Psychology of Addictive Behaviors 2014, Vol. 28, No. 2, 431– 442

© 2014 American Psychological Association 0893-164X/14/$12.00 DOI: 10.1037/a0036226

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Do Peer Perceptions Mediate the Effects of ADHD Symptoms and Conduct Problems on Substance Use for College Students? Kathryn Van Eck

Robert S. Markle, Lauren Dattilo, and Kate Flory

University of South Carolina and University of Maryland

University of South Carolina

Although extensive research suggests that attention-deficit/hyperactivity disorder (ADHD) and conduct problems (CP; symptoms of oppositional defiant disorder, conduct disorder, and antisocial personality disorder) contribute to risk for substance use, why these symptoms increase risk for substance use remains unclear. Given that research indicates that perceived peer tolerance and perceived peer substance use affect substance use, we evaluated the degree to which these peer-perception constructs mediated the association that ADHD symptoms, CP symptoms, and their interaction share with substance use (i.e., alcohol use, marijuana use, and illicit drug use). Participants were college students (N ⫽ 627; 60% female; 47% European American) with a mean age of 20.23 years. Results indicated that perceived peer use significantly mediated the association of ADHD symptoms with alcohol, marijuana, and illicit drug use, whereas perceived peer use only mediated the association between CP symptoms and alcohol use. Perceived peer tolerance significantly mediated the association that both CP and ADHD symptoms had with marijuana use, but not alcohol or illicit drug use. Finally, CP symptoms moderated the indirect effect that ADHD symptoms had on alcohol use through perceived peer tolerance. At low levels of CP symptoms, increases in ADHD symptoms corresponded to increases in perceived peer tolerance, which was related to increased alcohol use. However, at high levels of CP symptoms, increases in ADHD symptoms corresponded to decreases in perceived peer tolerance, which was associated with decreased alcohol use. Implications of findings are discussed. Keywords: substance use, ADHD, conduct problems, peer perceptions

important implications for prevention and intervention development. For example, as ADHD and/or CP symptoms increase, college students may require programming with a targeted focus on perceptions of peer tolerance and use.

Although many studies have investigated the link between attention-deficit/hyperactivity disorder (ADHD) symptoms and substance use (e.g., Malone, Van Eck, Flory, & Lamis, 2010; see Lee, Humphreys, Flory, Liu, & Glass, 2011 for a review), it is unclear whether symptoms of ADHD and conduct problems (CP; e.g., symptoms of oppositional defiant disorder, conduct disorder, and antisocial personality disorder) interact or independently contribute to substance use. Further, the mechanisms explaining substance use for individuals with these symptoms are not well understood. Specifically, peer influence may mediate the relations among ADHD symptoms, CP symptoms, and substance use (Marshal & Molina, 2006), yet few studies have examined this. Thus, we examined the degree to which ADHD symptoms, CP symptoms, and their interaction increased three types of substance use (alcohol, marijuana, and illicit drug use) among college students, and the degree to which peer perceptions mediated these associations. Understanding the degree to which peer perceptions contribute to substance use as ADHD and CP symptoms increase has

ADHD Symptoms and Substance Use Use of diagnosis rather than continuous ADHD symptoms may have contributed to inconsistent findings across the numerous studies that have assessed the risk for substance use among adults with ADHD (Clure et al., 1999; Katusic et al., 2005; Szobot et al., 2007). ADHD symptoms and other disruptive behavior disorders occur on a continuum of symptom severity (Marcus & Barry, 2005). Thus, ADHD and CP symptoms are more accurately represented as continuous symptoms rather than discrete diagnoses, which fall prey to the methodological shortcomings associated with dichotomizing a continuous variable (see Keselman et al., 1998; MacCallum, Zhang, Preacher, & Rucker, 2002). Further, ADHD symptom severity below the diagnostic cutoff point may increase risk for substance use, which would fail to be represented in analyses if discrete diagnoses were used (Faraone, 2006). Use of diagnosis rather than symptoms is particularly problematic for young adults. The heterotypic symptom presentation of ADHD between childhood and young adulthood is poorly represented in current diagnostic criteria, including use of developmentally inappropriate symptoms for young adults (e.g., climbing, excessive running; Rostain & Ramsay, 2006; Willoughby, 2003). Despite research support for the use of symptoms rather than diagnosis

Kathryn Van Eck, Division of Child & Adolescent Psychiatry, School of Medicine, University of Maryland and Department of Psychology, University of South Carolina; Robert S. Markle, Lauren Dattilo, and Kate Flory, Department of Psychology, University of South Carolina. Correspondence concerning this article should be addressed to Kathryn Van Eck, Department of Psychology, Barnwell College, University of South Carolina, 1512 Pendleton Street, Columbia, SC 29208. E-mail: [email protected] 431

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VAN ECK, MARKLE, DATTILO, AND FLORY

(Farmer, Seeley, Kosty, & Lewinsohn, 2009; Fergusson & Horwood, 1995), few studies have used this method. Studies using continuous ADHD symptoms indicate that current ADHD symptoms increase illicit drug use (Glass & Flory, 2011; Upadhyaya et al., 2005), whereas studies using ADHD diagnosis have produced inconsistent findings. Some studies indicate that ADHD is associated with increased alcohol and illicit drug use (Blase et al., 2009; Rooney, Chronis-Tuscano, & Yoon, 2012); others suggest that ADHD symptoms are associated with decreased alcohol use but increased illicit stimulant use (Janusis & Weyandt, 2010). Finally, other studies report no differences between those with and without ADHD (Rabiner, Anastopoulos, Costello, Hoyle, & Swartzwelder, 2008). Given these inconsistencies, further research is needed regarding the role of ADHD symptoms in substance use.

