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Social Density of Gambling and Its Association with Gambling Problems: An Initial Investigation
Springer Science+Business Media, LLC
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Journal of Gambling Studies
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Department of Psychology
University of Georgia
Athens, GA, 30602-3013, USA
eelitt@uga.edu
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James
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Department of Psychology
University of Georgia
Athens, GA, 30602-3013, USA
Center for Alcohol and Addiction Studies
Brown University
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W. Keith
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University of Georgia
Athens, GA, 30602-3013, USA

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The role of social factors in pathological gambling has received relatively little systematic research. The goal
of the current study was to examine the relationship between a target individual’s gambling behavior and the
gambling behavior among that individual’s parents, siblings and five closest friends. The specific aims were,
first, to apply a novel brief assessment to study the social density of factors relating to pathological gambling;
second, to replicate previously observed findings involving the social aggregation of alcohol and tobacco use;
and third, to examine social density findings among the three domains. Participants were 128 frequent
gamblers from the Athens, Georgia area, 79.7 % male with a mean age of 34.2 (SD = 11.7). Participants were
assessed using the Diagnostic Interview for Gambling Severity for gambling severity, the Alcohol Use
Disorders Identification Test for alcohol abuse, the Fagerstrom Test of Nicotine Dependence for tobacco use,
and the novel Brief Social Density of Gambling, Alcohol, and Tobacco Assessment. Significant relationships
were observed between participants’ and friends’ activity within all domains: gambling (ps = .001), alcohol
use (p < .001) and tobacco use (p < .001). Relationships with friends’ activity across domains were less strong.
Distinct patterns of associations with parents and siblings were not observed. Thus, social aggregation was
observed across the three domains of potentially addictive behaviors, generally with specificity within
domains and with friends, not biological relatives. Methodological considerations and potential applications
of these findings are discussed.
Gambling - Pathological gambling - Social density - Tobacco - Alcohol

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DOI 10.1007/s10899-012-9303-3
1 ORIGINAL PAPER
Author Proof
2 Social Density of Gambling and Its Association
3 with Gambling Problems: An Initial Investigation
4 Erica E. Fortune • James MacKillop • Joshua D. Miller •
5 W. Keith Campbell • Allan D. Clifton • Adam S. Goodie
6
7 Ó Springer Science+Business Media, LLC 2012
8 Abstract The role of social factors in pathological gambling has received relatively little
9 systematic research. The goal of the current study was to examine the relationship between
10 a target individual’s gambling behavior and the gambling behavior among that individual’s
11 parents, siblings and five closest friends. The specific aims were, first, to apply a novel
12 brief assessment to study the social density of factors relating to pathological gambling;
13 second, to replicate previously observed findings involving the social aggregation of
14 alcohol and tobacco use; and third, to examine social density findings among the three
15 domains. Participants were 128 frequent gamblers from the Athens, Georgia area, 79.7 %
16 male with a mean age of 34.2 (SD=11.7). Participants were assessed using the Diag-
17 nostic Interview for Gambling Severity for gambling severity, the Alcohol Use Disorders
18 Identification Test for alcohol abuse, the Fagerstrom Test of Nicotine Dependence for
19 tobacco use, and the novel Brief Social Density of Gambling, Alcohol, and Tobacco
20 Assessment. Significant relationships were observed between participants’ and friends’
21 activity within all domains: gambling (ps=.001), alcohol use (p\.001) and tobacco use
22 (p\.001). Relationships with friends’ activity across domains were less strong. Distinct
23 patterns of associations with parents and siblings were not observed. Thus, social aggre-
24 gation was observed across the three domains of potentially addictive behaviors, generally
25 with specificity within domains and with friends, not biological relatives. Methodological
26 considerations and potential applications of these findings are discussed.
