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research-article2014

IJOXXX10.1177/0306624X14548530International Journal of Offender Therapy and Comparative CriminologyMaahs et al.

Article

Prescribing Some Criminological Theory: An Examination of the Illicit Use of Prescription Stimulants Among College Students

International Journal of Offender Therapy and Comparative Criminology 1­–19 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0306624X14548530 ijo.sagepub.com

Jeffrey R. Maahs1, Robert R. Weidner1, and Ryan Smith2

Abstract Recent evidence indicates that the illicit use of prescription stimulants such as Adderall and Ritalin is common across college campuses and in professions (e.g., trucking) where staying awake and focused is valued. Existing research has established use patterns and explored respondents’ reasons for using these stimulants. Less is known, however, about whether or how well mainstream criminological theory explains this type of illegal activity. This article reports results from a survey (N = 484) of college students from a Midwestern university, examining whether measures of strain, selfcontrol, and social learning predict the illicit use of prescription stimulants. Measures from social learning and social control theories were significant predictors of illicit use of prescription stimulants, whereas the measure of academic strain was not; the strongest predictor of illicit use of prescription stimulants was general deviance. Implications of these findings are discussed. Keywords prescription stimulants, illicit use, criminological theory, college students In recent years, the nonmedical use of prescription drugs has become a prominent concern on college campuses across the country. Psychotherapeutic drugs susceptible to abuse include pain relievers (brand names include Lortab and Oxycontin), sleeping 1University 2Financial

of Minnesota Duluth, USA Assistance Specialist Supervisor, Anoka County, Andover, MN, USA

Corresponding Author: Jeffrey R. Maahs, Associate Professor, University of Minnesota Duluth, 228 Cina Hall, 1123 University Drive, Duluth, MN 55812, USA. Email: [email protected]

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International Journal of Offender Therapy and Comparative Criminology 

medications (Ambien, Lunesta), sedatives/tranquilizers (Prosac, Zoloft), and stimulants (Ritalin, Adderall). As a result, some journalists and academics have labeled young adults coming of age during the new millennia as “Generation Rx” (Associated Press, 2005; Critser, 2005). Stimulants such as Ritalin and Adderall, used legitimately to treat symptoms of attention deficit hyperactivity disorder (ADHD), are currently among the most commonly abused prescription drugs on college campuses. Indeed, the diversion of prescription stimulants to illicit markets (primarily colleges) is commonly cited as one of the reasons for recent shortages of these drugs (Harris, 2011). Media outlets have tended to portray stimulants as “study drugs” or “cognitive enhancers” (Talbot, 2009). This construction implies that prescription stimulant use is a response to academic pressure, and that it is used by already strong students seeking a further competitive edge. For example, in a New York Times interview, a Columbia University student stated, “I don’t think I could keep a 3.9 average without this stuff” (Jacobs, 2005, p. 1). Even among academics, these drugs have drawn comparisons to the use of steroids in athletics, in that they allow students to increase their grade point averages (GPAs) with less effort (Vagra, 2012). The emerging literature on use patterns, however, suggests a more complicated picture. Students use prescription stimulants (often in conjunction with alcohol) to party, for appetite suppression, and even as an exercise aid (Low & Gendaszek, 2002; White, Becker-Blease, & Grace-Bishop, 2006). Prescription stimulants are Schedule II drugs with a high potential for abuse. They mimic many of the effects of cocaine and amphetamines. When used as prescribed for legitimate medical conditions, they are relatively safe, though not without side effects such as increased blood pressure, insomnia, and anxiety. Recreational use, particularly when the drugs are crushed and snorted and/or mixed with alcohol, may produce additional psychological and physical harm (Adams & Kopstein, 1993). In response to these potential harms and to increased media attention, a body of research is now emerging to describe and explain the illicit use of prescription stimulants among college students. Understandably, the initial wave of research, largely from researchers in the fields of public health and psychology, focused on the prevalence and correlates of use (DeSantis, Webb, & Noar, 2008; Pilkinton & Cannatella, 2012; Shillington, Reed, Lange, Clapp, & Henry, 2006; Teter, McCabe, Cranford, Boyd, & Guthrie, 2005; Webb, Valasek, & North, 2013; White et al., 2006). More recently, researchers have started to examine recreational use of pharmaceutical drugs generally (cf. Peralta & Steele, 2010; Schroeder & Ford, 2012), and more specifically stimulants (Ford & Schroeder, 2009) using mainstream theories of crime. The purpose of this study then, is to contribute to this emerging literature. Specifically, we explore the nonmedical use of prescription stimulants (i.e., Adderall and Ritalin) using concepts from social control, social learning, and strain theory. In addition, we examine the relationship between the recreational use of stimulants and other forms of deviance and crime.

