Decisional balance and collegiate drinking.
The study examined the perceived benefits and costs of alcohol use among undergraduates (N = 462) perceiving their drinking as normal or abnormal as well as those undergraduates who met or did not meet the DSM-IV-TR criteria for an alcohol disorder. A 2x2 MANOVA and univariate analyses on the benefits (pros) and costs (cons) scales of the Alcohol Decisional Balances Scale (ADBS) revealed significantly higher perceived benefits of alcohol use among students reporting normal drinking behaviors and meeting the DSM-IV-TR diagnostic criteria. A significant interaction revealed that students who met the DSM-IV-TR criteria but perceived their drinking as normal reported the highest perceived benefits of drinking. Findings supported prior research highlighting the link between perceived benefits of alcohol use and problematic drinking.
Key words: alcohol, college, attitudes, DSM-IV-TR, decisional balance
Recent estimates report that 10 percent to 31 percent of the approximately 8 million college students (ages 18-24) meet the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; American Psychiatric Association, 2000) criteria for alcohol abuse. Another six percent to 11 percent meet the DSM-IV-TR criteria for alcohol dependence (Clements, 1999; Knight, Wechsler, Kuo, Siebring, Weitzman, & Schuckit, 2002). Heavy drinking leads to negative events such as sleep disruption, academic difficulties, and violence (Wechsler, Lee, & Kuo, 2000). Consequently, heavy collegiate alcohol use and the associated negative consequences continue to be recognized as a significant problem on college campuses (NIAAA, Alcoholism Advisory Council, 2002).
The significance of these negative consequences influenced colleges to implement primary alcohol prevention programs (Wechsler, Kelley, & Weitzman, 2000) that typically utilize alcohol education, alcohol-free activities or alcohol-free living quarters (e.g., dry residence halls), and greater enforcement of campus alcohol policies (Larimer & Cronce, 2002; Wechsler, Seibring, Liu, & Ahl, 2004). However, these programs are typically ineffective because the students do not perceive their drinking as being problematic (Vik, Culbertson, & Sellers, 2000). Gintner and Choate (2007) underscore the fact that, although the collegiate drinking research emphasizes a need to understand the motivations of collegiate drinkers, most college campuses still rely upon primary prevention and alcohol psychoeducation.
These primary prevention programs are being utilized with problematic college drinkers mandated to treatment for campus alcohol policy infractions, a scenario on the rise in recent years (Tevyaw, Borsari, Colby, & Monti, 2007). For instance, Anderson and Gadaleto (2006) surveyed 196 colleges and universities and found that 74 percent of these campuses mandated participation in an alcohol education/primary prevention program for alcohol policy violators. Consequently, college/university counseling centers must provide services to individuals mandated to treatment while also providing a welcoming resource for a population that typically does not volunteer for assistance with alcohol problems.
One intervention that moves beyond primary prevention and education by targeting the motivations of the collegiate drinker is motivational interviewing (MI; Miller & Rollnick, 2002). Motivational interviewing may reduce problematic drinking by demonstrating the discrepancies between drinking behaviors and goals. The concept of MI originates in the Transtheoretical Model of Behavior Change (Prochaska & DiClemente, 1986) which theorizes several stages of motivation (precontemplation, contemplation, preparation, action, and maintenance) to change a problematic behavior. Each stage represents an increased degree of motivation for change with the action stage reflecting a willingness to change behavior. However, most college students are not ready to initiate change, especially if the drinking behavior is not perceived as problematic. Thus, it is crucial to motivate and guide the individual to the action stage so behavior change may begin.
One component of MI is decisional balance (DB), derived from the decision-making model by Janis and Mann (1977), which reviews how decision-making entails careful consideration of all options while producing a "balance sheet" of potential benefits (pros) and costs (cons) of engaging in the behavior (Velicer, DiClemente, Prochaska, & Brandenburg, 1985). DB provides the individual an opportunity to articulate ambivalence regarding current behavior by endorsing the benefits (pros) and costs (cons) of that behavior (Miller, 1999). Research reports mixed results with studies advocating or refuting the potential efficacy of DB as a mechanism to facilitate change in problematic drinking behaviors.
