The impact of alcohol and marijuana use on academic achievement among college students

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The impact of alcohol and marijuana use on academic achievement among college students夽 Riane M. Bolin a,∗ , Margaret Pate b , Jenna McClintock c a

Department of Criminal Justice, Radford University, 801 East Main Street, PO Box 6934, Radford, VA 24142, United States Department of Criminal Justice, Radford University, 801 East Main Street, Radford, VA 24142, United States c Department of Criminal Justice and Criminology, University of North Carolina — Charlotte, 9201 University City Blvd, Charlotte, NC 28223, United States b

a r t i c l e

i n f o

Article history: Received 23 December 2016 Received in revised form 3 August 2017 Accepted 11 August 2017 Available online xxx Keywords: Alcohol use Marijuana use Skipping class Academic achievement

a b s t r a c t The present study explored the direct and indirect relationship between substance use, specifically alcohol and marijuana use, and academic achievement among college students, using skipping class as a mediator. Online self-administered surveys were distributed to undergraduate students at a mid-size university in the southeast. Individually, both alcohol and marijuana use significantly predicted GPA; as the frequency of students’ reported alcohol and marijuana use increased, GPAs decreased. However, when included in the same model, marijuana use appeared to mediate the relationship between alcohol use and GPA. Additionally, it was found that skipping class partially mediated the relationship between both alcohol use and GPA and marijuana use and GPA. Given the negative relationship that was found between substance use and academic achievement for all students in our sample, we highlight the importance of substance use prevention efforts that target students throughout the entirety of their college careers. We also discuss the limitations of current prevention efforts across college campuses that focus on alcohol use alone. We recommend that prevention efforts include a focus on marijuana use, in addition to alcohol use, especially given our current findings for marijuana use. Published by Elsevier Inc. on behalf of Western Social Science Association.

1. Introduction Since the 1990s, substance use among college students has been steadily increasing in the United States (Johnston, O’Malley, Bachman, Schulenberg, & Miech, 2015). The two most prevalent substances being used on college campuses today are alcohol and marijuana. According to the most recent data from the 2014 Monitoring the Future survey, approximately 76% of students reported alcohol use and

夽 This research was supported in part by a College Research Award from the Radford University College of Humanities and Behavioral Sciences. ∗ Corresponding author. E-mail address: [email protected] (R.M. Bolin).

34% reported marijuana use within the last year. Though alcohol use remains more common among college students, the trend in marijuana use has been increasing at a much quicker rate. In fact, when examining daily use, marijuana has now surpassed alcohol with 5.9% of students reporting daily use of marijuana compared to only 4.3% for alcohol. 1.1. The impact of alcohol and marijuana use on academic achievement The relative popularity of alcohol and marijuana use among college students has led many researchers to explore the impact that such use has on a variety of different areas of their lives including mental health (Buckner,

http://dx.doi.org/10.1016/j.soscij.2017.08.003 0362-3319/Published by Elsevier Inc. on behalf of Western Social Science Association.