The Role of CP in the Relation Between ADHD and Substance Use The degree to which ADHD symptoms contribute to substance use independent of CP symptoms is also unclear. Both childhood CP symptoms (Disney, Elkins, McGue, & Iacono, 1999; Fergusson, Horwood, & Ridder, 2007) and adult antisocial behavior (Knop et al., 2009) increase substance use and often co-occur with ADHD, making CP an important construct to include in analyses. Research suggests that controlling for childhood CP symptoms reduces or even eliminates the link between ADHD symptoms and substance use (Fergusson et al., 2007; Glass & Flory, 2011; Lynskey & Fergusson, 1995; Torok, Darke, & Kaye, 2012), whereas, in other studies, CP symptoms moderate the link between ADHD symptoms and substance use (Flory, Milich, Lynam, Leukefeld, & Clayton, 2003; Molina & Pelham, 2003; Van Eck, Markle, & Flory, 2012). Regardless of the use of continuous symptoms or diagnosis, ADHD and CP together appear to increase illicit drug use more than ADHD or CP alone. Although CP symptoms may decline in young adulthood (Monahan, Steinberg, & Cauffman, 2009), research suggests that many individuals with antisocial traits manage to function in society, complete college, and transition into the workforce (LeBreton, Binning, & Adorno, 2006). Studies have estimated that about 5–15% of the population display subclinical symptoms of psychopathy, which include impulsivity, callousness, and need for novel stimulation despite maintaining employment and personal relationships (Gustafson & Ritzer, 1995; Pethman & Erlandsson, 2002). Thus, a college-student population is likely to present subclinical, yet meaningful, CP symptom severity. These individuals may experience high risk for substance use, given their sensitivity to novel stimuli and heightened sense of boredom (see Van Eck et al., 2012).

Peer-Perception Mediators of Substance Use Research suggests that perceiving peers to be tolerant of substance use (perceived peer tolerance) and perceiving that peers engage in substance use (perceived peer use) lead to increases in substance use (Neighbors, Lee, Lewis, Fossos, & Larimer, 2007). In fact, aiding youth in accurately perceiving peer norms related to substance use is a target area included in many substance-use prevention programs (Neighbors, Larimer, Lostutter, & Woods,

2006). However, these programs have demonstrated mixed results, with some demonstrating reduced substance use (Perkins, Haines, & Rice, 2005), whereas others are not associated with significant changes in substance use (Wechsler et al., 2003). Thus, it may be helpful to better define under which conditions substance-use behaviors are related to perceptions of peer norms and tolerance of use (Mattern & Neighbors, 2004). Specifically, ADHD and CP symptoms may influence the link between perceptions of peer norms and tolerance of use. As individuals with ADHD and CP symptoms tend to affiliate with other youth with CP symptoms (Bagwell, Molina, Pelham, & Hoza, 2001; Kendler, Jacobson, Myers, & Eaves, 2008), they may perceive, perhaps accurately, greater degrees of peer tolerance and peer drug use, increasing their risk for substance use. Only two studies have examined peer mediators of the association between ADHD and substance use (Marshal & Molina, 2006; Marshal, Molina & Pelham, 2003). Combining perceived peer tolerance and perceived peer use (referred to as “deviant peer affiliation”) with an adolescents sample (N ⫽ 142), Marshal and colleagues (2003) found that this construct mediated the relation between ADHD and four types of substance use (i.e., tobacco, alcohol, marijuana, and illicit drug). Further, the relation between deviant peer affiliation and substance use was stronger for adolescents with ADHD than controls. Marshal and Molina (2006) found that inattention symptoms predicted a strong association between deviant peer affiliation and substance use, but only for those with high CP symptoms. Taken together, these studies suggest that: (a) ADHD increases susceptibility to peer influence, which increases substance use during adolescence, and (b) CP symptoms increase the influence that perceptions of peer tolerance and use have on substance use for adolescents with ADHD. Although these studies examined perceptions of peer tolerance and use as one variable, these constructs are likely distinct. Perceived peer tolerance is an injunctive norm, indicating the perception that one’s peer-group members approve or disapprove of substance use. Perceived peer substance use is a descriptive norm indicating perceptions of group-member substance use. Injunctive and descriptive peer norms appear to independently affect substance use (Elek, Miller-Day, & Hecht, 2006; Larimer, Turner, Mallett, & Geisner, 2004; Neighbors, Geisner, & Lee, 2008). Descriptive norms better predict current use, whereas injunctive norms better predict long-term use and substance-related consequences (Larimer et al., 2004). Given the apparent independence of these constructs, we examined perceptions of peer tolerance and use as separate constructs and hypothesized that they would have independent effects on substance use.

The Current Study The current study investigated the degree to which ADHD symptoms, CP symptoms, and their interaction were related to three types of substance use (alcohol, marijuana, other illicit drugs) in college students. We also investigated the degree to which perceived peer tolerance and perceived peer use mediated these associations. This is the first study to evaluate how perceptions of peer use and tolerance independently mediate these associations. Clarifying the roles of peer perceptions in substance use for those with ADHD and CP symptoms can inform prevention and intervention development, contributing to understanding which college

ADHD, CP, PEER PERCEPTIONS, AND SUBSTANCE USE

students may respond to programs that target peer norms and tolerance of use. For example, as ADHD and/or CP symptoms increase, college students may display inflated perceptions of peer tolerance and use, and may require more programming that improves accurate peer perceptions for the avoidance and reduction of substance use. We hypothesized that ADHD symptoms, CP symptoms, and their interaction would significantly relate to all three types of substance use and that both perceptions of peer tolerance and use would mediate these associations.

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Method Participants and Procedure Participants were 627 college students ages 18 to 25 years (M ⫽ 20.23, SD ⫽ 1.40; 60% female) enrolled at a Southeastern university. Many students were freshmen (43%), and 47% identified as European American, 12% as African American, 3% as Asian, and 3% as Latino. Around 31% identified as multiracial; 4% percent did not specify race/ethnicity. Childhood household income was ⱕ $40,000/year for 14% of participants and ⱖ $100,000 for 48%; 41% of participants’ mothers had completed some college coursework. Recruitment occurred through class announcements in undergraduate psychology courses. Participants consented online and completed an Internet-based survey (M ⫽ 52 minutes; range: 18 –162 min). No significant inconsistencies were apparent between items with similar content, and response time on the survey was unrelated to study variables. Participants received extra course credit and the university’s institutional review board approved all procedures.

