Addictive Behaviors 47 (2015) 42–47
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Marijuana use, craving, and academic motivation and performance among college students: An in-the-moment study Kristina T. Phillips a,⁎, Michael M. Phillips a, Trent L. Lalonde b, Kayla N. Tormohlen c a b c
School of Psychological Sciences, Campus Box 94, University of Northern Colorado, Greeley, CO 80639, United States Applied Statistics and Research Methods, Campus Box 124, University of Northern Colorado, Greeley, CO 80639, United States Center for Addictions, Personality, and Emotion Research, Department of Psychology, University of Maryland, College Park, MD 20742, United States
H I G H L I G H T S • • • •
Craving predicted use in college students who frequently use marijuana. Craving was negatively associated with academic effort and motivation. Average minutes spent smoking marijuana was negatively related to GPA. Greater academic self-efﬁcacy positively predicted GPA.
a r t i c l e
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Available online 27 March 2015 Keywords: Marijuana Craving Ecological momentary assessment Academics Motivation College students
a b s t r a c t Introduction: Marijuana is the most commonly used illicit substance in the U.S., with high rates among young adults in the state of Colorado. Chronic, heavy marijuana use can impact cognitive functioning, which has the potential to inﬂuence academic performance of college students. It is possible that craving for marijuana may further contribute to diminished cognitive and affective functioning, thus leading to poor outcomes for students. Methods: College student marijuana users (n = 57) were recruited based on heavy use and completed ecological momentary assessment (EMA) via text-messaging. The association between marijuana use and craving in a college setting was explored, as well as how these variables might relate to academic motivation, effort and success. The participants were sent text messages for two weeks, three times per day at random times. Results: A temporal association between craving and marijuana use was found, where momentary craving positively predicted greater marijuana use. Similarly, as craving levels increased, the number of minutes spent studying decreased at the next assessment point. A negative association between momentary craving for marijuana and academic motivation was found in the same moment. Greater academic self-efﬁcacy positively predicted cumulative GPA, while average minutes spent smoking marijuana was negatively related. Conclusions: Using EMA, marijuana craving and use were signiﬁcantly related. These ﬁndings provide further evidence that heavy marijuana use is negatively associated with academic outcomes. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Marijuana is the most commonly used illicit drug in the U.S., with over 7% of the general population and 19% of 18–25 year olds reporting use of marijuana within the last month (Substance Abuse & Mental Health Services Administration [SAMHSA], 2014). In the state of Colorado, rates of marijuana use are among the highest in the nation, with 25% of 18–25 year olds reporting use within the last month (SAMHSA, 2012). Approximately one-third of college students report use of marijuana ⁎ Corresponding author. Tel.: +1 970 351 2428. E-mail addresses: [email protected]
(K.T. Phillips), [email protected]
(M.M. Phillips), [email protected]
(T.L. Lalonde), [email protected]
http://dx.doi.org/10.1016/j.addbeh.2015.03.020 0306-4603/© 2015 Elsevier Ltd. All rights reserved.
