Marijuana and the Effects on College Students Peer Review Article

• Journal List
• HHS Author Manuscripts
• PMC4586361

Psychol Aficionado Behav. Author manuscript; available in PMC 2016 Sep i.

Published in final edited course every bit:

PMCID: PMC4586361

NIHMSID: NIHMS700707

The Bookish Consequences of Marijuana Use during Higher

Amelia One thousand. Arria

Heart on Young Adult Wellness and Development, Department of Behavioral and Community Health, University of Maryland School of Public Health

Kimberly M. Caldeira

Center on Young Developed Health and Evolution, Department of Behavioral and Community Health, University of Maryland School of Public Health

Brittany A. Bugbee

Middle on Young Developed Wellness and Development, Section of Behavioral and Customs Health, University of Maryland School of Public Health

Kathryn B. Vincent

Center on Young Developed Health and Evolution, Department of Behavioral and Community Health, University of Maryland School of Public Health

Kevin East. O'Grady

Department of Psychology, University of Maryland

Supplementary Materials

Effigy.

GUID: 9508A638-011E-4FBC-82EF-DD4AAFDD2661

Tabular array.

GUID: E1CF22B7-F8AA-4777-87AE-84C825D0A958

Abstruse

Although several studies have shown that marijuana apply can adversely affect academic achievement among adolescents, less research has focused on its impact on post-secondary educational outcomes. This report utilized data from a large longitudinal cohort study of college students to test the direct and indirect effects of marijuana use on higher GPA and time to graduation, with skipping class every bit a mediator of these outcomes. A structural equation model was evaluated taking into account a diverseness of baseline risk and protective factors (i.e., demographics, college appointment, psychological performance, alcohol and other drug apply) thought to contribute to college bookish outcomes. The results showed a meaning path from baseline marijuana use frequency to skipping more classes at baseline to lower start-semester GPA to longer time to graduation. Baseline measures of other drug use and alcohol quantity exhibited similar indirect furnishings on GPA and graduation time. Over time, the rate of alter in marijuana utilise was negatively associated with rate of alter in GPA, but did not account for whatever additional variance in graduation time. Percentage of classes skipped was negatively associated with GPA at baseline and over time. Thus, fifty-fifty accounting for demographics and other factors, marijuana use adversely afflicted college academic outcomes, both directly and indirectly through poorer grade attendance. Results extend prior research by showing that marijuana use during college can be a barrier to academic accomplishment. Prevention and early intervention might exist important components of a comprehensive strategy for promoting post-secondary academic accomplishment.

Keywords: alcohol, cannabis, educational achievement, GPA, illicit drugs

Marijuana use is common amid college students in the United states, with one in 3 using inside the by yr (Johnston, O'Malley, Bachman, Schulenberg, & Miech, 2014) and xix.viii% reporting past-month utilize (Substance Abuse and Mental Wellness Services Administration, 2014). The proportion of adolescents and young adults who perceive take a chance associated with smoking marijuana has been decreasing quite dramatically during the by ten years (Johnston et al., 2014; Substance Abuse and Mental Wellness Services Administration, 2014). These trends parallel the timing of legislative deportment to relax or eliminate legal penalties for either use or possession (Hopfer, 2014). For case, the expansion of medical marijuana in Colorado has been linked to declining take a chance perceptions and increasing prevalence of marijuana abuse and dependence (Schuermeyer et al., 2014).

Marijuana utilise can bear on educational achievement. Cross-exclusive and longitudinal research studies accept demonstrated the negative influence of marijuana on loftier school grades (Ellickson, Tucker, Klein, & Saner, 2004; Homel, Thompson, & Leadbeater, 2014), high school degree completion (Bray, Zarkin, Ringwalt, & Qi, 2000; Horwood et al., 2010; van Ours & Williams, 2009), and the likelihood of inbound college (Fergusson, Horwood, & Beautrais, 2003; Homel et al., 2014; Horwood et al., 2010). A few studies have focused on the clan betwixt marijuana apply and post-secondary educational outcomes. A recent study by Homel et al. (2014) on trajectories of marijuana utilize from ages fifteen to 25 found that, relative to not-users, occasional marijuana users were more than probable to delay enrollment in or drop out of post-secondary education, and frequent users were significantly less likely to enroll. Chase, Eisenberg, and Kilbourne (2010) analyzed national epidemiologic data and observed that individuals with marijuana use disorder were more likely to drib out of higher (OR=1.26). Furthermore, heavy marijuana users who do enroll in college are more likely to experience gaps in enrollment (Arria et al., 2013b), even when decision-making for a number of potentially confounding variables.

The mechanisms underlying the association between marijuana employ and poor educational outcomes are most probable very circuitous and not completely understood. Marijuana apply, especially heavy use, has been shown to bear on working memory, learning, and information processing; functions that are necessary for bookish performance (Crean, Crane, & Stonemason, 2011; Jager, Block, Luijten, & Ramsey, 2010; Solowij et al., 2011). Additionally, long-term, heavy use of marijuana has been linked to long-term changes in the construction of the brain, including the hippocampus, prefrontal cortex, and amygdala (Battistella et al., 2014; Churchwell, Lopez-Larson, & Yurgelun-Todd, 2010; Hall, 2015; Volkow, Baler, Compton, & Weiss, 2014; Yücel et al., 2008). These changes are associated with impairments in data processing, IQ, memory, attention, and neurocognitive performance (Block et al., 2002; Bolla, Brown, Eldreth, Tate, & Cadet, 2002; Fontes et al., 2011; Medina et al., 2007; Meier et al., 2012; Solowij et al., 2002), and these effects can remain even after several weeks of abstinence (Bolla et al., 2002; Medina et al., 2007; Schweinsburg et al., 2008).

It is possible that these neurocognitive effects of marijuana could contribute to bookish bug among marijuana-using students, particularly if utilize begins during boyhood and is regular and heavy (Fontes et al., 2011; Volkow et al., 2014). Brook, Stimmel, Chenshu, and Brook (2008) found that early onset of marijuana use was associated with lower levels of bookish functioning at historic period 27. A possible link between marijuana use and amotivation has been suggested (Bloomfield et al., 2013; van Hell et al., 2010), which could contribute to a lack of engagement in college and difficulties in sustaining a focus on academic pursuits. Skipping classes is a possible manifestation of the lack of delivery to one'south academic life during college and could also be exacerbated past the acute neurocognitive effects of marijuana smoking or withdrawal symptoms associated with more regular apply. Changes in such bookish behaviors (i.e., missing classes, studying less) appear to play a function in explaining the relationship between excessive drinking and academic performance (Powell, Williams, & Wechsler, 2004; Williams, Powell, & Wechsler, 2003; Wolaver, 2002), and it is plausible that similar mechanisms might occur with marijuana utilize. Bear witness from an before report of our longitudinal cohort of college students indicated that, as students' marijuana use problems intensified over time, they tended to experience associated declines in class omnipresence and, consequently, GPA (Arria et al., 2013c). Nevertheless, this phenomenon remains largely unexplored, as we could find no other studies examining the possible office of class attendance or other bookish behaviors as mediators underlying the human relationship between marijuana apply and academic performance problems.

Research has consistently shown that marijuana use and heavy drinking tend to co-occur (Jones, Oeltmann, Wilson, Brener, & Hill, 2001; O'Grady, Arria, Fitzelle, & Wish, 2008; Wechsler, Dowdall, Davenport, & Castillo, 1995), and employ of other drugs is common amid marijuana users (Mohler-Kuo, Lee, & Wechsler, 2003). When examining the complex human relationship betwixt marijuana apply and academic performance, it is therefore critical to account for the concurrent use of alcohol and other drugs.

Moreover, mental wellness problems oft co-exist with marijuana and other substance use, especially anxiety and depression (Armstrong & Costello, 2002; Pottick, Bilder, Vander Stoep, Warner, & Alvarez, 2007; Sheidow, McCart, Zajac, & Davis, 2012). These mental health problems have been plant to independently contribute to bookish problems amid college students (Arria et al., 2013a; Eisenberg, Golberstein, & Hunt, 2009; Chase et al., 2010). Eisenberg et al. (2009) found that depression, and especially low-anxiety comorbidity, was associated with decreased GPA among higher students, and Arria et al. (2013a) plant that depressive symptoms were associated with a gap in enrollment during the commencement 2 years of college.

