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. Author manuscript; available in PMC: 2021 Mar 24.
Published in final edited form as: J Soc Clin Psychol. 2009 Sep;28(7):799–823. doi: 10.1521/jscp.2009.28.7.799

Alcohol Interventions for College Students Improves Antecedents of Behavioral Change: Results from a Meta-Analysis of 34 Randomized Controlled Trials

Lori A J Scott-Sheldon 1, Kelly S DeMartini 1, Kate B Carey 1, Michael P Carey 1
PMCID: PMC7990229  NIHMSID: NIHMS1598116  PMID: 33767529

Abstract

The current meta-analysis examined the effects of individual-level alcohol interventions on college students’ knowledge and psychological outcomes at first post-intervention assessment. Data from 34 randomized controlled trials published between 1980 and June 2007 (N = 8,569) were included. Independent raters coded participant characteristics, design and methodological features, and intervention content. Weighted mean effect sizes, using both fixed- and random-effects models, were calculated; positive effect sizes indicated greater improvement in alcohol-related knowledge or psychological outcomes. Compared to controls, alcohol interventions improved participants’ alcohol-related knowledge, attitudes toward drinking, and descriptive norms (vis-à-vis national college students), and intentions to consume alcohol but did not improve alcohol expectancies or self-efficacy. Several sample, study and intervention characteristics moderated the knowledge and psychological outcomes. Implications of these findings are discussed.

Keywords: meta-analysis, college students, alcohol, randomized clinical trials, intervention

Alcohol use and abuse is pervasive on college campuses. Approximately 64% of full-time college students between the ages of 18 and 22 report consuming alcohol in the past month compared with 53% of their non-college/part-time college peers (Substance Abuse and Mental Health Services Administration [SAMHSA], 2006). Heavy episodic (binge) drinking is also common with nearly half of all college students reporting at least one episode of binge drinking in the past two weeks or month (SAMHSA, 2006; Wechsler et al., 2002). Excessive drinking among college students is related to poor academic performance (e.g., Park, 2004), lower post-college wages (e.g., Jennison, 2004), risky sexual behavior, and sexual assault (e.g., Flack et al., 2007). In response to the negative consequences associated with college student drinking, the U.S. Department of Health and Human Services’ (2000) set an objective to reduce binge drinking to 20% by the year 2010.

Given the pervasiveness of young adults’ alcohol consumption and its consequences, colleges have implemented alcohol-related interventions to address key factors (e.g., normative beliefs) associated with alcohol use among college students. These interventions range from campus-wide prevention programs, such as alcohol education for incoming students, to individual-level interventions targeted to specific at-risk groups (e.g., heavy drinkers, fraternity members, athletes). Unfortunately, few campus-wide interventions have been systematically evaluated (see DeJong & Langford, 2002). In contrast, individual-level interventions have been extensively evaluated. Qualitative reviews of the literature support the efficacy of some individual-level interventions (e.g., brief motivational interventions) but not others (e.g., education-only programs) (Larimer & Cronce, 2007; Lewis & Neighbors, 2006; Walters & Bennett, 2000; Walters & Neighbors, 2005). A recent meta-analytic review of 62 randomized controlled trials (RCTs) evaluating individual-level alcohol interventions for college students found a significant reduction in the quantity of alcohol consumed compared with controls that lasted up to 6 months post-intervention (Carey, Scott-Sheldon, Carey, & DeMartini, 2007). Moreover, interventions targeting personal motives for change and/or changing exaggerated normative perceptions predicted greater reductions in alcohol-related problems.

Although qualitative and quantitative reviews support the efficacy of individual-level alcohol interventions on reducing consumption and related problems among college students, the impact of these interventions on hypothesized antecedents of behavior change (e.g., attitudes, intentions) has yet to be reviewed. Still, prior research shows the benefits of modifying antecedents of alcohol use; for example, Lewis and Neighbors (2007) find changes in gender-specific norms mediates the effect of normative feedback on alcohol consumption. Therefore, the current meta-analytic review uses the Theory of Planned Behavior (TPB; Ajzen, 1991) as a guide to examine the efficacy of alcohol-related interventions on antecedents of behavioral change. According to the TPB, salient beliefs and intentions predict subsequent behavior. Three types of beliefs are distinguished: behavioral beliefs (i.e., attitudes), normative beliefs (i.e., subjective norms), and control beliefs (i.e., perceived behavioral control/self-efficacy). Behavioral intentions are considered the central antecedent of behavior, provided that the behavior is under volitional control. Alcohol consumption, however, may occur in contexts that are outside of an individual’s immediate control. In the absence of control, the TPB postulates that control beliefs (i.e., perceived behavioral control), analogous to Bandura’s (1972) concept of perceived self-efficacy, impact behavior directly. In the TPB framework, both behavioral and normative beliefs mediate intentions to engage in a behavior but have no direct behavioral impact, whereas control beliefs may be mediated by intentions (when behavior is under volitional control) or may directly impact behavior.