annually (Johnston, O'Malley, Bachman, Schulenberg, & Miech, 2014; Mohler-Kuo, Lee, & Wechsler, 2003) and a signiﬁcant portion (25%) of past-year cannabis users meet criteria for a cannabis disorder (Caldeira, Arria, O'Grady, Vincent, & Wish, 2008). Chronic marijuana users experience signiﬁcant consequences as a result of their use, including a range of cognitive deﬁcits. Acute intoxication effects include deﬁcits in psychomotor functioning (e.g., speed, accuracy), attention (including sustained selective, focused and divided attention problems), pre-attentive sensory memory, and short-term/working memory (problems in verbal learning/memory, immediate and delayed free recall; see Solowij & Pesa, 2010 for a review). When examining long-term deﬁcits, studies have consistently shown problems with attention, inhibition, working memory, executive functioning, verbal memory, and time estimation in heavy, chronic users (Solowij & Pesa, 2010). Of
K.T. Phillips et al. / Addictive Behaviors 47 (2015) 42–47
important note, such deﬁcits appear to persist even after waiting for intoxication effects to diminish. The degree of such problems appears to depend on frequency and duration of use, dose, and age of onset (Solowij & Pesa, 2010). Many of these cognitive deﬁcits could impact college success, as a number of speciﬁc impairments (e.g., attention, inhibition, and executive functioning) are directly connected to self-regulation in a learning environment (Pintrich, 2004; Tangney, Baumeister, & Boone, 2004; Zimmerman, 2008; Zimmerman, Bandura, & Martinez-Pons, 1992). It is possible that academic problems and failure could be impacted not only by the substance use itself, but also other addictive processes. Craving is one such process that is often described as a strong or intense urge or desire to use a particular substance. Tiffany's Cognitive Processing Model offers a way to conceptualize the impact of craving on cognitive and academic skills (Tiffany, 1990; Tiffany & Conklin, 2000). Tiffany (1990) describes addictive behavior as largely an automatic process, whereby behaviors associated with long-term substance use become regulated outside of consciousness, develop with practice and become difﬁcult to control. Craving, on the other hand, is suspected to function more at the non-automatic level, though in parallel with the more automated behaviors of drug use. Because craving is demanding at the cognitive level and requires substantial effort, it can impede other non-automatic processes. Similar to a self-regulation model for nicotine addiction proposed by Sayette and Grifﬁn (2011), active marijuana users have to maintain some degree of self-control over their use, and at times, must delay using marijuana in circumstances where using is not acceptable (e.g., while at work, when in class). Such delays may lead to increased urge or craving, which has the potential to impact one's attentional control at the non-automatic level (Field, Munafò, & Franken, 2009). Baumeister and colleagues (Baumeister, Heatherton, & Tice, 1994; Baumeister, Vohs, & Tice, 2007; Muraven & Baumeister, 2000) have proposed a self-regulatory strength model whereby individuals are believed to have a limited capacity to engage in self-control, which could inﬂuence operations controlled by the cognitive executive system. This leads to a competition for resources and poor performance on subsequent self-regulatory tasks (e.g., Baumeister, Bratslavsky, Muraven, & Tice, 1998; Muraven, Tice, & Baumeister, 1998). As an example of how this may relate to substance use, Muraven, Collins, Shiffman, and Paty (2005) used ecological momentary assessment (EMA) to examine whether daily ﬂuctuations in self-control inﬂuenced alcohol consumption with underage drinkers. They found that when participants had greater demands on their self-control, they were more likely to violate their personal alcohol limits. When considering the academic environment, it is possible that heavy users will struggle to perform at their peak academically if craving impedes their attention and competition for cognitive resources exists. Increased cognitive effort associated with craving may interfere with other cognitively demanding tasks, such as focusing in class, reading comprehension, and managing academic goals. Craving may also lead to greater marijuana use, which could impact the academic performance of college students and interfere with their ability to fully beneﬁt from their academic studies. The association between craving and subsequent marijuana use has not been widely studied. As noted by Tiffany and Wray (2009), studies examining the association between craving and substance use have not always found the two to be related, or if they are, often the association is not particularly strong. Only one study (Buckner, Crosby, Silgado, Wonderlich, & Schmidt, 2012) has examined marijuana use and craving in college students. Though academic variables were not examined, Buckner et al. (2012) assessed 49 college student marijuana users with a 2-week EMA protocol using personal digital assistants (PDAs). When examined temporally, craving tended to increase in the hours before using marijuana and decreased after use. Craving ratings were higher on days when marijuana was used compared to days participants did not use. Further research is needed to explore whether marijuana craving and use are related and how.