Finally, the educational enquiry literature has highlighted numerous not-substance-utilise-related factors that impede academic achievement. Get-go-generation, minority, and male students tend to experience worse academic outcomes (Conger & Long, 2010; Pascarella, Pierson, Wolniak, & Terenzini, 2004; Steele-Johnson & Leas, 2013). Participation in living-learning programs is associated with positive bookish experiences (Inkelas et al., 2006; Pike, 1999; Pike, Kuh, & McCormick, 2011), whereas the issue of extracurricular interest is largely unstudied but might depend on the specific type of activity (Baker, 2008).

High priority is placed on bookish achievement by parents, educational institutions, and policymakers, and understanding the factors that hinder academic operation is essential for promoting college student success. For this reason, inquiry prove clarifying the nature of the relationship betwixt marijuana employ and bookish operation and possible underlying mechanisms is critically needed for college administrators and policymakers, and would exist especially useful for developing prevention and intervention programs.

The present study builds on prior inquiry by evaluating a latent variable growth bend model (LVGCM) that specified the possible impact of marijuana use frequency on 2 academic outcomes during college—semester GPA and time to graduation—and the extent to which skipping class might mediate those associations in the context of the role of a set of first-year chance factors idea to predict academic outcomes (i.due east., other substance use, demographics, college date, and psychological performance). Importantly, our longitudinal design permitted u.s.a. to evaluate these associations both cross-sectionally during the offset year of higher (i.e., baseline) and longitudinally past modeling the repeated measures of marijuana utilize, skipping class, and semester GPA as latent variables representing slope, or rate of change over time. Thus, we evaluated a structural equation model to exam the post-obit hypotheses: (a) marijuana employ intercept and gradient will be inversely related to GPA intercept and gradient; (b) marijuana use intercept and slope will be directly related to time to graduation; and (c) skipping class intercept and slope volition mediate the in a higher place hypothesized associations. Specifically, we hypothesized that marijuana intercept volition exist straight related to skipping class intercept, which in plow volition exist directly related to GPA intercept and graduation fourth dimension, and that marijuana slope will be straight related to skipping class slope, which in turn will be straight related to GPA slope and graduation time. The structural model as well included the straight and indirect effects of several baseline covariates, including alcohol use, psychological hazard factors, demographics, and college engagement variables, in order to isolate the unique issue of marijuana employ on the hypothesized mediators and outcomes.

Method

Pattern

Data were nerveless during eight annual assessments with a cohort of 1253 young adults. Participants were originally recruited as incoming outset-time, offset-year students at ane large public university in the mid-Atlantic region (Arria et al., 2008a). The baseline assessment (Year 1) was conducted one-time during their first year of college (i.e., 2004 to 2005) and consisted of a two-hr personal interview and self-administered questionnaires covering substance utilize, bookish behaviors, and a broad range of other health-related information. Subsequent almanac follow-up assessments (Years 2 through 8) were similar in format and content. All of the original baseline participants were followed upwards regardless of continued higher attendance. Follow-upwards rates ranged from 76 to 91% annually (Vincent et al., 2012). Cash incentives were provided for completion of each assessment. Informed consent was obtained. The study received IRB approval. Interviewers were trained extensively in confidentiality protections, and a federal Certificate of Confidentiality was obtained.

Participants

For the present assay, the sample was restricted to the 1117 individuals for whom valid data on college graduation were available from either administrative data from the university or self-study by Twelvemonth viii of the report (come across beneath). Individuals who did not complete a 4-twelvemonth higher degree (n=34) or were missing information on graduation (n=102) were necessarily excluded because the distal outcome of interest was fourth dimension to graduation. Characteristics of the inclusion sample are presented in Table 1. Compared with excluded individuals, those in the inclusion sample were slightly more probable to exist female (54% vs. 31%), involved in living-learning programs (54% vs. 38%) or fraternity/sorority organizations (28% vs. 11%), and earned higher GPAs during their first semester of higher [M (SD) 3.fifteen (.64) vs. 2.46 (.89), all psouthward<.05]; however, they were like with respect to race, parents' education, and baseline alcohol and marijuana use frequency. Missing data within each assessment was minimal (see Tabular array 2).

Table 1

Sample Characteristics [n (%) or One thousand (SD)] (N=1117)

Demographics
Male	514 (46.0%)
White	823 (73.7%)
Parents with college caste	902 (86.2%)
Higher engagement
Living-learning program	605 (54.iii%)
Number of extracurricular activities	2.five (ii.ane)
Fraternity/sorority involvement	291 (27.nine%)
Baseline substance use
Marijuana use frequency (days, past calendar month)	ii.4 (5.7)
Alcohol employ frequency (days, past month)	6.3 (five.6)
Typical number of drinks/twenty-four hour period	4.five (two.9)
Number of other illicit drugs used (by year)	0.5 (1.1)
Higher bookish behaviors
Percent of classes skipped (Year 1)	nine.viii (12.seven)
Baseline psychological risk factors
Behavioral dysregulation	27.9 (xi.eight)
Feet symptoms	7.v (vii.0)
Depressive symptoms	5.2 (iv.nine)
ADHD diagnosis (self-reported)	63 (five.6%)
Impulsive sensation-seeking	three.5 (2.2)
College bookish outcomes
Get-go semester GPA (10 ten)	31.5 (six.4)
Cumulative GPA (ten 10) at graduation	33.i (4.3)
Time to graduation
Less than 4 years	71 (6.4%)
4 years	740 (66.two%)
5 years	248 (22.2%)
More than 5 years	58 (5.2%)
School of graduation
Dwelling house university	1067 (95.5%)
Another institution	fifty (4.5%)