The predictive validity of the TPB on alcohol-related behaviors has been confirmed in a number of studies with college students (e.g., Collins & Carey, 2007; Dempster, Newell, & Marley, 2005; Johnston & White, 2003). Despite the success of the TPB in predicting alcohol use, some variability remains unexplained. Some have also suggested and examined modifications to the TPB that may account for some of the unexplained variance in alcohol use (e.g., Cooke, Sniehotta, & Schüz, 2007; Kuther, 2002). One suggested modification is the use of specific measures of alcohol-related attitudes, for example, Wall, Hinson, and McKee (1998) used a modified version of the TPB that included alcohol expectancies (in addition to alcohol-related attitudes) and found that gender-specific alcohol expectancies enhance the power of the TPB to predict alcohol consumption among undergraduate students.

A second modification includes more detailed assessment of normative beliefs (Cooke et al., 2007; Kuher, 2002). Deutsch and Gerard (1955) distinguish between two types of norms: (a) injunctive (i.e., what significant others think they ought to do) and (b) descriptive (i.e., what significant others do). The TPB conceptualizes subjective norms as the product of normative beliefs (i.e., whether significant others approve/disapprove of a behavior) and a person’s motivation to comply with those norms (Ajzen, 1991). Because the subjective norm component of the TPB is concerned with others’ approval, it is an injunctive norm. Across a variety of behaviors, Rivis and Sheeran (2003) found that descriptive norms explained more variance in intentions after controlling for TPB predictors (i.e., attitudes, subjective [injunctive] norms, and perceived behavioral control). However, using a modified TPB to examine undergraduate binge-drinking behavior, Cooke et al. (2007) found no evidence of either subjective [injunctive] or descriptive norms as predictors of intentions after controlling for other TPB predictors. Because evidence exists for an extended TPB model, the present meta-analysis also examines the efficacy of alcohol-related interventions in changing expectancies and descriptive norms (relative to friends, local and national college students).

As an extension of our initial meta-analytic review examining behavioral outcomes (Carey et al., 2007), we use meta-analytic techniques to examine the efficacy of individual-level alcohol interventions on theoretically-identified, psychological antecedents of alcohol use among college students. Intervention success at modifying these antecedents was determined from self-reports of alcohol-related knowledge and psychological outcomes (i.e., attitudes and alcohol-related expectancies, descriptive and injunctive norms, self-efficacy, and behavioral intentions). We expected that college students exposed to an intervention would demonstrate greater alcohol-related knowledge and improved psychological outcomes compared with controls.

Method

Search Strategy and Study Selection

Several strategies were used to search for studies: (a) electronic reference databases (PsycINFO, PubMED, ERIC, CRISP, and the Cochrane Library) using a Boolean search strategy with the following abbreviated and full keywords: (alcohol OR drink* OR binge) AND (college OR university) and (intervention OR prevention) and (random* OR control*); (b) reference sections of relevant review or published studies; (c) examining online contents of relevant journals (e.g., Addiction, Addictive Behaviors), and (d) sending requests for published or in press manuscripts to authors. Studies were included if they (a) examined any psychological, behavioral, or educational alcohol-related intervention; (b) sampled undergraduates; (c) used a RCT; (d) assessed knowledge or psychological outcomes; and (e) provided sufficient information to calculate between-group effect size estimates. Consistent with these criteria, studies were excluded if the intervention did not specifically focus on alcohol (e.g., comprehensive drug and alcohol intervention; McCambridge & Strang, 2004), included participants not attending college and the outcomes were not separated by college status (e.g., Monti et al., 1999). When studies reported insufficient details, authors were contacted for additional information. Of the 11 authors contacted, 10 (91%) responded.