Prior research of the positive and negative aspects of alcohol use (Migneault, Velicer, Prochaska, & Stevenson, 1999) found a strong positive relationship between positive aspects of alcohol use and problematic drinking coupled with a smaller negative relationship between negative aspects of alcohol use and problematic drinking. LaBrie, Pedersen, Earleywine, and Olsen (2006) found that one-month after completing a DB exercise the participants reported a statistically significant increase in desire to change drinking behavior. Noar, LaForge, Maddock and Wood (2003) found that college students who believed in the negative costs of alcohol use were less likely to drink or drink heavily. The research also suggests that negative elements of alcohol use may not directly influence problematic drinking behavior, highlighting the need for further study of the costs (cons) of alcohol use in the DB process. Despite research highlighting the potential for DB in reducing problematic drinking, two recent studies found no support for DB effectiveness on changing problematic drinking behaviors (Carey, Carey, Maisto, & Henson, 2006; Collins & Carey, 2005).
We could find no published studies that utilized the DSM-IV-TR criteria of alcohol abuse/dependence in studying DB and college alcohol use. Considering the mixed results of the DB research and the absence of DSM-IV-TR criteria within the research, one area of further inquiry entails DB differences between college drinkers presenting with and without a DSM-IV-TR alcohol use disorder. Although recent meta-analytic work reports that brief motivational interventions (which include the use of MI and DB) are not intended for individuals meeting the DSM-IV-TR diagnostic criteria for alcohol abuse or dependence (Moyer, Finney, Swearingen, & Vergun, 2002), there does exist ample empirical evidence underscoring the utilization and efficacy of MI with substance abusers in treatment (e.g., Martino, Carroll, Nich, & Rounsaville, 2006). Thus, a study of brief motivational intervention components (such as DB) seems warranted in a college student population where it is estimated that upwards of forty percent meets the DSM-IV-TR alcohol abuse or dependence criteria (Clements, 1999; Knight et al., 2002). Research on the benefits (pros) and costs (cons) of alcohol use with college students meeting or not meeting the DSM-IV-TR criteria for alcohol use disorder could illuminate the issues associated with the use of DB in designing the secondary prevention programs that Gintner et al. (2007) argue are needed on college campuses. Furthermore, this population typically does not voluntarily seek assistance for problematic drinking behaviors (Nye, Agnostelli, & Smith, 1999). Therefore, a study of students who do or do not consider themselves problematic drinkers also seems warranted.
This study proposes three hypotheses. First, those meeting the DSM-IV-TR diagnostic criteria for abuse/dependence would report significantly greater benefits (pros) and significantly fewer costs (cons) of alcohol use than those not meeting the DSM-IV-TR diagnostic criteria. Second, those reporting abnormal drinking behavior would exhibit significantly greater benefits (pros) and significantly fewer costs (cons) of alcohol use than those reporting drinking behavior as normal. Third, those meeting the DSM-IV-TR abuse/dependence criteria and not reporting drinking behavior as abnormal would exhibit significantly greater benefits (pros) and significantly fewer costs (cons) of alcohol use as compared to the other experimental conditions.
Undergraduate student volunteers in a Psychology Department participant pool (N=462) participated in this study. Students were enrolled in the participant pool because they were in a psychology course. Participants were given the option in their course of registering for studies being run by the Department of Psychology or reading various psychology articles and answering questions about the research (for those who objected to research participation). Research participation (whether through study participation or reading the psychology articles) was for course credit.
Most of the sample were freshmen (43.1%) with the remainder consisting of sophomores (19.0%), juniors (16.5%), and seniors (20.8%). Consistent with the demographics of this northeastern university, the majority of the sample was female (71.6%) and Caucasian (87.2%) whereas 5.6% identified as Hispanic, 2.4% as African-American, 1.9% as Asian, and 1.9% as other. Most lived on campus in university housing (46.1%) with others living off campus with friends/roommates (18.0%), family (25.1%), spouse/significant other (6.5%), or alone (2.2%). Average age was 20.43 years (SD = 4.77) and average GPA (on a four-point scale) was 3.26 (SD = 0.48; Table 1).