No studies have examined the contributions of craving and marijuana use on speciﬁc academic factors that lead to college success. Furthermore, although some studies have found associations between marijuana use, academic performance, college completion, and hours spent studying (Arria et al., 2013a,b; Bell, Wechsler, & Johnston, 1997; Buckner, Ecker, & Cohen, 2010; Fergusson, Horwood, & Beautrais, 2003; Horwood et al., 2010), none have assessed a range of other academic components that might inﬂuence completion of one's college degree among marijuana users, such as academic motivation and self-efﬁcacy. In the general college student population, these factors are well-known to inﬂuence academic performance and retention (see review by Robbins et al., 2004). The primary aim of this study was to examine the association between marijuana use and craving and how these variables might relate to academic motivation and academic effort when assessed in the moment with college students. A secondary aim focused on exploring associations between academic performance (GPA) and time spent smoking marijuana, time spent studying, academic self-efﬁcacy, and consequences related to marijuana use. It was hypothesized that craving at one instance would predict marijuana use and time spent studying at the next time point and that higher craving would be associated with lower academic motivation in the moment. Finally, it was believed that academic self-efﬁcacy, problems related to marijuana use, time spent studying, and time spent smoking marijuana would predict academic performance (GPA). 2. Methods 2.1. Participants Participants included 57 college students (63% female) who were recruited through ﬂyers and announcements made in psychology and science courses at a mid-sized university in Colorado. Recruitment ﬂyers advertised a study on marijuana use that speciﬁed students would be screened for eligibility by phone or in-person before participating. Potential participants were eligible for the study if they 1) were age 18 or older, 2) were enrolled at the university for a minimum of one prior semester,
Table 1 Demographic and background characteristics. Measure/variable Age GPA (cumulative) Gender Male Female Race/ethnicity Caucasian Hispanic/Latino Other African American Asian Living situation Off-campus Campus residence hall At home with family Major Science/nursing/pre-health Other social sciences Education Business Psychology Other Undeclared University class status Freshmen Sophomore Junior Senior Did not respond
Mean (SD; range) 20.05 (2.60; 18–33) 2.90 (.72; .80–4.00)
21 (37%) 36 (63%) 44 (77%) 6 (11%) 4 (7%) 2 (4%) 1 (2%) 28 (49%) 26 (46%) 3 (5%) 12 (21%) 11 (19%) 9 (16%) 9 (16%) 7 (12%) 5 (9%) 4 (7%) 26 (46%) 9 (16%) 13 (23%) 4 (7%) 5 (9%)
Note: Totals may not sum to 100% because of rounding.
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3) reported using marijuana at least two days per week, 4) reported that their last marijuana use was within the last week, 5) tested positive on a marijuana urine screen, and 6) owned a cell phone with text messaging capabilities. Demographic information is available in Table 1.
were both assessed on a 1 (None) to 10 (High) scale. This rating scale was selected based on similar past EMA studies (e.g., Buckner et al., 2012; Shrier et al., 2012). 2.4. Baseline measures
2.2. Procedures To help protect participants' conﬁdentiality, only ﬁrst names were collected and used throughout the study. Phone numbers for the EMA portion of the study were saved in a password-protected web-based text messaging service and deleted after study completion. All paper documents used for contact purposes were destroyed immediately upon completion of the study. All other paper documents were stored in a secure location with only participant ID numbers. Though marijuana use became legal for adults over age 21 in Colorado in December 2012, all data for this study were collected between March 2011 and November 2012, prior to implementation of the new state law. All procedures were approved by the university IRB. Students meeting eligibility criteria were scheduled for a baseline appointment that lasted approximately 60 min. Marijuana urine screens (Redwood Toxicology Laboratory) were conducted after informed consent to conﬁrm study eligibility. All students who presented for the baseline were eligible to participate and tested positive for marijuana on the urine screen. Participants completed a structured interview with a trained research assistant that focused on marijuana use and completed a series of paper-and-pencil baseline measures. Detailed instructions on how to complete the signal-contingent EMA portion of the study were provided. Before leaving the lab, the research assistant and study participant reviewed the nine EMA questions and discussed the two-week protocol. Participants were sent a practice text message to their cell phones and asked to respond while in the lab. Any errors were corrected