27 Keywords Gambling Pathological gambling Social density Tobacco Alcohol
A1
A2
A3
A4
A5
E. E. Fortune (&) J. MacKillop J. D. Miller W. K. Campbell A. S. Goodie
Department of Psychology, University of Georgia, Athens, GA 30602-3013, USA
e-mail: eelitt@uga.edu
UNCORRECTED PROOF
J. MacKillop
Center for Alcohol and Addiction Studies, Brown University, Providence, RI, USA
A6
A7
A8
A. D. Clifton
Department of Psychology, Vassar College, 124 Raymond Ave., Box 713, Poughkeepsie,
NY 12604-0713, USA
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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
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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
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121 individual difference variables. Individuals were excluded from participation if they: (1)
122 did not gamble at least once a week, (2) currently lived with someone who already
123 completed the study, (3) could not use a computer, (4) reported symptoms of psychosis, or
124 (5) were older than 65.
125 Procedure
126 Eligible individuals completed a single in-person session, in which they first completed a
127 demographic questionnaire on a computer in an individual lab room, next took part in a
128 one-on-one diagnostic interview with a trained masters-level interviewer to determine
129 gambling pathology, and finally completed the assessment of gambling, alcohol use, and
130 tobacco use within their social networks. All procedures were approved by the University
131 of Georgia Institutional Review Board and were consistent with the Helsinki Declaration.
132 Participants were paid $30 US for their participation in the overall study.
133 Measures
134 Diagnostic Interview for Gambling Severity (DIGS; Winters et al. 1996)
135 This semi-structured clinical interview is based on the 10 DSM-IV diagnostic criteria for
136 PG. The 20 items are paraphrased directly from the DSM-IV criteria, with 2 items for each
137 criterion, grouped into pairs to reflect the dichotomous nature of the 10 criteria. Following
138 a face-to-face interview, a trained interviewer determined whether each item was false,
139 somewhat true, orvery true, based upon the participants’ responses. Within each of the
140 item pairs, two classifications of somewhat true or at least one answer of very true resulted
141 in the participant receiving a point. The total score was assessed on a scale of 1–10, with a
142 score of 5 or higher indicating PG status. Administration of the DIGS probes specific
143 gambling modalities including playing cards, betting on sporting events, playing the lottery
144 and playing slot machines, but it does not mandate that gambling-related problems be
145 restricted to these forms of gambling. Participants reported engaging in a wide range of
146 gambling activities, including lottery play, cards, dice, sports betting and internet gaming.
147 Alcohol Use Disorders Identification Test (AUDIT; Saunders et al. 1993)
148 This 10-item assessment addresses the frequency, amount, and possible consequences of
149 alcohol consumption. Total scores can range from 0 to 40 with a score of 8 or higher
150 indicating hazardous drinking.
151 Fagerstrom Test for Nicotine Dependence (FTND; Heatherton et al. 1991)
UNCORRECTED PROOF
152 The FTND is used to assess nicotine dependence by assigning a nicotine rating from 1 to
153 10, as based on particular smoking behaviors that individuals endorse. The FTND has been
154 shown to have good convergent validity with various biochemical measures of smoking,
155 including cotinine and nicotine in saliva (Heatherton et al. 1991).
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156 Brief Addictive Behavior Social Density Assessment: Gambling, Alcohol, and Tobacco
157 (BASDA; See ‘‘Appendix’’)
Author Proof
158 The BASDA adapted elements of validated screening measures of gambling, alcohol use,
159 and tobacco use to investigate these behaviors among each member of a participant’s social
160 network, including the five closest non-biological social associates (e.g., friends, romantic
161 partners and other close acquaintances, whom we refer to collectively as ‘‘friends’’), living
162 parents, and living siblings. Participants estimated the gambling and substance use of each
163 individual and rated their confidence in their estimate. In completing the BASDA for their
164 five closest friends, participants were given the following description:
165 Please provide estimates of these behaviors for your CLOSEST non-biologically
166 related individuals, including spouses and significant others. Please note, the term
167 ‘‘non-biologically related individual’’ is meant specifically to exclude parents and
168 siblings. It is however acceptable for your closest people to include other relatives
169 (e.g., cousins, aunts, uncles, etc.), but please note that in the appropriate location
170
171
below.