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Ecker, & Cohen, 2010; Perera, Torabi, & Kay, 2011), mental functioning (Caldeira, Arria, O’Grady, Vincent, & Wish, 2008), involvement in risky behaviors (Bell, Weschler, & Johnston, 1997; Brown & Vanable, 2007; Cooper, 2002; Shillington & Clapp, 2001), and even death (Hingson, Zha, & Weitzman, 2009; Hingson, Heeren, Zakocs, Kopstein, & Wechsler, 2002). One area that has received less attention has been the impact of alcohol and marijuana use on academic achievement. Studies have found mixed results when examining the relationship between alcohol use and academic achievement. Some studies have found that a significant negative relationship exists, with students who reported increased alcohol use also reporting falling behind in class, missing class, doing poorly on exams and papers, and having lower grades/GPA (Aertgeerts & Buntinx, 2002; Burt et al., 2016; Engs, Diebold, & Hanson, 1996; Piazza-Gardner, Barry, & Merianos, 2016; Powell, Williams, & Wechsler, 2004; Singleton, 2007; Singleton & Wolfson, 2009; Thombs et al., 2009; Wechsler et al., 2000; Wechsler et al., 2002; Wolaver, 2002). However, other studies have found the two were not significantly related (El Ansari, Stock, & Mills, 2013; Paschall & Freisthler, 2003). The research regarding marijuana use seems to be more consistent. A number of academic problems appear to be related to college student marijuana use including skipping classes (Arria, Caldeira, Bugbee, Vincent, & O’Grady, 2015; Caldeira et al., 2008), falling behind in schoolwork (Bell et al., 1997), performing inadequately on exams (Shillington & Clapp, 2001), receiving lower grades (Arria, Garnier-Dykstra et al., 2013; Arria, Wilcox et al., 2013; Arria et al., 2015; Bell et al., 1997; Buckner et al., 2010; Suerken et al., 2016), and even dropping out of college (Tucker, Ellickson, Orlando, Martino, & Klein, 2005; Suerken et al., 2016). To date, only one study has looked at the combined effect of alcohol and marijuana use on academic performance (Meda et al., 2017). Students were categorized into three groups of users: (1) non-users or light users of alcohol and marijuana, (2) moderate/large users of alcohol while being non-users or light users of marijuana, or (3) heavy users of both alcohol and marijuana. Individuals in the third group, heavy users of both alcohol and marijuana, had the lowest GPA, followed by individuals in group two, those who used alcohol in moderate/large amounts while not using marijuana or using marijuana in small amounts. This study confirmed and added to previous literature, finding that substance use has a negative influence on academic performance even when exploring the effects of marijuana and alcohol together. 1.2. Substance use, skipping class, and academic achievement While studies have sought to explore the impact of alcohol and marijuana use on academic achievement, many of these studies are flawed in that they fail to control for a number of non-substance use related factors found to impact student success. One particularly important variable that has been largely excluded from the literature is skipping class. Many studies have found skipping class to

be a significant predictor of student success (Dobkin, Gil, & Marion, 2010; LeBlanc, 2005; Shimoff & Catania, 2001). Credé, Roch, and Kieszcynka (2010), for example, conducted a meta-analysis of the relationship between class attendance and both class grades and GPA and found that class attendance was strongly correlated with both variables. Based on their findings, they concluded that class attendance is a better predictor of college grades than any other known predictor of academic performance. Due to the importance of class attendance in predicting student success in the classroom, it can be argued that any study exploring the relationship between substance use and academic achievement should control for this variable. More specifically, we believe that it is important for studies to determine whether the relationship between substance use and academic achievement is mediated by class attendance. To our knowledge, only three studies have been conducted exploring whether skipping class mediates the relationship between substance use and academic achievement (Arria, Wilcox et al., 2013; Arria et al., 2015; Conway & DiPlacido, 2015). Utilizing a sample of first-semester college students, Conway and DiPlacido (2015) found an indirect effect of alcohol use on GPA through skipping class. In their longitudinal prospective study, Arria, Wilcox et al. (2013) found that skipping class mediated the relationship between both alcohol use and marijuana use and GPA. Specifically, they found that students who were diagnosed with either an alcohol use or cannabis use disorder were more likely to skip class, and, in turn, were more likely to have lower GPAs. Utilizing the same data, Arria et al. (2015) explored both the direct and indirect relationship of marijuana use and GPA, using skipping class as the mediator. Consistent with their previous findings, it was found that marijuana use not only had a direct impact on GPA, but an indirect impact through poorer class attendance as well. 1.3. The current study The purpose of the current study is twofold. The first purpose is to examine the relationship between substance use, specifically alcohol and marijuana use, and academic achievement among college students. Specifically, the present study tests the hypothesis that college students who use alcohol and marijuana on more occasions will have lower GPAs than those students who report using on fewer occasions. The second purpose is to expand on prior research in this area by exploring the potentially mediating role of skipping class on the relationship between substance use and GPA. Due to the limited amount of research on this topic, we sought to determine whether findings from previous research on the relationship between substance use, skipping class, and GPA could be replicated at a mid-size, public university. 2. Method 2.1. Sample and data collection The sample for the present study consists of undergraduate students who were enrolled at a mid-size,