Measures ADHD symptoms. The Current Symptoms Scale, Self-Report Form (CSS; Barkley & Murphy, 2006) assesses continuous ADHD symptoms. The 36 items have a 4-point scale (0 ⫽ Never or rarely to 3 ⫽ Very often) and correspond to the Diagnostic and Statistical Manual (4th text rev.; DSM–IV–TR; American Psychiatric Association, 2000) criteria for ADHD. Eighteen items asked about current symptoms, and another 18 items assessed childhood symptoms. Validity concerns of retrospective self-report (Mannuzza, Klein, & Moulton, 1993) led us to use only current symptoms. Items were summed for a total scale score, given the multicollinearity between inattention and hyperactivity (r ⫽ .78, p ⬍ .05). Reliability was strong (␣ ⫽ .92). Prior research indicates sensitivity to symptom change (Safren et al., 2005; Wilens et al., 1996), and self-report is correlated with parent and spousal report (Barkley et al., 2008, 2011). Sixty-one participants (9%) scored in the clinical range (i.e., the number of their symptoms was higher than the CSS clinical cutoff score for ADHD; Barkley & Murphy, 2006), which is slightly higher than ADHD prevalence rates with other college-student samples (2– 8%; DuPaul et al., 2009; Heiligenstein et al., 1998). Conduct problems. The Self-Reported Delinquency Scale (SRD; Elliot, Huizinga, & Ageton, 1985) assesses conduct problems with 18 items that correspond to DSM–IV–TR (APA, 2000) diagnostic criteria for CD and is frequently used to asses delinquent behavior (e.g., Loeber, Farrington, Stouthamer-Loeber, & van Kammen, 1998). Participants indicated if (0 ⫽ No, 1 ⫽ Yes)

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they had engaged in the behavior described in each item in the last 6 months. Responses were summed (range: 0 to 11). Research suggests that responses correspond well with parent and teacher report (Pardini, Obradovíc, & Loeber, 2006) and legal records (Farrington et al., 1996). CP symptoms were related to self-report of both current and retrospective childhood symptoms of oppositional defiant disorder (current ODD r ⫽ .270, p ⬍ .001; childhood ODD r ⫽ .266, p ⬍ .001). Perceived peer substance use and tolerance. Perceived peer substance-use and tolerance of substance-use items are from the Monitoring the Future study (Johnston, O’Malley, & Bachman, 1988) and adapted by Chassin and colleagues (1993). Perceived peer-use items (9 items) were measured on a 6-point scale (1 ⫽ None my friends to 6 ⫽ All of my friends). Perceived peer tolerance (8 items) used a 5-point scale (1 ⫽ Strongly approve to 5 ⫽ Strongly disapprove). Peer tolerance items were reverse scored, with higher scores showing greater approval. Items for each type of substance use were summed to create separate subscales for perceived peer tolerance and peer use. These procedures created a total of six subscales where peer use of alcohol and peer tolerance of alcohol use each had four items; peer tolerance of marijuana use and peer use of marijuana each included three items; and peer tolerance of illicit drug use and peer use of illicit drugs each had two items. The peer-tolerance subscales (alcohol: ␣ ⫽ .84; marijuana: ␣ ⫽ .93, illicit drugs: ␣ ⫽ .91) and peer-use subscales (alcohol: ␣ ⫽ .88; marijuana: ␣ ⫽ .84, illicit drugs: ␣ ⫽ .91) had strong internal consistency in this sample. Although peer perception items were combined into one factor of “deviant peer association” in previous studies (Marshal & Molina, 2006; Marshal et al., 2003), we considered perceived peer tolerance and peer use as separate factors. These variables are similar to injunctive and descriptive norms, which are distinct constructs (White, Smith, Terry, Greenslade, & McKimmie, 2009) and share different associations with substance use behaviors (Lee, Geisner, Lewis, Neighbors, & Larimer, 2007; Neighbors et al., 2008). Given that perceptions for one type of substance may not extend to other substances, we also considered separately peer tolerance and peer use for each substance. To support this approach, we compared two confirmatory factor analysis (CFA) models conducted with maximum likelihood (ML) estimation, using Mplus 6.1 (Muthén & Muthén, 2010). In the first model, peer-perception items represented one factor for each type of substance (e.g., alcohol, marijuana, illicit drugs). Results did not meet the accepted cutoff points on fit indices: ␹2(101) ⫽ 2,593.54, p ⬍ .001; comparative fit index (CFI) ⫽ .63; standardized root mean residual (SRMR) ⫽ .13; and root mean square error of approximation (RMSEA) ⫽ .20; Yu & Muthén, 2002. In the second model, perceived peer-tolerance items and perceived peeruse items represented separate factors for each type of substance (six total factors). Results also did not display acceptable fit, ␹2(89) ⫽ 1,008.96, p ⬍ .001; CFI ⫽ .86; SRMR ⫽ .06; RMSEA ⫽ .13. Although the models were not nested, subjective comparison indicated that the second model provided better fit for the data, with perceived peer tolerance and peer use best represented as distinct factors. Further, correlations among perceived peer-tolerance factors and among perceived peer use factors ranged from .43 to .65, whereas correlations between perceived peer tolerance factors and perceived peer-use factors ranged from ⫺.01 to ⫺.40. Although additional research on the psycho-

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metric properties of this measure is warranted, results are consistent with our theoretical argument that perceived peer tolerance and perceived peer substance use are distinct constructs. Substance use. The Substance Use Questionnaire (SUQ; Molina & Pelham, 2003) has been used in several longitudinal studies (e.g., Molina et al., 2007). The sum of responses from three items represented alcohol use. The first item (“On average, how much do you drink in one sitting?”) had a 6-point scale (1 ⫽ 1–3 drinks to 6 ⫽ 15 or more drinks). The second item (“On average, how much do you drink in one week?”) had an 8-point scale (1 ⫽ 1–3 drinks to 8 ⫽ ⱖ 21 drinks). The third item (“On average, how many times in one week do you get drunk?”) had an 8-point scale (1 ⫽ Never to 8 ⫽ ⱖ 40 times). One item with an 8-point scale (1 ⫽ Never to 8 ⫽ ⱖ 40 times) assessed past year frequency of marijuana use. Lifetime illicit drug use was represented with 8 items (0 ⫽ No or 1 ⫽ Yes), using the stem “Have you ever used . . .?” Illicit substance included downers, cocaine (in any form), hallucinogens, narcotics, amphetamines (and any “uppers”), steroids, heroin, and inhalants. A “yes” to any item was coded 1 to represent lifetime illicit drug use.