172 When completing the sibling portion of the BASDA, participants were instructed to
173 start with the sibling closest in age to themselves and continue to siblings of
174 increasingly distant ages until completing the questionnaire for all living siblings. With
175 regard to the gambling behaviors, participants estimated each individual’s weekly fre-
176 quency of gambling on an 8-point scale from ‘‘no days’’ to ‘‘every day’’ as well as
177 weekly expenditure on gambling-related activities in an open response format. These
178 items were based on the South Oaks Gambling Screen (SOGS; Lesieur and Blume
179 1987). The alcohol portion of the BASDA used the AUDIT-Quantity-Frequency index,
180 the first three items pertaining to heaviness of alcohol use, which has been validated as
181 a measure of problematic alcohol consumption (Bush et al. 1998). Tobacco use for
182 individuals was assessed as cigarettes/day, which is a validated indicator of cigarette
183 consumption and an item on the FTND. To be maximally representative, a composite of
184 the five closest friends was used. Internal consistency was calculated for distinct
185 BASDA categories by treating each of the five closest friends’ responses as items. The
186 internal consistency levels were good for gambling frequency (a=.79), gambling
187 expenditure (a=.78), and alcohol use (a=.70), but suboptimal for tobacco use
188 (a=.41). This last finding may reflect the fact that the social network questionnaire
189 asked one tobacco-use question about each network member, whereas it asked multiple
190 questions about gambling and alcohol use. Individual data are reported for parents and
191 siblings.
192 Lastly, participants rated their confidence level for the accuracy of their responses for
193 each individual with three options: No Confidence, Confident, or Highly Confident. Par-
194 ticipants’ confidence in their response accuracy ranged from an average of 2.45–2.72 for
195 each individual (with No Confidence=1, Confident=2, and Highly Confident=3) and
196 participants had an overall mean confidence level of 2.51 (84 % of scale maximum) across
197 all social categories. Participants indicated having slightly more confidence in answers for
198 their parents (M=2.68, SD=.51) than for their five closest friends (M=2.48,
199 SD=.45; t=3.57, p\.001). Participants also reported having more confidence in their
200 answers for their siblings (M=2.56, SD=.56) than for their friends, although this dif-
201 ference was not significant (t=1.28, p[.05). Data were not included for individuals
202 within a participants’ social network if the participants reported having no confidence in
203 their answers for that network member.
UNCORRECTED PROOF
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204 Data Analyses
Author Proof
205 Missing data were present for a number of participants who did not realize that the BASDA
206 form was double-sided, resulting in a sample of N=112 with complete data. The Ns for
207 parent and sibling ratings are smaller than the total N, as would be expected based upon
208 variation in number of siblings and the stipulation that questionnaires be completed only
209 for parents who are currently living. Additionally, a sibling composite was calculated that
210 includes the data for a maximum of the four siblings closest in age to the participant, as
211 those closest in age should have the greatest impact on participants’ behaviors and those
212 furthest in age could actually dampen possible findings. The data were initially examined
213 for distributions and outliers, which determined there was a very wide range of dollars
214 gambled with per week, with some outlying values. This was resolved via a log 10 trans-
215 formation. Additionally, as zero values cannot be log transformed and the log 10 of 1 is 0,
216 all $0 gambled/week data points were recoded as $1/week before transformation. The
217 primary analyses used Pearson’s product-moment correlations (r) to investigate the con-
218 tinuous relationships between participant-level characteristics and network-level charac-
219 teristics. Statistical significance was defined as a=.05 and the study had sufficient power
220 (b=.80) to detect significant associations of r=|.26| in the primary analyses, which
221 corresponds to a medium effect size magnitude.
