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A social network analysis approach to alcohol use and co-occurring addictive behavior in young adults
Addictive Behaviors 51 (2015) 72–79
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Addictive Behaviors
A social network analysis approach to alcohol use and co-occurring addictive behavior in young adults Matthew K. Meisel a,⁎, Allan D. Clifton b, James MacKillop c, Adam S. Goodie a a b c
Department of Psychology, University of Georgia, Athens, GA, United States Department of Psychology, Vassar College, Poughkeepsie, NY, United States Peter Boris Center for Addictions Research, Department of Psychiatry and Behavioural Neurosciences, McMaster University/St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
H I G H L I G H T S • Addictive behaviors tend to co-occur among network members. • Network members cluster together based on co-occurring addictive behavior. • At-risk drinkers perceive frequent use of addictive behaviors in their networks.
a r t i c l e
i n f o
Available online 21 July 2015 Keywords: Social network analysis Co-occurring Addictive behavior Alcohol Marijuana Egocentric
a b s t r a c t Introduction: The current study applied egocentric social network analysis (SNA) to investigate the prevalence of addictive behavior and co-occurring substance use in college students' networks. Specifically, we examined individuals' perceptions of the frequency of network members' co-occurring addictive behavior and investigated whether co-occurring addictive behavior is spread evenly throughout networks or is more localized in clusters. We also examined differences in network composition between individuals with varying levels of alcohol use. Method: The study utilized an egocentric SNA approach in which respondents (“egos”) enumerated 30 of their closest friends, family members, co-workers, and significant others (“alters”) and the relations among alters listed. Participants were 281 undergraduates at a large university in the Southeastern United States. Results: Robust associations were observed among the frequencies of gambling, smoking, drinking, and using marijuana by network members. We also found that alters tended to cluster together into two distinct groups: one cluster moderate-to-high on co-occurring addictive behavior and the other low on co-occurring addictive behavior. Lastly, significant differences were present when examining egos' perceptions of alters' substance use between the networks of at-risk, light, and nondrinkers. Conclusions: These findings provide empirical evidence of distinct clustering of addictive behavior among young adults and suggest the promise of social network-based interventions for this cohort. Published by Elsevier Ltd.
1. Introduction Excessive alcohol consumption is a serious problem in the United States, particularly in college populations (Hingson, Zha, & Weitzman, 2009). The most recent Monitoring the Future Survey found that 63% of college students consumed alcohol in the past month (Johnston, O'Malley, Bachman, Schulenberg, & Miech, 2014). Furthermore, addictive behaviors tend to co-occur, such that individuals who frequently consume alcohol also tend to frequently engage in tobacco use, marijuana use, and gambling (Reboussin, Song, Shrestha, Lohman, & Wolfson, ⁎ Corresponding author at: Department of Psychology, University of Georgia, Athens, GA 30602-3013, United States. E-mail address: [email protected] (M.K. Meisel).
http://dx.doi.org/10.1016/j.addbeh.2015.07.009 0306-4603/Published by Elsevier Ltd.
2006; Shillington & Clapp, 2006; Weitzman & Chen, 2005). Individuals who engage in co-occurring substance use are at a greater risk of developing substance use related problems (Shillington & Clapp, 2006; Martens et al., 2009). Specifically, college students who use both marijuana and alcohol are more likely to drink and drive and to experience problems with alcohol than only those who consume alcohol (Shillington & Clapp, 2006). It is widely accepted that social motives are among the most endorsed reasons for drinking among college students (Brennan, Walfish, & AuBuchon, 1986; Cronin, 1997; LaBrie, Hummer, & Pedersen, 2007). In a regression model, even after controlling for personal motives, social motives account for unique variance in alcohol use (Cronin, 1997; Haden & Edmundson, 1991; MacKillop et al., 2013). Furthermore, social motives in high school predict alcohol use in college (Corbin, Iwamoto,
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& Fromme, 2011), suggesting that younger adults' alcohol use is prospectively influenced by the desire to be socially compatible with peers. Social network analysis (SNA) is a technique that quantifies and examines the structure of individuals' social networks. A social network refers to the individual's immediate social environment, such as friends, romantic partners, and family members. SNA allows researchers to examine compositional and structural characteristics of individuals' networks. Compositional characteristics refer to the proportion of individuals in a network with particular socio-demographic characteristics or behavioral traits. Structural characteristics refer to the pattern of relationships between individuals in a network. Structural characteristics, such as clustering in the network, can only be evaluated using SNA. In an egocentric SNA, the respondent (termed “ego”) enumerates the most important individuals in his or her life (termed “alters”), answers questions pertaining to the alters, and classifies the relationships among alters. Previous studies have examined compositional characteristics associated with addictive behavior. For example, individuals' alcohol consumption and misuse are positively associated with the proportion of network members who are drinkers, heavy drinkers, and “drinking buddies” (Fondacaro & Heller, 1983; MacKillop et al., 2013; Reifman, Watson, & McCourt, 2006). Similarly, individuals' smoking, drinking, and gambling are significantly associated with the same behaviors by peers (Andrews, Tildesley, Hops, & Li, 2002; Fortune et al., 2013; MacKillop et al., 2013; Meisel et al., 2013). Furthermore, heavy drinkers' networks have an overall greater rate of alcohol use as well as a greater proportion of “drinking buddies” compared to regular and infrequent drinkers' networks (Leonard, Kearns, & Mudar, 2000). In the current study, we utilized an egocentric network design to extend past findings in three ways. First, we investigated the frequency of co-occurring addictive behaviors in college students' networks, hypothesizing that alters who were perceived to engage in one addictive behavior would also be perceived to engage in multiple addictive behaviors. Second, we examined whether addictive behaviors tended to co-occur among clusters of alters within networks. Third, we examined differences in not only alters' alcohol use, but alters' marijuana use, tobacco use, and gambling as well, between ego's with varying degrees of alcohol use and alcohol-related problems, the most common form of substance misuse among young adults. Based on previous studies, we hypothesized that, compared to light and nondrinkers, at-risk drinkers would report greater frequencies of addictive behavior among their alters. Based on the accuracy of ego's perceptions of their alters' substance use, two different interventions may be implemented. If the ego misperceives the