Analytic Procedures Mediation procedures. For alcohol and marijuana use mediation models, we used ML estimation and bootstrapping with 5,000 generated samples, where 95% confidence intervals represented the possible range of point estimates (Preacher & Hayes, 2008). Weighted least squares, means, and variance-adjusted chisquare (WLSMV) estimation was used for the illicit drug use model. WLSMV provides robust estimation of standard errors for path coefficients for discrete outcome variables; a probit link is used to estimate coefficients (Asparouhov, 2005). Probit estimates represent the z-score change in the outcome for every standard deviation change in the predictor. As models were fully saturated, fit statistics were not applicable. Independent variables were mean centered (Cohen, Cohen, West, & Aiken, 2003).

Covariates. Covariates were selected based on previous research and associations with the mediator and outcome variables in this dataset. Covariates included participant sex, race/ethnicity (European American or non-European American), maternal education, childhood household income, and current ADHD medication status. We did not include age as a covariate, given that age was unrelated to the variables of interest. Missing data. The only missing data were for 29 participants who did not disclose their racial/ethnic background (4.4% of data). We used full information maximum likelihood (FIML) estimation, which generates accurate parameter estimates and standard errors for data that is missing at random (MAR) and generates estimates that are less biased than other strategies (Schafer & Graham, 2002).

Results Descriptive Statistics We used Mplus 6.1 (Muthén & Muthén, 2010) for mediation analyses and SPSS 19 (IBM, Chicago, IL) for other analyses. Descriptive statistics and correlations were assessed for continuous variables (see Table 1). Nearly 18% of the sample reported having used illicit drugs; 47% reported having used marijuana. About 83% of the sample reported having been drunk, whereas 28% reported getting drunk more than three times in a week and 39% have had more than five drinks in one sitting. For skewed variables, we ran analyses with and without log transformations of these variables. We retained analyses with log-transformed marijuana, but all other results were consistent between analyses with and without log-transformed variables. ADHD symptoms displayed small but significant correlations and CP symptoms displayed moderate correlations with most peer-perception variables, as well as alcohol and marijuana use. CP and ADHD symptoms shared a small positive association (r ⫽

Table 1 Descriptive Statistics and Correlations Among Continuous Variables Variable

1

2

3

4

5

6

7

1. ADHD 1 2. CP .273ⴱⴱ 1 3. Peer tolerance: Alcohol .027 .284ⴱⴱ 1 4. Peer tolerance: Marijuana .118ⴱⴱ .347ⴱⴱ .555ⴱⴱ 1 5. Peer tolerance: Other illicit drugs .178ⴱⴱ .192ⴱⴱ .242ⴱⴱ .536ⴱⴱ 1 6. Peer use: Alcohol .122ⴱⴱ .318ⴱⴱ .447ⴱⴱ .334ⴱⴱ .083ⴱ 1 7. Peer use: Marijuana .219ⴱⴱ .376ⴱⴱ .297ⴱⴱ .488ⴱⴱ .265ⴱⴱ .754ⴱⴱ 1 8. Peer use: Other illicit drugs .268ⴱⴱ .357ⴱⴱ .174ⴱⴱ .449ⴱⴱ .337ⴱⴱ .575ⴱⴱ .886ⴱⴱ 9. Alcohol use .171ⴱⴱ .509ⴱⴱ .406ⴱⴱ .408ⴱⴱ .250ⴱⴱ .532ⴱⴱ .533ⴱⴱ 10. Marijuana use .121ⴱⴱ .389ⴱⴱ .242ⴱⴱ .512ⴱⴱ .251ⴱⴱ .320ⴱⴱ .504ⴱⴱ 11 Childhood household income .041 .138ⴱⴱ .145ⴱⴱ .093ⴱ .005 .233ⴱⴱ .172ⴱⴱ 12. Mother’s education level .026 ⫺.045 ⫺.095ⴱ ⫺.030 .042 ⫺.165ⴱⴱ ⫺.104ⴱⴱ Mean 14.26 1.8 8.13 2.73 10.40 7.91 3.49 Standard deviation 9.39 1.83 4.03 2.33 2.98 3.21 1.80 Skew 1.14 1.54 .972 1.10 ⫺.49 ⫺.057 1.26 Kurtosis 1.99 3.07 .30 ⫺.17 ⫺.05 ⫺.74 1.34

8

9

10

11

12

1 .420 1 .471 .503ⴱⴱ 1 .123 .260ⴱⴱ .057 1 ⫺.035 ⫺.176ⴱⴱ ⫺.072 ⫺.324ⴱ 1 14.96 8.45 11.86 5.21 2.43 4.92 3.33 4.74 2.15 1.10 ⫺.33 .22 .64 ⫺.21 .67 ⫺.62 ⫺.44 .45 ⫺1.09 .49

Note. CP ⫽ conduct problems; peer tolerance ⫽ perceived peer tolerance of; peer use ⫽ perceived peer use of; N ⫽ 627; for variable response scales, see the Method section. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

ADHD, CP, PEER PERCEPTIONS, AND SUBSTANCE USE

.27, p ⬍ .001). These results support including CP symptoms as a moderator of both direct and indirect effects of ADHD symptoms on substance-use outcomes as well as the association of ADHD symptoms with perceived peer-tolerance and peer-use variables. Mother’s education and childhood household income were related to peer-perception variables and both alcohol and marijuana use. Given that mediators and substance use outcomes were significantly different across sex, race, and current use of stimulant medication as prescribed by a doctor (8% of the sample), these variables were included as covariates.

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Mediation Results Model description. We evaluated the degree to which peer perception variables mediated the associations that ADHD symptoms, CP symptoms, and their interaction had with substance use. In the model, ADHD symptoms, CP symptoms, and their interaction were linked to both perceived peer tolerance of use and perceived peer use. Both constructs were then linked with substance use. The model also included the direct links from ADHD symptoms, CP symptoms, and their interaction to substance use (see Figures 1, 3, and 4). Separate models were tested for three types of substance use (e.g., alcohol, marijuana, illicit drugs). Alcohol use. Results demonstrated that only perceived peer alcohol use mediated the association between ADHD symptoms and alcohol use (see mediation effects in Table 2; see Figure 1 for path coefficients). Only the indirect effect of ADHD symptoms on alcohol use through perceived peer use was significant. Both perceived peer tolerance of alcohol use and perceived peer alcohol use provided significant mediation of the association between CP symptoms and alcohol use. The total effect and total indirect effect were also significant and positive. The total effect represents the combined effect of the indirect and direct effects of CP symptoms on alcohol use. The indirect effect of the interaction of ADHD and CP symptoms on alcohol use through perceived peer tolerance of alcohol use was significant and positive.

