling is a more socially desirable activity for boys. That is, the peer group is a context to share gambling activities and to

Table 4Logistic regressionwith gambling behavior as dependent variable (no problem/at-risk and problem) in males: statistics for each predictor of the final model.

Predictorsa B S.E. Wald df p O. R.

Probabilistic reasoning �.14 .05 8.35 1 <.01 .87Sensation seeking .05 .02 9.27 1 <.001 1.05Superstitious thinking .05 .02 12.70 1 <.001 1.05Economic perception of gambling .03 .02 5.06 1 <.05 1.04Parental gambling behavior .21 .20 1.04 1 ns 1.23Peer gambling behavior .53 .27 3.80 1 <.05 1.70

Goodness-of-fit test: Hosmer & Lemeshow: c2 ¼ 7.13, df ¼ 8, p ¼ .52.Cox and Snell R2 ¼ .11; Nagelkerke R2 ¼ .14.Correct classification: 67.8%.

a Overall model evaluation: Likelihood ratio test: c2 ¼ 62.47, df ¼ 6, p < .001.

Table 5Logistic regression with gambling behavior as dependent variable (No Problem/At-Risk and Problem) in Females: Statistics for each predictor of the finalmodel.

Predictorsa B S.E. Wald df p O. R.

Probabilistic reasoning �.02 .09 .045 1 ns .98Sensation seeking .11 .03 10.83 1 <.01 1.11Superstitious thinking .06 .03 5.08 1 <.05 1.07Economic perception of gambling .06 .03 2.77 1 ns 1.06Parental gambling behavior 1.08 .33 10.57 1 <.01 2.94Peer gambling behavior .37 .35 1.13 1 ns 1.45

Goodness-of-fit test: Hosmer & Lemeshow: c2 ¼ 3.96, df ¼ 8, p ¼ .86.Cox and Snell R2 ¼ .12; Nagelkerke R2 ¼ .19.Correct classification: 81.4%.

a Overall model evaluation: Likelihood ratio test: c2 ¼ 40.82, df ¼ 6, p < .001.

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demonstrate maturity status through gambling wins. As a consequence, gambling is perceived as a mean to gain money. Viceversa, gambling is a less peer-approved activity for girls, who are rather influenced by their parents’ behavior. Finally, genderdifferences in probabilistic reasoning might be related to a different approach when solving the tasks, with male adolescentsappearing to be more intuitive.

Limitations of the present study

Although this study had a number of strengths, including the large sample size, and the investigation of female adoles-cents’ gambling behavior, there are several limitations. First, findings in this study were based on self-report data, so it doesnot necessarily follow that participants’ responses completely corresponded to their actual gambling behavior. Second, thiswas a cross-sectional study involving a sample of public school students, thus, generalizability to other populations is limited.Finally, whereas the characteristics of the gambling phenomenon highlighted in the present study are in line with theinternational literature, this study has been conducted with Italian adolescents, and some limitations regarding externalvalidity might be related to the specificity of the sample.

Conclusions and future prospects

Our results have implications for programs aiming to prevent problem gambling among adolescents acting on cognitive,dispositional, and social factors. Acquisition of correct understanding of randomness and chance may serve as tools that canbe used to over-ride any emotionally driven thoughts concerning the profitability of gambling, or the ability to control, predictor influence outcomes. Specifically, it would be useful to work with youth in fictitious gambling situations, designed to elicitthe fallacies that may generally occur. In fact, providing adolescents with objective probabilistic information would notinfluence their beliefs about gambling (Blaszczynski & Nower, 2001), whereas through reasoning in gambling situations,adolescents might understand that specific irrational beliefs and biases may interfere with normative probabilistic reasoning.Another focal point to consider should be to stress the economic disadvantages and risks associatedwith gambling, in order tomodify adolescents’ misconceptions about the returns of gambling, and the reversibility of money lost in gambling. Thus,although it might be difficult to modify sensation seeking predispositions in a preventive program, all the above-mentionedinterventions could play a role in reducing the likelihood of developing gambling-related problems. Finally, it is important totake into account the effect of social influence, and related gender differences. Indeed, for boys it should be important todevelop social skills which are necessary to resist peer pressure, and for girls to reinforce their ability that they do not modelparental behaviors. Thus, when a female adolescent with gambling problems is identified, it should be interesting to deeplyinvestigate her family environment, whereas when a male adolescent is identified, it should be interesting to investigate thepeer group.

Despite the limitations, overall our results provide a model to explain at-risk/problem gambling among boys and girls,stressing gender similarities and differences fromwhich suggestions for preventive actions can be drawn. Future studies areneeded to confirm and extend the current findings. Further research might be conducted in other countries, with studentsattending private schools, and with adolescent workers, also in order to better investigate the potential sources of genderdifferences found in this study. Finally, there is a need for testing the proposed model by defining a preventive program basedon the identified risk factors.

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