and questions answered. Participants were asked to respond to text messages promptly when possible and were not texted during hours they were in class. At the end of the appointment, participants were scheduled for a 2-week follow-up where they completed one additional measure (data not part of this study) and were compensated with a $30 gift card. Text messaging began the morning after the day of the baseline appointment. Participants were texted three times per day randomly to their personal cell phones for two weeks (total of 42 signal-contingent text prompts), a time period that has been demonstrated to be adequate to assess substance use behaviors in past EMA studies (Buckner et al., 2012; Freedman, Lester, McNamara, Milby, & Schumacher, 2006; Shrier, Walls, Kendall, & Blood, 2012). A texting schedule was created for each participant using a randomization schedule with three time blocks (8:00 a.m.–12:00 p.m., 12:30 p.m.–4:30 p.m., and 5:00 p.m.–10:00 p.m.) in 30-minute increments. The same nine questions were texted at every instance, with ﬁve questions used in the current study. EMA text messages were managed through an online text messaging service (www. redoxygen.com). Responses were time stamped and later downloaded by the researchers. Participants were not sent a reminder text if they did not respond to the initial text message. The next text message was sent at the scheduled time in the next time block based on the randomization schedule. The response rate over the two-week period was 89% and ranged from 85% to 96% per day across the 14-day period. Further details on procedures, response rates, and feasibility of the EMA protocol are described elsewhere (Phillips, Phillips, Lalonde, & Dykema, 2014). 2.3. EMA questions Nine questions were developed by the researchers and texted to participants during three time blocks randomly on each of 14 consecutive days (all questions outlined in Phillips et al., 2014). For the purposes of this study, ﬁve EMA questions (craving, minutes spent smoking, number of times smoked since last texted, minutes spent studying, and academic motivation) were used for data analyses. Ratings of craving for marijuana and academic motivation to complete school work
2.4.1. Demographics Participant age, gender, race/ethnicity, major, year at university, class schedule (for texting purposes), and living situation were assessed during the baseline appointment. Due to the low n, race/ethnicity was dichotomized as Caucasian/Non-minority (n = 44) and Minority (n = 13) in analyses. 2.4.2. Marijuana use measure (MUM) and urine test Created for use in this study, MUM interview questions assessed marijuana use frequency (e.g., number of days used in last month) and history of marijuana use (e.g., age of ﬁrst marijuana use). A single panel marijuana urine dip test (Redwood Toxicology Laboratory) was used to conﬁrm whether a participant tested positive or negative for marijuana. 2.4.3. Rutgers Marijuana Problem Index (RMPI) A 23-item version of the RMPI (White, Labouvie, & Papadaratsakis, 2005) was used to assess negative consequences associated with marijuana use within the last year. Items were rated from 0 to 3 (“none” to “more than 5 times”) based on the frequency of each consequence (e.g., went to work high). Internal consistency for scores in the current sample was α = .84. 2.4.4. GPA With informed consent, research assistants veriﬁed participants' cumulative GPA through the online university records system. 2.4.5. Motivated Strategies for Learning Questionnaire (MSLQ) The MSLQ (Pintrich, Smith, Garcia, & McKeachie, 1991, 1993) is a psychometrically-sound questionnaire designed to examine student motivational orientation and learning strategies. In the current study, the 8item Self-Efﬁcacy (MSLQ-SE) subscale was used to assess students' conﬁdence in their ability to complete academic tasks and expectations for success. Item responses were rated on a 7-point scale from 1 (Not at all true of me) to 7 (Very true of me). Questions were adapted to focus on all coursework instead of just one course (e.g., “I expect to do well in my classes.”). For the current study, Cronbach's alpha was .91 for scores on this measure. 3. Results 3.1. Sample characteristics and patterns of marijuana use Table 2 includes participant marijuana use history, RMPI total score, and means and SDs for EMA marijuana-related items. Though the initial aim of this study was to recruit individuals who smoked weekly or
Table 2 Marijuana use and history. Measure/variable
RMPI total score Marijuana frequency in the last 30 days Age of ﬁrst marijuana use Age of regular marijuana use EMA minutes smoked (number of minutes smoked/day)a EMA frequency of marijuana use (number of times smoked/day)a EMA marijuana craving (1–10 scale)a
13.95 (9.32) 24.95 (5.89) 15.62 (2.43) 17.78 (2.25) 14.79 (29.11) 1.07 (1.48) 3.16 (2.65)
Note: RMPI = Rutgers Marijuana Problem Index (RMPI). a All EMA variables averaged over entire two-week assessment.