frequency of addictive behavior by alters (e.g., the ego perceives that alters engage in addictive behavior more frequently than reality), normative interventions may be conducted to correct egos' perceptions. However, if the alters actually engage in multiple addictive behaviors frequently, social network interventions may be utilized to dissolve or attenuate the ego's ties to these network members. Alternatively, a social network intervention could focus on increasing the ego's ties to low-risk individuals. Social network interventions may be especially useful if addictive behaviors tend to co-occur among clusters of alters within social networks. If addictive behaviors tend to co-occur in clusters, clinicians can inform clients to not only dissolve ties to separate alters, but multiple alters connected to one another. Furthermore, it is important to understand that the relationships among alters may be critical to the ego. For example, alter A who engages in addictive behavior modestly, may be connected to alters B, C, and D who engage in addictive behavior frequently. It may be important for the ego to not only sever ties to alters B, C, and D, but also A because of his or her ties to the more problematic alters. 2. Method 2.1. Participants 281 undergraduates (189 females) enrolled in lower-level psychology courses at a large university participated in the study. Respondents
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were compensated with course credit for their participation. The mean age was 19.4 (SD = 1.44). The majority of respondents classified themselves as Caucasian (82.2%), followed by African American (7.5%), Asian American (6.8%), and Other (3.5%). 2.2. Procedure Respondents provided informed consent at the beginning of the study, completed a battery of assessments in an on-campus laboratory, and then were debriefed. The study was approved by the University of Georgia Institutional Review Board. 2.3. Measures 2.3.1. Ego questionnaires Respondents completed the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). This questionnaire assesses individuals' alcohol use and alcoholrelated problems. Based on recommendations by DeMartini and Carey (2012), respondents were classified into three categories based on their total AUDIT score: nondrinkers were those who scored 0, light drinkers scored 1–7, and at-risk drinkers scored ≥ 8. Based on this classification, the sample included 57 nondrinkers, 115 light drinkers, and 109 at-risk drinkers. Respondents answered questions pertaining to their own frequency of alcohol use, tobacco use, marijuana use, and gambling. Each behavior was assessed on a 6-point Likert frequency scale: 1) Not in the past year, 2) Less than once a month, 3) Once a month, 4) Once a week, 5) Multiple times a week, and 6) Daily. 2.3.2. Social network questionnaire We utilized an egocentric network analysis approach, in which the respondent (i.e., “ego”) enumerated 30 individuals (i.e., “alters”) who had the most significant impact on the respondent's life in the past year. Each alter was classified as a friend, family member, romantic partner, or co-worker. The ego answered questions pertaining to the ego's perceptions of each alter's alcohol use, tobacco use, marijuana use, and gambling frequency on a 6-point Likert frequency scale: 1) Not in the past year, 2) Less than once a month, 3) Once a month, 4) Once a week, 5) Multiple times a week, and 6) Daily. Egos then evaluated the relationships among alters listed. Specifically, the ego answered questions about the closeness among the alters on a 5-point Likert scale. For purposes of analysis, a tie between alters was considered to be present if they were classified as either very or moderately close. 2.4. Data analysis To test the first aim, whether alters engaged in co-occurring addictive behaviors, Pearson correlations were conducted. To examine the second aim, whether alters tended to cluster together based on similarities in co-occurring addictive behaviors, two unique cluster analyses were conducted. First, we partitioned the 281 egocentric networks into unique, non-overlapping clusters using the Markov Clustering algorithm (Van Dongen, 2000). Because egos are connected to each
Table 1 Frequency of alcohol use, marijuana use, tobacco use, and gambling by respondents. All values are percentages. Frequency
Alcohol
Marijuana
Tobacco
Gamble
Not in the past year Less than once a month Once a month Once a week Multiple times a week Daily
21.0 15.3 9.6 23.5 30.3 0.3
55.5 22.1 8.2 5.3 5.7 3.2
72.2 12.5 6.8 2.5 3.6 2.4
80.1 15.7 1.4 2.2 0.3 0.3
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Table 2 Marijuana use, tobacco use, and gambling frequency between egos with varying levels of alcohol use based on AUDIT results. Variable
Marijuana Tobacco Gamble
Nondrinker (n = 57) AUDIT = 0
Light (n = 115) AUDIT = 1–7
At-risk Drinker (n = 109) AUDIT ≥ 8
M
SD
M
SD
M
SD
1.11 1.05 1.16
0.56 0.40 0.37
1.73 1.38 1.24
1.24 1.00 0.73
2.58 2.12 1.39
1.50 1.47 0.76
Table 3 Correlations between the frequencies of alter alcohol use, marijuana use, tobacco use, and gambling.
Alcohol Marijuana Tobacco Gamble a
Alcohol
Marijuana
Tobacco
– 0.474a 0.347a 0.142a
– 0.474a 0.125a
– 0.230a
two operations (e.g., expansion and inflation) to assign alters into disconnected clusters (Van Dongen, 2000). Second, we conducted a two-step hierarchical and nonhierarchical cluster analysis (Hair, Black, Babin, & Anderson, 2010). In the first stage, we conducted Ward's agglomerative hierarchical squared Euclidean distance analysis, using the average frequency of alcohol use, marijuana use, tobacco use, and gambling of the initial clusters. In the second stage, we conducted a k-means clustering analysis to verify the clusters in the first step. After the two-step cluster analysis, we performed an ANOVA with the clusters as the independent variable, and alcohol use, marijuana use, tobacco use, and gambling as the dependent variables. This allowed us to test whether the clusters differed in alcohol use, marijuana use, tobacco use, and gambling. To test the third aim, Jonckheere–Terpstra tests were conducted to examine differences in the frequency of alters' substance use between the networks of at-risk drinkers, light drinkers, and nondrinkers. For each effect, medians were reported along with the effect size. Effect pffiffiffiffi sizes were calculated using the formula r = Z / ( N. Data were analyzed using UCINET and SPSS 21.0.
Correlation is significant at the 0.01 level.
3. Results 3.1. Ego characteristics alter in their network, only the connections among alters were of interest. The Markov Clustering algorithm maximizes ties between alters within clusters and minimizes ties between clusters. The Markov Clustering algorithm utilizes a bootstrapping approach and
Table 1 displays egos overall frequency of substance use. In the past year, 79% of respondents consumed alcohol, 44.5% used marijuana, 27.8% used tobacco, and 19.9% gambled. These percentages
A: Nondrinker Alcohol Network Key Friend Family member Romantic partner Co-worker
B: Light Drinker Alcohol Network
C: At-Risk Drinker Alcohol Network
Fig. 1. Graphical representations of a nondrinker's, a light drinker's, and an at-risk drinker's network. Across the panels, darker colors reflect more frequent alcohol use by alters. Because each ego has a tie (i.e., connected) to all 30 of his or her alters, only the ties among alters are displayed.