Decisional balance. Decisional balance was measured using the 16-item Alcohol Decisional Balance Scale (ADBS; Maddock, 1997). The ADBS consists of eight items that assess the benefits (pros) of alcohol use and eight items that assess the costs (cons) of alcohol use. The scale asks "How important to you are the following statements in your decision about how much to drink?" and respondents' answer 1 (not at all important) to 5 (extremely important) on the benefits (pros) and costs (cons) scales. Items include "Drinking gives me courage" and "I feel happier when I drink" on the benefits (pros) scale, and "Drinking could get me addicted" and "I can hurt people close to me when I drink too much" on the costs (cons) scale. Reliability was strong for both the benefits (pros; [alpha] =. 93) and costs (cons; [alpha] = .94) scales within this study sample.
Alcohol abuse or dependence. Alcohol use disorder was measured using a self-report checklist based on the DSM-IV-TR criteria for alcohol abuse or dependence. To meet the DSM-IV-TR criteria for alcohol abuse a student must experience at least one of the following symptoms in the past 12 months: (1) Failure to fulfill obligations (e.g., poor grades, missing class); (2) drinking in hazardous situations (e.g., while on medication or while driving); (3) legal problems (e.g., arrested for underage drinking, fighting, penalty for violating campus alcohol policy, violent or sexual assault); (4) interpersonal problems (e.g., repeated conflicts with friends or significant other).
Alcohol dependence is met by experiencing three of more of the following symptoms in the past 12 months: (1)Tolerance (e.g., increasing number of drinks needed for intoxication); (2) withdrawal (e.g., physical symptoms in the absence of alcohol use); (3) drinking larger quantities than intended; (4) persistent desire and/or unsuccessful attempts to control drinking; (5) significant time and effort spent in obtaining alcohol, drinking and recovering from drinking; (6) reducing non-alcohol related activities (e.g., hobbies, school, job); (7) continued drinking despite physical and/or psychological consequences. Respondents endorsed a yes or no to the questions directly derived from the DSM-IV-TR language and criteria for alcohol abuse or dependence. In addition, we asked the participants (via a dichotomous yes/no variable) if they felt their drinking behavior was normal.
Participants voluntarily signed-up for a study on "college health." Alcohol was not mentioned in the study title or the brief study description in an effort to eliminate any self-selection in or out of the study based on alcohol experiences and/or attitudes. As a component of the study the participants completed the DSM-IV-TR checklist for alcohol abuse or dependence. Following the completion of data collection, the checklist was computed to produce the two experimental groups of presence or absence of a DSM-IV-TR alcohol use disorder. An individual cannot possess both abuse and dependence diagnoses for the same substance; thus, for sake of simplicity, the variables of abuse and dependence were collapsed into one level.
All participants were encouraged, through a detailed verbal and written debriefing process, to contact the university counseling center if they felt their drinking was problematic and/or their participation in the study triggered self-reflection on their drinking behaviors. All research assistants received training in confidentiality and ethics pertaining to research in general as well as research issues relevant to substance use disorders. The study was reviewed and approved by the university Institutional Review Board.
Alcohol Problem Analyses
The participants (N = 462) were nearly evenly split on whether they met (51.9%; n = 240) or did not meet (48.1%; n = 222) the DSM diagnostic criteria for alcohol abuse or dependence. Two-thirds of the sample felt they were normal drinkers (66.7%; n = 308). To better understand the relationship between self-report of normal or abnormal drinking and whether drinking behavior meets the DSM-IV-TR diagnostic criteria for abuse or dependence, a chi-square with risk estimates was performed. The analysis was significant, [chi square] (1) = 20.48, p < .001. Risk estimates indicate that participants were not willing to admit and/or were aware of their problematic drinking. The relative risk of a self-reported normal drinker not meeting the DSM-IV-TR diagnostic criteria was .64 (95% CI for risk estimate = .53 to .77) whereas the relative risk of a self-reported normal drinker meeting the DSM-IV-TR diagnostic criteria was 1.61 (95% CI for risk estimate = 1.28 to 2.02).