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greater, participants reported smoking on average 25 days out of the last 30 (range = 6–30 days) at the baseline interview. 3.2. Does craving predict marijuana use? Two time-lagged hierarchical (mixed-effects) models (Lee & Nelder, 1996, 2001; Lee, Nelder, & Pawitan, 2006) examined whether craving at one assessment point predicted marijuana use at the next instance, controlling for day of the week. Marijuana use was operationalized by the amount of time (in minutes) participants spent smoking (Model 1) and the number of times participants smoked (Model 2), both at the next assessment point. Estimates of the effects for each day of the week are omitted for simplicity of presentation. Model 1 showed that craving signiﬁcantly predicted the amount of time participants spent smoking at the next time assessment (F[1,1771] = 1869.35, p b .001), with a positive association (β = .13). Similarly, Model 2 showed that craving also positively predicted the number of times participants smoked at the next time assessment (F[1,1704] = 136.74, p b .001; β = .11). To account for excessive zero counts (Kassahun, Neyens, Molenberghs, Faes, & Verbeke, 2014) with the number of times smoked variable in Model 2, data were re-ﬁt using a mixed Zero-Inﬂated Poisson (ZIP) distribution model (Cameron & Trivedi, 1998; Hu, Pavlicova, & Nunes, 2011) and similar results were observed. Thus as hypothesized, when craving increased, the number of minutes spent smoking and the frequency of participants' use at the next reported assessment also increased. 3.3. Is craving associated with academic effort and motivation? Similar to the previous time-lagged models, Model 3 examined whether craving at one assessment point would predict the amount of effort (measured as time in minutes participants spent studying) at the next assessment point. Day of the week was controlled for and cumulative GPA was added as a control variable due to the relevance of examining the academic variable time spent studying. Consistent with what would be expected, craving negatively predicted the amount of time in minutes spent studying at the next time point (F[1,1701] = 230.96, p b .001; β = −.03). Thus, as craving levels increased, the number of minutes spent studying decreased at the next time point. In this model, cumulative GPA was not
a signiﬁcant factor (F[1,1701] = .48, p = .48; β = −.11) and was dropped from the model. Data were re-ﬁt using a mixed ZIP model to account for excessive zero counts and similar results were observed. A hierarchical (mixed-effects) linear model (Fitzmaurice, Laird, & Ware, 2011; Laird & Ware, 1982; Robinson, 1991) was applied to evaluate the signiﬁcance of the association between momentary craving (criterion variable) and academic motivation (outcome variable) while in the same moment (Model 4), controlling for day of week, and adjusting for the minutes spent studying (p b .001; β = .009) and minutes spent smoking (p = .09; β = −.0004). Craving was negatively associated with academic motivation at the momentary level (F[1,1969] = 5.06, p = .025; β = −.06). Thus, when craving at a particular moment was higher, the level of academic motivation at that same moment was lower and vice versa (see Fig. 1).