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are comparable to national samples with young adults (Johnston et al., 2014). Egos varied widely in their AUDIT scores (M = 6.15; SD = 5.53). Table 2 displays the average frequency of ego's addictive behaviors based on ego drinker categorization. As expected, at-risk drinkers engaged in addictive behaviors more frequently than light drinkers and nondrinkers. 3.2. Aim 1. Alter overlap of addictive behaviors As shown in Table 3, egos' perceptions of alters' frequency of gambling, smoking, drinking, and marijuana use were all significantly correlated, although correlations involving gambling were weaker. Alter marijuana, alcohol, and tobacco use was moderately to highly correlated. Figs. 1, 2, 3, and 4 display graphical representations of a nondrinker's, a light drinker's, and an at-risk drinker's network, with darker colors reflecting greater frequencies of alcohol use, marijuana use, tobacco use, and gambling, respectively. These three egos were chosen because each of their networks contains the average characteristics of that ego drinking group. When displaying network graphs, the ego is not displayed in the network, because the ego has a tie (i.e., is connected) to every alter in his or her network. Visual examination of the figures indicates that egos' perceive that alters tend to engage in co-occurring addictive behaviors. For example, examining only the at-risk drinker's network, alters who tend to frequently engage in alcohol use (Panel C of Fig. 1), also tend to frequently engage in marijuana use (Panel C of Fig. 2), tobacco use (Panel C of Fig. 3), and gambling (Panel C of Fig. 4).
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3.3. Aim 2. Clustering of co-occurring addictive behaviors We utilized the Markov Clustering algorithm to partition each network into non-overlapping clusters of alters (Van Dongen, 2000). The algorithm identified 1286 clusters, with the number of clusters varying between one and nine in each egocentric network. Once the clusters were identified, we averaged the alcohol, marijuana use, tobacco use, and gambling of each cluster. Next, a two-step hierarchical and nonhierarchical cluster analysis was conducted on the clusters (Hair et al., 2010) to identify the types of clusters in individuals' networks. In the first step, a hierarchical cluster analysis on the average substance use of each cluster was performed, using two criteria to ascertain the number of clusters: visual examination of the dendogram and the agglomeration schedule coefficient (Hair et al., 2010). Both of these criteria indicated that a two-cluster solution was optimal. In the second step, we conducted a k-means cluster analysis for descriptive purposes, using the same variables to verify the clusters. In this type of cluster analysis, the clusters are chosen to maximize the differences between the clusters. Then, we conducted an ANOVA to examine differences between the clusters. Cluster 1 contained greater frequencies of co-occurring addictive behavior, supporting the two-cluster solution (see Table 4). Visual representations of the clustering effects in Figs. 1, 2, 3, and 4 show that all four addictive behaviors tend to co-occur in the same regions of the network. 3.4. Aim 3. Network compositional differences Fig. 1 illustrates graphical differences in the frequency of alters' alcohol use between a nondrinker's, a light drinker's, and an at-risk drinker's
A: Nondrinker Marijuana Network Key Friend Family member Romantic partner Co-worker B: Light Drinker Marijuana Network
C: At-Risk Drinker Marijuana Network
Fig. 2. Graphical representations of the same nondrinker's, light drinker's, and at-risk drinker's networks from Figs. 1, 3, and 4. Across the panels, darker colors reflect more frequent marijuana use by alters.
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A: Nondrinker Tobacco Network Key Friend Family member Romantic partner Co-worker B: Light Drinker Tobacco Network
C: At-Risk Drinker Tobacco Network
Fig. 3. Graphical representations of the same nondrinker's, light drinker's, and at-risk drinker's networks from Figs. 1, 2, and 4. Across the panels, darker colors reflect more frequent tobacco use by alters.
network. There were significant differences in drinking frequency between at-risk drinkers' (median = 4, ‘once a week’), light drinkers' (median = 3, ‘once a month’), and nondrinkers' alters (median = 1, ‘not in the past year’; Z = 37.08, p b .001; r = .41). Notably, at-risk drinkers perceived that 66% of alters in their network consumed alcohol at least once a week compared to 43% in light drinkers' networks and 17% in nondrinkers' networks (see Fig. 5). Fig. 2 illustrates graphical differences in alters' marijuana use between the same egos' networks. Although the medians were the same for all three groups (median = 1, ‘not in the past year’), alters' frequency of marijuana use significantly differed between at-risk drinkers, light drinkers, and nondrinkers' networks (Z = 26.43, p b .001; r = .29). At-risk drinkers perceived that 22% of alters used marijuana at least once a week compared to 13% in light drinkers' networks and 4% in nondrinkers' networks (see Fig. 5). Figs. 3 and 4 display graphical differences in alters' tobacco use and gambling, respectively, between the same egos. There were significant differences in alters' smoking and gambling frequency between at-risk drinkers, light drinkers, and nondrinkers' networks (Z = 18.11, p b .001, and Z = 5.57, p b .001, respectively), albeit with smaller effect sizes (r = .19 for smoking and r = .06 for gambling). The median in all groups for both behaviors was 1 (‘not in the past year’) indicating that these behaviors were relatively infrequent (see Fig. 5). 4. Discussion This is the first study to utilize egocentric SNA to examine perceptions of co-occurring addictive behaviors among alters (e.g., network
members), investigate whether alters cluster together based on similar magnitudes of co-occurring addictive behaviors, and utilize a large-scale egocentric network design to investigate differences in the frequency of alters' addictive behavior between egos (e.g., respondents) with different degrees of alcohol use. Previous studies have only examined differences in small networks (e.g., less than 10 alters); this is the first study to use a large network design. Although individuals who consume alcohol tend also to use marijuana and tobacco (Reboussin et al., 2006; Shillington & Clapp, 2006), this study empirically demonstrated for the first time that this populationlevel trend was also clearly observable within individuals' social networks. The frequency of alter drinking, smoking, and marijuana use was moderately to strongly correlated, suggesting either that egos perceived that alters tend to engage in multiple addictive behaviors or that alters actually tend to engage in multiple addictive behaviors. A similar pattern was observed for gambling but at weaker levels of correlation. Expanding on previous studies, which have found that alters who drink heavily may be more problematic to the ego (Leonard et al., 2000), our findings indicate that these individuals who drink frequently systematically engage in two or more addictive behaviors. Thus, the current SNA approach brings the overlap in substance use and addictive behaviors among young adults into sharp relief. The cluster analysis suggested that alters clustered together in two distinct types, one group moderate to high on co-occurring addictive behaviors and the other low on co-occurring addictive behaviors. This indicates that medium to high frequencies of co-occurring addictive behavior do not occur randomly across the egocentric networks, but are confined to distinct subgroups within the network. Alters with similar
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A: Nondrinker Gambling Network Key Friend Family member Romantic partner Co-worker
B: Light Drinker Gambling Network
C: At-Risk Drinker Gambling Network
Fig. 4. Graphical representations of the same nondrinker's, light drinker's, and at-risk drinker's networks from Fig. 1, 2, and 3. Across the panels, darker colors reflect more frequent gambling by alters.