A 2 (DSM diagnostic status) x 2 (self-perception of being a normal drinker) MANOVA was conducted on two dependent measures: The benefits (pros) and costs (cons) scales of the ADBS. The MANOVA procedure was chosen, rather than two individual 2x2 ANOVAs with a Bonferroni alpha-adjustment procedure, to avoid the drop in power associated with alpha-adjustment techniques (Sedlmeier & Gigerenzer, 1989).
Box's M was not significant (Box's M = 13.75, p = .14), providing insufficient evidence that the covariance matrices differ. Thus, these data were appropriate for the MANOVA procedure. MANOVA results revealed significant main effects for diagnostic status, Hotelling's Trace = .12, F (2, 442) = 25.61, p < .001, as well as for self-report of abnormal drinking behavior, Hotelling's Trace = .11, F (2, 442) = 23.42, p < .001. Results found a marginally significant interaction effect, Hotelling's Trace = .01, F (2, 442) = 2.66, p=.07 (see Table 2). Considering the highly significant two main effects, we chose to review the univariate results for the interaction in addition to the main effects for both benefits (pros) and costs (cons) scores in regard to alcohol use.
Benefits (pros). Univariate results revealed significant main effects for diagnostic status, F (1,443) = 49.39, p < .001, as well as for self-report o f abnormal drinking behavior, F (1,443) = 44.05, p < .001. Those who met the DSM-IV-TR criteria for alcohol abuse or dependence reported significantly greater benefits (pros) scores regarding alcohol use (M = 22.77, SE = .52) as compared to those not meeting the DSM-IV-TR criteria (M = 17.81, SE=.47). Those who self-reported normal drinking behavior reported significantly greater benefits (pros) scores (M = 22.63, SE = .40) as compared to those reporting abnormal drinking behavior (M = 17.95, SE = .58). Univariate results also revealed a significant interaction effect, F (1,443) = 5.22, p < .05, where those who believed they engaged in normal drinking (regardless of DSM-IV-TR diagnostic status) produced the highest benefits (pros) scores on alcohol use. Furthermore, those who reported normal drinking behaviors but met the DSM-IV-TR criteria for alcohol abuse or dependence registered the highest benefits (pros) scores for alcohol use (see Figure 1).
Costs (cons). Univariate results revealed only one significant main effect for self-report of normal drinking behavior, F (1,443) = 5.20, p < .05, with those self-reporting normal drinking behaviors (M = 27.24, SE = .82) scoring significantly higher on the costs (cons) toward alcohol use as compared with those reporting abnormal drinking behavior (M = 24.97, SE = .57). Statistical significance was not reached for either the main effect of DSM-IV-TR diagnostic criteria, F (1,443) = .60, p =.44, or the interaction between self-report of abnormal drinking behavior and DSM-IV-TR diagnostic criteria, F (1,443) = .02, p = .90.
We examined the attitudes of alcohol use among college students who viewed their drinking behaviors as normal or abnormal as well as those who met or did not meet the DSM-IV-TR diagnostic criteria for alcohol abuse/dependence. A 2x2 MANOVA was performed to test the following hypotheses: 1) Those meeting the DSM-IV-TR diagnostic criteria for abuse/dependence would exhibit significantly greater benefits (pros) scores and significantly fewer costs (cons) scores than those not meeting the DSM-IV-TR diagnostic criteria; 2) those reporting abnormal drinking behavior would exhibit significantly greater benefits (pros) scores and significantly fewer costs (cons) scores than those reporting drinking behavior as normal; 3) those meeting the DSM-IV-TR abuse/dependence criteria and not reporting drinking behavior as abnormal would exhibit significantly greater benefits (pros) scores and significantly fewer costs (cons) scores as compared to the other experimental conditions. MANOVA and subsequent univariate results supported all three hypotheses for the benefits (pros) scores, whereas only one hypothesis was supported for the costs (cons) scores.