Studies that fulfilled the search criteria and were available by June 1, 2007 were included. In some cases, several publications provided information about the same intervention or outcomes (e.g., baseline and follow-up results in separate papers). In these instances, information from multiple publications reporting on the same sample was pooled for content coding, and effect sizes were calculated separately for each measurement occasion. When more than one control or comparison condition was used (e.g., standard education and wait-list), the control condition with the least contact (e.g., wait-list) was used as the comparison condition. Using these criteria, 34 manuscripts with 54 separate interventions (k) qualified for the meta-analysis (Figure 1).

Figure 1.

Figure 1.

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Selection process for study inclusion in the meta-analysis.

Study Outcomes

Effect sizes were calculated for alcohol-related knowledge and psychological outcomes; the latter refers to attitudes toward drinking, alcohol-related expectancies, normative beliefs (descriptive norms: friends, students at the local institution, and college students nationally; injunctive norms), alcohol-related self-efficacy, and intentions to reduce consumption.

Content Coding and Reliability

Two researchers independently coded overall study information (e.g., publication year), sample characteristics (e.g., ethnicity, gender, age), target group (e.g., heavy drinkers, Greek members, freshmen), design and measurement specifics (e.g., recruitment method, number of follow-ups), and content of control and intervention condition(s) (e.g., number of sessions, intervention content). Twenty studies were randomly selected to evaluate interrater reliability. For the categorical dimensions, raters agreed on 40% to 100% of the judgments (mean Cohen’s ϰ = .65). Reliability for the continuous variables was calculated using the intraclass correlation coefficient (ϱ); ϱ ranged from 0.81 to 1.00, with an average ϱ = 0.98 across categories (Mdn = 1.00). Because all studies were double-coded, disagreements were resolved through discussion.

Effect Size Derivation

Effect sizes (d) were calculated as the mean differences between the treatment and control group divided by the pooled standard deviation (Cohen, 1988). If the pooled SD was unavailable or could not be derived from the reported statistics, the denominator was another form of SD (e.g., the SD of the paired comparisons). When means and standard deviations were unavailable, other statistical information (e.g., t- or F-values) was used (Lipsey & Wilson, 2001). If a study reported dichotomous outcomes (e.g., frequencies), we calculated an odds ratio and transformed it to d using the Cox transformation (Sánchez-Meca, Marín-Martínez, & Chacón-Moscoso, 2003). If no statistical information was available (and could not be obtained from the authors) and the study reported no significant between-group differences, we estimated that effect size to be zero (Lipsey & Wilson, 2001). In calculating d, we controlled for baseline differences when pre-intervention measures were available, and effect sizes were corrected for sample size bias (Hedges, 1981). We calculated multiple effect sizes from individual studies when they had more than one outcome, multiple intervention conditions, or when outcomes were separated by sample characteristics (e.g., gender). Effect sizes calculated for each intervention and by sample characteristic were analyzed as a separate study (Lipsey & Wilson, 2001). When a study contained multiple measures of the same outcome (e.g., intentions to limit consumption, intentions to limit maximum consumption), the effect sizes were averaged. A positive sign indicated that the treatment group improved compared to controls. Effect sizes were calculated using DSTAT 2.0 (Johnson & Wood, 2006).

Statistical Analysis

Weighted mean effect sizes, d+s, were calculated using fixed- and random-effects procedures (Lipsey & Wilson, 2001), such that individual studies’ effect sizes were weighted by the inverse of their fixed- or random-effects variance. The homogeneity statistic, Q, was computed to determine whether each set of d+s shared a common effect size; a significant Q indicates a heterogeneous relationship. To further assess heterogeneity, the I2 index was calculated, to assess the proportion of variability in a set of effect sizes attributable to true heterogeneity (Higgins & Thompson, 2002; Huendo-Mendina, Sanchez-Meca, Marin-Martinez, & Botella, 2006). Percentages of 25%, 50%, and 75%, are considered low, medium, and high heterogeneity respectively (Higgins, Thompson, Deeks, & Altman, 2003). If the 95% uncertainty interval around the I2 index includes a zero, the set of effect sizes are considered homogeneous. To explain variability in the effect sizes, the relation between study characteristics and the magnitude of the effects was examined using a modified least squares regression analysis. The I2 index and corresponding 95% uncertainty intervals were calculated using published formulas (Higgins & Thompson, 2002). Analyzes were conducted in Stata 10.0 (StataCorp, 2007) using macros provided by Lipsey and Wilson (2001).