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public university in the southeast during the Spring 2016 semester. A list of student e-mail addresses was obtained from the campus directory in February 2016.1 At the time of the survey, there were 8471 undergraduate students listed in the campus directory. An online questionnaire was used to collect data from the students. Prior to survey administration, the college’s institutional review board approved the study. All undergraduate students listed in the campus directory were sent an initial e-mail invitation, which contained an explanation of the importance of the study and provided a link to the survey. All participants were informed that their responses were anonymous and their participation voluntary. To further help solicit participation, a week after the initial student e-mail was sent, an e-mail was sent to all faculty members listed in the campus directory requesting that they consider encouraging their students to participate in the study. Approximately three weeks after sending the initial survey, a final reminder e-mail containing a link to the survey was sent to all students. Of the 8471 undergraduate students who were administered the survey, 1104 students responded, representing a 13% response rate. However, after removing individuals with missing data on the key dependent variable, GPA, and both substance use measures, our total sample size was 946. The final sample was approximately 33% male, 79% white, 22% Greek affiliated, 67% upperclassman, with an average age of 222 (Table 1). 2.2. Measures GPA was used to indicate students’ academic achievement. To determine GPA, respondents were asked to self-report their current cumulative GPA. We had to rely on students’ self-report of their GPA due to the anonymous nature of our study. Our key independent measure was substance use, specifically alcohol and marijuana use. Consistent with prior studies, self-report measures of past year alcohol and marijuana use were utilized.3 Students were asked to report the number of occasions that they had engaged in alcohol and/or marijuana use in the past 12

1 It is important to note that the campus directory only contains contact information for those students who allow their contact information to be included; therefore, not all university student e-mail addresses could be obtained. Further, as the directory is only periodically updated, it is also possible that students listed in the directory were no longer at the university due to graduation, transfer, dismissal, etc. 2 Based upon information retrieved about the population from the university’s fact book, t-tests were conducted which confirmed that the sample is significantly different than the university population. Specifically, females, Whites, older students, upperclassmen, and Greek affiliated students were overrepresented in our sample. Concerns about sample representativeness are addressed in the discussion section. 3 Response rates for alcohol use and marijuana use were 93.1% and 92.7%, respectively. T-tests were conducted to determine whether significant differences existed between those who answered the question and those who did not. Significant differences were found for all variables. Specifically, those who failed to respond were more likely to be non-white, male, younger, an underclassman, have a lower GPA, skip class more frequently and indicate more frequent use of marijuana (if missing alcohol) or alcohol (if missing marijuana). In regards to Greek affiliation, individuals missing alcohol data were less likely to be Greek affiliated, while those missing marijuana data were more likely to be Greek affiliated.

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Table 1 Descriptive statistics for GPAa , substance use variables, and control variables.

Dependent variable Cumulative GPA .00–.99 1.00–1.99 2.00–2.99 3.00–3.99 4.00

Frequency

Percent

3 18 262 643 52

0.3 1.8 26.5 66.7 4.7

Independent variables Number of occasions of past year alcohol use 92 0 70 1–2 86 3–5 6–9 64 10–19 127 112 20–39 360 40 or more Number of occasions of past year marijuana use 480 0 1–2 95 3–5 57 41 6–9 10–19 33 24 20–39 177 40 or more Control variables

10.1 7.7 9.4 7.0 13.9 12.3 39.5 52.9 10.5 6.3 4.5 3.6 2.6 19.5

Frequency of skipping class Never Rarely Sometimes Very often All of the time

307 448 174 14 3

32.5 47.4 18.4 1.5 0.3

Gender Male Female

309 634

32.7 67.0

Race White Non-white

747 198

79.0 20.9

Greek affiliation Greek affiliation No Greek affiliation

208 738

22.0 78.0

Class standing Upper classmen Lower classmen

631 315

66.7 33.3

Mean 21.9

SD 3.8

Age

a GPA was a continuous measure; however, to more easily show the distribution of GPAs, an ordinal variable was used to compute the descriptive statistics. The continuous measure was used for all other analyses.