Table 2

Measures Used in the Present Study

Construct	Description	Years (Source)
Outcome Measures
GPA	Semester GPA for coursework at home university, captured from authoritative information for autumn and spring semesters each year. Range 0.33 to 4.00 in first semester. Available sample sizes for semesters 1 through 10 were 1116, 1110, 1073, 1064, 1039, 930, 1020, 978, 213, and 93, respectively. Data from subsequent semesters were censored due to bereft sample sizes (n≤18).	one–half-dozen (A)
Time to graduation	Computed initially as the number of semesters from college entry to completion of first 4-year degree, either at the domicile university (administrative data) or other institution (self-study). To create a more even distribution, data were and so consolidated into an ordinal variable coded as less than four years (1); four years (2); more than than four years, up to 5 years (3); and more than five years (iv).	5–8 (A, I)
Hypothesized Mediator
Skipping class	Computed as the percentage of classes skipped, based on responses to "How many class sessions per week do you lot routinely skip?" and their total number of class sessions per week. Range 0.0 to 87.5 in Year one. Available sample sizes for years 1 through v were 1116, 1037, 1000, 964, and 151. Data from subsequent assessments were censored due to insufficient sample size (north≤22).	ane–8 (I)
Primary Explanatory Variable
Marijuana use	Frequency of marijuana use in the past thirty days (range 0 to xxx). Available sample sizes for years 1 through 5 were 1116, 1042, 1018, 977, and 151. Information from subsequent assessments were censored due to insufficient sample size (north≤23).	1–8 (I)
Baseline Covariates
Substance utilize
			Items	Available northward
Booze frequency	Frequency of alcohol utilize in the past 30 days (range 0 to thirty).	1 (I)		1117
Alcohol quantity	Number of drinks consumed on a typical drinking 24-hour interval (range 0 to 20).	1 (I)		1111
Other drug utilise	Computed equally the number of other illicit drugs that were used at least once in the past year, based on responses regarding past-yr frequency of utilize for hallucinogens, inhalants, cocaine, heroin, amphetamine/methamphetamine, ecstasy, and nonmedical employ of prescription stimulants, analgesics, and tranquilizers (range 0 to eight).	1 (I)	9	1116
College Engagement
Extracurricular involvement	Computed every bit the sum of regular (2), irregular (1) and no (0) involvement in seven unlike activities (volunteer work, religious/church groups, exercise, athletics, and upwardly to three other open up-concluded extracurricular activities). Range 0 to 12.	i (I)	seven	1116
Fraternity/sorority involvement	Dichotomized equally irregular or regular involvement versus none, due to the very small-scale number endorsing irregular involvement (<2%).	2 (I)	1	1043
Living-learning program involvement	Dichotomized every bit whatsoever versus none, based on administrative data obtained at higher entry. Living-learning programs provide students with a specialized residential experience that is specifically tied to an academic unit, with the goal of fostering deeper integration of classroom material. A various range of living-learning programs were represented in our sample, including interests in community service and/or specific academic disciplines (e.m., foreign-linguistic communication, humanities, engineering), many of which were focused on loftier-achieving (i.eastward., "honors") students. Student athletes were non counted every bit living-learning program participants.	1 (A)		1115
Psychological run a risk factors
Behavioral dysregulation	Subscale score from the Dysregulation Inventory (Mezzich, Tarter, Giancola, & Kirisci, 2001). College scores bespeak higher levels of disinhibition and behavioral undercontrol. Range ii to 91. Cronbach's α=.90	1 (S)	34	1059
Impulsive sensation-seeking	Subscale score from the Zuckerman-Kuhlman Personality Questionnaire (Zuckerman, 2002). College scores reflect greater preference for novelty and excitement. Range 0 to7. Cronbach's α=.74	1 (S)	vii	1106
Depressive symptoms	Brook Low Inventory (Brook, Ward, Mendelson, Mock, & Erbaugh, 1961). For each detail, participant selects one of four statements that is almost applicable to their current feelings. Higher scores indicate higher levels of depressive symptoms. Range 0 to 34. Cronbach's α=.84	one (Due south)	21	1114
Anxiety symptoms	Beck Anxiety Inventory (Beck & Steer, 1990). Uses Likert-blazon response options for participant to indicate how much each symptom is bothersome (non at all, mildly, moderately, and severely). College scores indicate higher levels of anxiety symptoms. Range 0 to 51. Cronbach's α=.88	1 (Due south)	21	1094
ADHD diagnosis at higher entry	Dichotomous variable based on self-reported electric current health conditions.	1 (I)		1117
Demographics		1 (S)
Sex	Dichotomized as male and female.	1 (I)		1117
Race/Ethnicity	Dichotomized as White versus non-White due to the preponderance of Whites in the sample (74%).	one,iii (Due south)		1117
Parents' pedagogy	Highest level of educational attainment past either parent was dichotomized as college caste versus no higher degree, to emphasize the distinction betwixt students who were and were non first-generation college students.	1 (S)		1046

Measures

A description of the measures used in this study is presented in Tabular array 2. Participants provided informed consent for the researchers to admission their bookish data from the home academy, which was the sole source of data on semester GPA, and the primary source of data on graduation for individuals who graduated from the dwelling house university. For the nowadays report, GPA information were rescaled by a factor of ten in club to facilitate interpretation of model results. In order to retain the 50 individuals who had left the home academy and graduated from another institution, we supplemented administrative data on college graduation with participants' self-report data nerveless in Years 5 through eight. A dichotomous variable on the school of graduation (home institution vs. elsewhere) was used as a control variable. Substance utilise measures were adapted from standard surveys (Substance Corruption and Mental Wellness Services Assistants, 2003).

Analysis

Because time to graduation was the long-term outcome variable of interest in this written report, data on marijuana use that were collected for the flow after graduation were considered missing for the purposes of the nowadays analyses, and data on skipping grade and GPA were necessarily missing after the pupil's graduation. Encounter Table 2 for available sample sizes at each time point for each variable. For the 58 participants who graduated later on their fifth year in higher, data from their first v years were included, and all subsequent observations were omitted from all analyses, because the sample sizes for graduation in years 6 (n=49), seven (n=7), and 8 (northward=2) were bereft to allow estimation.

Our hypothesized conceptual model was fit using a latent variable model (LVM; Muthén, 2002; Muthén & Muthén, 2012) that included three latent variable intercepts and three latent variable growth curves equally elements of the LVM. Our LVM can exist considered a structural equation model that included every bit a component a latent variable growth curve model (LVGCM; Duncan, Duncan, Strycker, Li, & Alpert, 1999; Muthén, 2008). LVGCM itself can exist viewed as an extension to repeated measures analysis of variance because it examines mean differences over time. Information technology can also exist viewed every bit an extension to confirmatory factor assay, because the rates of change over time in a variable are considered to be unmeasured, or latent. The goal of a LVGCM is to approximate the growth trajectory (rate of alter over time) of a latent variable (Duncan et al., 1999; Muthén, 2008). The LVGCM component of our model consisted of the latent endogenous variables that represented linear rates of change in frequency of marijuana use, skipping class, and semester GPA, each of which were hypothesized to predict time to graduation, likewise as the latent intercepts for these same three variables, which represented first-yr marijuana use, first-year skipping class, and first-semester GPA. In addition to the LVGCM component of the model, our LVM also included exogenous variables—namely, the baseline substance utilize (i.e., booze and drugs other than marijuana), demographics, college appointment, and psychosocial risk factors—all of which were hypothesized to predict both the endogenous intercept and slope variables for marijuana utilize, skipping form, and GPA, likewise as the distal effect of time to graduation. Figure 1 depicts the putative model under examination.

Schematic Delineation of Hypothesized Model of the Longitudinal Relationship betwixt Marijuana Use, Skipping Grade, GPA, and Time to College Graduation

Note. In addition to the mediation paths depicted here, we also tested all possible straight paths from the covariates to the intercept and slope variables (i.eastward., marijuana, skipping, GPA), and all possible direct paths from the intercept and slope variables to the distal outcome of fourth dimension to graduation. The observed variables for the latent variables of intercept and slope for marijuana apply frequency, skipping class, and GPA have been omitted from this effigy for ease of presentation; refer to Figure 3A in the supplemental online materials for a more complete depiction of the trajectory component of the model and its parameterization.

Our arroyo tested the possible direct relationship between rate of change over time in frequency of marijuana use and rate of change over time in skipping classes, and the possible straight relationship between rates of modify over time in skipping classes and semester GPA, together with the possible straight and indirect relationships between rates of alter over time in frequency of marijuana use and rate of change over time in semester GPA. Moreover, within the context of this model it was possible to parameterize the 3 trajectories (meet Figure 3A for detailed information regarding parameterization of the LVGCM component of the LVM) such that the intercept terms for each trajectory represented marijuana apply frequency and skipping class during the first year of higher, and semester GPA during the first semester, respectively. Thus, these three intercept terms correspond behavior during the first year of college. Additionally, nosotros tested all possible direct paths from the intercept and gradient variables to the distal outcome, fourth dimension to graduation, and all possible straight paths from the baseline covariates to the three intercept and three gradient variables.

The three latent variables were parameterized in the same manner, in which the intercept was fixed to correspond the first-semester behavior, and the gradient parameter was divers by a starting time-caste polynomial (i.e., a linear term; see Effigy 3A for greater detail regarding the parameterization of the three trajectories). The jitney examination of model fit and the test of competing models used the Satorra-Bentler scaled χ ⁱⁱ goodness-of-fit tests (Satorra & Bentler, 1988). Robust methods were used to gauge the standard errors associated with the free parameters (Chou, Bentler, & Satorra, 1991). Given that Hu and Bentler (1999) have indicated the need to utilize joint criteria to determine adequacy of model fit, with suggestions for cutoff values for the comparative fit index (CFI; ≥.95) and the standardized root mean square residual (SRMR; ≤.09), any model needed to meet these criteria to be considered acceptable. All paths remaining in the model had to be statistically pregnant at α=.05. Our arroyo was to examination our hypothesized model and then alter the model as needed based on test of the parameter estimates and overall model fit. Therefore, our approach was to test our hypothesized model, then change it on the footing of examination of the results, first deleting not-significant paths from the model, refitting the revised model, and then allowing paths with significant modification indices to enter the revised model. Finally, the MacKinnon and Lockwood asymmetric distribution of products method was used to examination the significance of the mediation effects, with estimates, standard errors, and confidence intervals based on i,000 bootstrap samples (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). The dichotomous variable assessing graduation from the domicile university versus any other school was held constant in all models. Analyses were performed using Mplus seven.i (Muthén & Muthén, Los Angeles, CA).