Results

Descriptive Outcomes

Table 1 provides study and participant characteristics, research design, and intervention and comparison condition details. Of the 34 RCTs included in the meta-analysis, all were published (or in press) in English-language journals between 1980 and 2007. Studies were typically conducted at large public universities in the U. S. northeast or southeast and targeted heavy drinkers. The modal participant was a Caucasian first year student (M age = 19.78, SD = 1.79) who volunteered for the study. The interventions were typically conducted in groups, and met for a median of 33 to 45 minutes. Intervention content usually included alcohol education, normative comparisons, and moderation strategies. A wait-list/no treatment control was the typical comparison condition. Studies had a median of 1 post-intervention assessment (range = 1 to 4). Due to the small number of studies with multiple follow-ups (i.e., 12 of 34 studies), we focused on the first assessment only. Complete summary statistics are reported in Table 2.

Table 1.

Study, sample, and intervention characteristics of the 34 studies included in the meta-analysis.

Intervention Details
Study Locationa N F W Control No. of Interventions Delivery Sessions Doseb
Barnett et al. (2007) US-NE, L private 227 51% 76% Relevant, not matched 3 I/I/C 1/2/2 60/70/85
Bersamin et al. (2007) US-SW, L public 622 52% 30% WL/NT 1 C NR NR
Borsari & Carey (2000) US-NE, L private 60 57% 88% WL/NT 1 I 1 60
Corbin et al. (2001) US-SE, L public 87 50% NR Relevant, not matched 1 G 3 255
Curtin et al. (2001) US-SE, L public 76 100% 95% WL/NT 5 I 2 60
Czuchry et al. (1999) US-SW, L private 187 68% NR Irrelevant, matched 1 G 1 65
Darkes & Goldman (1993) US-SE, L-public 74 0% 95% WL/NT 2 G 3 315
Darkes & Goldman (1998) US-SE, L-public 67 0% 87% Irrelevant, not-matched 2 G 4 240
Faris & Brown (2003) US-SE, L public 84 53% 90% Irrelevant, matched 2 G 2 25.5
Geisner et al. (in press) US-NW, L public 177 70% 49% WL/NT 1 M 1 10
Gonzalez (1980) US-SE, L public 126 NR NR Irrelevant, matched 1 G 1 240
Graham et al. (2004) US-NE, L public 634 55% 85% WL/NT 1 G 2 70
Jewell & Hupp (2005) US-MW, L public 251 79% NR Irrelevant, not matched 3 G 1 5/15/15
Jewell et al. (2004) US-MW, L public 163 75% NR Education 2 G 1 15
Keillor et al. (1999) US-SW; L public 33 0% NR Relevant, matched 1 G 2 180
Kulick & Rosenberg (2001) US-MW, L public 108 70% 87% Irrelevant, matched 2 G 1 51/53
Lysaught et al. (2003) US-NE; colleges 60 53% 78% WL/NT 1 I 1 10
Meacci (1990) US-NE, L public 135 45% NR WL/NT G 13 650
Intervention Details
Study Locationa N F W Control No. of Interventions Delivery Sessions Doseb
Meier (1988) US-NE, L public 71 72% NR Irrelevant, matched 2 C/P 1 40
Murgraff et al. (2007) UK 573 70% NR WL/NT 1 P 1 10
Musher-Eizenman & Kulick (2003) US-MW, L public 70 100% 94% Relevant, matched 2 G 3 315
Neal & Carey (2004) US-NE, L private 92 55% 85% Relevant, matched 2 G 1 40
Neighbors et al. (2004) US-NW, L private 252 59% 80% WL/NT 1 C 1 5
Neighbors et al. (2006) US-MW, M private 214 56% 98% WL/NT 1 C 1 5
Peeler et al. (2000) US-NW; L public 258 62% 84% Irrelevant, matched 1 G 13 650
Reis et al. (2000) US-MW; L public 643 61% 64% WL/NT 2 C/G NR/1 NR/50
Saitz et al. (2006) US-NE; L private 650 55% 81% Education 1 C 1 30
Sharmer (2001) US-NE; M public 360 60% NR WL/NT 2 C/G 1/3 60/180
Stamper et al. (2004) US-SE; L public 1152 61% 75% Relevant, matched 1 G 1 55
Trockel et al. (2002) US-MW; L public 184 72% NR WL/NT 1 G 1 40
Walters et al. (2000) US-SW; L public 43 40% 62% WL/NT 2 M/G&M 1/2 10/130
Walters et al. (2007) US-SW; S & L public 106 48% 73% WL/NT 1 C 1 125
Werch et al. (2000) US-SE; L public 634 64% 83% WL/NT 1 M 3 15
Wiers et al. (2005) Netherlands; S vocational college & M public 96 50% 88% Relevant, not matched 1 G 1 210
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Note. N, number of consenting participants; F, proportion female; W, proportion White; WL/NT, wait-list/no treatment control; C, computer; G, group; I, individual; P, printed materials; M, mailed; NR, not reported.

a

Location refers to university location, including size and type (public, private, vocational college). US = United States; UK = United Kingdom; NE = Northeast; SE = Southeast; MW = Midwest; SE = Southeast; SW = Southwest; S = Small; M = Medium; L = Large.

b

Estimated number of minutes of intervention content excluding measurement.