Literature Review Stimulants are the most commonly prescribed medication for treatment of ADHD symptoms. There are two general classes of prescription stimulants. Methylphenidate-based

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drugs include brand names such as Concerta and Ritalin whereas mixed-salts amphetamines include Adderall and Dexedrine (McCabe, Knight, Teter, & Wechsler, 2005). Both types of drugs stimulate nerve and brain activity (central nervous system) to control impulses and lower hyperactivity (White et al., 2006). For a variety of reasons, treatment of ADHD using stimulants has increased substantially over the past 20 years, especially in the United States. Recent estimates from the Center for Disease Control suggest a national prevalence for ADHD diagnosis among children aged 4 to 17 of roughly 8%. About half of diagnosed children, or 4.4 million, reported taking prescription stimulants (Centers for Disease Control, 2005). One implication of the increased use of prescription stimulants is the spread of these drugs to illicit markets. In recent years, an abundance of studies have examined the nonmedical use of prescription stimulants among young adults. Most of these studies (e.g., Johnston, O’Malley, Bachman, & Schulenberg, 2009; McCabe & Teter, 2007; McCabe, Teter, & Boyd, 2006; Shillington et al., 2006) have focused on the prevalence of use among college populations. The prevalence rates that emerge from this research, however, are somewhat inconsistent. Large nationally representative samples suggest relatively low rates of recreational stimulant use among college students. The University of Michigan’s annual Monitoring the Future survey (MTF) has measured prevalence rates for Ritalin use among full-time college students as well as college-aged individuals not enrolled in college full-time since 2002 (Johnston et al., 2009). In 2008, 3.2% of full-time college students reported using Ritalin nonmedically in the past year (Johnston et al., 2009). MTF has found that illicit drug use in general is lower among college students than among their age peers. However, this pattern is reversed in the case of Ritalin; compared with full-time college students, only 2.1% of the noncollege group used Ritalin nonmedically in the past year (Johnston et al., 2009). Similarly, combined 2006 and 2007 data from the National Household Survey on Drug Use and Health (NSDUH) reveal that 6.4% of full-time college students aged 18 to 22 used Adderall nonmedically in the past year; more than their counterparts who were not full-time college students (3.0%; Substance Abuse and Mental Health Services Administration [SAMHSA], Office of Applied Studies, 2009). Similar estimates were uncovered in the College Alcohol Survey (CAS). This survey, completed by more than 10,000 students across 119 universities, indicated that 6.9% of American undergraduate students had used prescription stimulants illicitly in their lifetimes (McCabe et al., 2005). The conclusion from national-level studies then, is that illicit use of stimulants among college students, whereas higher than their noncollege peers, is relatively low. Estimates for lifetime use range from 3% to 7%. Some studies of single universities, however, have found the rate of illicit prescription stimulant use to be much higher (Advokat, Guidry, & Martino, 2008; Low & Gendaszek, 2002; Peralta & Steele, 2010). For example, a study at a small competitive college found that one third (35.5%) of the students surveyed had taken prescription stimulants illicitly (Low & Gendaszek, 2002). More recently, a 2008 study of 1,550 respondents at Louisiana State University found that as many as 43% of the students had used prescription stimulants without a prescription (Advokat et al., 2008).

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Other single-site studies find prescription stimulant misuse at levels closer to the national estimates (cf. Shillington et al., 2006) Overall then, although national-level rates are low, there does appear to be heterogeneity across specific colleges. To be sure, much of the observed variability stems from the type of stimulant measure used (e.g., use of specific brands such as Adderall or Ritalin) and the particular reference period (e.g., “past year” or “used in lifetime”). Still, among the sample of colleges in the CAS data, where measurement was held constant, past year use rates ranged from 0% to 25%. Universities in the Northeast, and those with competitive admissions standards had higher rates of stimulant misuse (McCabe et al., 2005).

Correlates of the Nonmedical Use of Prescription Stimulants Among College Students As noted previously, stimulant use appears to be the exception to the general rule that illicit drug use is higher among noncollege youth than college students. In addition, research indicates that non-Latino Whites are more likely to abuse prescription drugs than non-Whites and Hispanics. In the SAMHSA (2009) report, for example, White, non-Hispanic students (8%) were more likely than Black (1%), Hispanics (2.2%), or Asians (2.1%), to have used Adderall. Aside from these differences, correlates of stimulant use are fairly consistent with predictors of other types of illicit drug use. Among college students, males are more likely to report stimulant misuse than females. Furthermore, those students who are fraternity/sorority members, from public high schools, and have lower grade point averages are more likely to report illicit use of prescription stimulants (e.g., Hall, Irwin, Bowman, Frankenberger, & Jewett, 2005; McCabe et al., 2005; McCabe & Teter, 2007; McCabe et al., 2006; Shillington et al., 2006). Finally, there appears to be a consistent correlation between nonmedical prescription drug use (including stimulants), the use of illicit drugs (e.g., marijuana, cocaine, ecstasy), and binge drinking (McCabe et al., 2005; Shillington et al., 2006).

Prescription Stimulant Use and Criminological Theory To date, research on the illicit use of prescription stimulants has focused largely on establishing the extent of use among different populations. With very few exceptions (e.g., Ford & Schroeder, 2009; Higgins, Mahoney, & Ricketts, 2009), studies that have examined correlates of Adderall or Ritalin use have focused on demographic predictors (e.g., sex, race, type of residence) or very limited theoretical measures. Where theory was incorporated, it was often derived from perspectives outside of mainstream criminology (cf. Low & Gendaszek, 2002). To be sure, there is an emerging literature linking nonmedical use of prescription drugs generally to theories of crime (Peralta & Steele, 2010; Schroeder & Ford, 2012). Given the unique motivation (e.g., as a “study drug”) for some nonmedical prescription stimulant use, however, it is unclear whether the misuse of prescription opiates or anxiety medication will have the same causes and correlates as prescription stimulant misuse.

