Research News

Research indicates that special events (e.g., 21st birthdays, holidays, etc.) are times of increased alcohol consumption and alcohol-related negative consequences for college students (Lewis, Lindgren, Fossos, Neighbors, & Oster-Aaland, 2009; Perkins, 2002). This week’s DRAM reviews a study that examined the association between alcohol intake and negative consequences among freshman college students during Spring Break (SB) (Lee, Lewis, & Neighbors, 2009). 

Methods

• Participants (n=726) were freshman college students who participated in a longitudinal web-based survey regarding alcohol intake; these students qualified as heavy drinkers (i.e., one or more incidents of heavy drinking in the past month; 4+ drinks for women, 5+ drinks for men) at baseline (i.e., first quarter of freshman year).
• Measures, assessed one week after SB, included:
• Typical weekly alcohol intake – average number drinks/week during the past three months using the Daily Drinking Questionnaire (Collins, Parks, & Marlatt, 1985).  
• SB alcohol intake – total number of SB drinks using a retrospective Timeline Feedback report (Sobell & Sobell, 2002).  
• SB alcohol-related negative consequences – adapted from the Rutger’s Alcohol Problem Index (White, 1989) and the Young Adult Alcohol Problems Screening Test (Hurlbut & Sher, 1992).  

Results

• 70% of participants drank during SB; participants consumed an average of 15.26 drinks across the 10 days of SB.
• 36% of participants reported experiencing at least one SB alcohol-related negative consequence; drinking during SB was positively associated with negative consequences.
• As Figure 1 shows, participants who were typically light drinkers at the time of SB were more likely to report negative consequences if they drank during SB than typically heavy drinkers.

Figure 1: Odds Ratios for Reporting Zero Negative Consequences during SB by Typical Drinking Groups (from Lee et al., 2009) 

 
Note: The Y axis of the figure is the odds-ratio of experiencing no negative consequences due to drinking during SB. The plot shows the decreasing likelihood of no negative consequences as the number of SB drinks increases (X axis) for the three groups defined by recent typical amount of drinking.) Consequences included – 1. Had a hangover the morning after you had been drinking; 2. Felt sick to your stomach or threw up after drinking; 3. Passed out or fainted suddenly; 4. Had a fight, argument, or bad feelings with a friend; 5. Received an injury as a result of drinking; 6. Got into fights, acted bad, or did mean things; 7. Got into sexual situations that you later regretted; and 8. Had a fight, argument or bad feelings with a family member.

Limitations

• All alcohol consumption information is retrospective self report.
• Inclusion criteria included heavy drinking at baseline. Thus, even “light” drinkers at the six month follow-up had qualified as heavy drinkers in the first quarter assessment, and results should be interpreted accordingly.
• The study analyzed number of consequences, not type of consequence, despite the diversity and differing prevalence of consequences (e.g., hangover vs. injury).

Discussion

The results suggest that drinking during SB is associated with negative consequences for all levels of drinking, but is particularly problematic for students who have been light drinkers in the months surrounding SB. Since all of the participants in the study were initially classified as heavy drinkers in the first quarter of their freshman year, it is unclear whether these results generalize to all light drinkers or specifically to students who have recently limited their drinking. More research is needed to test these findings in a more diverse range of students. If confirmed, the results might inform intervention efforts for specific events associated with higher alcohol intake, suggesting a focus on students who typically drink infrequently.

-Tasha Chandler

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References

Collins, R. L., Parks, G. A., & Marlatt, G. A. (1985). Social determinants of alcohol consumption: The effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting Clinical Psychology, 53(2), 189-200.

Hurlbut, S. C., & Sher, K. J. (1992). Assessing alcohol problems in college students. Journal of American College Health, 41(2), 49-58.

Lee, C. M., Lewis, M. A., & Neighbors, C. (2009). Preliminary examination of spring break alcohol use and related consequences. Psychology of Addictive Behaviors, 23(4), 689-694.

Lewis, M. A., Lindgren, K. P., Fossos, N., Neighbors, C., & Oster-Aaland, L. (2009). Examining the relationship between typical drinking behavior and 21st birthday drinking behavior among college students: Implications for event-specific prevention. Addiction, 104(5), 760-767.

