Ray Franke, PhD Assistant Professor Higher Education Administration University of Massachusetts Boston

Transcription

1 Drinking, Smoking, Partying And Still Graduate On Time? Examining the Impact of Adverse Study Behavior on 4-Year Degree Completion in the United States Ray Franke, PhD Assistant Professor Higher Education Administration University of Massachusetts Boston

2 Background & Motivation Alcohol and drug use appear to be an increasing problem in U.S. higher education (Califano, 2007) About 50% or all full-time students (3.8 mill.) binge drink, abuse prescription drugs and/or illegal substances Trend towards abuse of prescription drugs (Ritalin etc.) Problem is so widespread (and accepted?) that almost 1 in 4 students meet medical criteria for substance abuse or dependence (Palmer et al., 2012) About 2.5 times the rate of adult population in U.S.

3 Trends at U.S. Campuses

4 % of college freshmen Experiences with Alcohol and Cigarettes among College Freshmen, Wine/Liquor Alcohol Cigarettes 10 0 Source: Pryor, Hurtado, Saenz, Santos, and Korn, 2007

5 Trends in Drug Use Among College Students, 1993 to % 46% 46% 47% 40% 41% Lifetime Marijuana Use 30% Lifetime Illicit Drug Use 20% 10% 11% 13% 13% 14% 0% Source: Mohler-Kuo, Lee, and Weschler, 2003

6 % of Full-time College Students Illicit Drug Use by Race/Ethnicity, % 25% 20% 20,60% 22,70% 25,60% 22,00% 15% 13,20% 10% 5% 0% Asian Hispanics White African-American Total Source: US Dept. of Health and Human Services, 2012 National Survey on Drug Use and Health

7 Prescription Stimulants Evidence that the non-medical use of prescription stimulants such as Ritalin, Dexedrine, and Adderall is a growing problem among college students in the U.S. (SAMSHA, 2003; Teter et al., 2003). Studies report the prevalence of use from 8% to 34% of college students. A recent study of nearly 11,000 2% 2% randomly selected U.S. college students reported lifetime nonmedical use of prescription 1% 0% stimulants among 7% of college Lifetime Past Year Past Month students, past year stimulant use among 4% of college students, and 2% of past month stimulant use. Source: McCabe, Knight, Teter, and Wechsler, % 7% 6% 5% 4% 3% Non-medical Prescription Stimulant Use among College Students, % 4%

8 Prescription Stimulants (cont.) Stimulant use higher among college students who are male, white, members of fraternities/sororities, and have lower grade point averages. Higher prevalence at colleges in the Northeast, and colleges with competitive admission standards. Stimulant users were also more likely to report using alcohol, cigarettes, marijuana, ecstasy, cocaine, and other adverse behaviors. Motives for non-medical use of stimulants: Help with concentration & study; increase alertness Get high; experiment Is the non-medical use of prescription stimulants a form of academic dishonesty ( doping )? Sources: McCabe, 2008

9 Purpose & Conceptual Framework Purpose: To examine the impact of student behavior that is commonly associated with adverse effects (smoking, drinking, partying) on 4-year degree completion in the U.S. For student-level variables, building mostly on Nora s (2003) Student/ Institution Engagement model and previous research (i.e. Tinto, 1975, 1993; Bean, 1982; Nora & Carbrera, 1996; Arbona & Nora, 2007) Using Titus s (2006) and Oseguera and Rhee s (2009) frameworks to guide institutional-level variable selection

10 Data Source Longitudinal data on college students gathered from two sources 2004 The Freshman Survey, Cooperative Institutional Research Program (CIRP) Enrollment and Completion Data for National Student Clearinghouse (NSC) 243,676 students at more than 400 institutions Used multiple-imputation (MVA in Stata 11) to compensate for missing values Dataset has been weighted to represent the national population of freshman in 2004

11 Methodology DV coded (1) if graduate in 4 years at the same institution, and (0) if not Analytical method Hierarchical Generalized Linear Modeling (HGLM) to account for nested data structure (Raudenbush et al., 2007) Student-level variables (blocks) Socio-demographic characteristics College preparation Financial resources College attendance (Social & Academic) Institutional-level variables Institutional type Selectivity

12 Findings Descriptive Statistics N Min Max Mean S.D. Graduated 4-Years Smoking 0 1 Occasionally Frequently Drinking Beer Occasionally Frequently Drinking Liquor/Hard Alc. Occasionally Frequently Partying <1h/week h/week h/week h/week h/week

13 Findings Key Measures Odds Ratio S.E. Sig. Smoking Occasionally ** -- Frequently ** -- Drinking Beer Occasionally ** ++ Frequently ** ++ Drinking Liquor/Hard Alc. Occasionally Frequently ** -- Partying <1h/week h/week h/week h/week h/week ** -- * = p<.01; ** = p<.000

14 Findings Student-Level (Full Model) Variable Odds Ratio S.E. Sig. Gender (Female) ** ++ Race/Ethnicity (Ref. group - White) American Indian ** -- Asian American African American ** -- Latino ** -- Multi-Ethnicity ** -- Other Race/Ethnicity First Generation Status * + Income (Parental) ** ++ Mother's Education ** ++ Father's Education ** ++ High School GPA ** ++ SAT Score ** ++ Hours/week Studying ** ++ Hours/week Student Clubs ** ++ * = p<.01; ** = p<.000

15 Findings Student-Level (cont.) Variable Odds Ratio S.E. Sig. Financial Resources/Concern Family Support ** ++ Grants Loans Some Fin. Concern (Ref. group: no concern) Major Fin. Concern (Ref. group: no concern) ** -- Degree Aspirations Reasons to Attend The Cost of Attending this College ** ++ I Wanted to Live Near Home ** ++ Graduates Get Good Jobs ** -- Transfer to Another College ** -- Live with Family (Ref. group - on-campus) ** -- Other Living Situation (Ref. group - on-campus) ** -- TFS College Involvement ** ++ Major declared ** -- Work Full-time while Attending College ** -- * = p<.01; ** = p<.000

16 Findings Institutional-Level Variable Odds Ratio Private University (Public U reference) ** ++ Public 4yr College (Public U reference) ** + Nonsect. 4yr College (Public U reference) ** ++ Catholic 4yr College (Public U reference) ** ++ Other Relig. 4yr College (Public U reference) ** ++ Selectivity ** ++ S.E. Sig. Intercept ** ++ * = p<.01; ** = p<.000 Model Fit Variance explained at Level-2: 61.9%

17 Conclusions/Implications Smoking cigarettes (all levels) and drinking hard alcohol (freq.) lower chances to graduate in time (4 years) Drinking beer (all levels) significantly increases odds to complete a degree More pronounced effect for more frequent drinking Spending more than 11 hours/week partying also lowers chances to graduate within 4 years (Institutional) implications & points for discussion: Interaction of socially accepted use of alcohol (beer) and social integration into campus life? Isolating (smoking, hard liquor) vs. integrating (light alcohol) drug use? Should campuses ban/restrict certain behavior (going back to in loco parentis)?

18 Thank you! Ray Franke