222 Results
223 Social Density of Gambling, Alcohol and Tobacco Use Among Friends
224 The sample, which was recruited for high levels of gambling activity, had a mean DIGS
225 score of 4.5, including the entire scale range of 0–10 symptoms (SD=3.2). The two
226 measures of gambling activity (frequency and expenditure) correlated strongly with each
227 other (r=.80).
228 Correlations between self-scores and friends’ composite scores are depicted in
229 Table 1, and reveal significant associations between participants’ and friends’ activity in
230 all three domains of behavior. As expected, the composite for the five closest friends’
231 gambling frequency and expenditure was significantly associated with the participant’s
232 gambling severity, with correlations of r=.31 and r=.30, respectively. Similarly,
233 participants’ AUDIT scores were significantly related to composite scores of alcohol
234 consumption for friends (r=.39) and participants’ FTND total scores were significantly
235 related to their friends’ composite scores for tobacco use (r=.34). These primary
236 results are depicted within the shaded boxes in Table 1. Further investigation into the
237 differential effects of various types of relationships within the broader category of
238 ‘‘friends’’ (e.g., comparing the influence of significant others vs. platonic friends on
239 participants’ behaviors) was hindered by the small number of participants who included
240 significant others among their five closest friends, which resulted in low power.
241 Regardless of relationship type, when focusing on the collective behaviors of the five
242 closest friends, we find evidence of significant social density of all three addictive
243 behaviors among participants’ proximal social networks. Importantly, comparable results
244 were not obtained across domains of behavior. Participants’ gambling pathology was
245 not significantly related to their friends’ alcohol or tobacco use. Their tobacco use was
246 also not significantly related to their friends’ gambling or alcohol use. It is interesting
247 to note, however, that participants’ alcohol use did correlate significantly with their
UNCORRECTED PROOF
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Table 1 Cross-modal correlations (p values) between participants and non-biological friends
Self-alcohol 0.19 (.045)*
Self Friends
Self-gambling Self-alcohol Self-tobacco Friends-GambFreq Friends-Gamb$ Friends-alcohol
Self-tobacco 0.23 (.016)* 0.27 (.004)**
Friends-GambFreq 0.31 (.001)** 0.17 (.066) 0.14 (.142)
Friends-Gamb$ 0.30 (.001)** 0.19 (.051) 0.08 (.423) 0.80 (.000)**
Friends-alcohol 0.01 (.950) 0.39 (.000)** 0.09 (.372) 0.40 (.000)** 0.25 (.008)**
UNCORRECTED PROOF
Friends-tobacco 0.15 (.119) 0.36 (.000)** 0.34 (.000)** 0.45 (.000)** 0.39 (.000)** 0.34 (.000)**
Self-gambling: DIGS score; self-alcohol: AUDIT score; self-tobacco: FTND score; friend-GambFreq, friend-Gamb$, friend-Alc and friend-Nic: gambling frequency,
gambling expenditure, alcohol use and tobacco use measures from BASDA. Italicized values represent correlations within modalities (e.g., self and network gambling)
* p\.05; ** p\.01; p\.10; N=112
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248 friends’ tobacco use, and showed a trend toward an association with their friends’
249 gambling. Within-modality relations thus appear to be stronger than between-modality
250 relations, between participants and their friends. Finally, many studies have observed
251 that alcohol and tobacco use tend to co-occur in individuals. The current data replicate
252 this finding, both for the individual and among the friends’ behaviors (Table 1).
253 Social Density of Gambling, Alcohol and Tobacco Use Among Family Members
254 Findings relating self-behavior to parents’ and siblings’ behaviors in gambling, alcohol and
255 tobacco use are depicted in Table 2. The clear patterns of social density observed among
256 friends were not seen as strongly among family members. Participants’ gambling severity
257 was significantly associated with fathers’ frequency of gambling (r=.30) but was not
258 associated with fathers’ gambling expenditure. No significant relationships were found
259 between participants’ gambling severity and the gambling variables for mothers, a parental
260 composite, or sibling composite. For alcohol use, a significant correlation was observed
261 between participants’ AUDIT scores and Mothers’ alcohol consumption, but not other
262 significant relationships emerged from the alcohol use of parents or siblings. In the domain
263 of tobacco use, no significant associations were observed between participants and any
264 biological relatives.
