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Journal : Small 10899 Dispatch : 4-4-2012 Pages : 14 Article No. : 9303 h LE h TYPESET MS Code : JOGS637 h4 CP h4 DISK J Gambl Stud 28 Introduction Author Proof 29 Social network analysis (SNA) is a promising methodology for the study of psychopa- 30 thology in general (Cramer et al. 2010) and addictive behavior in particular (Christakis and 31 Fowler 2008). For example, in large social networks, clusters of drinkers can be quanti- 32 tatively discerned, and network-level changes in drinking over time significantly affect 33 individual-level drinking patterns (Rosenquist et al. 2010). Familial and social factors are 34 commonly theorized to influence the development and trajectory of addictive disorders, 35 and pathological gambling (PG) is no exception. There is an inherent social component to 36 various gambling modalities, including many casino games, poker, and sports pools, which 37 proves to be a significant motivation for gambling involvement among many gamblers 38 (Lee et al. 2006; Neighbors et al. 2002). There is a small but consistent literature to suggest 39 that social factors contribute to PG among juveniles (Gupta and Derevensky 1997), ado- 40 lescents (Fried et al. 2010), college students (Neighbors et al. 2002) and older adults 41 (McNeilly and Burke 2001). 42 Although the most apparent social motivation for gambling may be socializing with 43 friends and the opportunity to meet new people, other social factors may also be operative. 44 For example, social norms and perceptions can influence decisions to engage in certain 45 risky activities (Welte et al. 2006). Greater social availability (the favorability of one’s 46 social group’s views of an activity) is related to gambling behavior, with participants’ 47 gambling activity being significantly correlated with more favorable perceptions of gam- 48 bling from friends and family (Delfabbro and Thrupp 2003; Martin et al. 2010; Welte et al. 49 2006). Understandably, those family members and friends who have more favorable views 50 are also more likely to actively engage in gambling themselves. In a sample of juvenile 51 gamblers aged 9–14, Gupta and Derevensky (1997) found that 81 % gambled with 52 immediate family (i.e., parents and siblings) and other relatives. While this familial 53 influence remained relatively stable from the youngest to the oldest juveniles in the sample, 54 gambling with friends increased dramatically from 33 % in 4th Grade, to 60 % in 6th 55 Grade, and to 76 % in 8th grade. Based on these findings, the authors suggest that parents’ 56 gambling habits may be more directly related to the acquisition of gambling behavior, 57 whereas friends’ gambling habits may be more associated with the maintenance of 58 gambling. 59 It has also been found that relatives of PGs are at increased risk to suffer from a 60 gambling disorder themselves. For example, relatives of PGs are nearly four times as likely 61 to suffer from a gambling disorder than relatives of nonpathological gamblers (NPGs), 62 with lifetime rates of PG for these relatives being 8.3 and 2.1 %, respectively (Black et al. 63 2006). Focusing specifically on the parent–child correlation, the paternal influence on 64 offspring has been shown to be greater than that of the maternal influence. In a sample of 65 189 families, Oei and Raylu (2004) found that mothers’ scores on the South Oaks Gam- 66 bling Screen (SOGS; Lesieur and Blume 1987) had essentially no bearing on offspring 67 SOGS scores (r=0.02), although fathers’ SOGS scores had a highly significant rela- 68 tionship to offspring SOGS scores (r=0.74). From a social learning perspective (Bandura 69 1977), direct engagement in gambling activity with parents is not a necessary precursor for 70 influential effects to occur on one’s own gambling behaviors. Through modeling, indi- 71 viduals may be influenced by exposure to the behaviors of others, such as how their friends 72 and family allocate their time and money to gambling-related activities. Indeed, the 73 children of parents who effectively model good money management skills expressed less 74 interest in future gambling than children whose parents did not emphasize the importance 75 of managing money responsibly (Delfabbro and Thrupp 2003). UNCORRECTED PROOF 123
J Gambl Stud Journal : Small 10899 Dispatch : 4-4-2012 Pages : 14 Article No. : 9303 h LE h TYPESET MS Code : JOGS637 h4 CP h4 DISK Author Proof 76 Modeling has also been implicated in connection to substance use in adolescents, with 77 both friends and parents playing an important role in the acquisition of alcohol con- 78 sumption (Bank et al. 1985) and tobacco habits (Flay et al. 1994). The combined influence 79 of friends and parents have been shown to account for more than 30 % of the variance in 80 both tobacco use and alcohol consumption among adolescents (Kinard and Webster 2010), 81 but friends ultimately play a more significant and direct role in adolescents’ substance use 82 than parents (Bank et al. 1985; Flay et al. 1994; Kinard and Webster 2010). In addition, it 83 has also been suggested that siblings play a more critical role in substance use than parents, 84 even when controlling for shared environmental experiences (Fagan and Najman 2005). 85 Peer and familial influences on substance use are clearly present during adolescence and 86 can still be seen in adulthood, with SNA showing increased drinking for individuals with 87 heavy drinkers in their social network (Rosenquist et al. 2010) and alcohol-specific support 88 from one’s social network impacting treatment success (Longabaugh et al. 2010). Using 89 the Important People Inventory (IPI) in a clinical context, Longabaugh and colleagues 90 found post-treatment percentage days abstinent was significantly related to the frequency 91 of alcohol use observed in patients’ social networks. Similarly, smoking cessation main- 92 tenance has been shown to have a negative relationship with the existence of smokers in 93 one’s social network (Mermelstein et al. 1986) and SNA for tobacco use indicates that it is 94 more common for successful smoking cessation to simultaneously occur for multiple 95 smokers within the same social network as opposed to an individual smoker (Christakis 96 and Fowler 2008). 97 Despite the clear relevance of social network influences, we are aware of no studies to 98 date that have applied SNA to PG. Therefore, the current study was an initial investigation 99 of the relationship between an individual’s gambling behavior and the gambling behavior 100 in the individual’s social network, to lay the groundwork for more methodologically 101 rigorous SNA gambling studies in the future. More specifically, the primary aim of the 102 current study was to use a novel social density measure to characterize the relationship 103 between the severity of participants’ PG and the frequency of gambling and monetary 104 losses for individuals within their social network. Participants’ social networks were op- 105 erationalized to include: (a) parents, (b) siblings, and (c) the five other closest people in 106 their lives. We predicted that we would observe a significant association between partic- 107 ipants’ level of pathology and the levels of gambling observed in their proximal social 108 networks. A secondary aim of the current study was to investigate the same relationships 109 for alcohol and tobacco use. We expected aggregation within social networks on these 110 behaviors as well. As a third aim, we examined whether participants’ behaviors in one 111 domain (e.g., gambling) would be related to network members’ behaviors in alternative 112 domains (e.g., alcohol use).We did not make specific predictions for these cross-modal 113 relationships. 114 Methods 115 Participants UNCORRECTED PROOF 116 Participants included 128 frequent gamblers (i.e., gambled at least weekly), who were 117 recruited with newspaper advertisements and word of mouth in Athens, GA, USA. Par- 118 ticipants were 79.7 % male with a mean age of 34.2 (SD=11.7) who primarily self- 119 identified as white (39.8 %) or black (57.0 %). The current results are part of a larger 120 ongoing study that includes measures of personality, behavioral risk taking and other 123
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