frequencies of co-occurring addictive behaviors tend to cluster together, although we cannot be certain whether this results from social selection or from social influence. Last, we utilized a large-scale network design to investigate differences between nondrinkers, light drinkers, and at-risk drinkers' perceptions of their alters' substance use. Medium-to-large effect sizes were seen between groups when examining differences in the frequency of alter alcohol and marijuana use, and smaller but significant differences were also seen in the frequency of gambling and tobacco use. These results support previous findings that egos' networks are comprised of others who engage in similar levels of substance use (Leonard et al., 2000) and, for the first time, demonstrate two unique findings. First, it is not only the frequency of alters' drinking that greatly differentiates at-risk, light, and nondrinkers' networks, but also the perceived frequency of marijuana use, tobacco use, and gambling. Second, these network differences in addictive behavior are not only found among individuals' closest friends and family members, but among their entire social network, indicating that problematic drinkers may surround
themselves not only with a few alters who use multiple addictive behaviors, but with an extended group of alters who use multiple addictive behaviors. Strikingly, for example, in at-risk drinkers' networks, egos' perceived weekly marijuana use by 23% of alters. These findings have important implications for prevention specialists and treatment providers. First, in at-risk drinkers' social networks, there is a strong perceived prevalence of substance use, especially alcohol and marijuana use. Current treatment interventions such as those
Table 4 Cluster characteristics based on the k-means analysis. Variable
N Alcohol Marijuana Tobacco Gamble
Cluster 1
Cluster 2
M (SD)
M (SD)
510 4.25 (.69) 2.68 (1.17) 2.05 (1.16) 1.19 (.39)
775 2.12 (.92) 1.177 (.34) 1.16 (.37) 1.10 (.26)
Fig. 5. The percentage of network members who engage in alcohol, tobacco, marijuana use, and gambling at least once a week by ego drinking status.
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that utilize social networks to discontinue drinking have found that changing network support for abstinence is strongly associated with favorable treatment outcomes. Specifically, increasing the number of nondrinkers in the respondent's social network by one significantly impacts the probability that the respondent would be abstinent for two years following treatment (Litt, Kadden, Kabela-Cormier, & Petry, 2007, 2009). In our study, in at-risk drinkers' networks, ego's perceived that approximately one-fifth of alters did not consume alcohol and close to two-thirds did not use marijuana in the past year. We posit that strategies to increase the number of alters who do not engage in substance use may be an effective intervention strategy. Utilizing SNA as an assessment tool in clinical interventions may also be useful in clarifying where there are clusters of co-occurring addictive behaviors in respondent's networks. If the clinician can help the client recognize where the problematic co-occurring addictive behavior is occurring in their network and by whom, then the clinician can help the client reduce or even sever ties with the problematic alters. Severing ties with problematic alters may reduce the probability of relapse, as may increasing ties to positive alters, and this appears to be one of the mechanisms of participating in Alcoholics Anonymous (Kelly, Stout, Magill, & Tonigan, 2011). However, severing ties may not be perceived as a viable strategy for significant others or family members. In this case, SNA may be helpful in a clinical context by informing where the individual's social network is most amenable to change. The main limitation of the current study is that the egos reported on the behavior of alters in their network. Some research suggests that individuals project their behavior onto others (Ross, Greene, & House, 1977). Still, it may not be others' actual behavior that drives our own addictive behavior, but our perceptions of their behavior, where the two conflict. In the current sample, the self-reported frequency of gambling by respondents was lower than in other studies, which have found that as many as 70% of college students gambled in the past year (Barnes, Welte, Hoffman, & Tidwell, 2010). In the current sample, less than 20% gambled in the past year. We attribute the lack of network gambling to the low self-reported frequency of gambling by respondents. Also, it is not certain whether the same alters were reported in multiple ego's networks because of the anonymity of alters (i.e., egos only indicated each alters first name and last initial due to privacy concerns). Future studies may utilize a sociocentric design, wherein each alter in a network reports on their own behavior, to replicate and extend the current findings. In addition, the current study used a cross-sectional design, which precludes determining whether social selection or social influence models of co-occurring substance use were dominant. Another limitation of the current study is its low representation of racial minorities, although this reflects the university population where the data were collected. Nonetheless, different cultures and ethnicities vary on the influence of family members and friends on behavior, and future studies should replicate these analyses on other samples to examine the robustness of the findings. A final consideration is the novel and exploratory cluster analysis on the sample. The current study is the first to examine younger adults' perceptions of their network members co-occurring substance use. Network members were perceived to engage in multiple forms of addictive behaviors, and clustered together into two groups based on connections to other network members and their frequency of co-occurring addictive behaviors. One cluster was low on co-occurring addictive behaviors and the other cluster was higher on co-occurring addictive behaviors. There were significant differences in the perceived frequency of substance use between at-risk, light, and nondrinkers' network members, such that effects were strongest with regard to marijuana and alcohol use, and weaker but still significant with regard to gambling and tobacco use. These findings suggest that social networks both reflect an individual's severity of alcohol and other addictive behavior and may also be viable targets for future interventions.
Role of funding source James MacKillop is the holder of the Peter Boris Chair in Addiction Research, which partially supported his contributions and NIH grant P30 DA027827.
Contributors MKM contributed to the primary design, analyzed the results of the study, and was the primary contributor to the composition of the manuscript. ADC, JM, and ASG provided oversight of all aspects of the study and were the secondary contributors to the composition of the manuscript.