Table 2.

Description of studies, samples, intervention, and control conditions.

Study characteristics (k = 34)
Year of publication
 Mdn (Range) 2003 (1980–2007)
Year of data collection
 Mdn (Range) 2000 (1977–2006)
Region
 US Northeast 26%
 US Southeast 24%
 US Midwest 21%
 US Southwest 15%
 US Northwest 9%
 Non-US region 6%
Targeted Intervention 68%
Target Group (k = 23)
 Heavy drinkers 46%
 Drinkers 22%
 College freshman 17%
 Alcohol violators 4%
 Depression (BDI >14) 4%
 Females 4%
 Males 2%
Type of Institution (k = 32)
 Private university 20%
 Public university 80%
Institution Size (k = 32)
 <6000 students 9%
 6000 – 10,000 students 11%
 >10,000 students 80%
Research design and implementation (k = 34)
Recruitment procedures
 Volunteered 85%
 Recruited 9%
 Mandated 6%
Randomization
 Randomized individuals 62%
 Matched, randomized 18%
 Randomized groups 21%
No. post-intervention assessments
 M (SD) 1.47 (0.71)
 Mdn 1
 Range 1 – 4
First post-intervention assessment
 M weeks (SD) 2.13 (3.23)
 Mdn 0
 Range 0 – 13
Intervention characteristics (continued)
Computer/mailing interventions (k = 8)
 No. sessions (Mdn) 1
 No. minutes (Mdn) 35
 No. facilitators (Mdn) 0
 No. participants (Mdn) 1
Intervention content tailored
 Individual 48%
 Group 15%
 None/NR 37%
*Facilitators (k = 43)
 Peers 5%
 Paraprofes sionals 16%
 Professional-in-training 44%
 Professionals 21%
 None 28%
*intervention content
 Alcohol/BAC education 80%
 Normative comparisons 41%
 Moderation strategies 39%
 Feedback on consumption 33%
 Feedback on expectancies/motives 30%
 Goal-setting 30%
Sample characteristics (k = 34)
Sample size (N)
 Total 8,569
 M (SD) 252.03 (257.87)
 Mdn 149
Year in school (M%)
 Freshman
 Sophomore 20%
 Junior 8%
 Senior 4%
Age in years (k = 25)
 M (SD) 19.78 (1.79)
 Range 18 – 26
% women (k = 33)
 M (SD) 0.56 (0.22)
% Greek Members (k = 7)
 M (SD) 0.25 (0.12)
% White
 M (SD) 0.79 (0.16)
% Black
 M (SD) 0.07 (0.05)
% Hispanic
 M (SD) 0.07 (0.08)
% Asian
 M (SD) 0.14 (0.16)
Intervention characteristics (k = 54)
No. of Intervention Conditions
 M (SD) 2.02 (1.05)
 Mdn (Range) 2.00 (1 – 5)
Level of intervention
 Individual 13%
 Group 57%
 Computer/web 13%
 Mailing/email 4%
 Individual 13%
Individual interventions (k = 10)
 No. sessions (Mdn) 2
 No. minutes (Mdn) 33
 No. facilitators (Mdn) 1
 No. participants (Mdn) 1
Group interventions (k = 33)
 No. sessions (Mdn) 1
 No. minutes (Mdn) 45
 No. facilitators (Mdn) 1
 No. participants (Mdn) 6
Intervention characteristics (continued)
 Focus on high-risk situations 26%
 Feedback on problems 24%
 Skills training 17%
 Expectancy challenge 13%
 Values clarification 9%
 Decisional balance exercise 6%
 Breathalyzer feedback 2%
Comparison characteristics (k = 54)
Type of control/comparison
 Wait-list/no treatment 46%
 Irrelevant content, time matched 19%
 Relevant content, matched 11%
 Irrelevant content, not matched 9%
 Relevant content, not matched 9%
 Education-only 6%
Active comparison conditions (k = 29)
 No. sessions (Mdn) 1
 No. minutes (Mdn) 40
 No. facilitators (Mdn) 1
 No. participants (Mdn) 6
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Note. k = number of interventions; NR, not reported.