Results

Marijuana Utilize

When assessed during their offset twelvemonth of higher, more than than one-third (37.iv%) of the sample had used marijuana one time or more during the past month, with an average frequency of six.5 days during the past month amongst those who used (data non shown in a table). In subsequent years, marijuana employ was similarly prevalent (e.chiliad., past-month use was 38.0%, 37.9%, 33.0% in Years two through 4, respectively) merely more frequent, with average frequency of employ betwixt 7.vii and 8.8 days during the by month among those who used in Years 2 through 4 (information not shown in a table). More than data on longitudinal patterns of marijuana use in this cohort is available elsewhere (Caldeira, O'Grady, Vincent, & Arria, 2012).

Model Selection

The model selection process resulted in a final model that fit the data reasonably well (χ ^two=957.5, df=468, p<.0001, RMSEA=.034, CFI=.939). The terminal model included all the baseline covariates shown in Figure one, with the exception of self-reported ADHD diagnosis at baseline, which did not contribute to whatsoever significant pathways and was therefore omitted from the final model. Figure two depicts the pathways related to the intercepts and slopes for marijuana use, skipping class, and academic outcomes that were significant and therefore retained in the final model, and are discussed in particular below. Results pertaining to the baseline covariates are available in a supplementary online table (see Table 3A).

Construction Coefficients (Standard Errors) for the Latent Variable Model of the Human relationship between Intercept and Slope for Marijuana Use, Skipping Class, GPA, and Fourth dimension to College Graduation

Note. All paths shown are statistically significant (p<.05). Non-significant paths were dropped from the model. For ease of presentation, boosted significant paths between the covariates and the intercepts, slopes, and result were omitted from this effigy, although they were retained in the model (meet Table 3A in supplemental online materials for estimates of the structure coefficients and their standard errors, for significant paths that are not shown here). Baseline covariates that were included in the model were booze quantity and frequency, number of other drugs used during the past twelvemonth, sex, race/ethnicity, parents' education, living-learning program involvement, fraternity/sorority involvement, number of other extracurricular activities, impulsive sensation-seeking, behavioral dysregulation, depressive symptoms, and anxiety symptoms.

Overall Results

In general, results supported the hypothesized mediation effect in that, at baseline, marijuana use frequency predicted skipping class, which in plow predicted GPA, which in turn predicted time to graduation. Additionally, over time, marijuana gradient was inversely associated with GPA slope. The last model accounts for moderate amounts of variance in the skipping form intercept (R ²=.37), GPA intercept and slope (R ²=.24 and .57, respectively), and fourth dimension to graduation (R ²=.32; run into Figure 2). Nevertheless, interestingly just one of the exogenous variables (i.east., parents' education) and none of the endogenous variables predicted rate of change in skipping class over fourth dimension, and thus the model accounted for comparatively piffling variance in the slope of skipping class (R ²=.04).

Effects of Marijuana Utilise on First-semester GPA

Although at that place was no direct path from marijuana intercept to GPA intercept (see Figure 2), marijuana use at baseline contributed indirectly to lower baseline GPA through its positive influence on skipping course (b=−.07, SE=.02, p=.002; run across Tabular array 3).

Table three

Specific Directly and Indirect Paths [b(SE)] between Marijuana Use, Skipping Class, and Academic Outcomes from the Final Model

Explanatory Variable	Mediating Path(s)	Outcome Variable	Specific Effect	Total Indirect	Total Effect
Marijuana intercept	Skipping intercept	GPA intercept	−.072 (.023)	−.072 (.023)	−.072 (.023)
Skipping intercept	(Direct)		−.161 (.027)		a
Marijuana intercept	Skipping intercept, GPA intercept	GPA slope	.005 (.002)	.000 (.004)	.000 (.004)
	Marijuana slope		−.005 (.004)
Marijuana slope	(Direct)		−.051 (.024)		a
Skipping intercept	GPA intercept		.011 (.002)	.011 (.002)	.011 (.002)
Skipping gradient	(Directly)		−.126 (.024)		a
GPA intercept	(Straight)		−.069 (.005)		a
Marijuana intercept	Skipping intercept	Time to Graduation	.003 (.002)	.008 (.003)	.008 (.003)
	Skipping intercept, GPA intercept		.005 (.002)
	Skipping intercept, GPA intercept, GPA slope		−.002 (.001)
	Marijuana gradient, GPA slope		.002 (.002)
Skipping intercept	(Direct)		.007 (.003)	.007 (.002)	.014 (.003)
	GPA intercept		.012 (.003)
	GPA intercept, GPA gradient		−.005 (.002)
GPA intercept	(Direct)		−.075 (.006)	.033 (.007)	−.042 (.005)
	GPA slope		.033 (.007)
Marijuana slope	GPA gradient		.025 (.013)	.025 (.013)	.025 (.013)
Skipping slope	(Direct)		−.066 (.025)	.060 (.028)	−.006 (.019)
	GPA gradient		.060 (.028)
GPA gradient	(Straight)		−.480 (.078)		a

Effects of Marijuana Use on GPA Gradient

Marijuana intercept had no net outcome on GPA gradient, despite having a negligible contribution via ane meaning indirect pathway (i.e., marijuana intercept to skipping intercept to GPA intercept to GPA gradient). With respect to rate of alter in marijuana utilise over fourth dimension, marijuana slope was direct and negatively associated with GPA slope (b=−.05, SE=.02, p=.030), just at that place was no corresponding indirect path through skipping gradient. Thus, increases in marijuana use frequency over time contributed directly to decreases in GPA, without any associated indirect outcome via rate of modify in skipping form.

Effects of Marijuana Use on Time to Graduation

No significant straight paths were observed betwixt marijuana intercept and graduation fourth dimension (see Figure 2), yet the internet result of marijuana intercept on graduation time was positive (b=.008, SE=.003, p=.005), which was the net result of ii competing indirect paths. The more than predominant (positive) path was from marijuana intercept to skipping intercept to GPA intercept to graduation time (b=.005, SE=.002, p=.003), which overshadowed a smaller negative effect from the indirect path from marijuana intercept to skipping intercept to GPA intercept to GPA slope to graduation time (b=−.002, SE=.001, p=.010). The other ii possible indirect paths (i.e., marijuana intercept to skipping intercept to graduation time, and marijuana intercept to marijuana slope to GPA slope to graduation time) were not statistically significant.

The marijuana slope latent variable had no result on graduation time, either directly or indirectly. Although the indirect pathway from marijuana slope to GPA gradient to graduation fourth dimension trended toward a positive consequence, information technology was non statistically significant (b=.025, SE=.013, p=.056).

Effects of Baseline Alcohol and Other Drug Use on Bookish Outcomes

In that location were no direct paths from any of the baseline substance use variables (i.e., booze frequency, alcohol quantity, other drug employ) to whatever of the academic effect variables (i.e., GPA intercept, GPA gradient, graduation fourth dimension). Indirectly, however, all 3 substance use covariates were significantly associated with lower GPA intercept via paths involving the intercepts of skipping and/or marijuana (see Table 3A, online supplemental materials). The seemingly contradictory finding that the baseline substance use variables were associated with lower baseline GPAs but college increases in GPA over time is consistent with a ceiling effect, in that the individuals with minimal substance utilise and high grades at baseline had little if any opportunity to improve their grades over fourth dimension. Because the hypothetical model did not include slopes of booze and other drug employ, it was not possible to evaluate the impact of rates of alter in use of these substances on GPA over time. Finally, with respect to graduation fourth dimension, both baseline alcohol quantity and other drug use—but not booze frequency—had positive net effects via indirect paths involving the intercepts of skipping class and GPA.