Conflict of interest No conflict declared.
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Contents lists available at ScienceDirect
Addictive Behaviors
A social network analysis approach to alcohol use and co-occurring addictive behavior in young adults Matthew K. Meisel a,⁎, Allan D. Clifton b, James MacKillop c, Adam S. Goodie a a b c
Department of Psychology, University of Georgia, Athens, GA, United States Department of Psychology, Vassar College, Poughkeepsie, NY, United States Peter Boris Center for Addictions Research, Department of Psychiatry and Behavioural Neurosciences, McMaster University/St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
H I G H L I G H T S • Addictive behaviors tend to co-occur among network members. • Network members cluster together based on co-occurring addictive behavior. • At-risk drinkers perceive frequent use of addictive behaviors in their networks.
a r t i c l e
i n f o
Available online 21 July 2015 Keywords: Social network analysis Co-occurring Addictive behavior Alcohol Marijuana Egocentric
a b s t r a c t Introduction: The current study applied egocentric social network analysis (SNA) to investigate the prevalence of addictive behavior and co-occurring substance use in college students' networks. Specifically, we examined individuals' perceptions of the frequency of network members' co-occurring addictive behavior and investigated whether co-occurring addictive behavior is spread evenly throughout networks or is more localized in clusters. We also examined differences in network composition between individuals with varying levels of alcohol use. Method: The study utilized an egocentric SNA approach in which respondents (“egos”) enumerated 30 of their closest friends, family members, co-workers, and significant others (“alters”) and the relations among alters listed. Participants were 281 undergraduates at a large university in the Southeastern United States. Results: Robust associations were observed among the frequencies of gambling, smoking, drinking, and using marijuana by network members. We also found that alters tended to cluster together into two distinct groups: one cluster moderate-to-high on co-occurring addictive behavior and the other low on co-occurring addictive behavior. Lastly, significant differences were present when examining egos' perceptions of alters' substance use between the networks of at-risk, light, and nondrinkers. Conclusions: These findings provide empirical evidence of distinct clustering of addictive behavior among young adults and suggest the promise of social network-based interventions for this cohort. Published by Elsevier Ltd.
1. Introduction Excessive alcohol consumption is a serious problem in the United States, particularly in college populations (Hingson, Zha, & Weitzman, 2009). The most recent Monitoring the Future Survey found that 63% of college students consumed alcohol in the past month (Johnston, O'Malley, Bachman, Schulenberg, & Miech, 2014). Furthermore, addictive behaviors tend to co-occur, such that individuals who frequently consume alcohol also tend to frequently engage in tobacco use, marijuana use, and gambling (Reboussin, Song, Shrestha, Lohman, & Wolfson, ⁎ Corresponding author at: Department of Psychology, University of Georgia, Athens, GA 30602-3013, United States. E-mail address: [email protected] (M.K. Meisel).
http://dx.doi.org/10.1016/j.addbeh.2015.07.009 0306-4603/Published by Elsevier Ltd.
2006; Shillington & Clapp, 2006; Weitzman & Chen, 2005). Individuals who engage in co-occurring substance use are at a greater risk of developing substance use related problems (Shillington & Clapp, 2006; Martens et al., 2009). Specifically, college students who use both marijuana and alcohol are more likely to drink and drive and to experience problems with alcohol than only those who consume alcohol (Shillington & Clapp, 2006). It is widely accepted that social motives are among the most endorsed reasons for drinking among college students (Brennan, Walfish, & AuBuchon, 1986; Cronin, 1997; LaBrie, Hummer, & Pedersen, 2007). In a regression model, even after controlling for personal motives, social motives account for unique variance in alcohol use (Cronin, 1997; Haden & Edmundson, 1991; MacKillop et al., 2013). Furthermore, social motives in high school predict alcohol use in college (Corbin, Iwamoto,
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& Fromme, 2011), suggesting that younger adults' alcohol use is prospectively influenced by the desire to be socially compatible with peers. Social network analysis (SNA) is a technique that quantifies and examines the structure of individuals' social networks. A social network refers to the individual's immediate social environment, such as friends, romantic partners, and family members. SNA allows researchers to examine compositional and structural characteristics of individuals' networks. Compositional characteristics refer to the proportion of individuals in a network with particular socio-demographic characteristics or behavioral traits. Structural characteristics refer to the pattern of relationships between individuals in a network. Structural characteristics, such as clustering in the network, can only be evaluated using SNA. In an egocentric SNA, the respondent (termed “ego”) enumerates the most important individuals in his or her life (termed “alters”), answers questions pertaining to the alters, and classifies the relationships among alters. Previous studies have examined compositional characteristics associated with addictive behavior. For example, individuals' alcohol consumption and misuse are positively associated with the proportion of network members who are drinkers, heavy drinkers, and “drinking buddies” (Fondacaro & Heller, 1983; MacKillop et al., 2013; Reifman, Watson, & McCourt, 2006). Similarly, individuals' smoking, drinking, and gambling are significantly associated with the same behaviors by peers (Andrews, Tildesley, Hops, & Li, 2002; Fortune et al., 2013; MacKillop et al., 2013; Meisel et al., 2013). Furthermore, heavy drinkers' networks have an overall greater rate of alcohol use as well as a greater proportion of “drinking buddies” compared to regular and infrequent drinkers' networks (Leonard, Kearns, & Mudar, 2000). In the current study, we utilized an egocentric network design to extend past findings in three ways. First, we investigated the frequency of co-occurring addictive behaviors in college students' networks, hypothesizing that alters who were perceived to engage in one addictive behavior would also be perceived to engage in multiple addictive behaviors. Second, we examined whether addictive behaviors tended to co-occur among clusters of alters within networks. Third, we examined differences in not only alters' alcohol use, but alters' marijuana use, tobacco use, and gambling as well, between ego's with varying degrees of alcohol use and alcohol-related problems, the most common form of substance misuse among young adults. Based on previous studies, we hypothesized that, compared to light and nondrinkers, at-risk drinkers would report greater frequencies of addictive behavior among their alters. Based on the accuracy of ego's perceptions of their alters' substance use, two different interventions may be implemented. If the ego misperceives the frequency of addictive behavior by alters (e.g., the