Perkins, H. W. (2002). Surveying the damage: A review of research on consequences of alcohol misuse in college populations. Journal of Studies on Alcohol – Supplement(14), 91-100.

Sobell, L. C., & Sobell, M. B. (2002). Alcohol Timeline Followback (TLFB). In A. P. Association (Ed.), Handbook of psychiatric measures (pp. 477-479). Washington, DC: American Psychiatric Association.

White, H. R., & Labouvie, E.W. (1989). Towards the assessment of adolescent problem drinking. Journal of Studies on Alcohol, 50(1), 30-37.

It’s that time of the year again…March Madness. Next to the Super Bowl, this event stimulates the most sports wagering in the U.S.  During 2004, the National Collegiate Athletic Association (NCAA) conducted a national survey of gambling and other high risk behaviors among college athletes, which is described in a number of publications or reports (Ellenbogen, Jacobs, Derevensky, Gupta, & Paskus, 2008; Huang, Jacobs, Derevensky, Gupta, & Paskus, 2007a, 2007b; Petr, Paskus, & Dunkle, 2004). This survey revealed that college athletes (especially males) evidenced sports betting despite the NCAA rules prohibiting student-athletes from wagering on college or professional sports. In response to these results, NCAA president, Myles Brand, commissioned a task force of key figures in college athletics and the gambling research/treatment community to examine the data and recommend a course of action. The resulting recommendations were wide ranging and included various forms of educational outreach. During 2008, the NCAA conducted a subsequent national study of student-athletes. Analyses of these data are underway and the NCAA plan to release an initial report about the study later this year.  This week’s WAGER reviews some preliminary findings of the 2008 NCAA study (Paskus, Petr, Vicente, & Derevensky, 2009; Petr et al., 2004).

Methods

• Using a cluster or three-stage sampling design in both the 2004 and 2008 studies, researchers approached all 1000+ NCAA member colleges to participate through faculty athletics representatives. Each school was asked to survey all members of 1-3 teams selected by the NCAA through a stratified random sampling procedure, with the intent of providing samples representative of all sports and NCAA divisions.
• Participation was anonymous at both the student and school level. Based on the number of responses and experience with similar surveys, the NCAA estimated a school-level response rate of 60-65% for both 2004 and 2008. Investigators did not calculate the student-level or team response rate for either survey.
• Without the student or team response rate, we cannot calculate a study-wide response rate. However, because this rate emerges from a product of school, team and student response rates, we know that the study response rate is going to be < .60-.65.
• Rigorous data cleaning procedures were employed, including the use of item response theory (IRT) to enhance validity checks.
• To enhance comparability, investigators retroactively applied the 2008 data cleaning methods to the 2004 survey data and weighted both data sets to the NCAA’s estimate of the 2008 participation rates for 22 sports (11 male and 11 female). 
• The 2008 survey contained a single item to assess sports betting prevalence and frequency among respondents, whereas the sports betting data for the 2004 survey was based on a composite of items in the survey.
• Researchers reanalyzed the 2004 data set and the modified 2004 results to make these comparable to the 2008 results.
• The 2004 sample included 19,354 student-athletes and the 2008 sample included 19,371 student-athletes.
• Both the 2004 and 2008 surveys included 10 questions based on the DSM-IV criteria for pathological gambling (American Psychiatric Association, 1994) to assess gambling status. Each affirmative answered was scored as 1 with the total score being a summation of scores for each item.
• Researchers classified respondents as social gamblers if they received a score between 0-2, at-risk gamblers if they received a score of 3 or 4 and probable pathological gamblers if they received a score of 5 or more.

Results

• As shown in Table 1, social levels of sports wagering (i.e., defined as having gambled on sports in the past year) appear to have increased from 2004 to 2008. Researchers reported that estimates of student-athletes gambling once per month or more was similar across the two cohorts and heavy sports gambling (i.e., once/week or more) decreased since 2004.
• Analysis of the gambling-type classification according to the DSM-based gambling screen indicates that the proportion of non-gamblers and social gamblers is similar across surveys. However, the 2008 survey reveals fewer at-risk gamblers and more probable pathological gamblers.
• Overall, the proportion of male athletes endorsing any number of gambling-related problems declined from 6.7% in 2004 to 5.4% in 2008.