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In this article, we explore prescription stimulant use from the three most popular individual-level theories of crime—informal social control, social learning, and strain theory. The purpose of this analysis is twofold. First, we seek to identify whether predictors from any of these theories can explain stimulant use. Second, we examine which theory offers the strongest explanation of prescription stimulant use when the theories directly compete against one another (Hirschi, 1989). Thus, although it is possible that each theory may be useful, it remains important to identify the most promising targets for intervention and future empirical study. Social learning theory.  Within criminology, the social learning perspective stems from Sutherland’s differential association theory. In brief, Sutherland (1947) argued that crime was learned by a process of (usually verbal) communication among intimate personal groups. Sutherland emphasized that both the behaviors and rationales (attitudes he called “definitions” of the legal code) for the behaviors was learned. Akers’s (1985) social learning theory brought differential association in line with the psychological principles of learning such as reinforcement and role modeling. Although social learning theory is complex, tests of the theory usually involve measures of delinquent associations (e.g., parents or peers) and attitudes that relate to antisocial behavior (moral values or antisocial attitudes). Akers, Krohn, Lanza-Kaduce, and Radosevich (1979) first test of social learning theory included illicit drug use as a dependent variable. This test and ones that follow generally support social learning theory as a predictor of illicit drug use (Akers & Jensen, 2005; Warr, 2002). It seems logical to extend social learning theory to explain illicit use of prescription stimulants among college students. Indeed, college campuses are unique, in that they offer environments that likely foster imitation, peer association, and verbal communication of attitudes toward deviance. Peralta and Steele (2010) found that the central variables in social learning theory, including peer associations and differential reinforcement, were related to a composite measure (which included stimulants) of illicit use of prescription medication. Specifically, students with a higher proportion of friends who misused prescription drugs, those whose friends had attitudes supportive of recreational prescription drug use, and those who had low estimates of the social and nonsocial costs of misusing such drugs were more likely to report the illicit use of prescription drugs. Two studies have specifically examined social learning and prescription stimulant use. Using MTF data, Higgins et al. (2009) found that peer delinquency and nonsocial reinforcement predicted the nonmedical use of prescription stimulants. Although not the central focus of their article, Ford and Schroeder (2009) found that peer measures (friends’ binge drinking, socialization with friends) also predict nonmedical stimulant use. In short, there is strong reason to believe that social learning theory can help account for the nonmedical use of stimulants such as Ritalin or Adderall. Control theory. Control theories utilize one or more of three types of informal control—direct, indirect, and internal—to explain crime. Hirschi’s (1969) social bond theory focuses largely on indirect control, postulating that the strength of one’s social bond to society explains delinquency. Hirschi identified four elements of the social

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International Journal of Offender Therapy and Comparative Criminology 

bond that emphasized the importance of indirect control. The concepts of “attachment” and “commitment” both suggest that crime is more common when a person has something (e.g., an emotional bond, reputation, social capital) that a person will not risk losing by engaging in crime. As with learning theory, measures from Hirschi’s (1969) social bond theory have successfully predicted the use of illicit substances (cf. Akers & Cochran, 1985; Ford, 2005), and can therefore logically be extended to prescription stimulants. Hirschi’s (2004) most recent statement suggests that he now views both indirect and internal forms of control as evidence of low self-control. Using this theoretical reconceptualization, Higgins et al. (2009) found that measures of school commitment and teacher influence exerted an impact on some forms of prescription drug (e.g., sedatives, tranquilizers) misuse, but not on stimulant misuse.1 In contrast to social bond theory, Gottfredson and Hirschi (1990) proposed the concept of low self-control as a critical cause of both crime and “behaviors analogous to crime”—those that share the same nature of criminal behaviors (e.g., adultery, smoking cigarettes). Low self-control is described as a constellation of traits, including impulsivity, insensitivity, low verbal ability, and a risk-taking orientation. Low self-control results from ineffective parenting, or more specifically, the inability of parents to recognize and consistently punish deviance in their children. In this formulation then, direct control (parenting) creates a form of internal control.2 Tests of Gottfredson and Hirschi’s central concepts have consistently explained a variety of crimes and deviant acts among many different populations (Gottfredson, 2006; Pratt & Cullen, 2000). More specifically, low self-control has consistently predicted the use of illicit drugs (Arneklev, Grasmick, Tittle, & Bursik, 1993; Piquero, Gibson, & Tibbetts, 2002). Given this success, we expect low self-control would also predict the illicit use of Ritalin or Adderall. While there is little evidence testing the effect of low self-control on prescription stimulant use, psychological studies on “sensation seeking” appear to tap into riskseeking dimension of low self-control. “[S]ensation-seekers are students who like novel experiences, who want to try something new and a little dangerous, like jumping off the highest diving board or placing themselves in high-risk situations” (Arria, Caldeira, Vincent, O’Grady, & Wish, 2008, p. 194). Research has indicated that sensation seeking does predict nonmedical use of prescription stimulants (Arria et al., 2008; Low & Gendaszek, 2002). A conceptual issue related to low self-control, and presumably the misuse of Adderall and Ritalin, concerns the generality of deviance. As noted earlier, prescription stimulants are often portrayed as “study drugs,” or “intellectual steroids,” that are atypical of other forms of drug use—something that elite students use to maintain a 4.0 GPA. In contrast, the misuse of stimulants might be conceptualized as simply another form of crime or deviance. Indeed, the relationship between some forms of deviance and others, and between past and future crimes, has been well-documented in criminology (Farrington, 2003; Gottfredson & Hirschi, 1990). This line of reasoning would suggest that students who misuse stimulants would also abuse alcohol, use illicit drugs, and engage in other forms of deviance. There is evidence that students who use prescription stimulants illicitly are more likely to use other illicit drugs. Low and Gendaszek (2002) found that 19.3% of students