ego perceives that alters engage in addictive behavior more frequently than reality), normative interventions may be conducted to correct egos' perceptions. However, if the alters actually engage in multiple addictive behaviors frequently, social network interventions may be utilized to dissolve or attenuate the ego's ties to these network members. Alternatively, a social network intervention could focus on increasing the ego's ties to low-risk individuals. Social network interventions may be especially useful if addictive behaviors tend to co-occur among clusters of alters within social networks. If addictive behaviors tend to co-occur in clusters, clinicians can inform clients to not only dissolve ties to separate alters, but multiple alters connected to one another. Furthermore, it is important to understand that the relationships among alters may be critical to the ego. For example, alter A who engages in addictive behavior modestly, may be connected to alters B, C, and D who engage in addictive behavior frequently. It may be important for the ego to not only sever ties to alters B, C, and D, but also A because of his or her ties to the more problematic alters. 2. Method 2.1. Participants 281 undergraduates (189 females) enrolled in lower-level psychology courses at a large university participated in the study. Respondents
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were compensated with course credit for their participation. The mean age was 19.4 (SD = 1.44). The majority of respondents classified themselves as Caucasian (82.2%), followed by African American (7.5%), Asian American (6.8%), and Other (3.5%). 2.2. Procedure Respondents provided informed consent at the beginning of the study, completed a battery of assessments in an on-campus laboratory, and then were debriefed. The study was approved by the University of Georgia Institutional Review Board. 2.3. Measures 2.3.1. Ego questionnaires Respondents completed the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). This questionnaire assesses individuals' alcohol use and alcoholrelated problems. Based on recommendations by DeMartini and Carey (2012), respondents were classified into three categories based on their total AUDIT score: nondrinkers were those who scored 0, light drinkers scored 1–7, and at-risk drinkers scored ≥ 8. Based on this classification, the sample included 57 nondrinkers, 115 light drinkers, and 109 at-risk drinkers. Respondents answered questions pertaining to their own frequency of alcohol use, tobacco use, marijuana use, and gambling. Each behavior was assessed on a 6-point Likert frequency scale: 1) Not in the past year, 2) Less than once a month, 3) Once a month, 4) Once a week, 5) Multiple times a week, and 6) Daily. 2.3.2. Social network questionnaire We utilized an egocentric network analysis approach, in which the respondent (i.e., “ego”) enumerated 30 individuals (i.e., “alters”) who had the most significant impact on the respondent's life in the past year. Each alter was classified as a friend, family member, romantic partner, or co-worker. The ego answered questions pertaining to the ego's perceptions of each alter's alcohol use, tobacco use, marijuana use, and gambling frequency on a 6-point Likert frequency scale: 1) Not in the past year, 2) Less than once a month, 3) Once a month, 4) Once a week, 5) Multiple times a week, and 6) Daily. Egos then evaluated the relationships among alters listed. Specifically, the ego answered questions about the closeness among the alters on a 5-point Likert scale. For purposes of analysis, a tie between alters was considered to be present if they were classified as either very or moderately close. 2.4. Data analysis To test the first aim, whether alters engaged in co-occurring addictive behaviors, Pearson correlations were conducted. To examine the second aim, whether alters tended to cluster together based on similarities in co-occurring addictive behaviors, two unique cluster analyses were conducted. First, we partitioned the 281 egocentric networks into unique, non-overlapping clusters using the Markov Clustering algorithm (Van Dongen, 2000). Because egos are connected to each
Table 1 Frequency of alcohol use, marijuana use, tobacco use, and gambling by respondents. All values are percentages. Frequency
Alcohol
Marijuana
Tobacco
Gamble
Not in the past year Less than once a month Once a month Once a week Multiple times a week Daily
21.0 15.3 9.6 23.5 30.3 0.3
55.5 22.1 8.2 5.3 5.7 3.2
72.2 12.5 6.8 2.5 3.6 2.4
80.1 15.7 1.4 2.2 0.3 0.3
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Table 2 Marijuana use, tobacco use, and gambling frequency between egos with varying levels of alcohol use based on AUDIT results. Variable
Marijuana Tobacco Gamble
Nondrinker (n = 57) AUDIT = 0
Light (n = 115) AUDIT = 1–7
At-risk Drinker (n = 109) AUDIT ≥ 8
M
SD
M
SD
M
SD
1.11 1.05 1.16
0.56 0.40 0.37
1.73 1.38 1.24
1.24 1.00 0.73
2.58 2.12 1.39
1.50 1.47 0.76
Table 3 Correlations between the frequencies of alter alcohol use, marijuana use, tobacco use, and gambling.
Alcohol Marijuana Tobacco Gamble a
Alcohol
Marijuana
Tobacco
– 0.474a 0.347a 0.142a
– 0.474a 0.125a
– 0.230a
two operations (e.g., expansion and inflation) to assign alters into disconnected clusters (Van Dongen, 2000). Second, we conducted a two-step hierarchical and nonhierarchical cluster analysis (Hair, Black, Babin, & Anderson, 2010). In the first stage, we conducted Ward's agglomerative hierarchical squared Euclidean distance analysis, using the average frequency of alcohol use, marijuana use, tobacco use, and gambling of the initial clusters. In the second stage, we conducted a k-means clustering analysis to verify the clusters in the first step. After the two-step cluster analysis, we performed an ANOVA with the clusters as the independent variable, and alcohol use, marijuana use, tobacco use, and gambling as the dependent variables. This allowed us to test whether the clusters differed in alcohol use, marijuana use, tobacco use, and gambling. To test the third aim, Jonckheere–Terpstra tests were conducted to examine differences in the frequency of alters' substance use between the networks of at-risk drinkers, light drinkers, and nondrinkers. For each effect, medians were reported along with the effect size. Effect pffiffiffiffi sizes were calculated using the formula r = Z / ( N. Data were analyzed using UCINET and SPSS 21.0.
Correlation is significant at the 0.01 level.
3. Results 3.1. Ego characteristics alter in their network, only the connections among alters were of interest. The Markov Clustering algorithm maximizes ties between alters within clusters and minimizes ties between clusters. The Markov Clustering algorithm utilizes a bootstrapping approach and
Table 1 displays egos overall frequency of substance use. In the past year, 79% of respondents consumed alcohol, 44.5% used marijuana, 27.8% used tobacco, and 19.9% gambled. These percentages
A: Nondrinker Alcohol Network Key Friend Family member Romantic partner Co-worker
B: Light Drinker Alcohol Network
C: At-Risk Drinker Alcohol Network
Fig. 1. Graphical representations of a nondrinker's, a light drinker's, and an at-risk drinker's network. Across the panels, darker colors reflect more frequent alcohol use by alters. Because each ego has a tie (i.e., connected) to all 30 of his or her alters, only the ties among alters are displayed.