.232 .210 (.077)

CP Symptoms -.023 -.153 (.007)

ADHD x CP

We probed the interaction of ADHD and CP symptoms with simple slope analyses at high (⫹1 SD ⬎ mean) and low levels (⫺1 SD ⬍ mean) of CP symptoms (Cohen et al., 2003; see Figure 2). When CP symptoms were low, perceived peer tolerance of use increased as ADHD symptoms increased (␤ ⫽ .040, SE ⫽ .011, p ⬍ .001). When CP symptoms were high, perceived peer tolerance decreased as ADHD symptoms increased (␤ ⫽ ⫺.044, SE ⫽ .011, p ⬎ .001). Perceived peer tolerance was positively related to alcohol use. We also evaluated the simple effects of the indirect effect of the interaction of ADHD and CP symptoms on alcohol use through perceived peer tolerance (see Table 3). When CP symptoms were high, increases in ADHD symptoms corresponded to decreases in perceived peer tolerance of alcohol use, indirectly decreasing alcohol use. When CP symptoms were low, perceived peer tolerance of alcohol use increased as ADHD symptoms increased, indirectly increasing alcohol use. We also calculated the total indirect effect for each mediator and the total direct effect of ADHD symptoms, CP symptoms, and their interaction simultaneously on alcohol use. We summed the a path point estimates of ADHD symptoms, CP symptoms, and their interaction to perceived peer tolerance of alcohol use and then to perceived peer alcohol use; we multiplied this value by the b path of each mediator to alcohol use. Standard errors were calculated with bootstrapping. For perceived peer tolerance, the total a path (a ⫽ .207, SE ⫽ .073, p ⫽ .005; LCL ⫽ ⫺.088, UCL ⫽ ⫺.374) times the b path resulted in a significant total indirect effect. For perceived peer alcohol use, the total a path (a ⫽ .300, SE ⫽ .127, p ⫽ .018; LCL ⫽ .090, UCL ⫽ .566) times the b path also resulted in a significant total indirect effect. Effect size. According to MacKinnon (2008), the proportion mediated indicates how the indirect effect compares to the total effect, and is calculated by dividing the indirect effect by the total effect. The proportion mediated may not be bounded by zero; thus, caution is suggested in interpreting it as a strict proportion ranging from 0 to 1 (Preacher & Kelley, 2011). The proportion that

Perceived Peer Tolerance of Alcohol Use

-.002 -.008 (.015)

ADHD Symptoms

Perceived Peer Alcohol Use

.041 .088 (.023)

.019 .045 (.016) .152 .102 (.093)

.265 .145 (.130) -.012 -.049 (.011)

435

.303 .224 (.050) .319 .392 (.031)

Alcohol Use

.017 .085 (.010)

Figure 1. Alcohol use mediation model. Note. Solid black lines indicate statistically significant paths. Standardized regression coefficients are reported in italics between unstandardized coefficients and the standard error. According to MacKinnon (2008), standardized regression coefficients can serve as effect sizes for individual paths in a mediation model, and indicate the change in the outcome variable for every one standard deviation change in the predictor.

VAN ECK, MARKLE, DATTILO, AND FLORY

436 CP 1 SD Below

CP Mean

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Peer Tolerance of Alcohol Use

9

CP 1 SD Above

*

8 7

*

6 5 4 3 2 1 0 ADHD 1 SD Below

ADHD Mean

ADHD 1 SD Above

ADHD Symptoms

Figure 2. The interaction of ADHD and CP symptoms predicting the a path of perceived peer tolerance. Note. The lines represent the effect of the interaction of ADHD symptoms and CP symptoms on perceived peer tolerance of alcohol use. The lines represent at one standard deviation below the mean (⫺1 SD), at the mean, and at one standard deviation above the mean (⫹1 SD) for CP symptoms, following the procedures outlined by Cohen et al., 2003. ⴱ Indicates that the simple slope was statistically significant, p ⬍ .01.

perceived peer alcohol use mediated of the association between ADHD symptoms and alcohol use was .441, indicating that perceived peer alcohol use mediated approximately 44.1% of the variance in the total effect of ADHD symptoms on alcohol use. Perceived peer tolerance of alcohol use mediated 22.8% and perceived peer alcohol use mediated 27.7% of the total effect of CP symptoms on alcohol use. The proportion mediated could not be calculated when the direct and indirect effect had opposite signs. Also, standardized regression coefficients serve as effect sizes for individual paths in a mediation model, and indicate the change

Perceived Peer Tolerance of Marijuana Use

.017 .049 (.014)

ADHD Symptoms

Perceived Peer Marijuana Use

.032 .087 (.015)

-.001 -.028 (.001)

.290 .250 (.057)

CP Symptoms -.010 -.062 (.007)

ADHD x CP

in the outcome variable for every one standard deviation change in the predictor (MacKinnon, 2008; see Table 2 and Figure 1). Marijuana use. Only the indirect effect of ADHD symptoms on marijuana use through perceived peer marijuana use reached statistical significance (see Table 4 and Figure 3 for path coefficients), although the lower confidence interval was zero. Both indirect effects of CP symptoms on marijuana use were significant and positive, as well as the direct, total indirect, and total effects. No effects of the interaction of ADHD and CP symptoms were significant. We also calculated the total indirect effect for each mediator and the total direct effect of ADHD symptoms, CP symptoms, and their interaction simultaneously on marijuana use (see Table 4). For perceived peer tolerance, the summative a path (a ⫽ .317, SE ⫽ .054, p ⬍ .001; LCL ⫽ .210, UCL ⫽ .418) times the b path resulted in a significant total indirect effect. For perceived peer marijuana use, the summative a path (a ⫽ .309, SE ⫽ .073, p ⬍ .001; LCL ⫽ .160, UCL ⫽ .446) times the b path also resulted in a significant total indirect effect. Effect size. Peer tolerance of marijuana use mediated 34.4% and perceived peer marijuana use mediated 28.1% of the total effect of CP symptoms on marijuana use. See Table 4 and Figure 3 for standardized coefficients. Illicit drug use. The indirect effect of ADHD symptoms on illicit drug use through perceived peer illicit drug use was significant (see Table 5). Interpreting the probit threshold of .997 as a z score, the probability of illicit drug use when all independent variables are at their mean was .832. For every SD increase in ADHD symptoms, risk for illicit drug use indirectly increases .014 z-score units through perceive peer use. Neither the direct nor total effects of ADHD symptoms on illicit drug use were significant. The indirect effect of CP symptoms on illicit drug use through perceived peer illicit drug use was significant and positive, as well as the direct and total effects. The indirect effects of ADHD and CP symptoms on illicit drug use through perceived peer tolerance of illicit drug use approached but did not reach significance,