Word

In this sample of higher graduates, students who used marijuana more than oft during the commencement yr of college tended to skip more of their classes, which, in plow, contributed to their tendency to earn lower grades. Similar effects were too observed for baseline measures of booze use and other illicit drug use. Moreover, these findings were pregnant fifty-fifty in the context of a broad range of baseline covariates encompassing higher engagement, psychological functioning, and demographic characteristics. These results provided back up for our first hypothesis that marijuana utilise during the first year of college would contribute to poorer academic outcomes, and that these effects would exist mediated by skipping class.

Results likewise confirmed the hypothesized longitudinal relationships betwixt marijuana utilize and bookish outcomes. Specifically, increases in marijuana employ over time predicted declines in GPA, although this did not necessarily interpret to a later on graduation. Perhaps more strikingly, nevertheless, baseline marijuana employ frequency during the starting time year of higher had an enduring effect on delaying graduation several years later, via its influence on the path from skipping class to GPA at baseline. In fact, any additional contributions to delayed graduation arising from longitudinal changes in marijuana apply and/or skipping grade were either mixed or negligible, as can be seen through exam of the specific indirect and total effects in Tabular array three. This pattern of findings highlights the importance of the get-go year of college as a disquisitional period in which students' long-term academic trajectories brainstorm to take shape, based in part on how they balance engagement in academic life—peculiarly grade omnipresence—with marijuana utilize.

The present findings extend our prior enquiry on the mediating part of skipping form on the relationship between nonmedical prescription drug use and GPA by the stop of the first year of higher (Arria, O'Grady, Caldeira, Vincent, & Wish, 2008c). Findings are also largely consequent with an earlier study of this cohort spanning their first iv years of college, in which skipping class mediated the relationship between marijuana use bug (as defined by cocky-reported DSM-IV criteria) and GPA (Arria et al., 2013c). Whereas that study and the nowadays analyses support the mediation effect of skipping class at baseline, the nowadays model, unlike the prior report, revealed a direct human relationship between rate of change in marijuana use and GPA over time (but no arbitration result from changes in skipping class). This discrepancy is likely attributable to methodological differences such as differences in our marijuana utilise measures or the present report's inclusion criteria being more than restrictive (than in the same study) in order to focus on fourth dimension to graduation among the subset who graduated.

Results must exist interpreted in low-cal of the study's limitations. Although our model accounted for race/ethnicity in a broad fashion, we did not accept sufficient numbers of individuals in whatsoever specific minority groups to explore race/ethnicity differences in detail. Considering participants were all recruited from one university, results might accept express generalizability to students in other areas or at other types of colleges. Generalizability is also express by our conclusion to restrict the sample to individuals who completed their college caste; all the same, given that individuals with the near severe levels of marijuana involvement were at loftier gamble for dropping out of college (Arria et al., 2013a; Arria et al., 2013b), the fact that we were however able to detect more subtle bookish consequences even amid a relatively successful sample lends farther confidence to our findings. Administrative data on GPA and graduation were available simply from the home university; therefore, we could non fully account for how GPA changed amidst the students who left the home academy, and we cannot say how the results might have been affected past this omission. Nosotros attempted to mitigate this limitation by controlling for graduation from the home institution or elsewhere. Information technology is also possible that some of the 102 students who were excluded due to missing graduation information actually completed a college degree from another institution. Although we acknowledge that both academic behaviors and outcomes are probable to vary by option of major, given that some majors are intrinsically more than demanding than others, the number of singled-out majors in our sample was large (>100) and therefore difficult to analyze in a meaningful way. Our model did not include other factors that are associated with bookish achievement, namely financial stress, having a job, and bookish cocky-efficacy (Joo, Durband, & Grable, 2008; Krumrei-Mancuso, Newton, Kim, & Wilcox, 2013; Mattern & Shaw, 2010; Thruway, Kuh, & Massa-McKinley, 2009; Robb, Moody, & Abdel-Ghany, 2012). We cannot say how the results might have differed if marijuana use frequency had been modeled bold a not-normal distribution (i.eastward., Poisson or zero-inflated Poisson); unfortunately, model convergence became possible only after we specified a normal distribution, afterwards exhausting other specification options. Finally, although we used standard substance employ measures, past-calendar month behaviors might non reflect typical use patterns, and the temporal correspondence betwixt semester-level GPA data and annually assessed behavioral measures was imperfect.

Despite the higher up limitations, the written report's strengths include its longitudinal pattern and superior response rates and follow-up rates. This report also demonstrates the utility of a novel measure of academic behaviors (i.eastward., percent of classes skipped). Some other important advantage of this study is its integration of self-written report behavioral information with administrative data on academic outcomes, which is non always bachelor in college student studies. Finally, the impact of the findings is enhanced by the latitude of risk factors that were assessed in multiple domains.

With respect to research implications, the nowadays findings underscore the importance of enhancing our understanding of the mechanisms underlying the human relationship between substance use and academic performance during college. Baseline marijuana, alcohol, and other drug utilise had both short-term and long-term impacts on academic outcomes among this sample. Prior research has demonstrated that college drinking patterns are often a continuation of patterns that were established before college entry (Arria et al., 2008b; Sher & Rutledge, 2007). More extensive longitudinal enquiry is warranted to understand the possible impact of marijuana use on motivation to pursue academic goals, preferably starting in middle and high school. It is possible that marijuana employ contributes to the deterioration of academic values and motives, and thereby has potential to deflect students away from an otherwise promising academic trajectory. Long-term rewards associated with academic pursuits can exist overshadowed by brusk-term rewards associated with marijuana utilize, thereby leading to lower academic achievement during college. Finally, fifty-fifty though this report statistically adjusted for a number of covariates such every bit impulsive sensation-seeking, whether underlying neurocognitive factors predisposed individuals to both marijuana use and lower GPAs during higher remains to exist determined.

Looking beyond the first year of college, results provide strong evidence that as students employ marijuana more often over time, their GPA tends to decline. The finding that this association was not mediated by rate of change in form attendance—which itself likewise contributed negatively to GPA slope—was unexpected and highlights the importance of alternative underlying mechanisms that might be responsible for marijuana-related declines in bookish functioning. While information technology is tempting to speculate that the cognitive effects of chronic marijuana utilize might account for this blueprint of findings, we cannot rule out the possible contribution of whatsoever number of factors, such as the onset of a mental health status, perhaps exacerbated by marijuana use, or some other stressful event. In prior research with this sample, time spent studying did not mediate the human relationship between marijuana employ and GPA (Arria et al., 2013c), but it might be of import in other samples. Future research should include these variables as possible influences on academic achievement in college.

The present findings regarding marijuana use, if replicated in future studies, could have important implications for college administrators and parents of college-bound students. Despite the popular view that heavy drinking and marijuana use are a normal "rite of passage" endemic to the college feel, too every bit decreasing perceptions of risk from marijuana apply, marijuana use was far from innocuous in this sample. Rather, heavier patterns of marijuana use were incompatible with regular class attendance, with clear consequences for students' grades. Future inquiry should focus on specifically evaluating the possible impact of recovery or cessation of marijuana use on academic outcomes. The findings of this study suggest that recovery would accept a benign touch. The research question in this assay focused on the impact of marijuana use on fourth dimension to graduation, and therefore all the students in the sample eventually graduated. Heavier marijuana use did contribute indirectly to delayed graduation, and results strongly suggest that when students engaged in heavier marijuana employ patterns, they might accept done so at the expense of their learning experience.

Every bit an increasing number of states legalize marijuana, higher administrators must determine how to address marijuana use on campus in a fashion that promotes student success. The present findings describe a clear merchandise-off between academic outcomes and marijuana employ. College administrators interested in optimizing academic outcomes should acknowledge the part of marijuana utilize in maybe undermining students' ability to succeed. Rather than acceding to trends in public opinion virtually marijuana use, administrators tin indicate to the growing body of research evidence—including the nowadays findings—as a solid rationale supporting their decision to maintain a potent stance on enforcing their school'south anti-drug policies. A commitment to prioritizing the implementation of prove-based drug prevention and intervention is likely to promote higher levels of educatee appointment in bookish life and ultimately might amend institutional measures of success such every bit retention and on-time graduation.