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surveyed at a U.S. college reported using prescription stimulants in combination with alcohol for recreational purposes. McCabe et al. (2005) found that students who used prescription stimulants illicitly were also more likely to report using alcohol, cigarettes, marijuana, ecstasy, cocaine, and to drive although impaired. Given these findings, it appears likely (though at this point untested) that stimulant misuse would also be related to other forms of deviance and crime. Strain theory. Robert Merton’s (1938) classic strain theory focuses on “meansends” disjunctures in American society. Merton believed that America’s intense cultural emphasis on economic achievement produced pressure to succeed using any means, rather than just legitimate means. At the individual level, Merton also pointed out that there was unequal access to the legitimate means for achieving economic success. Thus, people who lacked meaningful access to education and employment, but felt pressure to succeed, were placed under a “strain.” In some individuals, this strain produced “innovation,” the use criminal means to achieve success. Consistent with this view, stimulant use might be characterized as an illegitimate means for achieving academic success—especially among those who lack legitimate means. Empirical tests of this aspect of Merton’s theory have largely been negative. Much of the negative evidence comes from Hirschi’s (1969) operationalization of strain as the gap between educational aspirations and expectations (cf. Burton & Cullen, 1992). Although some alternative measures of strain have predicted crime, they tend not to be robust predictors (Agnew, Cullen, Burton, Evans, & Dunaway, 1996). More recent research on strain theory, including one study of prescription stimulants, has been focused on Robert Agnew’s (1992) revision of strain theory. Agnew’s General Strain Theory (GST) posits that strain arises from a three basic sources—the failure to achieve goals, the inability to escape noxious stimuli, and the removal of positively valued theory. Within GST, strain produces negative emotional states such as anger and depression, which motivate individuals for delinquency and crime. Using data from the Harvard School of Public Health’s College Alcohol Study, Ford and Schroeder (2009) found that academic strain is associated with the nonmedical use of prescription stimulants. Specifically, their structural equation models showed that students who experience academic strain report higher levels of depression (their measure of Agnew’s “negative affect”), and students who report higher levels of depression are more likely to report the nonmedical use of prescription stimulants. Although this study found an indirect effect (through depression) of strain on stimulant use consistent with GST, it seems reasonable to expect a direct effect of academic strain on stimulant use more consistent with Merton’s strain theory. In sum, it appears that criminological theory would have much to say about the illicit use of prescription stimulants among college students. This study adds to the emerging literature the nonmedical use of prescription stimulants by examining variables from, informal social control theory, social learning/differential association theory, and strain theory in a sample of college students from a 4-year public university in the Midwest.

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Method Sample College students are commonly a focus of researchers who study trends in substance abuse, as changes in drug abuse patterns among college students tend to presage similar changes among the general public (Johnston, O’Malley, Bachman, & Schulenberg, 2006). This phenomenon is attributed to the fact that “college life has traditionally been associated with experimentation and following trends” (Ford & Schroeder, 2009, p. 29). To examine prescription stimulant use among college students, we distributed self-administered questionnaires to a convenience sample of undergraduate students enrolled in the University of Minnesota Duluth (UMD). UMD, which is part of the University of Minnesota system, is located in northeastern Minnesota and enrolls roughly 10,000 undergraduates. The student body of UMD is disproportionately (93%) White, and split almost equally between males and females. The data were collected over a 2-month period from January to March of 2009. Students were recruited for this study by obtaining permission to distribute the survey from the professors of large introductory criminology/sociology courses; in an effort to obtain upperclassmen, students in upper division courses were surveyed as well. In surveyed classes, the instrument was administered during the first 15 min of a class period, and returned immediately after completion to a sealed box. Surveys were distributed to 512 undergraduate students enrolled in criminology or sociology classes. Participation in this study was completely voluntary, there was no financial or academic benefit offered to those students who completed it. Of the 512 students surveyed, 484 usable surveys were returned—a 94.5% completion rate. The primary limitation of convenience samples is that some members of the population (in this case, a student body) are more likely to be sampled than others. Because some members of the population have no chance of being sampled, the extent to which a convenience sample actually represents the entire population cannot be known. Thus, the sample may not accurately reflect students at UMD. Two factors suggest that that this is not a serious limitation in the current study. First, the sample was drawn disproportionately from sections of large, introductory-level criminology and sociology courses. Both courses meet liberal education criteria and therefore draw from a wide swath of students. Second, basic demographic information from the sample roughly mirrors that of the population. For example, the sample was 46% male and 93% White, whereas the UMD population figures for the year of the survey are 51% male and 93% White.