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are comparable to national samples with young adults (Johnston et al., 2014). Egos varied widely in their AUDIT scores (M = 6.15; SD = 5.53). Table 2 displays the average frequency of ego's addictive behaviors based on ego drinker categorization. As expected, at-risk drinkers engaged in addictive behaviors more frequently than light drinkers and nondrinkers. 3.2. Aim 1. Alter overlap of addictive behaviors As shown in Table 3, egos' perceptions of alters' frequency of gambling, smoking, drinking, and marijuana use were all significantly correlated, although correlations involving gambling were weaker. Alter marijuana, alcohol, and tobacco use was moderately to highly correlated. Figs. 1, 2, 3, and 4 display graphical representations of a nondrinker's, a light drinker's, and an at-risk drinker's network, with darker colors reflecting greater frequencies of alcohol use, marijuana use, tobacco use, and gambling, respectively. These three egos were chosen because each of their networks contains the average characteristics of that ego drinking group. When displaying network graphs, the ego is not displayed in the network, because the ego has a tie (i.e., is connected) to every alter in his or her network. Visual examination of the figures indicates that egos' perceive that alters tend to engage in co-occurring addictive behaviors. For example, examining only the at-risk drinker's network, alters who tend to frequently engage in alcohol use (Panel C of Fig. 1), also tend to frequently engage in marijuana use (Panel C of Fig. 2), tobacco use (Panel C of Fig. 3), and gambling (Panel C of Fig. 4).
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3.3. Aim 2. Clustering of co-occurring addictive behaviors We utilized the Markov Clustering algorithm to partition each network into non-overlapping clusters of alters (Van Dongen, 2000). The algorithm identified 1286 clusters, with the number of clusters varying between one and nine in each egocentric network. Once the clusters were identified, we averaged the alcohol, marijuana use, tobacco use, and gambling of each cluster. Next, a two-step hierarchical and nonhierarchical cluster analysis was conducted on the clusters (Hair et al., 2010) to identify the types of clusters in individuals' networks. In the first step, a hierarchical cluster analysis on the average substance use of each cluster was performed, using two criteria to ascertain the number of clusters: visual examination of the dendogram and the agglomeration schedule coefficient (Hair et al., 2010). Both of these criteria indicated that a two-cluster solution was optimal. In the second step, we conducted a k-means cluster analysis for descriptive purposes, using the same variables to verify the clusters. In this type of cluster analysis, the clusters are chosen to maximize the differences between the clusters. Then, we conducted an ANOVA to examine differences between the clusters. Cluster 1 contained greater frequencies of co-occurring addictive behavior, supporting the two-cluster solution (see Table 4). Visual representations of the clustering effects in Figs. 1, 2, 3, and 4 show that all four addictive behaviors tend to co-occur in the same regions of the network. 3.4. Aim 3. Network compositional differences Fig. 1 illustrates graphical differences in the frequency of alters' alcohol use between a nondrinker's, a light drinker's, and an at-risk drinker's
A: Nondrinker Marijuana Network Key Friend Family member Romantic partner Co-worker B: Light Drinker Marijuana Network
C: At-Risk Drinker Marijuana Network
Fig. 2. Graphical representations of the same nondrinker's, light drinker's, and at-risk drinker's networks from Figs. 1, 3, and 4. Across the panels, darker colors reflect more frequent marijuana use by alters.
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A: Nondrinker Tobacco Network Key Friend Family member Romantic partner Co-worker B: Light Drinker Tobacco Network
C: At-Risk Drinker Tobacco Network
Fig. 3. Graphical representations of the same nondrinker's, light drinker's, and at-risk drinker's networks from Figs. 1, 2, and 4. Across the panels, darker colors reflect more frequent tobacco use by alters.
network. There were significant differences in drinking frequency between at-risk drinkers' (median = 4, ‘once a week’), light drinkers' (median = 3, ‘once a month’), and nondrinkers' alters (median = 1, ‘not in the past year’; Z = 37.08, p b .001; r = .41). Notably, at-risk drinkers perceived that 66% of alters in their network consumed alcohol at least once a week compared to 43% in light drinkers' networks and 17% in nondrinkers' networks (see Fig. 5). Fig. 2 illustrates graphical differences in alters' marijuana use between the same egos' networks. Although the medians were the same for all three groups (median = 1, ‘not in the past year’), alters' frequency of marijuana use significantly differed between at-risk drinkers, light drinkers, and nondrinkers' networks (Z = 26.43, p b .001; r = .29). At-risk drinkers perceived that 22% of alters used marijuana at least once a week compared to 13% in light drinkers' networks and 4% in nondrinkers' networks (see Fig. 5). Figs. 3 and 4 display graphical differences in alters' tobacco use and gambling, respectively, between the same egos. There were significant differences in alters' smoking and gambling frequency between at-risk drinkers, light drinkers, and nondrinkers' networks (Z = 18.11, p b .001, and Z = 5.57, p b .001, respectively), albeit with smaller effect sizes (r = .19 for smoking and r = .06 for gambling). The median in all groups for both behaviors was 1 (‘not in the past year’) indicating that these behaviors were relatively infrequent (see Fig. 5). 4. Discussion This is the first study to utilize egocentric SNA to examine perceptions of co-occurring addictive behaviors among alters (e.g., network
members), investigate whether alters cluster together based on similar magnitudes of co-occurring addictive behaviors, and utilize a large-scale egocentric network design to investigate differences in the frequency of alters' addictive behavior between egos (e.g., respondents) with different degrees of alcohol use. Previous studies have only examined differences in small networks (e.g., less than 10 alters); this is the first study to use a large network design. Although individuals who consume alcohol tend also to use marijuana and tobacco (Reboussin et al., 2006; Shillington & Clapp, 2006), this study empirically demonstrated for the first time that this populationlevel trend was also clearly observable within individuals' social networks. The frequency of alter drinking, smoking, and marijuana use was moderately to strongly correlated, suggesting either that egos perceived that alters tend to engage in multiple addictive behaviors or that alters actually tend to engage in multiple addictive behaviors. A similar pattern was observed for gambling but at weaker levels of correlation. Expanding on previous studies, which have found that alters who drink heavily may be more problematic to the ego (Leonard et al., 2000), our findings indicate that these individuals who drink frequently systematically engage in two or more addictive behaviors. Thus, the current SNA approach brings the overlap in substance use and addictive behaviors among young adults into sharp relief. The cluster analysis suggested that alters clustered together in two distinct types, one group moderate to high on co-occurring addictive behaviors and the other low on co-occurring addictive behaviors. This indicates that medium to high frequencies of co-occurring addictive behavior do not occur randomly across the egocentric networks, but are confined to distinct subgroups within the network. Alters with similar
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A: Nondrinker Gambling Network Key Friend Family member Romantic partner Co-worker
B: Light Drinker Gambling Network
C: At-Risk Drinker Gambling Network
Fig. 4. Graphical representations of the same nondrinker's, light drinker's, and at-risk drinker's networks from Fig. 1, 2, and 3. Across the panels, darker colors reflect more frequent gambling by alters.