Students who are facing the dual challenges of both academic failure and marijuana use problems might exist peculiarly receptive to targeted interventions aimed at reducing their marijuana apply if they are approached in the context of helping them stay in college. Screening students who visit academic assistance centers—whether past mandate or voluntarily—for both drug use and sporadic grade attendance could be a novel approach to identifying and intervening with students whose academic difficulties are linked to their marijuana use. On the other paw, given that many substance use patterns are established before college entry (Arria et al., 2008b; Sher & Rutledge, 2007), it is besides important to screen incoming students for existing marijuana utilize and intervene accordingly to promote long-term success.

Finally, given prior evidence supporting the importance of parental influences on college students' utilize of marijuana and booze (Abar & Turrisi, 2008; Abar, Turrisi, & Mallett, 2014; Napper, Hummer, Chithambo, & LaBrie, 2015), parents should actively stress the value of long-term rewards associated with academic engagement and regular grade attendance over substance apply during college.

Supplementary Cloth

Figure

Table

Acknowledgments

This enquiry was supported by the National Plant on Drug Abuse Grant R01DA014845 awarded to Amelia G. Arria. Special thanks are given to Angelica Barrall, the interviewing team, and the participants.

Footnotes

No conflicts of interest for whatever author.

Contributor Information

Amelia M. Arria, Center on Young Adult Health and Evolution, Section of Behavioral and Community Health, University of Maryland Schoolhouse of Public Health.

Kimberly M. Caldeira, Center on Young Developed Health and Development, Department of Behavioral and Customs Health, University of Maryland Schoolhouse of Public Health.

Brittany A. Bugbee, Center on Young Adult Health and Development, Department of Behavioral and Customs Health, Academy of Maryland Schoolhouse of Public Health.

Kathryn B. Vincent, Center on Young Adult Wellness and Development, Section of Behavioral and Community Health, University of Maryland Schoolhouse of Public Health.

Kevin E. O'Grady, Department of Psychology, University of Maryland.