months, based on seven response options ranging from 0 occasions to 40 or more occasions. Both of these variables were treated as continuous. A number of control variables found in prior studies to be related to substance use were included in the analyses (Table 1) (El Ansari et al., 2013; Arria, Garnier-Dykstra et al., 2013; Arria et al., 2015; Shillington & Clapp, 2001; Singleton & Wolfson, 2009; Suerken et al., 2016). Selfreports were used to assess all of the control variables. Age was coded as the student’s age in years. Gender, race, and Greek affiliation were dichotomous variables, with males (=1) compared to females (=0), white students (=1)

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4 Table 2 Correlation matrix of all variables (N = 978)

1 GPA 2 Alcohol use 3 Marijuana use 4 Skipping class 5 Male 6 White 7 Upper class 8 Age 9 Greek

1

2

3

4

5

6

7

8

9

– −.16*** −.20*** −.27*** −.14*** .12*** .03 .05 −.03

– .43*** .25*** .07* .08* .14*** −.00 .15***

– .29*** .13*** −.03 −.06 −.11** .00

– .03 .01 −.01 −.04 .02

– .05 .05 .11** −.04

– .14*** .09** .04

– .37*** .08*

– −.02

–

*p < .05, **p < .01, ***p < .001.

compared to non-white students (=0), and Greek affiliated students (=1) compared to non-Greek affiliated students (=0). Class standing was based on students’ self-report of their current class standing. Responses ranged from freshman (=1) to senior (=4). The variable was collapsed into a dichotomous variable with juniors and seniors representing upper classmen (=1) and freshmen and sophomores representing lower classmen (=0). To measure skipping class, students were asked to respond, based on a five-point Likert scale, to the question, “how frequently do you skip class?” Response options ranged from never (=1) to all of the time (=5). Higher scores indicate a higher frequency of skipping class. 2.3. Analytic strategy In order to examine the relationship between substance use and GPA, as well as the potential mediating role of skipping class, data analysis was conducted in three phases. In phase one, a bivariate correlation analysis was conducted with all of the independent, control, and dependent measures. Next, three Ordinary Least Squares (OLS) regressions were conducted in order to further examine the relationship between each substance use variable and GPA, as well as the two together, when controlling for other variables. Finally, mediational analyses were conducted to determine whether skipping class mediated the potential relationships between the substance use variables and GPA. Utilizing the PROCESS procedure for SPSS, all mediational analyses were ran using bias-corrected 95% confidence intervals via bootstrapping with 10,000 resamples (Hayes, 2013). All analyses were conducted using IBM SPSS Statistics 22.0. Any individuals with missing data were automatically removed from the analysis. 3. Results Almost 75% of the sample reported having a 3.00 or above GPA. The average GPA for the sample was a 3.21; thus, our sample consisted largely of above average students given that the university’s average undergraduate GPA during Spring 2016 was 2.928 (t = 15.76, p < .000). Approximately 90% of the sample reported using alcohol on at least one occasion during the past 12 months, with approximately 40% of the sample reporting use on 40 or more occasions (Table 1). As expected and consistent with prior findings, substantially fewer students indicated using

marijuana. In fact, almost 53% of the sample indicated no marijuana use in the past 12 months. 3.1. Bivariate correlations Table 2 displays the results of the bivariate correlations. As expected, both alcohol and marijuana use were significantly, negatively correlated with GPA (r = −.16, p < .001; r = −.20, p < .001, respectively). Individuals who reported more frequent use of marijuana and/or alcohol were more likely to report having a low GPA than those individuals who reported less frequent use. Only two of the control variables were significantly correlated with GPA. Individuals who were white and female were more likely to report having a high GPA than their counterparts. Additionally, individuals who reported skipping class less frequently were more likely to report having a high GPA than those who reported frequently skipping class. 3.2. Multivariate analysis In order to examine the relationship between substance use and GPA more accurately, OLS regressions were conducted. Table 3 presents the results for all OLS regressions. Model 1 included only the alcohol use variable as an independent measure of substance use. The overall model predicting GPA was significant, F (7, 869) = 16.53, p < .001, and explained 11.8% (R2 = .118; Adjusted R2 = .111) of the variance in GPA. As shown in the table, alcohol use remained a significant predictor of GPA (t = −2.92, p = .004), even when controlling for the other variables, with students who reported more frequent alcohol use also reporting lower GPAs. For the control variables, only sex and race predicted GPA. Male students (t = −4.39, p < .001) and non-white students (t = 3.74, p < .001) had lower GPAs than their counterparts. Finally, the frequency of skipping class had a significant, negative relationship with GPA (t = −7.31, p < .001), indicating that as individuals skipped class more often, their GPAs tended to be lower. Of these variables, frequency of skipping class (ˇ = −.24) was the most important predictor of GPA. Similar results were found in an OLS regression predicting GPA with marijuana as the sole substance use variable (Model 2). The overall model predicting GPA was significant, F (7, 870) = 17.98, p < .001, and explained 12.7% (R2 = .127; Adjusted R2 = .120) of the variance in GPA. As expected, occasions of marijuana use significantly pre-