3.4. How does marijuana use relate to academic performance? For the ﬁnal analysis (Model 5), a sequential multiple regression model was used to examine whether race/ethnicity, gender, academic self-efﬁcacy, time spent studying (averaged EMA variable), problem marijuana use, and time spent using marijuana (averaged EMA variable) predict academic performance (cumulative GPA). At the ﬁrst level, race/ ethnicity (β = −.32, p = .03) and gender (β = .25, p = .08) together predicted cumulative GPA (F[2,43] = 4.04, p = .03) and accounted for approximately 15.80% of the variation in GPA. At the second level, the linear combination of academic self-efﬁcacy (MSLQ-SE; β = .31, p = .02) and problem marijuana use (RMPI; β = .12, p = .38) together were signiﬁcant (F[4,41] = 3.80, p = .01) and accounted for approximately 11.20% of additional variation in cumulative GPA. While accounting for these four predictor variables, average EMA minutes spent studying (β = −.10, p = .48) at the third level was not a signiﬁcant predictor of cumulative GPA (F[5, 40] = 3.10, p = .02), accounting for only 1% of additional variation in cumulative GPA. Finally, accounting for all ﬁve of the previous predictor variables, the average EMA minutes spent smoking (β = − .29, p = .04) was a signiﬁcant negative predictor of cumulative GPA (F[6, 39] = 3.54, p b .01; R2 = .35), accounting for approximately 7.30% of additional variation in cumulative GPA.
Rating from 1 – 10
8 7 6 5 4 3
D1_1 D1_2 D1_3 D2_1 D2_2 D2_3 D3_1 D3_2 D3_3 D4_1 D4_2 D4_3 D5_1 D5_2 D5_3 D6_1 D6_2 D6_3 D7_1 D7_2 D7_3 D8_1 D8_2 D8_3 D9_1 D9_2 D9_3 D10_1 D10_2 D10_3 D11_1 D11_2 D11_3 D12_1 D12_2 D12_3 D13_1 D13_2 D13_3 D14_1 D14_2 D14_3
Fig. 1. Average EMA values across participants for academic motivation and marijuana craving over the two-week assessment. Each time point on the x-axis refers to the day of the study (D) and the speciﬁc time point when the data was collected (out of three random times each day).
K.T. Phillips et al. / Addictive Behaviors 47 (2015) 42–47
4. Discussion This study was designed to explore the association between marijuana use and craving in a college setting and how these variables might relate to academic motivation, effort and success. While there has been considerable research exploring the association between marijuana use and academic success (e.g., cumulative GPA, dropout, etc.) with high school students (e.g., Bray, Zarkin, Ringwalt, & Junfeng, 2000; Bryant & Zimmerman, 2002; Horwood et al., 2010; Lynskey & Hall, 2000), there has been less emphasis on exploring academic factors in college students using marijuana. Results indicate that marijuana craving is associated with marijuana use in college students who frequently use marijuana. EMA affords an opportunity to examine a temporal association between craving and marijuana use by gathering data closer to the moment of usage. Even though data for the current study was based on self-report, it allowed for a closer examination of craving in its ability to predict future participant behavior within the next several hours. Marijuana use was conceptualized in two different ways — the amount of time (in minutes) smoked and the number of times participants smoked since they were last texted. When participants experienced greater levels of craving at one point in time, they tended to spend more time smoking and reported more frequent smoking at the next assessment point. Findings from this study are consistent with the only other past study (Buckner et al., 2012) that examined marijuana use and craving among college students, which also used EMA. Further research on this topic will help better understand these relations and whether they generalize to other cannabis-using populations. Findings from this study indicate that craving is related to and may impact academic effort and motivation. Craving negatively predicted the subsequent amount of time spent studying. In the same moment, when craving was higher academic motivation was lower and vice versa. These ﬁndings are consistent with a self-regulation model suggesting that craving can potentially lead to a competition for cognitive resources. Distractions that take students away from their studies will lead to academic struggles. Furthermore, if students can't focus or don't feel compelled to study, they may not complete their degree. Certain factors, such as academic self-efﬁcacy, have been shown to play a considerable role in college success (e.g., Chemers, Hu, & Garcia, 2001; Robbins et al., 2004) and this was similar for our sample. No prior studies have examined academic motivation or self-efﬁcacy in relation to college student marijuana use. In the current study, time spent smoking marijuana accounted for 7% of the variance in participants' GPA, while already accounting for other important factors. Importantly, race/ethnicity accounted