frequencies of co-occurring addictive behaviors tend to cluster together, although we cannot be certain whether this results from social selection or from social influence. Last, we utilized a large-scale network design to investigate differences between nondrinkers, light drinkers, and at-risk drinkers' perceptions of their alters' substance use. Medium-to-large effect sizes were seen between groups when examining differences in the frequency of alter alcohol and marijuana use, and smaller but significant differences were also seen in the frequency of gambling and tobacco use. These results support previous findings that egos' networks are comprised of others who engage in similar levels of substance use (Leonard et al., 2000) and, for the first time, demonstrate two unique findings. First, it is not only the frequency of alters' drinking that greatly differentiates at-risk, light, and nondrinkers' networks, but also the perceived frequency of marijuana use, tobacco use, and gambling. Second, these network differences in addictive behavior are not only found among individuals' closest friends and family members, but among their entire social network, indicating that problematic drinkers may surround
themselves not only with a few alters who use multiple addictive behaviors, but with an extended group of alters who use multiple addictive behaviors. Strikingly, for example, in at-risk drinkers' networks, egos' perceived weekly marijuana use by 23% of alters. These findings have important implications for prevention specialists and treatment providers. First, in at-risk drinkers' social networks, there is a strong perceived prevalence of substance use, especially alcohol and marijuana use. Current treatment interventions such as those
Table 4 Cluster characteristics based on the k-means analysis. Variable
N Alcohol Marijuana Tobacco Gamble
Cluster 1
Cluster 2
M (SD)
M (SD)
510 4.25 (.69) 2.68 (1.17) 2.05 (1.16) 1.19 (.39)
775 2.12 (.92) 1.177 (.34) 1.16 (.37) 1.10 (.26)
Fig. 5. The percentage of network members who engage in alcohol, tobacco, marijuana use, and gambling at least once a week by ego drinking status.
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that utilize social networks to discontinue drinking have found that changing network support for abstinence is strongly associated with favorable treatment outcomes. Specifically, increasing the number of nondrinkers in the respondent's social network by one significantly impacts the probability that the respondent would be abstinent for two years following treatment (Litt, Kadden, Kabela-Cormier, & Petry, 2007, 2009). In our study, in at-risk drinkers' networks, ego's perceived that approximately one-fifth of alters did not consume alcohol and close to two-thirds did not use marijuana in the past year. We posit that strategies to increase the number of alters who do not engage in substance use may be an effective intervention strategy. Utilizing SNA as an assessment tool in clinical interventions may also be useful in clarifying where there are clusters of co-occurring addictive behaviors in respondent's networks. If the clinician can help the client recognize where the problematic co-occurring addictive behavior is occurring in their network and by whom, then the clinician can help the client reduce or even sever ties with the problematic alters. Severing ties with problematic alters may reduce the probability of relapse, as may increasing ties to positive alters, and this appears to be one of the mechanisms of participating in Alcoholics Anonymous (Kelly, Stout, Magill, & Tonigan, 2011). However, severing ties may not be perceived as a viable strategy for significant others or family members. In this case, SNA may be helpful in a clinical context by informing where the individual's social network is most amenable to change. The main limitation of the current study is that the egos reported on the behavior of alters in their network. Some research suggests that individuals project their behavior onto others (Ross, Greene, & House, 1977). Still, it may not be others' actual behavior that drives our own addictive behavior, but our perceptions of their behavior, where the two conflict. In the current sample, the self-reported frequency of gambling by respondents was lower than in other studies, which have found that as many as 70% of college students gambled in the past year (Barnes, Welte, Hoffman, & Tidwell, 2010). In the current sample, less than 20% gambled in the past year. We attribute the lack of network gambling to the low self-reported frequency of gambling by respondents. Also, it is not certain whether the same alters were reported in multiple ego's networks because of the anonymity of alters (i.e., egos only indicated each alters first name and last initial due to privacy concerns). Future studies may utilize a sociocentric design, wherein each alter in a network reports on their own behavior, to replicate and extend the current findings. In addition, the current study used a cross-sectional design, which precludes determining whether social selection or social influence models of co-occurring substance use were dominant. Another limitation of the current study is its low representation of racial minorities, although this reflects the university population where the data were collected. Nonetheless, different cultures and ethnicities vary on the influence of family members and friends on behavior, and future studies should replicate these analyses on other samples to examine the robustness of the findings. A final consideration is the novel and exploratory cluster analysis on the sample. The current study is the first to examine younger adults' perceptions of their network members co-occurring substance use. Network members were perceived to engage in multiple forms of addictive behaviors, and clustered together into two groups based on connections to other network members and their frequency of co-occurring addictive behaviors. One cluster was low on co-occurring addictive behaviors and the other cluster was higher on co-occurring addictive behaviors. There were significant differences in the perceived frequency of substance use between at-risk, light, and nondrinkers' network members, such that effects were strongest with regard to marijuana and alcohol use, and weaker but still significant with regard to gambling and tobacco use. These findings suggest that social networks both reflect an individual's severity of alcohol and other addictive behavior and may also be viable targets for future interventions.
Role of funding source James MacKillop is the holder of the Peter Boris Chair in Addiction Research, which partially supported his contributions and NIH grant P30 DA027827.
Contributors MKM contributed to the primary design, analyzed the results of the study, and was the primary contributor to the composition of the manuscript. ADC, JM, and ASG provided oversight of all aspects of the study and were the secondary contributors to the composition of the manuscript.
Conflict of interest No conflict declared.
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