Consistent with past research (Migneault et al., 1999; Noar et al., 2003) the benefits (pros) of alcohol use, and not the costs (cons), were linked with problematic drinking. However, unlike prior published findings, this study extended the DB research on collegiate alcohol use by demonstrating the same relationship when the DSM-IV-TR diagnostic criteria for alcohol abuse or dependence were met as for self-reports of normal drinking behavior. Considering the broad array of measurement strategies within the literature used to evaluate problematic drinking, the finding that benefits (pro) of alcohol use is more strongly linked with problematic drinking seems robust.
Although those who felt they were not problematic drinkers reported significantly higher costs (con) of alcohol use scores than those who did self-report abnormal drinking, there were no significant costs (con) score differences between those meeting or not meeting the DSM-IV-TR diagnostic criteria. Drinkers who met the DSM-IV-TR criteria regarded the costs (cons) of alcohol use the same as those not meeting the diagnostic criteria. Furthermore, the difference between these scores was quite small (less than one point on an 8 to 40 point scale). This finding may underscore the tendency to under-estimate the problematic nature of drinking behavior based on distorted campus norms regarding alcohol consumption (Neighbors, Dillard, Lewis, Bergstrom, & Neil, 2006). Participants may not have perceived their drinking as abnormal when compared to their peers, but when evaluated using the DSM-IV-TR diagnostic criteria, many who endorsed having normal drinking behavior were found to meet the diagnostic criteria.
This finding also highlighted why psychoeducation for campus alcohol infractions does not work (Gintner et al., 2007) because drinkers who met or did not meet the DSM-IV-TR diagnostic criteria already understood and knew the negatives associated with alcohol use. For years, social psychological research has underscored that, if a message recipient does not have a strong desire to attend to a message, the information conveyed will not be processed and retained (see Petty & Cacioppo, 1986). Consequently, this finding supports the call (Gintner et al., 2007) for a targeted secondary-prevention strategy that addresses the personal underlying reasons why alcohol is used, as opposed to primary-prevention educational methods reporting the hazards of drinking.
Alcohol counseling within a college/university counseling center is a considerable challenge (Birky, 2005), with one of the obstacles being students not willing to seek services for problematic drinking (Nye et al., 1999). Many of the students who do report for alcohol services are doing so due to an administrative mandate (Anderson et al., 2006). Consequently, students may not seek services because they believe they do not have an alcohol problem. This paper supported this scenario, as students who self-reported normal drinking had a relative risk of meeting the DSM-IV-TR diagnostic criteria for alcohol abuse or dependence approximately 2.5 times greater (risk estimates of 1.61 versus 0.64) than those self-reporting abnormal drinking behavior. Whether this was due to the core addiction element of denial (Yalisove, 1998), social norms for campus drinking behaviors (see Neighbors, Lee, Lewis, Fossos, & Larimer, 2007), or some interaction of these two constructs, should be the focus of future research.
Several limitations should be considered when interpreting the present results. The primary limitation, inherent in all laboratory-based psychological research using volunteering undergraduate participants, is that the use of an undergraduate psychology research participant pool threatens external validity. Years of research highlights how participants in an undergraduate research pool who voluntarily enter a research study may not be representative of the larger population (Berkowitz & Donnerstein, 1982; Jackson, Procidano, & Cohen, 1989; Sears, 1986). The present research (conducted with 462 students from one northeastern university) is no exception. Though the present research was a good first step in taking DB/alcohol research in a new direction, the one university sample impedes our generalizing the findings across universities nationwide. Future research with multiple universities of varying size, demographics, and geographical locations would produce a study sample that can better generalize to the larger nationwide collegiate population.