*

Multiple categories were possible.

Intervention Impact

As detailed in Table 3, alcohol interventions improved alcohol-related knowledge (d+ = 0.25, CI.95 = 0.16, 0.34), attitudes toward drinking (d+ = 0.19, CI.95 = 0.10, 0.28), normative beliefs regarding local students (d+ = 0.35, CI.95 = 0.26, 0.45) and national college students (d+ = 0.31, CI.95 = 0.19, 0.43), and intentions to reduce alcohol consumption (d+ = 0.09, CI.95 = 0.02, 0.15) relative to controls. No improvements emerged for the other variables at first post-intervention assessment. These effects were parallel using either fixed- or random-effects assumptions (except for norms for local student groups and intentions). The hypothesis of homogeneity was rejected for alcohol-related knowledge, attitudes toward alcohol consumption, normative comparisons regarding local college students, normative comparisons regarding national college students, and intentions; examination of the I2 index confirmed moderate to high levels of heterogeneity. Moderator tests were conducted on each effect separately to examine whether study features related to the variability. Consistent with meta-analytic procedures (e.g., Hoffman, Papas, Chatkoff, & Kerns, 2007), moderator tests were conducted only if the dependent variable consisted of a minimum of six effect sizes. Therefore, moderator tests were not conducted for normative beliefs regarding local or national students.

Table 3.

Weighted Mean Effect Sizes and homogeneity Statistics for the 54 alcohol-reduction interventions at first measurement occasion.

Weighed mean d (95% CI) Homogeneity of effect sizes I2 (95% CI)
Outcome k Fixed effects Random effects Q P
Knowledge 10 0.25 (0.16, 0.34) 0.49 (0.07, 0.90) 162.77 <.001 94% (92%, 96%)
Attitudes 14 0.19 (0.10, 0.28) 0.21 (0.04, 0.39) 46.50 <.001 72% (52%, 84%)
Alcohol Expectancies 17 0.05 (−0.05, 0.14) 0.05 (−0.05, 0.14) 15.19 .511 0%
Normative Beliefs
 Friends 3 0.03 (−0.10, 0.16) 0.04 (−0.13, 0.22) 3.12 .210 36% (0%, 79%)
 Local college students 4 0.35 (0.26, 0.45) 0.25 (−0.07, 0.58) 27.32 <.001 89% (75%, 95%)
 National college students 5 0.31 (0.19, 0.43) 0.39 (0.10, 0.67) 18.96 <.001 79% (50%, 91%)
 Injunctive norms 2 0.08 (−0.10, 0.25) 0.08 (−0.10, 0.25) 0.10 .75 0%
Self-Efficacy 9 0.01 (−0.08, 0.11) 0.02 (−0.10, 0.13) 8.99 .343 11% (0%, 52%)
Intentions 24 0.09 (0.02, 0.15) 0.06 (−0.04, 0.16) 44.29 .005 48% (16%, 68%)
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Note. k, number of interventions; CI, confidence interval.

Moderators of Intervention Impact on Knowledge, Attitudes, National Descriptive Norms, and Intentions

Overview.

Univariate regression analyses were conducted to examine potential moderators of knowledge, attitudes, and intentions. Specific moderators examined were participant characteristics (gender, ethnicity, age), target group (heavy drinkers), recruitment method (volunteered), delivery format (group vs. non-groups), tailoring of the intervention, intervention dose (total minutes of contact excluding measurement) and content (see Table 2 for list of intervention components). Multivariate regressions analyses were not conducted due to the small sample of studies.

Moderators of change in alcohol-related knowledge (k = 10).

Interventions were more successful at increasing alcohol-related knowledge if the content was tailored to the individual and/or group (β = 0.35, p =.02) and included values clarification (β = 0.54, p <.001) or moderation strategies (β = 0.52, p <.001) but did not focus on high risk situations (β = −0.44, p <.01).

Moderators of change in attitudes toward drinking (k = 14).

Interventions were more successful at improving attitudes toward drinking if the content was tailored to the individual and/or group (β = 0.36, p = .01), and provided values clarification exercises (β = 0.54, p <.001). Interventions were less successful at improving attitudes if they were delivered in a group (β = −0.32, p =.03) or focused on high risk situations (β = −0.42, p <.01).