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Table 3 OLS regressions predicting GPA Alcohol use (N = 870) b Alcohol use Marijuana use Male White Upper class Age Greek Skipping class R2 Adjusted R2 F

−.03** – −.17*** .17*** .02 .01 −.03 −.18***

SE .01 – .04 .05 .04 .01 .04 .02 .12 .11 16.29***

Marijuana use (N = 871)

Alcohol and marijuana use (N = 836)

ˇ

b

SE

ˇ

b

SE

ˇ

−.09 – −.14 .13 .02 .03 −.02 −.24

– −.03*** −.16*** .16*** −.01 .00 −.05 −.17***

–

– −.12 −.14 .12 −.01 .03 −.04 −.24

−.01 −.02** −.16*** .17*** −.00 .00 −.05 −.17***

.01 .01 .04 .05 .04 .01 .04 .03 .13 .12 15.01***

−.04 −.10 −.14 .12 −.00 .02 −.04 −.23

.01 .04 .04 .04 .01 .04 .02 .13 .12 17.98***

p < .05*, p < .01**, p < .001***.

dicted GPA (t = −3.50, p < .001); students who reported using marijuana on more occasions also tended to report having a lower GPA. In Model 2, all control variables found to be significant in Model 1 remained significant and followed the same patterns. As with Model 1, frequency of skipping class (ˇ = −.24) remained the most important predictor of GPA. A third regression analysis (Model 3) was conducted to determine the effects of each substance use variable, while controlling for the other. As expected, this model was also significant, F (8, 835) = 15.09, p < .001), and explained 12.7% (R2 = .127; Adjusted R2 = .119) of the variance in GPA. While both alcohol use and marijuana use were included as independent measures, only marijuana use was a significant predictor of GPA (t = −2.59, p = .01), with students who indicated more frequent marijuana use reporting lower GPAs. Similar to the regression analysis displayed in Model 2, the control variables that were significant in Model 1 (sex, race, and skipping class) also predicted GPA in the same direction for Model 3. Of these variables, frequency of skipping class (ˇ = −.23) was again the most important predictor of GPA.

3.3. Mediation analysis Given that alcohol use became non-significant after adding marijuana use to the model, the PROCESS procedure was used to determine whether marijuana use mediated the relationship between alcohol use and GPA. This process revealed that marijuana use mediated the relationship between alcohol use and GPA, ab = −.02, BCa CI [−.03, −.01]. (Fig. 1). Specifically, it was found that marijuana use accounted for a little less than half of the total effect of alcohol use on GPA, PM = .45. In order to better understand the relationship between alcohol use and GPA, we tested skipping class as a mediator. The mediational analysis revealed that the frequency of skipping class partially mediated the relationship between alcohol use and GPA, ab = −.02, BCa CI [−.02, −.01]. Skipping class accounted for approximately 40% of the total effect of alcohol use on GPA, PM = .37 (Fig. 2). Similarly, we wanted to explore whether the relationship between marijuana use and GPA was mediated by the frequency of skipping class. There was a significant indi-

Fig. 1. Mediation between alchol use and GPA marijuana use. Notes: P < .05*, P < .01**, P < .001***.

Fig. 2. Mediation between alchol use and GPA by skipping class. Notes: P < .05*, P < .01**, P < .001***.