Data The 59-question survey instrument included items which measured the prevalence and incidence of the illicit use of prescription stimulants, as well as students’ rationale for using these drugs. It also included items to measure the central concepts in social learning/differential association theory, strain, and informal social control theory.

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Finally, it included basic demographic items. Descriptive statistics for all of the measures used in this study are presented in Table 1. Demographic information.  Basic measures of demographic information included age (in years), race (dummied as White = 1) and sex (male = 1). In addition, we collected information on residency type (dormitory, on-campus apartment, off-campus housing), and college year (freshman, sophomore, junior, senior). Illicit use of prescription stimulants.  Students were asked whether they had been diagnosed with ADHD. Four percent of students reported a diagnosis of ADHD, which is consistent with national estimates (DuPaul & Weyandt, 2004). Individuals who reported a diagnosis of ADHD were excluded from analyses predicting the illicit use of prescription stimulants. Respondents were asked, “In the past year, how many times have you used Ritalin and/or Adderall?” Response categories included never, 2 to 3 times per year, once per month, and once per week or more. In addition, we created a dummy variable that measured whether or not an individual used prescription stimulants in the past year.3 Differential association/social learning. The two most commonly utilized measures of social learning theory are exposure to delinquent peers and the presence of antisocial attitudes. Exposure to delinquent peers is assumed to reflect an “intimate group” from which a person learns delinquent values and/or behaviors. Delinquent peers is a fouritem summated index that gauges the extent to which the respondent’s peers engaged in delinquent or deviant acts such as smoking marijuana, using hard drugs, getting drunk, and cheating. Response categories for each item ranged from “none of my friends” to “all of them.” Therefore, higher scores on this index suggest that a greater proportion of the respondents’ friends engaged in delinquent/deviant acts (α = .68). Antisocial attitudes (or moral/prosocial beliefs) represent, in part, the content of what is learned. Accordingly, the variable moral beliefs is a six-item index that measures the extent (from “not wrong” to “very wrong”) to which respondents view various acts as wrong. Specific behaviors included marijuana use, vandalism, theft, and using hard drugs (α = .73). Higher scores on this scale would indicate that the respondent has stronger moral beliefs and would be more likely to refrain from delinquent acts.4 Informal social control theory.  Control theories invoke some combination of direct, indirect, or internal control to explain crime. Hirschi’s (1969) social bond theory emphasized the importance of indirect controls, including one’s commitment to society. We included two measures that are consistent with Hirschi’s concept of commitment. School Importance is a four-item index that measures how important school is to the respondent and their parents. This scale includes questions such as, “How important are school/grades to you?” and “How important is it to you to complete college?” Response categories ranged from “not important at all” to “very important,” and higher values on this index indicates that college is more important to the respondent (Cronbach’s α = .56). Grade point average (GPA) is the student’s self-reported GPA.

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Table 1.  Descriptive Statistics. Variable Demographics  Age  Race  White  Black  Asian  Other  Sex  Male  Female   Residence type  Dormitory   On-campus apartment   Off-campus house/apartment   College year  Freshman  Sophomore  Junior  Senior Social control variables  GPA   School importance   Low self-control Social learning variables   Moral beliefs   Delinquent peers Strain Deviance (past year) Illicit prescription stimulant use (past year)  Never   Once or more

M

SD

Range

19.8

2.0

17-44                                  

466 484 484

3.1 17.8 8.4

0.55 1.9 1.6

1.2-4.0 9-20 4-13

484 477 447 463 484

20.0 11.1 2.0 2.9

3.3 2.7 1.4 1.3

7-28 4-20 1-4 0-6      

n

%

484 484 93 1 3 3 484 46 54 484 35 17 48 484 45 23 17 13

69 31

Note. GPA = grade point average.

Consistent with Hirschi’s (1969) operationalization, and with subsequent research on social bonds, we view GPA as indicative of a student’s investment in their education. In contrast to Hirschi’s social bond theory, Gottfredson and Hirschi’s “general theory” emphasizes a form of internal control—specifically, low self-control. The authors characterize low self-control as a constellation of traits including a risk-taking orientation, impulsivity, insensitivity, and low verbal ability. The measure low self-control is a three-item index (α = .52) that is consistent with past attitudinal measures of low self-control (Grasmick, Tittle, Bursik, & Arneklev, 1993). Respondents assessed