Moderators of change in intentions (k = 24).

Only a single moderator of intentions to reduce alcohol consumption was found. Compared with controls, interventions were more successful at improving intentions when they sampled more men (β = −0.44, p <.01).

Discussion

The current meta-analytic review examined 34 RCTs evaluating 54 separate alcohol interventions among 8,569 college students. Our review was guided by a modified version of the TPB, evaluating alcohol-related expectancies and multiple normative beliefs (descriptive and injunctive norms). To our knowledge, this is the first meta-analysis to examine the efficacy of alcohol-related interventions on known antecedents of behavioral change (i.e., knowledge and psychological outcomes) for college drinkers. Our findings support the following conclusions.

First, we found that individual-level alcohol interventions for college students to be efficacious in improving alcohol-related knowledge, attitudes, descriptive norms regarding local and national college students, and intentions to reduce alcohol consumption at first post-intervention assessment. The magnitude of results was small to medium across study outcomes (d+s ranging from 0.09 to 0.35), consistent with other meta-analytic reviews examining antecedents of health-related behavioral change (e.g., Portnoy, Scott-Sheldon, Johnson, & Carey, 2008; Prendergast et al., 2001). Overall, these results support the capacity of alcohol-related interventions to improve hypothesized mediators of behavioral change (see Ajzen, 1981; Bandura, 1997; Fishbein & Ajzen, 1975; Fisher & Fisher, 1992). Moreover, given that the interventions tended to be brief (i.e., less than 45 minutes of contact), finding improvement in knowledge, attitudes, norms, and intentions is striking. In contrast, we did not find that these brief interventions improved self-efficacy relative to controls. Lack of change in self-efficacy is not surprising given the absence of intervention components known to improve self-efficacy such as experience successfully practicing the behavior (Bandura, 1972).

Second, we examined the impact of alcohol-related interventions on variables suggested as supplements to those in the TPB. Several authors have found that specific measures of attitudes (e.g., alcohol-related expectancies), predict intentions and behavior better than more global measures of alcohol-related attitudes (e.g., Kuther, 2002; Wall et al., 1998). Moreover, positive alcohol expectations have been repeatedly shown to be related to alcohol consumption whereas the relation between negative expectations and alcohol consumption is less clear (see Kuther, 2002 for review). In the current meta-analytic review, alcohol-related interventions did not improve alcohol expectancies but did improve global alcohol attitudes relative to controls. Lack of improvement may be expected given that (a) only half of the studies assessing alcohol expectancies included an expectancy component in the intervention (feedback or challenge; 9 out of 17 studies) and (b) composite measures of expectancies were typically used (13 out of 17 studies). Supplemental analyses examining the weighted mean effect sizes by presence of an expectancy component revealed no difference between studies that did or did not include an expectancy component, QB (1) = 0.62, p = .43. Because most studies used the Alcohol Expectancy Questionnaire (Brown, Christiansen, & Goldman, 1987) that assesses positive alcohol expectancies, we were unable to determine the impact of the intervention on positive and negative expectancies separately.

Others have suggested including descriptive norms (e.g., what friends, local college students, or national college students do) as an additional predictor in the TPB (Cooke et al., 2007; Kuher, 2002). Because so few studies included in this meta-analysis reported descriptive and/or injunctive norms, we were unable to fully evaluate the impact of the alcohol interventions on various types of normative comparisons. We did find individual-level alcohol interventions for college students successfully improved estimates of national college student norms (descriptive norm) but additional studies are necessary to determine whether such interventions can change injunctive and other types of descriptive (i.e., friends and local college students) norms. Moreover, examining changes in these antecedents of drinking may be particularly important among college students given recent meta-analytic findings (Rivis & Sheeran, 2003) revealing a stronger relation between descriptive norms and intentions (after controlling for other TPB predictors) among younger samples (vs. older samples) and for risky health behaviors (i.e., heavy drinking; vs. health-promotion behaviors).

Finally, several sample, study, and intervention characteristics moderated the impact of the intervention on alcohol-related knowledge, attitudes, and intentions to reduce alcohol consumption:

  1. First, consistent with literature and meta-analytic reviews examining health behavior change (see Noar, Benac, & Harris, 2007; Skinner, Campbell, Rimer, Curry, & Prochaska, 1999), interventions improved alcohol-related knowledge and attitudes when interventions were tailored to the individual. Individually tailored health message are more likely to induce change because they are more likely to be read, remembered, understood, perceived to be interesting, and thought to be credible (Kreuter & Holt, 2001). Positive benefits of message tailoring on knowledge and attitudes should lead to greater intentions to reduce future alcohol consumption.