Fig. 3. Mediation between marijuana use and GPA by skipping class. Notes: P < .05*, P < .01**, P < .001***.

rect effect of marijuana use on GPA through the frequency of skipping class, ab = −.02, BCa CI [−.02, −.01]. Skipping class accounted for approximately 35% of the total effect of marijuana use on GPA, PM = .33 (Fig. 3).

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4. Discussion In the present study, we examined the relationship between alcohol and marijuana on students’ self-reported GPA. We did this by conducting separate OLS regression analyses with alcohol use and marijuana use as predictors, as well as conducting an OLS regression with both substances included as predictors. It was found that both alcohol and marijuana use impacted students’ GPA. When analyzed independent of one another, both alcohol and marijuana use were found to be negatively related to GPA. Specifically, consistent with prior research, it was found that college students who reported using alcohol or marijuana on more occasions tended to also report having lower GPAs (Arria et al., 2015; Bell et al., 1997; Buckner et al., 2010; Engs et al., 1996; Singleton, 2007; Singleton & Wolfson, 2009; Suerken et al., 2016; Wechsler et al., 2000; Wechsler et al., 2002; Wolaver, 2002). However, when both alcohol and marijuana use were included in the same regression, the effects of alcohol became non-significant. This non-significance confirmed the need for additional analyses to explore the potential mediating effects of marijuana use on the relationship between alcohol use and GPA. Marijuana use accounted for just less than half of the total effect of alcohol use on GPA. The mediation would suggest that individuals who use alcohol are also more likely to use marijuana, which further impacts their academic achievement. Surprisingly, alcohol use did not mediate the relationship between marijuana use and GPA. The data for the current study were collected through a crosssectional design, so it is difficult to establish a time order. Future research should explore the potential impact both substances have on one another, and on academic achievement, in a longitudinal design. To date, only one study has looked at the comorbidity of alcohol and marijuana use on academic achievement (Meda et al., 2017). Another purpose of this study was to continue research exploring the mechanisms by which substances influence academic achievement. Previous research has found that the relationship between alcohol use and GPA is mediated by time spent studying (Williams, Powell, & Wechsler, 2003; Wolaver, 2002). Similarly, more recent research has found that the relationship between marijuana use and GPA, as well as alcohol use and GPA, is mediated by class attendance (Arria, Wilcox et al., 2013; Arria et al., 2015; Conway & DiPlacido, 2015). Through meditational analyses, we found that skipping class partially mediated the effects of alcohol use on GPA, accounting for approximately 40% of the relationship, which shows that alcohol use negatively affects GPA partially because it leads students to skip class more frequently. Similarly, skipping class partially mediated the effects of marijuana use on GPA, accounting for approximately 35% of the relationship. Overall, it was found that both substances both directly and indirectly affected GPA. Several limitations should be considered when interpreting these results. One limitation of the study is that we used self-reported measures of substance use, GPA, and skipping class. It is possible that alcohol/marijuana use as well as the frequency of skipping class were underreported due to the fact that these behaviors are not