265 Discussion
266 The present study investigated the relationship between individual-level PG, alcohol and
267 tobacco use, and the density of gambling behavior in participants’ social networks.
268 With regard to gambling, consistent with our hypothesis, participants’ gambling severity
269 was significantly and positively associated with both frequency of gambling and
270 monetary expenditure on gambling within a person’s immediate non-family social
271 network. Consistent with prior research (e.g., Oei and Raylu 2004), participants’
272 gambling severity was significantly correlated with fathers’ gambling frequency;
273 whereas participants gambling severity was not associated with either mothers’ or
274 siblings’ gambling practices. Overall, participants’ friends displayed a significant rela-
275 tionship with their current gambling behaviors, which may support the notion proposed
276 by Gupta and Derevensky (1997) that parents act as a dominant influence for the
277 development of gambling behaviors, which may occur in adolescence, whereas friends
278 aid in the maintenance of gambling activity through adolescence and adulthood. It is
Table 2 Correlations between participants’ DIGS scores and the frequency of gambling, gambling
expenditure, alcohol use, and tobacco use of parents and siblings
Relationship Frequency N Expenditure N Alcohol N Tobacco N
Parents
Father .30* 71 .16 68 .05 72 .21 63
Mother -.06 78 -.04 80 .32** 78 .15 72
UNCORRECTED PROOF
Composite .12 90 .10 91 .06 91 .17 84
Siblings
Composite .06 100 .11 101 .04 101 .02 93
* p\.05; ** p\.01
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279 possible that a similar study using a younger cohort would have revealed greater rel-
280 ative associations with family activities. The current findings related to alcohol and
281 tobacco use provide further support for previous research showing the importance of
282 peers in current alcohol consumption and tobacco use, in a sample that uniquely
283 combines investigation of these potentially addictive behaviors with gambling behav-
284 iors. This too is comparable to earlier studies that highlight the more significant impact
285 of peers over both parents and siblings.
286 Collectively, the gambling, alcohol, and tobacco findings create a relatively cohesive
287 view of the importance of nonbiological social relationships and the apparent reci-
288 procity between a person’s gambling, drinking, and smoking, and the prevalence of
289 those behaviors in his or her social network. Importantly, however, these are cross-
290 sectional data, making it difficult to assess whether these relationships reflect causal
291 links or consequences of self-selection. The presence of more frequent and heavy
292 gamblers may increase an individual’s gambling by providing partners and opportunities
293 to gamble, or may be observed because pathological gamblers seek out individuals with
294 similar interests. It is plausible that both causal chains are in operation, and that social
295 factors are recursive motivational factors. For example, an individual whose gambling is
296 escalating is increasingly likely to seek out and meet other heavy gamblers, who in turn
297 may become partners and provide opportunities, thus reflecting both causal and con-
298 sequential roles.
299 Strengths and Limitations
300 These findings should be considered in light of the study’s strengths and limitations. Study
301 strengths include the assessment of each participant’s level of gambling pathology using a
302 gold-standard clinical interview and the use of a racially diverse community sample.
303 However, one limitation that bears consideration is that the social network data were not
304 directly retrieved from the network members, but were supplied by the participants in this
305 preliminary study. Although the participants had high confidence in their response accu-
306 racy, the potential for error is present when estimating the behaviors of others. Perception
307 may systematically be skewed in the direction of a person’s behavior, with low severity
308 individuals underestimating behavior and high severity individuals overestimating
309 behavior. As such, these finding should be interpreted cautiously and not be considered
310 definitive. This issue itself is worthy of study. For example, future studies could examine
311 estimated behavior and self-reported behavior among cohorts of individuals to empirically
312 determine the extent to which a disconnect exists. The most rigorous application of this
313 approach is termed sociocentric SNA, and includes comprehensive assessment of all
314 members of a bounded population (both for the individual and their estimates of other
315 individual’s behaviors), permitting both objectivity and analysis of the discrepancy
316 between self-reports and other reports (e.g., Clifton et al. 2009), and would directly address
317 this issue. Further study of the social density of PG using diverse methodologies is clearly
318 warranted.