Besides the problem with external validity, the present research has three areas of sampling error and bias that can occur in any undergraduate participant pool sample. One, though the topic of the study was masked, the participants may have self-selected in or out of the study based on the "college health" title. In addition, although we requested that participants not share the alcohol theme of the study with any future participants, we had no way of checking if the true theme of the study leaked out to the other pool members. Thus, other students may have self-selected into or out of the study based on the actual alcohol topic. Two, Miller (1981) stressed that having multiple time-slots may cause some participants to not sign-up as they would not want to divulge sensitive information in a group setting. The present research used time slots with up to six appointments per slot. Three, empirical evidence (Masling, O'Neill, & Jayne, 1981; Stevens & Ash, 2001) suggests that when students sign-up for the study (early, mid, or late semester) influences their engagement with the study and the quality of the data. We did not code the data packets by when in the semester the data were collected; thus, we had no way to evaluate any temporal influences on these data.
In addition to these three principle limitations linked to the method of participant recruitment and sampling, other limits warrant discussion. One, although we did not find a statistically significant order effect (by randomizing the sequence of the DSM-IV-TR checklist and normal drinker question) there may be some influence of the DSM-IV-TR checklist on the question of normal drinking behavior. Two, adding a measure of social desirability would help clarify if the self-reported normal drinkers were responding in a manner to protect their image or standing within the university, i.e., respondents less than 21 years-old admitting to violating campus policy and state law. Three, self-report of alcohol use on the DSM checklist needs to be examined in the context of the abundant literature on the reliability of self-report data (see Akinci, Tarter, & Kirisci, 2001). Without a collateral measure true accuracy of alcohol use must be called into question. Fourth, unlike many of the published DB studies, this paper does not utilize longitudinal analysis of behavior change or stages of change measures. Consequently, future analyses along this line of inquiry would require an intervention to facilitate changes in drinking behavior as well as assessment of at which stage of change the drinker resides.
Despite these limitations, the present research offers a contribution of being the first examination of how DSM-IV-TR diagnostic status for alcohol abuse or dependence influences self-reported benefits (pros) and costs (cons) of alcohol use using a DB measure in a college-based (non-clinical) sample. In addition, this research highlighted how self-report of normal drinking behavior actually produced a greater relative risk of meeting the DSM-IV-TR diagnostic criteria for alcohol abuse or dependence. Additional studies examining multiple predictors of drinking behavior change using longitudinal designs are the appropriate next step. By understanding the relationship between self-perception of drinking behavior and DSM-IV-TR diagnostic level of problematic drinking, specific and personalized interventions can be designed that change problematic drinking behavior over time and may help develop more effective campus-based alcohol interventions.
Correspondence concerning this article should be addressed to: Keith Morgen, Ph.D., Centenary College, Box #403, 400 Jefferson Street, Hackettstown, NJ 07840, email@example.com.
Acknowledgments: The authors thank Kristina Glowzenski, Lauren Goslin, Leigh-Ann Javas, Lisa Maietta, and Kaitlyn Santangelo for their efforts in data collection and entry. This research was based on the second author's senior thesis. A prior version of this paper was presented at the 2008 Eastern Psychological Association Conference, Boston.
[FIGURE 1 OMITTED]
Akinci, I.H., Tarter, R.E., Kirisci, L. (2001). Concordance between verbal report and urine screen of recent marijuana use in adolescents. Addictive Behaviors, 26, 613-619.
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.
Anderson, D.S. & Gadaleto, A.F. (2006). Results of the 2006 College Alcohol Survey. Comparison with 2003 results, 1994 results, and baseline year. Fairfax, VA: George Mason University, Center for the Advancement of Public Health.
Berkowitz, L., & Donnerstein, E. (1982). External validity is more than skin deep: Some answers to criticisms of laboratory experiments. American Psychologist, 37, 245-257.
Birky, I.T. (2005). Evidence-based and empirically supported college counseling center treatment of alcohol related issues. Journal of College Student Psychotherapy, 20, 7-21.
Carey, K.B., Carey, M.P., Maisto, S.A., & Henson, J.M. (2006). Brief motivational interventions for heavy college drinkers: A randomized controlled trial. Journal of Consulting and Clinical Psychology, 74, 943-954.
Clements, R. (1999). Prevalence of alcohol-use disorders and alcohol-related problems in a college student sample. Journal of American College Health, 48, 111-118.