  2. Second, although group-based rather than individually-delivered interventions are more cost effective, more improvement in alcohol-related attitudes occurred when the intervention was delivered to individuals (either in-person or via computer/mailing) rather than groups. If alcohol consumption serves to maintain a socially desirable image (Sharp & Getz, 1996), then delivering alcohol-related interventions to a group of college students may elicit discussions that serve to enhance an individual’s favorable self-image thereby reinforcing negative alcohol attitudes.

  3. Third, when interventions included a values clarification component, knowledge and attitudes were improved compared with controls. Research on the motivational bases of attitudes suggests that people hold and express attitudes for different reasons (e.g., Katz, 1960; Smith, Bruner, & White, 1956). Attitudes toward drinking alcohol may be based on value-expressive motives (e.g., an individual may consume alcohol in order to shape his/her self-image and gain approval among peers). To change value-expressive attitudes, one must become dissatisfied with the self in comparison, for instance, with one’s peer group (Katz, 1960). By providing values clarification exercises, an individual may reassess his/her attitudes.

  4. Fourth, interventions including information regarding high-risk situations were less successful at improving knowledge and attitudes. Providing information regarding high-risk situations such as campus parties or 21st birthday celebrations is unlikely to change knowledge or attitudes because college students are already knowledgeable about the effects of alcohol in these situations (e.g., Gonzalez & Broughton, 1994).

  5. Fifth, when moderation strategies were included in the intervention, alcohol-related knowledge improved. Many modification strategies represent methods for avoiding high blood alcohol concentrations (BAC). Thus, discussion of these strategies, especially when personalized, may reinforce understanding of alcohol absorption and metabolism, alcohol content of beverages, and factors affective BAC.

  6. Finally, interventions improved alcohol-related intentions when samples included more men. Male college students generally experience more alcohol-related problems and drink more heavily than do women (American College Health Association, 2007; Wechsler et al., 2002). Perhaps college men are more likely to formulate alcohol-reduction intentions because they have more reasons and opportunities to decrease their consumption than do women.

Study Limitations

As with any meta-analytic review, several factors should be considered when interpreting these findings. First, retrieval of studies may have been hindered by authors’ use of keywords, publication source (i.e., not indexed in an electronic database), and researchers’ lack of response (Matt & Cook, 1994). Therefore, some studies may have inadvertently been excluded from the meta-analysis. Second, few published RCTs for college students report knowledge or psychological outcomes. Although there are nearly twice as many RCTs reporting behavioral outcomes (see Carey et al., 2007), we found only 34 studies evaluating antecedents of behavioral change. The limited size of the literature constrained analyses such that full evaluation of some antecedents of behavioral change (i.e., normative beliefs regarding friends and injunctive norms) was not possible. Third, we focused our analyses on immediate post-intervention efficacy because data from longer follow-ups were not available. To fully assess the effects of alcohol-related interventions on antecedents of behavioral change, multiple measurements of hypothesized mediators would be optimal. Advances in statistical modeling provide tools to track impacts of antecedents/mediators on outcomes over time (Stout, 2007).

Conclusion

Individual-level interventions for college students improved alcohol-related knowledge, attitudes, normative beliefs regarding national college students, and intentions to reduce alcohol consumption; however, no changes in alcohol expectancies or self-efficacy were seen at first post-intervention assessment. Longer follow-up assessments are needed to evaluate the maintenance of intervention effects on antecedents of behavioral change. As the number of RCTs examining both psychological and behavioral outcomes for increases, future meta-analytic reviews should examine the causal links between antecedents of behavioral change and alcohol consumption.

Acknowledgments

This work was supported by National Institute on Alcohol Abuse and Alcoholism Grants K02-AA15574 and R01-AA12518 to Kate B. Carey. The authors thank Jennifer Elliott for her assistance with this project. We thank the following study authors for providing additional intervention or statistical information: Nancy P. Barnett, PhD, William R. Corbin, PhD, Lisa Curtin, PhD, John Darkes, PhD, Dara R. Musher-Eizenman, PhD, Jeremy Jewell, PhD, Richard Saitz, MD, Scott T. Walters, PhD, Chudley C. Werch, PhD, and Reinout W. Wiers, PhD.

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