considered socially desirable (Akinci, Tarter, & Kirisci, 2001; Gruenewald & Johnson, 2006; Harrison & Hughes, 1997). Previous research has also found that self-reported GPAs tend to be rounded up or inflated by those with lower academic performance (Kuncel, Crede, & Thomas, 2005). However, it was necessary to use self-reported measures for our study to ensure anonymity. Guaranteed anonymity may have encouraged participants to be more honest with their responses (Durant, Carey, & Schroder, 2002; Ong & Weiss, 2000). Additionally, it is possible that student recall could be affected due to the timeframe in which they were asked to recall their substance use. However, the present study purposefully utilized past year use because we were interested in exploring student trends in alcohol and marijuana use. Past thirty-day use limits the ability to accurately assess trends (Greenfield, 2000; Stockwell et al., 1999) and lifetime use may have a significant impact on student recall (Cantor, 1984, 1985). Researchers have recommended using longer reference periods (past year) for assessing substance use behaviors in countries such as the United States (Dawson, 2003); thus, consistent with prior studies, we decided to use past year use. Future research should explore whether these same relationships exist when looking at both shorter and longer timeframes. Another limitation of the present study is that several factors that previous studies have found to be correlated with academic performance were not included such as employment (Trockel, Barnes, & Egget, 2000), socioeconomic status (Mushtaq & Khan, 2012), time spent studying (Lahmers & Zulauf, 2000), time management (Lahmers & Zulauf, 2000), study habits and skills (Nonis & Hudson, 2010), etc. This may explain why our predictors only explained a small percentage of the variance in GPA. Future studies may benefit from the inclusion of a more comprehensive set of predictors. A final limitation is our limited generalizability because we recruited participants from only one university. We also only received responses from a small number of the university’s students, which further decreases the generalizability of the results. However, our results are consistent with other studies on this topic. Our sample may also be limited due to selection bias; heavy substance users or those with poor grades may have been more reluctant to participate in the study (McCoy et al., 2009). Finally, our sample has limited generalizability to the population from which it was drawn. Only 13% of the population responded to the survey and as noted in the footnote above, the sample was significantly different from the population on key factors such as sex, race, and Greek affiliation. Future research should consider using students from several universities, both public and private, located in different geographical areas in order to further increase generalizability. The use of incentives may also be beneficial in increasing participation, reducing selection bias, and ensuring that a more representative sample is drawn from each university (Church, 1993; Edwards et al., 2002; Olsen, Abelsen, & Olsen, 2012). Given the negative relationship that was found between substance use and academic achievement, our study highlights the importance of prevention efforts

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on college campuses. Such efforts may help to reduce academic failure and thus increase student retention (Arria, Garnier-Dykstra et al., 2013; Arria, Wilcox et al., 2013; Liguori & Lonbaken, 2015; Martinez et al., 2009; Williams et al., 2003). Often, prevention efforts are largely aimed at incoming freshmen due to their increased risk of problematic drinking and drug use behaviors (Grekin & Sher, 2006; Lewis, Neighbors, Oster-Aaland, Kirkeby, Larimer, 2007; Schulenberg & Maggs, 2002; Thompson et al., 2006). For example, many colleges require incoming freshman to complete online educational programs such as AlcoholEdu, which teaches students about the risks of engaging in alcohol and substance use (https://everfi.com/higher-education-old/alcoholedu/). However, substance use initiation is not limited to freshmen; thus, prevention efforts should be focused on the student body as a whole in order to increase retention. Many campuses also focus their prevention efforts solely on the dangers of alcohol use (U.S. Department of Education, 2008); however, consistent with prior research, our study confirms the need to also target marijuana users who may be at risk for poor academic achievement. As marijuana becomes more readily available and more states legalize it, it will be important for colleges to consider how to implement appropriate prevention and intervention strategies for this particular drug, in addition to alcohol and other drugs that are popular on college campuses. In addition to implementing campus wide prevention programs for both alcohol and marijuana use, another potential solution to help negate the negative impact of substance use on academic achievement is screening students for substance use problems (Larimer & Cronce, 2002; Marlatt et al., 1998). Due to the fact that students often develop substance use patterns prior to entering college (Arria et al., 2008), screening incoming freshman and intervening appropriately may help to increase their likelihood of academic success (Arria et al., 2015). Based upon the findings from our study, we also recommend screening students for drug use problems who are experiencing academic difficulties as identified by low midterm grades, being placed on probation, or by visiting academic assistance. Students who are identified based on the screening as either having a substance use issue or are deemed at risk could then be referred to the counseling and/or health center for a brief intervention. Screening and brief intervention models such as what is described above have received growing support in terms of reducing substance use and the negative consequences related to such use (Denering & Spear, 2012; Hingson, 2010; Kazemi, Sun, Nies, Dmochowski, & Walford, 2011). Thus, college campuses may benefit from utilizing such a model as decreased substance use may have a positive impact on student academic achievement and ultimately student retention. References Aertgeerts, B., & Buntinx, F. (2002). The relation between alcohol use or dependence and academic performance in first-year college students. Journal of Adolescent Health, 31(3), 223–225. Akinci, I. H., Tarter, R. E., & Kirisci, L. (2001). Concordance between verbal report and urine screen of recent marijuana use in adolescents. Addictive Behaviors, 26, 613–619.

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