Table 1: Past-Year Gambling by Male Athletes

Limitations

• This survey gathers self-reported data without corroboration; therefore, this study is subject to the problems commonly associated with self-report.  For example, given the highly sensitive nature of the survey questions (e.g., sports gambling for college athletes is both illegal in nearly all states and violates NCAA policy), some participants might not have reported accurately their sports betting behaviors in spite of their anonymity. 
• General cohort shifts for gambling behaviors among student-athletes and the difficulties matching interventions received to specific students hampers our ability to isolate the effect of the NCAA’s educational efforts.
• Differences in the 2004 and 2008 survey instruments might have confounded the observed change in the prevalence rates of gambling on sporting events.
• The researchers chose survey procedures designed to enhance anonymity; however, these procedures make it difficult to calculate a student-level survey response rate. The NCAA estimated school level response rate of 60% to 65% sets a ceiling for the overall response rate in a study that employs multiple stage sampling. The survey response rate is likely lower because it is obtained by multiplying the school-level response rate by the team level response rate by the student-level response rate. Low response rates are a cause for concern about the representativeness of survey data and our ability to generalize its findings.
• In this case, disregarding the team level response rate, a student-level response rate lower than 80% will yield a survey-level response rate less than 50%. According to Mangione (1995, pgs. 60-61), “response rates between 50% and 60% are barely acceptable and really need some additional information that contributes to confidence about the quality of your data. Response rates below 50% really are not scientifically acceptable.” Thus, absent a very high level of team and student-level response, there are not sufficient safeguards against sampling bias.
• In addition, because evidence has shown that athlete gambling rates vary by NCAA division (e.g., Nelson et al., 2007), it is important to determine whether response rates are similar across NCAA divisions . Any inconsistencies across these rates can lead to over or under estimates of student-related gambling activities.

Conclusion
Among male college student athletes, the subsequent 2008 NCAA study shows an increase in the number of sports gamblers, a decrease in at-risk gambling behavior and an increase in the number of participants classified as a probable pathological gamblers. It is possible that prevention initiatives developed and implemented by the NCAA task force might have contributed to the changes in some measures of male college athlete gambling; however, other factors also could be at play.  Additional follow-up research is needed to have a better understanding of the effectiveness and impact of such intervention strategies.

In the meantime, the NCAA tournament will garner much attention and wagering. College students, both athletes and non-athletes, who choose to gamble, are best advised to exercise caution when wagering on the outcome.

-Erica Marshall

What do you think?  Please use the comment link below to provide feedback on this article.

References

American Psychiatric Association. (1994). DSM-IV: Diagnostic and statistical manual of mental disorders (Fourth ed.). Washington, D.C.: American Psychiatric Association.

Ellenbogen, S., Jacobs, D., Derevensky, J., Gupta, R., & Paskus, T. (2008). Gambling behavior among college student-athletes. Journal of Applied Sport Psychology, 20(3), 349-362.

Huang, J., Jacobs, D., Derevensky, J., Gupta, R., & Paskus, T. (2007a). Gambling and health risk behaviors among U.S. college student-athletes: findings from a national study. Journal of Adolescent Health, 40(5), 390-397.

Huang, J., Jacobs, D., Derevensky, J., Gupta, R., & Paskus, T. (2007b). A national study on gambling among US college student-athletes. Journal of American College Health, 56(2), 93-99.

Mangione, T. W. (1995). Mail surveys: improving the quality (Vol. 40, Applied Social Research Methods Series). Thousand Oaks, CA: SAGE Publications, Inc.

Nelson, T., LaBrie, R., LaPlante, D., Stanton, M., Shaffer, H., & Wechsler, H. (2007). Sports betting and other gambling in athletes, fans, and other college students. Research Quarterly for Exercise and Sport, 78(4), 271-283.

Paskus, T., Petr, T., Vicente, R., & Derevensky, J. (2009). Results from the 2008 NCAA Study on Collegiate Wagering: The National Collegiate Athletic Association.

Petr, T., Paskus, T., & Dunkle, J. (2004). 2003 NCAA National Study on Collegiate Sports Wagering and Associated Behaviors: The National Collegiate Athletic Association.