.012 .095 (.005)

.275 .214 (.075) .002 .012 (.008)

.037 .329 (.004) .033 .326 (.004)

Marijuana Use

.001 .056 (0.001)

Figure 3. Marijuana use mediation model. Note. Solid black lines indicate statistically significant paths. Standardized regression coefficients are reported in italics between unstandardized coefficients and the standard error. According to MacKinnon (2008), standardized regression coefficients can serve as effect sizes for individual paths in a mediation model, and indicate the change in the outcome variable for every one standard deviation change in the predictor.

ADHD, CP, PEER PERCEPTIONS, AND SUBSTANCE USE

Perceived Peer Tolerance of Illicit Drug Use

.024 .127 (.009)

Perceived Peer Illicit Drug Use

.098 .195 (.022)

ADHD Symptoms

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.110 .184 (.031)

.644 .248 (.110)

-.004 -.051 (.004)

Illicit Drug Use

.002 .035 (.004)

.001 .003 (.012)

ADHD x CP

.068 .113 (.029) .044 .192 (.013)

.008 .072 (.006)

.127 .128 (.044)

CP Symptoms

437

Figure 4. Illicit drug use mediation model. Note. Solid black lines indicate statistically significant paths. Standardized regression coefficients are reported in italics between unstandardized coefficients and the standard error. According to MacKinnon (2008), standardized regression coefficients can serve as effect sizes for individual paths in a mediation model, and indicate the change in the outcome variable for every one standard deviation change in the predictor. Paths leading to illicit drug use are probit coefficients.

toms on illicit drug use. Perceived peer illicit drug use mediated 19% of the total effect of CP symptoms on illicit drug use. Refer to Table 5 and Figure 4 for standardized coefficients. Additional analyses. Given that perceived peer tolerance of use and perceived peer use shared a moderate correlation for all three substances (see Table 1), single mediation models with each mediator were also analyzed to evaluate the impact of mediator collinearity on indirect effects. Differences were found only for the illicit drug use models. In the model for perceived peer tolerance of illicit drug use, ADHD and CP symptoms indirectly influenced illicit drug use; these indirect effects merely approached significance in the full model. We also tested the assumption that the

although both the a and b paths were significant. No effects of the interaction between ADHD and CP symptoms on illicit drug use were significant (see Figure 4). We calculated the total indirect and total direct effects for the illicit drug use model. For perceived peer tolerance, the summative a path (a ⫽ .147, SE ⫽ .042, LCL ⫽ .062, UCL ⫽ .228) times the b path resulted in a significant total indirect effect (see Table 5). For perceived peer illicit drug use, the summative a path (a ⫽ .743, SE ⫽ .103, LCL ⫽ .535, UCL ⫽ .951) times the b path also produced a significant total indirect effect. Effect size. Perceived peer illicit drug use mediated approximately 28.6% of the variance in the total effect of ADHD sympTable 2 Alcohol Use: Direct and Indirect Effects Perceived peer tolerance of alcohol use Effect

B

␤

ADHD symptoms Indirect effect ⫺.001 ⫺.002 Direct effect — — Total effect — — CP Symptoms Indirect effect .070ⴱⴱ .104ⴱⴱ Direct effect — — Total effect — — ADHD Symptoms ⫻ CP Symptoms Indirect effect ⫺.007ⴱⴱ ⫺.034ⴱⴱ Direct effect — — Total effect — — Total effects of all symptoms Indirect effect .063ⴱⴱ — Direct effect — —

SE

LCL

UCL

Perceived peer alcohol use B

␤

SE

LCL

Total effect of mediators together UCL

B

␤

SE

.004 ⫺.010 — — — —

.008 — —

.015ⴱ — —

.034ⴱ .007 — — — —

.002 .031 — — — —

.014 .019 .034

.033 .045 .077

.023 — —

.127 — —

.085ⴱ — —

.057ⴱ .040 — — — —

.019 .171 — — — —

.155ⴱⴱ .152 .307ⴱ

.104ⴱⴱ .056 .206 .093 .102ⴱ .137

.034 — —

.002 ⫺.012 ⫺.002 ⫺.004 — — — — — — — — .022 —

.028 —

.116 —

.096ⴱ —

⫺.019 — — — —

.004 ⫺.011 .003 ⫺.011ⴱ — — — .017t — — — .006 .039 —

.030 .179 — —

.158ⴱⴱ .189ⴱ

⫺.054ⴱ .032t .085 — —

LCL

.010 ⫺.004 .016 ⫺.012 .018 ⫺.002 .062 .007 .089

UCL .036 .052 .070 .280 .351 .601

.004 ⫺.019 ⫺.002 .010 .00 .038 .010 ⫺.011 .029 .054 .090

.066 .043

.277 .377

Note. All analyses include the following covariates: Prescribed use of ADHD stimulant medication, sex, race: 0 ⫽ European American (EA); 1 ⫽ non-EA, mother’s income, and household income; LCL ⫽ lower confidence limits; UCL ⫽ upper confidence limits; CP ⫽ conduct problems; B ⫽ unstandardized coefficients and ␤ ⫽ standardized coefficients. LCL and UCL were calculated with bootstrapping (Fritz & MacKinnon, 2007). t p ⬎ .05. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

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Table 3 Simple Effects for ADHD Symptoms ⫻ CP Symptoms in Alcohol Use Model Path a