Collins, S.E., & Carey, K.B. (2005). Lack of effect for decisional balance as a brief motivational intervention for at-risk college drinkers. Addictive Behaviors, 30, 1425-1430.
Gintner, G.G. & Choate, L.H. (2007). Counseling students who are problem-drinkers: Screening, assessment, and intervention. In J.A. Lippincott & R.B. Lippincott (Eds.), Special populations in college counseling. A handbook for mental health professionals (pp. 157-171). Alexandria, VA: American Counseling Association.
Jackson, J.M., Procidano, M.E., & Cohen, C.J. (1989). Subject pool sign-up procedures: A threat to external validity. Social Behavior and Personality, 17, 29-43.
Janis, I.L., & Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment. London: Cassel & Collier Macmillan.
Knight, J.R., Wechsler, H., Kuo, M., Siebring, M., Weitzman, E.R., & Schuckit, M.A. (2002). Alcohol abuse and dependence among U.S. college students. Journal of Studies on Alcohol, 63, 263-270.
LaBrie, J.W., Pedersen, E.R., Earleywine, M., & Olsen, H. (2006). Reducing heavy drinking in college males with the decisional balance: Analyzing an element of motivational interviewing. Addictive Behaviors, 31, 254-263.
Larimer, M.E. & Cronce, J.M. (2002). Identification, prevention, and treatment: A review of individual-focused strategies to reduce problematic alcohol consumption by college students. Journal of Studies on Alcohol, Supplement no. 14, 148-163.
Maddock, J.E. (1997). Development and validation of decisional balance and process of change for heavy, episodic drinking. Unpublished master's thesis, Kingston, Rhode Island: University of Rhode Island.
Martino, S., Carroll, K.M., Nich, C., & Rounsaville, B.J. (2006). A randomized controlled pilot study of motivational interviewing for patients with psychotic and drug use disorders. Addiction, 101, 1479-1492.
Masling, J., O'Neill, R., & Jayne, C. (1981). Orality and latency of volunteering to serve as experimental subjects. Journal of Personality Assessment, 45, 20-22.
Migneault, J.P., Velicer, W.F., Prochaska, J.O., & Stevenson, J.F. (1999). Decisional balance for immoderate drinking in college students. Substance Use and Misuse, 34, 1325-1346.
Miller, A. (1981). A survey of introductory psychology subject pool practices among leading universities. Teaching of Psychology, 8, 211-213.
Miller, W.R. (1999). Enhancing motivation for change in substance abuse treatment. Rockville, MD: U.S. Department of Health and Human Services.
Miller, W.R., & Rollnick, S. (2002). Motivational interviewing. Preparing people for change (2nd ed.). New York: Guilford Press.
Moyer, A., & Finney, J.W., Swearingen, C.E., & Vergun, P. (2002). Brief interventions for alcohol problems: A meta-analytic review of controlled investigations in treatment-seeking and non-treatment seeking populations. Addiction, 97, 279-292.
National Institute on Alcohol Abuse and Alcoholism (NIAAA). (2002). A call to action: Changing the culture of drinking at U.S. colleges. Rockville, MD: Author, NIH.
Neighbors, C., Dillard, A.J., Lewis, M.A., Bergstrom, R.L., & Neil, T.A. (2006). Normative misperceptions and temporal precedence of perceived norms and drinking. Journal of Studies on Alcohol, 67, 290-299.
Neighbors, C., Lee, C.M., Lewis, M.A., & Fossos, N. (2007). Are social norms the best predictor of outcomes among heavy-drinking college students? Journal of Studies on Alcohol and Drugs 68, 556-565.
Noar, S.M., LaForge, R.G., Maddock, J.E., & Wood, M.D. (2003). Rethinking positive and negative aspects of alcohol use: Suggestions from a comparison of alcohol expectancies and decisional balance. Journal of Studies on Alcohol, 64, 60-69.