References

• Abar C, Turrisi R. How important are parents during the college years? A longitudinal perspective of indirect influences parents yield on their higher teens' alcohol use. Addictive Behaviors. 2008;33(10):1360–1368. doi: 10.1016/j.addbeh.2008.06.010. [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]
• Abar CC, Turrisi R, Mallett KA. Differential trajectories of alcohol-related behaviors beyond the showtime yr of higher by parenting profiles. Psychology of Addictive Behaviors. 2014;28(ane):53–61. doi: 10.1037/a0032731. [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
• Armstrong TD, Costello EJ. Customs studies on adolescent substance use, abuse, or dependence and psychiatric comorbidity. Journal of Consulting and Clinical Psychology. 2002;lxx(6):1224–1239. doi: 10.1037//0022-006X.lxx.half dozen.1224. [PubMed] [CrossRef] [Google Scholar]
• Arria AM, Caldeira KM, O'Grady KE, Vincent KB, Fitzelle DB, Johnson EP, Wish ED. Drug exposure opportunities and use patterns among college students: Results of a longitudinal prospective accomplice study. Substance Abuse. 2008a;29(iv):19–38. doi: ten.1080/08897070802418451. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Arria AM, Kuhn V, Caldeira KM, O'Grady KE, Vincent KB, Wish ED. High school drinking mediates the relationship between parental monitoring and college drinking: A longitudinal analysis. Substance Abuse Treatment, Prevention, and Policy. 2008b;3(6):1–11. doi: 10.1186/1747-597X-3-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Arria AM, O'Grady KE, Caldeira KM, Vincent KB, Wish ED. Nonmedical use of prescription stimulants and analgesics: Associations with social and academic behaviors among higher students. Periodical of Drug Issues. 2008c;38(iv):1045–1060. doi: x.1177/002204260803800406. [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
• Arria AM, Caldeira KM, Vincent KB, Winick ER, Baron RA, O'Grady KE. Discontinuous college enrollment: Associations with substance utilise and mental health. Psychiatric Services. 2013a;64(ii):165–172. doi: ten.1176/appi.ps.201200106. [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
• Arria AM, Garnier-Dykstra LM, Caldeira KM, Vincent KB, Winick ER, O'Grady KE. Drug utilize patterns and continuous enrollment in college: Results from a longitudinal report. Journal of Studies on Alcohol and Drugs. 2013b;74(1):71–83. [PMC free article] [PubMed] [Google Scholar]
• Arria AM, Wilcox HC, Caldeira KM, Vincent KB, Garnier-Dykstra LM, O'Grady KE. Dispelling the myth of "smart drugs": Cannabis and alcohol use problems predict nonmedical use of prescription stimulants for studying. Addictive Behaviors. 2013c;38(iii):1643–1650. doi: 10.1016/j.addbeh.2012.10.002. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Bakery C. Under-represented college students and extracurricular involvement: The effects of various student organizations on academic performance. Social Psychology of Educational activity. 2008;eleven(3):273–298. doi: 10.1007/s11218-007-9050-y. [CrossRef] [Google Scholar]
• Battistella G, Fornari Eastward, Annoni JM, Chtioui H, Dao K, Fabritius 1000, Favrat B, Mall JF, Maeder P, Giroud C. Long-term effects of cannabis on brain construction. Neuropsychopharmacology. 2014;39(9):2041–2048. doi: 10.1038/npp.2014.67. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Beck AT, Ward CH, Mendelson Yard, Mock J, Erbaugh J. An inventory for measuring depression. Archives of Full general Psychiatry. 1961;4:561–571. doi: 10.1001/archpsyc.1961.01710120031004. [PubMed] [CrossRef] [Google Scholar]
• Beck AT, Steer RA. Brook Anxiety Inventory: Manual. San Antonio, TX: The Psychological Corporation, Harcourt Caryatid Jovanovich, Inc; 1990. [Google Scholar]
• Block RI, O'Leary DS, Hichwa RD, Augustinack JC, Boles Ponto LL, Ghoneim MM, Arndt S, Hurtig RR, Watkins GL, Hall JA, Nathan PE, Andreasen NC. Effects of frequent marijuana use on memory-related regional cerebral blood flow. Pharmacology, Biochemistry, and Beliefs. 2002;72(one–2):237–250. doi: 10.1016/S0091-3057(01)00771-7. [PubMed] [CrossRef] [Google Scholar]
• Bloomfield MA, Morgan CJ, Egerton A, Kapur Due south, Curran HV, Howes OD. Dopaminergic function in cannabis users and its human relationship to cannabis-induced psychotic symptoms. Biological Psychiatry. 2013;75(6):470–478. doi: 10.1016/j.biopsych.2013.05.027. [PubMed] [CrossRef] [Google Scholar]
• Bolla KI, Chocolate-brown M, Eldreth D, Tate K, Cadet JL. Dose-related neurocognitive effects of marijuana apply. Neurology. 2002;59(nine):1337–1343. doi: 10.1212/01.WNL.0000031422.66442.49. [PubMed] [CrossRef] [Google Scholar]
• Bray JW, Zarkin GA, Ringwalt C, Qi J. The relationship between marijuana initiation and dropping out of high school. Health Economics. 2000;9(1):9–18. doi: x.1002/(SICI)1099-1050(200001)9:1<nine::AID-HEC471>three.0.CO;two-Z. [PubMed] [CrossRef] [Google Scholar]
• Beck JS, Stimmel MA, Chenshu Z, Brook DW. The clan between earlier marijuana use and subsequent academic achievement and health problems: A longitudinal study. American Journal on Addictions. 2008;17(2):155–160. doi: 10.1080/10550490701860930. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Caldeira KM, O'Grady KE, Vincent KB, Arria AM. Marijuana use trajectories during the post-college transition: Health outcomes in young machismo. Drug and Alcohol Dependence. 2012;125(3):267–275. doi: ten.1016/j.drugalcdep.2012.02.022. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Chou CP, Bentler PM, Satorra A. Scaled test statistics and robust standard errors for not-normal data in covariance construction analysis: A Monte Carlo study. British Periodical of Mathematical and Statistical Psychology. 1991;44(2):347–357. doi: x.1111/j.2044-8317.1991.tb00966.x. [PubMed] [CrossRef] [Google Scholar]
• Churchwell JC, Lopez-Larson G, Yurgelun-Todd DA. Altered frontal cortical book and decision making in adolescent cannabis users. Frontiers in Psychology. 2010;one:225. doi: 10.3389/fpsyg.2010.00225. [PMC gratuitous article] [PubMed] [CrossRef] [Google Scholar]
• Conger D, Long MC. Why are men falling behind? Gender gaps in higher performance and persistence. Annals of the American Academy of Political and Social Science. 2010;627(1):184–214. doi: 10.1177/0002716209348751. [CrossRef] [Google Scholar]
• Crean RD, Crane NA, Bricklayer BJ. An evidence based review of acute and long-term effects of cannabis use on executive cognitive functions. Journal of Habit Medicine. 2011;5(1):1–8. doi: x.1097/ADM.0b013e31820c23fa. [PMC gratuitous commodity] [PubMed] [CrossRef] [Google Scholar]
• Duncan TE, Duncan SC, Strycker LA, Li F, Alpert A. An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Erlbaum; 1999. [Google Scholar]
• Eisenberg D, Golberstein E, Hunt JB. Mental health and academic success in college. Berkeley Electronic Journal of Economic Analysis and Policy. 2009;9(1):1–35. doi: 10.2202/1935-1682.2191. [CrossRef] [Google Scholar]
• Ellickson PL, Tucker JS, Klein DJ, Saner H. Antecedents and outcomes of marijuana apply initiation during boyhood. Preventive Medicine. 2004;39(5):976–984. doi: ten.1016/j.ypmed.2004.04.013. [PubMed] [CrossRef] [Google Scholar]
• Fergusson DM, Horwood LJ, Beautrais AL. Cannabis and educational achievement. Addiction. 2003;98(12):1681–1692. doi: 10.1111/j.1360-0443.2003.00573.x. [PubMed] [CrossRef] [Google Scholar]
• Fontes MA, Bolla KI, Cunha PJ, Almeida PP, Jungerman F, Laranjeira RR, Bressan RA, Lacerda ALT. Cannabis employ before age xv and subsequent executive functioning. British Journal of Psychiatry. 2011;198(vi):442–447. doi: 10.1192/bjp.bp.110.077479. [PubMed] [CrossRef] [Google Scholar]
• Hall Westward. What has research over the past two decades revealed about the adverse wellness effects of recreational cannabis utilise? Habit. 2015;110(1):nineteen–35. doi: x.1111/add.12703. [PubMed] [CrossRef] [Google Scholar]
• Homel J, Thompson Grand, Leadbeater B. Trajectories of marijuana use in youth ages 15–25: Implications for postsecondary education experiences. Journal of Studies on Booze and Drugs. 2014;75(4):674–683. [PMC free commodity] [PubMed] [Google Scholar]
• Hopfer C. Implications of marijuana legalization for adolescent substance use. Substance Abuse. 2014;35(4):331–335. doi: 10.1080/08897077.2014.943386. [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
• Horwood LJ, Fergusson DM, Hayatbakhsh MR, Najman JM, Coffey C, Patton GC, Silins E, Hutchinson DM. Cannabis use and educational achievement: Findings from three Australasian cohort studies. Drug and Alcohol Dependence. 2010;110(iii):247–253. doi: 10.1016/j.drugalcdep.2010.03.008. [PubMed] [CrossRef] [Google Scholar]
• Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance construction assay: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6(1):1–55. doi: x.1080/10705519909540118. [CrossRef] [Google Scholar]
• Chase J, Eisenberg D, Kilbourne AM. Consequences of receipt of a psychiatric diagnosis for completion of higher. Psychiatric Services. 2010;61(four):399–404. doi: 10.1176/appi.ps.61.4.399. [PubMed] [CrossRef] [Google Scholar]
• Inkelas KK, Johnson D, Lee Z, Daver Z, Longerbeam SD, Vogt G, Leonard JB. The role of living-learning programs in students' perceptions of intellectual growth at three large universities. NASPA Journal. 2006;43(1):115–143. doi: 10.2202/1949-6605.1574. [CrossRef] [Google Scholar]
• Jager Yard, Block RI, Luijten G, Ramsey NF. Cannabis use and retentiveness brain function in boyish boys: A cross-exclusive multicenter functional magnetic resonance imaging study. Journal of the American Academy of Child and Adolescent Psychiatry. 2010;49(vi):561–572. doi: x.1016/j.jaac.2010.02.001. [PMC gratuitous commodity] [PubMed] [CrossRef] [Google Scholar]
• Johnston LD, O'Malley PM, Bachman JG, Schulenberg JE, Miech RA. Monitoring the Future: National survey results on drug use, 1975–2013: Volume Ii: Higher students and adults ages nineteen–55. Ann Arbor, MI: Plant for Social Research, The University of Michigan; 2014. Retrieved from http://monitoringthefuture.org/pubs/monographs/mtf-vol2_2013.pdf. [Google Scholar]
• Jones SE, Oeltmann J, Wilson TW, Brener ND, Loma CV. Binge drinking amidst undergraduate college students in the United States: Implications for other substance utilise. Journal of American College Health. 2001;50(1):33–38. doi: 10.1080/07448480109595709. [PubMed] [CrossRef] [Google Scholar]