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statements (from “strongly agree” to “strongly disagree”) such as “I do not devote much thought and effort to preparing for the future,” and “I sometimes find it exciting to do things for which I might get in trouble.” Higher scores on this index reflect lower levels of self-control. Strain.  We also include a measure of academic strain, operationalized as the degree of disjunction between scholarly ambitions and outcomes. Replicating Ford and Schroeder’s (2009, p. 34) measure, we operationalized this concept by combining importance of academic work (“How important are school/grades?” (0 = “not important at all” to “somewhat important,” 1 = “pretty important” or “very important”) and GPA (0 = GPA less than 3.0, 1 = GPA of at least 3.0). Using these two variables, we constructed a four-category indicator, with higher values indicating greater levels of strain: the low-strain category comprises those who responded that school/grades are “pretty important” or “very important” and who have GPAs of at least 3.0; the lowmedium strain category includes those who do not place a lot of importance in school/ grades but who have GPAs of at least 3.0; those in the high-medium strain category do not place a lot of importance in school/grades and have GPAs of less than 3.0; finally, the high-strain category consists of those who responded that school/grades are “pretty important” or “very important,” and who have GPAs of less than 3.0. Delinquent/deviant behavior.  This variable deviance is a variety index where respondents accumulated one point for engaging in any of six specific delinquent/deviant acts. Specific acts include the use of marijuana, use of hard drugs (e.g., cocaine, methamphetamine), abuse of alcohol, cheating, stealing, and vandalism). Higher scores, therefore, reflect greater involvement in a variety of deviant/delinquent acts. Analysis.  The analyses proceed through four stages. First, we examine the prevalence and incidence of illicit use of prescription stimulants, as well as students’ rationale for using. Second, we conduct bivariate tests to examine predictors of stimulant use. In the third stage, we examine the illicit use of prescription stimulants using a series of multivariate logistic regression models. Finally, we examine the relationship between the illicit use of Ritalin and Adderall and the use alcohol, marijuana, and hard drugs (e.g., cocaine or methamphetamine).

Results Information pertaining to the illicit use of prescription stimulants is contained in Table 2. Among those students not diagnosed with ADHD, 28% of students report using prescription stimulants over the past year. Much of the Ritalin/Adderall use appears to be sporadic. Specifically, 21% of students reported limited (2-3 times per year) use. In contrast, the response categories tapping more regular use, defined as once per month (3%), 2-3 times per month (3%), and once per week or more (1%), were relatively rare. Respondents were asked to pick (among several options) the reason that they started taking Ritalin and/or Adderall. The most common response (44%) was school

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Table 2.  Profile of Illicit Prescription Stimulant Use. Variable Illicit prescription stimulant use among full sample  Never   2-3 times per year   Once a month   2-3 times per month   Once per week or more Reason for illicit prescription stimulant use   School pressure   Social events   Peer pressure   To get high   Multiple reasons   Other reasons Use prescription stimulants for recreation  Yes  No

n

Category (%)

462

  72 21 3 3 1   44 3 1 5 15 30   27 73

149

149

pressure, which is consistent both with mass media coverage and with strain theory. The high response rates for “multiple reasons” and “other reasons,” however, suggest that this question failed to offer adequate response categories. In addition, when asked to report whether they used prescription stimulants as a recreational drug (e.g., at social events), 27% responded affirmatively.

Bivariate Predictors of Prescription Stimulant Use Table 3 illustrates the results of a series of mean-level comparisons based on whether or not a person reported the illicit use of prescription stimulants. Among demographic variables, the only significant predictor of Adderall/Ritalin use is sex. Whereas 28% of the overall sample reported the use of prescription stimulants, males (32%) were more likely to report using than females (26%; p < .01). Among social control variables, GPA (t = −2.86, p < .01) and low self-control (t = 5.26, p < .001) emerged as predictors of prescription stimulant use. Those with a lower GPA and those with lower levels of self-control were more likely to report the illicit use of Ritalin or Adderall. Both social learning variables predicted prescription stimulant use. Specifically, those scoring higher on the delinquent peers index (t = 8.32, p < .001) and those scoring lower on moral beliefs (t = −7.73, p < .001) were more likely to use Ritalin or Adderall. Finally, general deviance during the past year was related to illicit use of prescription stimulants. Those who reported more deviance were more likely to also report Ritalin/ Adderall use (t = 9.73, p < .001).

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Table 3.  Mean Comparisons Predicting the Illicit Use of Prescription Stimulants (n = 463). Illicit use of prescription stimulants in past year? Variable Demographic  Age   Race (White = 1)   Sex (male = 1) Social control variables  GPA   School importance   Low self-control Social learning variables   Moral beliefs   Delinquent peers Strain Deviance (past year)

Yes (M)

No (M)

t

19.52 0.94 0.57

19.88 0.91 0.42

1.69 −1.00 −2.86*

2.97 17.86 8.13

3.13 17.81 8.98

2.86* −0.28 5.26**

18.24 12.67 2.17 3.69

20.76 10.46 1.94 2.58

7.73** −8.32** −1.56 9.73**

Note. GPA = grade point average. *p < .05. **p < .01.