for a substantial portion of the variance when examining predictors of GPA. Past data has shown that persons from diverse backgrounds (e.g., African American and Latino students) have lower college graduation rates within a 6-year period compared to White students across public universities with high acceptance rates (National Center for Education Statistics, 2013), similar to the university where data for this study was collected. Future work should more closely examine the role that ethnic and racial factors may play on both marijuana use and academic variables with a larger sample. Future researchers might consider testing the self-regulatory cognitive resource model through use of cue reactivity (Gray, LaRowe, & Upadhyaya, 2008; Gray, LaRowe, Watson, & Carpenter, 2011) in order to better understand whether craving directly impacts academic behaviors. As an example, Sayette, Schooler, and Reichle (2010) conducted a study on cigarette craving and the ability to sustain attention in a reading task. Findings suggested that participants randomized to a high craving cue condition were more likely to report mind-wandering compared to participants in a low craving condition. Though it is unclear whether these lab ﬁndings would translate into the academic environment or apply to marijuana users, it suggests that students who are craving might struggle to focus, which could lead to difﬁculty paying attention in college courses, completing reading and homework
assignments, studying, and other academic behaviors, such as regulating their academic motivation. There are several limitations that may restrict the generalizability of the current study. First, this study included a purposive sample of heavy marijuana users who reported smoking almost daily. Therefore, the association between craving and marijuana use should be understood within that context. Because participants were heavy users, ﬁndings related to marijuana craving and use in relation to academic factors (i.e., motivation, effort, and GPA) probably do not generalize to the occasional college-aged marijuana user. Second, though EMA was a useful methodology to answer our questions, other variables that may be mediating these associations were not assessed. For example, positive or negative affect in the moment or a range of psychological or other substance use (e.g., alcohol) variables that may be impacting students' academic success and marijuana use were not assessed. Finally, it may have been useful to incorporate event contingent assessments to learn more about marijuana use when it was occurring. Though using a single item to assess craving and academic motivation is a limitation (Tiffany, 1992), due to the intensive nature of EMA data collection, we did not want to overwhelm participants with additional questions during the two-week assessment. Past research has demonstrated the validity of one-item questions compared to multi-item questionnaires for a range of constructs, including craving and anxiety (Buckner, Silgado, & Schmidt, 2011; Davey, Barratt, Butow, & Deeks, 2007). One-item measures are more suitable for repeated measurement (McCormick, Horne, & Sheather, 1988), when one might expect frequent changes in a construct. However they do not necessarily capture the multidimensionality of certain constructs, such as craving (Rosenberg, 2009). In conclusion, study ﬁndings suggest that craving is related to frequency of students' marijuana use and academic effort and motivation. Students' marijuana use explained part of the variance in participants' cumulative GPA after already accounting for other important variables, such as ethnicity/race and academic self-efﬁcacy. These ﬁndings have possible implications for future work with college student marijuana users. Participants in the current study were part of a high-risk group of students who are at risk for attrition from university studies. Such students could potentially beneﬁt from a tailored intervention that would address academic concerns, psychological needs, and problematic substance use. Role of funding sources Funding for this study was provided by the University of Northern Colorado (PR104). The university had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
Contributors Authors K. Phillips and M. Phillips designed the study and protocol, conducted literature searches and provided summaries of previous research studies. Authors Lalonde and M. Phillips conducted the statistical analyses. All authors contributed to and have approved the ﬁnal manuscript. Conﬂict of interest All authors declare that they have no conﬂicts of interest.
Acknowledgments We would like to thank our research assistants from the Motivation and Addiction Research Lab at the University of Northern Colorado for their assistance with data collection.
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