• Joo SH, Durband DB, Grable J. The academic bear upon of financial stress on college students. Periodical of College Student Retention. 2008;10(3):287–305. doi: 10.2190/CS.10.3.c. [CrossRef] [Google Scholar]
• Krumrei-Mancuso EJ, Newton FB, Kim E, Wilcox D. Psychosocial factors predicting first-year college student success. Periodical of Higher Student Development. 2013;54(three):247–266. doi: 10.1353/csd.2013.0034. [CrossRef] [Google Scholar]
• MacKinnon DP, Lockwood CM, Hoffman JM, Due west SG, Sheets VL. A comparison of methods to examination mediation and other intervening variable effects. Psychological Methods. 2002;7(1):83–104. doi: 10.1037/1082-989X.7.ane.83. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Mattern KD, Shaw EJ. A look beyond cognitive predictors of academic success: Agreement the relationship between academic self-beliefs and outcomes. Journal of College Student Development. 2010;51(six):665–678. doi: 10.1353/csd.2010.0017. [CrossRef] [Google Scholar]
• Medina KL, Hanson KL, Schweinsburg AD, Cohen-Zion One thousand, Nagel BJ, Tapert SF. Neuropsychological functioning in adolescent marijuana users: Subtle deficits detectable subsequently a month of abstinence. Periodical of the International Neuropsychological Guild. 2007;13(5):807–820. doi: 10.1017/s1355617707071032. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Meier MH, Caspi A, Ambler A, Harrington H, Houts R, Keefe RSE, McDonald K, Ward A, Poulton R, Moffitt TE. Persistent cannabis users prove neuropsychological decline from childhood to midlife. Proceedings of the National University of Sciences. 2012;109(40):e2657–2664. doi: ten.1073/pnas.1206820109. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Mezzich AC, Tarter RE, Giancola PR, Kirisci Fifty. The dysregulation inventory: A new calibration to assess the hazard for substance utilise disorder. Journal of Kid and Adolescent Substance Abuse. 2001;ten(4):35–43. doi: 10.1300/J029v10n04_04. [CrossRef] [Google Scholar]
• Mohler-Kuo Chiliad, Lee JE, Wechsler H. Trends in marijuana utilize and other illicit drug apply among college students: Results from four Harvard Schoolhouse of Public Health Higher Booze Written report Surveys: 1993–2001. Journal of American College Health. 2003;52(1):17–24. doi: 10.1080/07448480309595719. [PubMed] [CrossRef] [Google Scholar]
• Muthén BO. Beyond SEM: General latent variable modeling. Behaviormetrika. 2002;29(i):81–117. doi: 10.2333/bhmk.29.81. [CrossRef] [Google Scholar]
• Muthén BO. Latent variable hybrids: Overview of old and new models. In: Hancock GR, Samuelsen KM, editors. Advances in latent variable mixture models. Charlotte, NC: Data Age Publishing; 2008. pp. 1–24. [Google Scholar]
• Muthén LK, Muthén BO. Mplus user's guide. seven. Los Angeles, CA: Muthén & Muthén; 2012. [Google Scholar]
• Napper LE, Hummer JF, Chithambo TP, LaBrie JW. Perceived parent and peer marijuana norms: The moderating outcome of parental monitoring during college. Prevention Science. 2015;16(iii):364–373. doi: x.1007/s11121-014-0493-z. [PubMed] [CrossRef] [Google Scholar]
• O'Grady KE, Arria AM, Fitzelle DB, Wish ED. Heavy drinking and polydrug utilize among higher students. Periodical of Drug Problems. 2008;39(two):445–466. doi: 10.1177/002204260803800204. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Pascarella E, Pierson C, Wolniak G, Terenzini P. Kickoff-generation college students: Additional prove on college experiences and outcomes. Journal of Higher Education. 2004;75(3):249–284. doi: 10.1353/jhe.2004.0016. [CrossRef] [Google Scholar]
• Pike GR. The effects of residential learning communities and traditional residential living arrangements on educational gains during the first yr of higher. Periodical of College Student Development. 1999;twoscore(3):269–284. [Google Scholar]
• Pike GR, Kuh Thousand, Massa-McKinley R. First-year students' employment, appointment, and academic achievement: Untangling the relationship between piece of work and grades. NASPA Journal. 2009;45(four):560–582. doi: 10.2202/1949-6605.2011. [CrossRef] [Google Scholar]
• Pike GR, Kuh GD, McCormick AC. An investigation of the contigent relationships between learning community participation and student appointment. Inquiry in Higher Education. 2011;52(three):300–322. doi: x.1007/s11162-010-9192-1. [CrossRef] [Google Scholar]
• Pottick KJ, Bilder S, Vander Stoep A, Warner LA, Alvarez MF. US patterns of mental health service utilization for transition-age youth and immature adults. Journal of Behavioral Health Services and Enquiry. 2007;35(four):373–389. doi: 10.1007/s11414-007-9080-4. [PubMed] [CrossRef] [Google Scholar]
• Powell 50, Williams J, Wechsler H. Study habits and the level of alcohol employ among college students. Education Economics. 2004;12(2):135–149. doi: 10.1080/0964529042000239159. [CrossRef] [Google Scholar]
• Robb CA, Moody B, Abdel-Ghany K. College student persistence to degree: The burden of debt. Journal of College Student Retentiveness. 2012;13(4):431–456. doi: 10.2190/cs.13.4.b. [CrossRef] [Google Scholar]
• Satorra A, Bentler PM. Scaling corrections for chi-foursquare statistics in covariance structure analysis. Newspaper presented at the Proceedings of the Business and Economic Section of the American Statistical Association; Alexandria, VA. 1988. [Google Scholar]
• Schuermeyer J, Salomonsen-Sautel S, Price RK, Balan South, Thurstone C, Min SJ, Sakai JT. Temporal trends in marijuana attitudes, availability and utilise in Colorado compared to non-medical marijuana states: 2003–11. Drug and Alcohol Dependence. 2014;140:145–155. [PMC free article] [PubMed] [Google Scholar]
• Schweinsburg Advertizing, Nagel BJ, Schweinsburg BC, Park A, Theilmann RJ, Tapert SF. Abstemious adolescent marijuana users show altered fMRI response during spatial working memory. Psychiatry Research. 2008;163(ane):forty–51. doi: 10.1016/j.pscychresns.2007.04.018. [PMC gratuitous article] [PubMed] [CrossRef] [Google Scholar]
• Sheidow AJ, McCart M, Zajac Thou, Davis M. Prevalence and impact of substance use among emerging adults with serious mental health conditions. Psychiatric Rehabilitation Journal. 2012;35(3):235–243. doi: 10.2975/35.3.2012.235.243. [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
• Sher KJ, Rutledge PC. Heavy drinking across the transition to college: Predicting kickoff-semester heavy drinking from precollege variables. Addictive Behaviors. 2007;32(4):819–835. doi: 10.1016/j.addbeh.2006.06.024. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller K, Christiansen One thousand, McRee B, Vendetti J. Cognitive functioning of long-term heavy cannabis users seeking treatment. Journal of the American Medical Association. 2002;287(9):1123–1131. doi: 10.1001/jama.287.9.1123. [PubMed] [CrossRef] [Google Scholar]
• Solowij Northward, Jones K, Rozman M, Davis South, Ciarrochi J, Heaven PL, Lubman D, Yücel M. Verbal learning and retentivity in adolescent cannabis users, booze users and not-users. Psychopharmacology. 2011;216(one):131–144. doi: x.1007/s00213-011-2203-10. [PubMed] [CrossRef] [Google Scholar]
• Steele-Johnson D, Leas K. Importance of race, gender, and personality in predicting academic performance. Journal of Practical Social Psychology. 2013;43(8):1736–1744. doi: 10.1111/jasp.12129. [CrossRef] [Google Scholar]
• Substance Abuse and Mental Health Services Administration. 2002 National Survey on Drug Use and Health Questionnaire. Rockville, Physician: Office of Practical Studies; 2003. Retrieved from http://world wide web.drugabusestatistics.samhsa.gov/nhsda/2k2MRB/2k2CAISpecs.pdf. [Google Scholar]
• Substance Corruption and Mental Health Services Administration. Results from the 2013 National Survey on Drug Use and Health: Detailed tables. Rockville, MD: US Department of Health and Human Services, Office of Practical Studies; 2014. Retrieved from http://www.samhsa.gov/data/sites/default/files/NSDUH-DetTabs2013/NSDUH-DetTabs2013.htm. [Google Scholar]
• van Hell HH, Vink G, Ossewaarde 50, Jager Chiliad, Kahn RS, Ramsey NF. Chronic furnishings of cannabis use on the human advantage organization: An fMRI report. European Neuropsychopharmacology. 2010;20(3):153–163. doi: 10.1016/j.euroneuro.2009.eleven.010. [PubMed] [CrossRef] [Google Scholar]
• van Ours JC, Williams J. Why parents worry: Initiation into cannabis use by youth and their educational attainment. Journal of Health Economics. 2009;28(1):132–142. doi: ten.1016/j.jhealeco.2008.09.001. [PubMed] [CrossRef] [Google Scholar]
• Vincent KB, Kasperski SJ, Caldeira KM, Garnier-Dykstra LM, Pinchevsky GM, O'Grady KE, Arria AM. Maintaining superior follow-up rates in a longitudinal study: Experiences from the College Life Study. International Periodical of Multiple Research Approaches. 2012;6(one):56–72. doi: 10.5172/mra.2012.6.1.56. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Volkow ND, Baler RD, Compton WM, Weiss SRB. Adverse health furnishings of marijuana use. New England Journal of Medicine. 2014;370(23):2219–2227. doi: 10.1056/NEJMra1402309. [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]
• Wechsler H, Dowdall GW, Davenport A, Castillo S. Correlates of college student binge drinking. American Journal of Public Health. 1995;85(7):921–926. doi: 10.2105/AJPH.85.vii.921. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
• Williams J, Powell LM, Wechsler H. Does alcohol consumption reduce human capital accumulation? Evidence from the College Booze Study. Applied Economics. 2003;35(10):1227–1239. doi: 10.1080/0003684032000090735. [CrossRef] [Google Scholar]
• Wolaver AM. Effects of heavy drinking in college on study endeavor, grade point average, and major choice. Contemporary Economic Policy. 2002;twenty(iv):415–428. doi: 10.1093/cep/20.4.415. [CrossRef] [Google Scholar]
• Yücel Chiliad, Solowij Northward, Respondek C, Whittle S, Fornito A, Pantelis C, Lubman DI. Regional encephalon abnormalities associated with long-term heavy cannabis use. Athenaeum of General Psychiatry. 2008;65(6):694–701. doi: ten.1001/archpsyc.65.half-dozen.694. [PubMed] [CrossRef] [Google Scholar]
• Zuckerman M. Zuckerman-Kuhlman Personality Questionnaire (ZKPQ): An alternative five-factorial model. In: de Raad B, Perugini M, editors. Big five assessment. Seattle, WA: Hogrefe & Huber; 2002. pp. 377–396. [Google Scholar]

goinsappredery.blogspot.com

Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4586361/

Share this post