Multivariate Prediction of Illicit Prescription Stimulant Use The results of a series of multivariate logistic regression models are reported in Table 4. Moving from left to right, the first regression (Model 1) serves a base/demographic model. Consistent with bivariate results, sex emerges a significant predictor (B = .67, p < .01); the odds ratio for this coefficient indicates that males are 48.8% more likely than females to use Adderall or Ritalin illicitly. Age is also significant in this model, with older respondents less likely to use Adderall or Ritalin illicitly. In Model 2, we introduce the three measures attributed to control theory. Among these measures, only low self-control (B = .335, p < .001) predicts Ritalin Adderall use. As in Model 1, sex is a significant predictor. In Model 3, we introduce the two measures of social learning theory (while removing the control theory measures). Inspection of the table reveals that both delinquent peers (B = .216, p < .001) and moral beliefs (B = −.128, p < .01) are associated with illicit use of prescription stimulants. The odds ratio for delinquent peers shows that a one-unit increase in this index is associated with a 30% increase in the odds of using Adderall or Ritalin. The odds ratio for moral beliefs indicates that the likelihood of Ritalin or Adderall use declines 12% with each unit increase in this measure. In Model 4, our measure of academic strain is included with the base/demographic variables. Its coefficient (B = .088) is not statistically significant.5 In Model 5, we allow social control measures to compete with social learning/differential association measures and the strain measure in a single model. In this model, both the delinquent peer and moral beliefs measures remain significant. With respect to control theory, self-control loses significance, whereas school importance emerges

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Table 4.  Logistic Regression Predicting the Illicit Use of Prescription Stimulants in Past Year.

Variable

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

b (odds ratio)

b (odds ratio)

b (odds ratio)

b (odds ratio)

b (odds ratio)

b (odds ratio)

n 463 447 456 447 441 441 Demographic  Age −.141* (.869) −.081 (.922) −.154 (.857) −.137 (.872) −.103 (.900) −.098 (.906)   Race (White = 1) .425 (1.530) .230 (1.258) −.063 (.938) .412 (1.510) −.034 (.967) −.113 (.893)   Sex (male = 1) .670** (1.488) .617** (1.460) .475* (1.378) .605** (1.454) .459 (1.368) .419 (1.343) Social control  GPA — −.229 (.796) — — −.424 (.654) −.450 (.638)   School importance — .079 (1.083) — — .144* (1.155) .135 (1.144)   Low self-control — .335*** (1.398) — — .161 (1.175) .121 (1.129) Social learning   Moral beliefs — — −.128** (.879) — −.145** (.865) −.102* (.903)   Delinquent peers — — .261*** (1.299) — .223*** (1.250) .157* (1.170) Strain — — — .088 (1.092) −.111 (.895) −.146 (.864) Deviance (past year) — — — — — .392** (1.480) Model χ2 14.40** 38.78*** 84.75*** 14.34** 91.63*** 101.20*** Nagelkerke R2 .044 .119 .244 .045 .269 .294

Note. Unstandardized coefficients reported. GPA = grade point average. *p < .05. **p < .01. ***p < .001.

as a significant predictor (B = .144, p < .05) of Ritalin/Adderall use, but in the opposite direction than anticipated by control theory. Specifically, those with higher expectations for education are more likely to report the use of stimulants. Again, the coefficient for strain is not significant. The final regression model (Model 6) introduces the measure of deviance into the previous regression model. Deviance emerges as the strongest predictor in the model (B = .392, p < .01). Its odds ratio indicates that the likelihood of illicit Adderall/Ritalin use increases 48% with each one-unit increase in this measure. Both measures of social learning also are significant in this model. None of the three social control measures is significant, nor is the measure of academic strain.6 Thus, both bivariate and multivariate tests revealed that illicit Ritalin/Adderall use is related to an index of overall deviance. Consistent with prior research (e.g., McCabe et al., 2005; SAMHSA, 2009), we also examined the relationship between nonmedical use of Adderall/Ritalin and the use of alcohol and illicit drugs. Table 5 shows that those who used Adderall or Ritalin nonmedically in the past year are almost twice as likely as other students to have used marijuana in the past year as well (81.2% vs. 42.1%). Table 5 also illustrates that users of Adderall/Ritalin were almost 17 times as likely as nonusers (20.3% vs. 1.2%) to also have used “hard” drugs (e.g., cocaine, methamphetamine) in the past year.

Discussion This study examined the illicit use of prescription stimulants among college students at a Midwestern university. We examined the prevalence of use, as well as predictors

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Table 5.  Other Substance Use in the Past Year, by Past Year Nonmedical Use of Adderall/ Ritalin. Substance Marijuana “Hard” drugs (e.g., Cocaine, Meth) Alcohol

Used Adderall/ Ritalin (%)

Did not use Adderall/Ritalin (%)