319 A last consideration is that participants were asked to provide information only for
320 living parents and siblings. However, if parents are primarily responsible for their
321 offspring’s acquisition of gambling behaviors early in life, as Gupta and Derevensky
322 (1997) suggest, even parents who are currently deceased could have potentially had a
323 significant impact on participants’ current gambling behaviors. Furthermore, the role of
324 genetics in PG should not be overlooked. Although research in this area is in its
325 infancy, a recent review on the topic indicated that genetic factors may explain between
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326 50 and 60 % of variance in the development of PG (Lobo and Kennedy 2009).
327 Moreover, there is evidence that certain genetic variants are responsible for differing
328 roles in social networks (Fowler et al. 2011), suggesting that genetic factors many play
329 diverse roles. For these reasons, future studies should obtain information for all parents
330 and siblings, even if those individuals are deceased, and ideally examine genetic var-
331 iation concurrently.
332 Conclusion
333 The current study provides an initial investigation into the social aggregation of
334 gambling and the contributions of this aggregation to gambling severity. As such, it
335 advances the prior literature exploring the social and familial influences related to PG.
336 Converging with previous studies on tobacco, alcohol, and illicit drug use, the current
337 study revealed the parallels between individual-level severity of PG and gambling
338 behaviors in the immediate non-biological social network. Although considerations
339 apply and further basic research is necessary, these findings support the importance of
340 quantifying social contextual factors and directly examining these influences on gam-
341 bling and other potentially addictive behaviors.
342 Appendix
343 Directions to participants: Please provide estimates of these behaviors for the people who
344 are closest to you. In each case, check the appropriate boxes or write in your answer. Please
345 answer each question even if you are not sure. Your answer should be your best guess.
346 Person # 1–5 [one for each]
347 Your [CLOSEST/second closest/etc.] non-biologically related individual, including
348 spouses and significant others. Please note, the term ‘‘non-biologically related individual’’
349 is meant specifically to exclude parents and siblings. It is however acceptable for your
350 closest people to include other relatives (e.g., cousins, aunts, uncles, etc.), but please note
351 that in the appropriate location below.
352
1. Over the last year, how often does this person have a drink containing alcohol?
___ Never ____Monthly or Less
____2-4x/Month ____2-3x/Week ____4 or More Times/Week
2. Over the last year, how many drinks containing alcohol does this person have on a typical day when
he or she is drinking?
___1 to 2 ___3 to 4 ___5 to 6 ___7 to 9 ___10 or more
3. Over the last year, how often does this person have six or more drinks on one occasion?
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___ Never ____Monthly or Less
____2-4x/Month ____2-3x/Week ____4 or More Times/Week
353
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4. Over the last year, how many cigarettes/day does this person typically smoke each day? _________
[If the person smokes weekly, write in the number and “per week”]
Author Proof
5. Over the last year, how many days/week does this person typically gamble in any form? (e.g., poker
or other card games, gambling machines, lottery, bingo, etc.) [If monthly, translate into the closest
number of days/week]
___ No days
___ 1 Day ___2 Day___3 Day ___4 Day___ 5 Day ___ 6 Days ___ Every Day
6. Over the last year, how much money does the person gamble with each week? $_____________
(What is the maximum possible loss each week?)
7. Is this person male or female? ___Male ___ Female
8. Is this person a spouse/significant other, friend, or a relative (other than a parent or sibling)?
___ Spouse/Significant Other ___Friend ___Relative
If you checked relative, please indicate how you are related: _________________________
9. How confident are you that your estimates about this person are accurate?
___Not confident ___Confident
Your Father/Mother [one for each]:
1. Over the last year, how often does this person have a drink containing alcohol?
___ Never ____Monthly or Less
____2-4x/Month ____2-3x/Week ____4 or More Times/Week
2. Over the last year, how many drinks containing alcohol does this person have on a typical day when
he or she is drinking?