Nye, E.C., Agnostelli, G., & Smith, J.E. (1999). Enhancing alcohol problem recognition: A self-regulation model for the effect of self focusing and normative information. Journal of Studies on Alcohol, 60, 685-693.
Petty, R.E., & Cacioppo, J.T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. New York: Springer-Verlag.
Prochaska, J.O., & DiClemente, C.C. (1986). Toward a comprehensive model of change. In W.R. Miller & N. Heather (Eds.), Treating addictive behaviors: Processes of change (pp. 3-27). New York: Plenum Press.
Rosenthal, R., & Rosnow, R.L. (1975). The volunteer subject. New York: Wiley.
Sedlmeier, P., & Gigerenzer, G. (1989). Do studies of statistical power have an effect on the power of studies? Psychological Bulletin, 105, 309-316.
Stevens, C.D., & Ash, R.A. (2001). The conscientiousness of students in subject pools: Implications for "laboratory" research. Journal of Research in Personality, 35, 91-97.
Tevyaw, T.O., Borsari, B., Colby, S.M., & Monti, P.M. (2007). Peer enhancement of a brief motivational intervention with mandated college students. Psychology of Addictive Behaviors, 21, 114-119.
Velicer, W.F., DiClemente, C.C., Prochaska, J.O., & Brandenburg, N. (1985). A decisional balance measure for predicting smoking cessation. Journal of Personality and Social Psychology, 48, 1279-1289.
Vik, P.W. & Culbertson, K.A. (2000). Readiness to change drinking among heavy-drinking college students. Journal of Studies on Alcohol, 61, 674-680.
Wechsler, Kelley, K., & Weitzman, E.R. (2000). What colleges are doing about student binge drinking: A survey of college administrators. Journal of American College Health, 48, 219-226.
Wechsler, H., Lee, J.E., & Kuo, M. (2000). College binge drinking in the 1990s: A continuing problem: Results of the Harvard School of Public Health 1999 College Alcohol Study. Journal of American College Health, 48, 199-210.
Wechsler, H., Seibring, M., Liu, I.C., & Ahl, M. (2004). Colleges respond to student binge drinking: Reducing student demand and limiting access. Journal of American College Health, 52, 159-168.
Yalisove, D. (1998). The origin and evolution of the disease concept of treatment. Journal of Studies on Alcohol, 59, 469-472.
Keith Morgen, Ph.D.
Department of Psychology
Table 1 Demographics (N = 462) Variable n % M SD Gender Female 331 71.6 Class Freshmen 199 43.1 Sophomore 88 19.0 Junior 76 16.5 Senior 96 20.8 Ethnicity Caucasian 403 87.2 Hispanic 26 5.6 African-American 11 2.4 Residence Dorm/On-Campus 213 46.1 Family 116 25.1 Off Campus with Friends 83 18.0 Spouse/Significant Other 30 6.5 Off-Campus Alone 10 2.2 DSM Criteria Meets Abuse/ Dependence Criteria 240 51.9 20.43 4.77 Self-Report of Normal Drinking Normal Drinking Behavior 308 66.7 3.26 0.48 Table 2 Multivariate and Univariate Analyses of Variance F Ratios for DSM Criteria x Normal Drinking Self-Report for Benefits (Pros) and Costs (Cons) of Drinking Measures ANOVA MANOVA Benefits Costs Variable F (2, 442) F(1, 443) F(l, 443) DSM Criteria (D) 25.61*** 49.39*** 0.60 Normal Drinker (N) 23.42*** 44.05*** 5.20** D x N 2.66* 5.22** 0.02 Note. MANOVA F ratios are Hotelling's Trace * p < .10, ** p < .05, *** p <.001
|Printer friendly Cite/link Email Feedback|
|Author:||Morgen, Keith; Gunneson, Lauren|
|Publication:||Journal of Alcohol & Drug Education|
|Date:||Dec 1, 2008|
|Previous Article:||Explaining small effects of information-based drug prevention: the importance of considering preintervention levels in risk perceptions.|
|Next Article:||Development of a low-alcohol drink similar in sensory properties to a full-alcohol drink.|