81.2 20.3 98.4

42.1 1.2 90.4

of use based on extant criminological theory. With respect to prevalence, 28% of students reported some use of Ritalin or Adderall. This estimate is consistent with other research on college populations using similar methodology (Advokat et al., 2008; Low & Gendaszek, 2002), though much higher than that in nationally representative studies (SAMHSA, 2009). In the current study, most of the illicit use of prescription stimulants was sporadic rather than regular. With regard to predictors of Ritalin/Adderall use, results generally conformed to theoretical explanations. In particular, measures from social control theory (low self-control) and social learning theory (moral beliefs, delinquent peer associations) were significant predictors. Individuals with lower levels of self-control, weaker moral beliefs, and greater exposure to delinquent peers were more likely to illicitly use prescription stimulants. The sole anomaly in the analyses was the index measuring the importance of college to the students and their parents. In one multivariate model, this measure was positively related to Adderall/Ritalin use, suggesting that use was more prevalent among those who rated the importance of college highly. Although the measure was intended to indicate social control, the findings are more consistent with strain theory—individuals to whom college generally, and specifically grades, were perhaps more likely to cheat. The inconsistency of this relationship in our models and post hoc nature of our interpretation suggest caution rather than any confidence in this explanation. Indeed, our measure of academic strain was not significant in the bivariate or any of the multivariate analyses. This finding is not entirely inconsistent with findings from a previous study that applied Agnew’s (1992) GST to the phenomenon of nonmedical use of prescription stimulants: Ford and Schroeder (2009) found no direct connection between strain and nonmedical use of prescription stimulants. However, they did find an indirect relationship, via their measure of depression. Thus, “classic” strain theory suggests that strain should be directly related to stimulant misuse, empirical findings suggest otherwise. It remains possible, of course, that this conceptualization of strain is inadequate. It is worth noting that when asked why they illicitly used stimulants, the most common answer was “school pressure.” With regard to polydrug use and the generality of deviance, prescription stimulant use appears to correlate with other measures of drug use and deviance. In particular, the measure of general deviance was the strongest predictor of illicit Adderall or Ritalin use in the logistic regression models. Similarly, those using these stimulants

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illicitly were also much more likely to use illicit drugs, such as marijuana and hard drugs, and alcohol. In short, it appears as though the illicit use of prescription stimulants follows the same pattern as other forms of crime and deviance. These findings run counter to the media’s portrayal of prescription stimulants as “academic steroids” used by talented students to achieve even better grades. There is at least one caveat to this conclusion. Given the wide variety of motivations for nonmedical use of stimulants, it is possible that research such as ours is confounding two different sorts of users—those who use the drug for recreation and those who use it to study and take exams. Although not possible with our data, future research might examine these users separately to see whether there are unique correlates of use for each type. There are, of course, other limitations in the current study. First, these analyses represent a rather basic test of the application of criminological theory to the use of Adderall and Ritalin. Future tests might include more robust measures of social control theory, as well as refined measures of strain. Second, conclusions about illicit prescription stimulant use from the current study may not generalize to students in different types of colleges (e.g., smaller and/or private institutions), or those attending college in different geographical locations. Finally, in the current analyses, we deleted those individuals who reported being diagnosed as ADHD, as were interested in the illicit use of prescription stimulants. It is still possible that students diagnosed with ADHD may use the drugs for reasons unrelated to their disorder. Despite these limitations, this study does suggest that a criminological approach to the study of illicit Ritalin/Adderall use can make a useful contribution. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes 1. Although the authors of this study view their measures as indicative of low self-control based on Hirschi’s (2004) most recent statement, we use them here as evidence of indirect control more consistent with Hirschi’s social bond theory. 2. Although it is clear that Hirschi’s (1969) social bond theory is markedly different from Gottfredson and Hirschi’s (1990) theory of low self-control, we include them both under the rubric of “social control theory” based on their assumption regarding human nature. Specifically, although they emphasize different types (indirect vs. direct and internal) of control, both assume that human nature is such that crime should be expected in the absence of some sort of social control. This stands in contrast to both strain and learning approaches, which imply that humans need positive motivation toward crime. 3. Adderall and Ritalin can be prescribed for conditions, such as Narcolepsy, other than attention deficit hyperactivity disorder (ADHD). In the vast majority of cases, however, these drugs are prescribed to alleviate symptoms of ADHD.

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4. Moral beliefs are used here as a measure of social learning/differential association. We should point that there is disagreement among theorists regarding the meaning of such measures. Specifically, Hirschi (1969) and others consider moral beliefs to be evidence of low “belief” in the validity of the law, which would be a measure of social control. 5. Given the null findings regarding our four-tier measure of strain, a reviewer suggested that we explore whether we would get different results were we to use a dichotomized measures of strain, comparing the determined underachievers (the high-strain group) to all other students (the no-strain group). Substituting this dichotomized measure of strain in Models 4 to 6 did not change our results in any meaningful way; as is the case with the four-category measure of strain, the coefficient for the dichotomized measure was nonsignificant in each of these multivariate models. 6. Regression diagnostics showed that in Models 5 and 6, multicollinearity potentially was an issue for two coefficients: grade point average (GPA) and strain. A variance inflation factor (VIF) for a coefficient of greater than 2.5 is a sign of multicollinearity (Allison, 1999). In both Models 5 and 6, the VIF for GPA is 3.027; the VIF for strain was 2.853 in Model 5, and 2.867 in Model 6. Multicollinearity makes it more difficult to find statistically significant coefficients. Yet the coefficients of each of the two variables in question were not significant in any of the models in which they were included, even those models that excluded the other factor. That is, GPA is nonsignificant in Model 2 (which excludes strain) and strain is nonsignificant in Model 4 (which excludes GPA). Thus, multicollinearity does not seem to be a major concern here.

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