Path b

ADHD ⫻ CP to perceived peer tolerance

Perceived peer tolerance to alcohol use

Indirect effect (ab path)

Effect

B

SE

B

SE

B

SE

z score

p value

High CP Low CP

⫺0.040 0.044

⫺0.011 0.011

⫺0.303 ⫺0.303

0.05 0.05

0.012 ⫺0.013

0.004 0.004

3.203 ⫺3.327

0.001 0.001

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Note. B ⫽ unstandardized coefficients; SE ⫽ standard error; standard error for simple effects of the indirect effect used the following equation: 公 (b2sa2 ⫹ a2s2b). See Figure 2 for a visual representation of the interaction of ADHD and CP symptoms on peer tolerance of alcohol use.

suggest potential constructs to target when attempting to reduce substance use for college students with ADHD and CP symptoms. Findings indicated that ADHD symptoms were not directly related to alcohol, marijuana, or illicit drug use, whereas CP symptoms had a positive direct effect on marijuana and illicit drug use, but not alcohol use. These results are consistent with a subset of literature (Copeland, Shanahan, Costello, & Angold, 2009; Falls et al., 2011; Glass & Flory, 2011) suggesting that controlling for CP symptoms reduces or even eliminates the effect of ADHD symptoms. These results highlight the importance of including both ADHD and CP symptoms in analyses regarding substance use. Perceived peer tolerance was a significant mediator of the association between alcohol use and the interaction of CP and ADHD symptoms. Perceived peer tolerance of alcohol use was at its highest when CP symptoms were high and ADHD symptoms were low, but perceived peer tolerance remained at about the same level when ADHD symptoms were high regardless of CP symptom severity. As ADHD symptoms increase, perceptions of one’s own

mediators did not function better as moderators (MacKinnon, 2008; VanderWeele, & Vansteelandt, 2010) by adding the interaction of each symptom area with each mediator in predicting the outcome variables. No meaningful changes in results were found.

Discussion The goal of this study was to investigate the degree to which perceived peer tolerance and perceived peer substance use mediated the association that ADHD symptoms, CP symptoms, and their interaction has with three types of substance use (alcohol, marijuana, other illicit drugs) for college students. Currently, little is known about the specific mechanisms underlying these associations. Further, many prevention and intervention programs targeting reductions in substance use for youth incorporate perceptions of peer use (Neighbors et al., 2006). This study suggests that perceived peer tolerance and use may play important roles in the increased risk for substance use associated with ADHD and CP symptoms. These findings are important given that they may

Table 4 Marijuana Use: Direct and Indirect Effects Perceived peer tolerance of marijuana use Effect ADHD symptoms Indirect effect Direct effect Total effect CP symptoms Indirect effect Direct effect Total effect ADHD Symptoms ⫻ CP Symptoms Indirect Effect Direct effect Total effect Total effects of all symptoms Indirect effect Direct effect

Perceived peer marijuana use

Total effect of mediators together

B

␤

SE

LCL

UCL

B

␤

SE

LCL

UCL

B

␤

.001ⴱ — —

.016ⴱ — —

.00 — —

.00 — —

.002 — —

.001ⴱ — —

.028ⴱ — —

.00 — —

.00 — —

.002 — —

.002ⴱ ⫺.001 .001

.045ⴱ ⫺.028 .016

.011ⴱⴱ — —

.082ⴱⴱ .003 — — — —

.007 — —

.016 — —

.009ⴱⴱ .070ⴱⴱ .003 .004 .015 — — — — — — — — — —

.020ⴱⴱ .012ⴱ .032ⴱⴱ

.00 — —

.00 — —

.00 — —

.016 —

.010ⴱⴱ —

.00 — — .011ⴱⴱ —

⫺.020 — — — —

.00 — — .002 —

⫺.001 — — .0007 —

.004 — — — —

.00 — —

.00 — —

.001 — —

.003 .005 .0016 — — —

.021ⴱⴱ .012ⴱ

SE

— —

UCL

.001 .00 .003 .001 ⫺.004 .001 .001 ⫺.002 .003

.152ⴱⴱ .004 .095ⴱ .005 .247ⴱⴱ .007 ⫺.017 .056 .039

LCL

.013 .027 .002 .023 .018 .046

.00 ⫺.001 .00 .001 ⫺.001 .003 .001 ⫺.001 .002 .004 .005

.014 .029 .002 .022

Note. All analyses include the following covariates: prescribed use of ADHD stimulant medication, sex, race: 0 ⫽ European American (EA); 1 ⫽ non-EA, mother’s income, and household income; LCL ⫽ lower confidence limits; UCL ⫽ upper confidence limits; CP ⫽ conduct problems; B ⫽ unstandardized coefficients and ␤ ⫽ standardized coefficients. LCL and UCL were calculated with bootstrapping (Fritz & MacKinnon, 2007). t p ⬎ .05. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

ADHD, CP, PEER PERCEPTIONS, AND SUBSTANCE USE

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Table 5 Illicit Drug Use: Direct and Indirect Effects Perceived peer tolerance of illicit drug use

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Effect ADHD symptoms Indirect effect Direct effect Total effect CP Symptoms Indirect effect Direct effect Total effect ADHD Symptoms ⫻ CP Symptoms Indirect effect Direct effect Total effect Total effects of all symptoms Indirect effect Direct effect Model threshold