___1 to 2 ___3 to 4 ___5 to 6 ___7 to 9 ___10 or more
3. Over the last year, how often does this person have six or more drinks on one occasion?
___ Never ____Monthly or Less
___Highly Confident
____2-4x/Month ____2-3x/Week ____4 or More Times/Week
4. Over the last year, how many cigarettes/day does this person typically smoke each day? _________
[If the person smokes weekly, write in the number and “per week”]
5. Over the last year, how many days/week does this person typically gamble in any form? (e.g., poker
or other card games, gambling machines, lottery, bingo, etc.) [If monthly, translate into the closest
number of days/week]
___ No days ___ 1 Day ___2 Day___3 Day ___4 Day___ 5 Day ___ 6 Days ___ Every Day
6. Over the last year, how much money does the person gamble with each week? $_____________
(What is the maximum possible loss each week?)
7. Is this your biological parent? ___Yes ___No
8. How confident are you that your estimates about this person are accurate?
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___Not confident ___Confident
___Highly Confident
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Your Sibling [closest in age/2 nd closest in age/etc.]
1. Over the last year, how often does this person have a drink containing alcohol?
Author Proof
___ Never ____Monthly or Less
3. Over the last year, how often does this person have six or more drinks on one occasion?
___ Never ____Monthly or Less
____2-4x/Month ____2-3x/Week ____4 or More Times/Week
4. Over the last year, how many cigarettes/day does this person typically smoke each day? _________
[If the person smokes weekly, write in the number and “per week”]
5. Over the last year, how many days/week does this person typically gamble in any form? (e.g., poker
or other card games, gambling machines, lottery, bingo, etc.) [If monthly, translate into the closest
number of days/week]
___ No days ___ 1 Day ___2 Day___3 Day ___4 Day___ 5 Day ___ 6 Days ___ Every Day
6. Over the last year, how much money does the person gamble with each week? $_____________
(What is the maximum possible loss each week?)
7. Is this person male or female? ___Male ___ Female
8. How many years older or younger is this sibling? ______ Years ____Older ___Younger
9. Is this your biological siblings? ___Yes ___No
____2-4x/Month ____2-3x/Week ____4 or More Times/Week
2. Over the last year, how many drinks containing alcohol does this person have on a typical day when
he or she is drinking?
___1 to 2 ___3 to 4 ___5 to 6 ___7 to 9 ___10 or more
10. How confident are you that your estimates about this person are accurate?
___Not confident ___Confident ___Highly Confident
354 BASDA Administration: Participants should be given forms for their five closest
355 friends plus any living parents and siblings. The closest five friends are numbered ‘‘Per-
356 son#1’’–‘‘Person #5’’ with #1 being the closest friend, #2 being the second closest friend,
357 and so on and so forth. The forms for the siblings should be completed sequentially,
358 starting with the sibling closest in age and finishing with the sibling furthest in age.
359 Scoring: All scaled responses should be coded so that the smallest value is coded as 1
360 and the subsequent values increase by 1. Example: For question #1, never=1, Monthly or
361 Less=2, 2–49month=3, 2–39/week=4, and 4 or more times/week=5.
362 Focus scores:
363 Gambling frequency=Question 5; Gambling expenditure=Question 6 (a log trans-
364 formation may be required); alcohol use=composite of Questions 1–3; and tobacco
365 use=Question 4
366 Experimenters may wish to use a composite score for the five closest friends (as done in
367 the current study). If doing so, internal reliabilities can be calculated by treating the five
368 closest friends’ responses for each activity as ‘‘items.’’ It is recommended that data only be
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369 used for individuals for whom the participants feel either confident or highly confident in
370 their response accuracy.
371
372
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