Perceived peer illicit drug use

B

␤

SE

LCL

UCL

B

.002t — —

.01t — —

.001 — —

.00 — —

.004 — —

.004ⴱⴱ — —

.009t — —

.015t — —

.005 — —

.001 — —

.022 — —

.00 — —

⫺.006 — —

.00 ⫺.001 — — — —

.000 — —

.010t — .997ⴱⴱ

— — .927ⴱⴱ

.005 — .368

.002 .024 — — .865 1.170

Total effect of mediators together

SE

LCL

UCL

B

.037ⴱⴱ — —

.002 — —

.002 — —

.008 — —

.006ⴱⴱ .008 .014ⴱ

.028ⴱⴱ — —

.048ⴱⴱ — —

.010 — —

.011 — —

.049 — —

.037ⴱⴱ .110ⴱⴱ .147ⴱⴱ

.00 — —

.001 — —

.001 ⫺.001 — — — —

.001 — —

.00 .002 .002

.011 —

.055 —

.043ⴱⴱ .120ⴱ

.033ⴱⴱ —

— —

.013 —

␤

SE

LCL

UCL

.052ⴱⴱ .072 .124ⴱ

.002 .006 .006

.003 ⫺.004 .003

.010 .020 .026

.062ⴱⴱ .184ⴱⴱ .246ⴱⴱ

.010 .031 .031

.018 .051 .089

.059 .173 .208

.001 .004 .004

⫺.001 ⫺.004 ⫺.005

.001 .009 .009

.011 .030

.021 .063

.065 .179

⫺.005 .035 .030 — —

Note. LCL ⫽ lower confidence limits; UCL ⫽ upper confidence limits; CP ⫽ conduct problems; B ⫽ unstandardized probit coefficients and ␤ ⫽ standardized probit coefficients; prob. ⫽ probability. The threshold or intercept can be interpreted as a z-score for risk of illicit drug use when all independent variables are at zero. This value can be transformed into a probability score based on the z score. Probit coefficients represent the z-score change in the threshold for one standard deviation change in the independent variable. The threshold can be used to interpret the indirect and total effects as well. t p ⬍ .10. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.

behavior and the behavior of others become less accurate (Flory et al., 2006; Jiang & Johnston, 2012; McQuade et al., 2011). It is possible that, as ADHD symptoms increase, ability to accurately perceive peer tolerance to alcohol use decreases. This association may be stronger than the link between CP symptoms and perceived peer tolerance, resulting in the same level of perceived peer tolerance when ADHD symptoms are high, regardless of the level of CP symptoms. Alternatively, CP symptoms are associated with avoiding social conformity and rule breaking behavior. As CP symptoms increase, students may attend more to peers who tolerate or approve of alcohol use, given that alcohol use is illegal for most college students (i.e., under 21 years of age). However, it should be noted that only perceptions of tolerance were assessed; the actual tolerance participant’s peers had toward alcohol use is unknown. The fact that perceived peer tolerance of use mediated the association between CP symptoms and marijuana use, but not illicit drug use, is consistent with research suggesting that college students are more likely to use marijuana than other illicit drugs (Johnston, O’Malley, Bachman, & Schulenberg, 2012). As a result, they may believe that their peers are less tolerant of illicit drug use than of marijuana use and may discuss their illicit drug use less frequently, leading others to perceive them as less tolerant of illicit drug use, despite their actual behavior. The significant mediation of perceived peer use for both ADHD and CP symptoms and all three substance use outcomes suggests that perceptions of peer use may play a larger role than perceptions of peer tolerance with effect that generalize to a wider array of substances. College students with ADHD and CP symptoms may be more likely to attend to peer behaviors rather than attitudes. Interestingly, CP and ADHD symptoms did not interact in relation to perception of peer use for any substance, suggesting the function

that perception of peer use serves in substance use for CP and ADHD symptoms may differ. For example, CP symptoms may lead to bonding with other students who engage in antisocial behavior (Poulin, Dishion, & Hass, 1999), whereas ADHD symptoms may lead to substance use as a strategy for social acceptance. Given that ADHD symptoms are associated with faulty cue encoding and inaccurate social inferences (Flory et al., 2006), using substance use for this purpose may be based on inflated perceptions of peer use and may actually further jeopardize peer acceptance. Despite the strengths of this study, limitations were present. First, our sample was cross-sectional, which limits evaluation of directional associations (MacKinnon, 2008; Maxwell, & Cole, 2007). For example, perceived peer use could lead to increased substance use, or frequent substance use may lead to inflated assumptions of peer use. However, given that very limited research has evaluated how perceived peer use and tolerance of use contribute to substance use for individuals with ADHD and CP symptoms, cross-sectional analyses provide an opportunity to consider a potentially important explanation for the risk of substance use associated with externalizing symptoms. Second, ADHD symptoms were measured continuously rather than using diagnosis. Although examining symptoms on a continuum may capture some individuals with transient attention problems unrelated to ADHD, measuring ADHD dimensionally has many advantages, as detailed earlier. Third, the psychometric properties of the items used to assess peer perception constructs require additional evaluation. Finally, this study used all self-reported measures, which is a frequently used measurement approach in research on ADHD, CP, and substance use. Self-report of ADHD symptoms corresponds closely with parent and spousal report for young adults (Barkley et al., 2008, 2011). Although results of some studies suggest a social

VAN ECK, MARKLE, DATTILO, AND FLORY

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desirability bias in self-reported delinquency (Piquero, MacIntosh, & Hickman, 2002), other studies indicate that self-report corresponds closely to legal records and parent report and a higher quantity of reported delinquent acts (Brame, Fagan, Piquero, Schubert, & Steinberg, 2004; Cashel, 2003). Further, disclosure of problem behaviors in general is highest when self-report is used and when it is collected in a confidential online format rather than a face-to-face interview (Kobak et al., 1997; Tourangeau & Smith, 1996), as done in this study. Little research has addressed the mechanisms by which ADHD and CP symptoms increase substance use. Despite the crosssectional design of this study, these results suggest that perceptions of peer tolerance and perceptions of peer substance use may play important roles in increasing risk for substance use for college students, particularly for those with ADHD and CP symptoms. These findings have important implications for prevention and intervention programming for college students, which have not consistently demonstrated significant reductions in substance use (Perkins, Haines, & Rice, 2005; Wechsler et al., 2003). These findings have led some researchers to suggest that next steps in substance prevention programming would be to identify for whom and under what conditions targeting perceptions of peer tolerance and use may reduce substance use (Mattern & Neighbors, 2004). Our findings suggest that youth with ADHD and CP symptoms may benefit from these types of programs. In conclusion, results of this study indicate that perceptions of peer tolerance of substance use and perceptions of peer substance use play an important mediating role in the associations between ADHD symptoms and substance use and CP symptoms and substance use. Further, only CP symptoms had a direct effect on substance use. Future research should replicate these results in a longitudinal design to evaluate the causal associations among model variables. Findings can inform the development of clinical interventions. Future research should identify ways to target misperceptions about peer substance use and peer tolerance of substance use within prevention programs for individuals with risk factors such as CP and ADHD symptoms.

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Received September 23, 2012 Revision received January 2, 2014 Accepted January 17, 2014 䡲