Skip to main content
PMC home page
Journal of Studies on Alcohol and Drugs logoLink to Journal of Studies on Alcohol and Drugs
. 2016 Dec 11;78(1):5–13. doi: 10.15288/jsad.2017.78.5

Does Alcohol Contribute to College Men’s Sexual Assault Perpetration? Between-and Within-Person Effects Over Five Semesters

Maria Testa a,*, Michael J Cleveland b
PMCID: PMC5148750  PMID: 27936357

Abstract

Objective:

The current longitudinal study was designed to consider the time-varying effects of men’s heavy episodic drinking (HED) and drinking setting attendance on college sexual assault perpetration.

Method:

Freshman men (N = 992) were recruited in their first semester and completed online measures at the end of their first five semesters. Using multilevel models, we examined whether men with higher frequency HED (or party or bar attendance) were more likely to perpetrate sexual assault (between-person, Level 2 effect) and whether sexual assault perpetration was more likely in semesters in which HED (or party or bar attendance) was higher than each individual’s average (within-person, Level 1 effect).

Results:

The between-person effect of HED on sexual assault was not significant after accounting for the between-person effects of antisocial behavior, impersonal sex orientation, and low self-control. The within-person effect of HED on sexual assault perpetration was not significant. However, models substituting frequency of party attendance or bar attendance revealed both between- and within-person effects. The odds of sexual assault were increased for men with higher bar and party attendance than the sample as a whole, and in semesters in which party or bar attendance was higher than their own average. Supplemental analyses suggested that these drinking setting effects were explained by hookups, with sexual assault perpetration more likely in semesters in which the number of hookups exceeded one’s own average.

Conclusions:

Findings point toward the importance of drinking contexts, rather than drinking per se, as predictors of college men’s sexual assault perpetration.

Alcohol is believed to play a significant role in college sexual assault, with half of sexual assault incidents occurring when the perpetrator, the victim, or both have been drinking (Abbey, 2002, 2011). College women’s heavy episodic drinking (HED) increases risk for sexual assault victimization, particularly incapacitated rape (Mohler-Kuo et al., 2004; Parks et al., 2008; Testa & Livingston, 2009). Because men and women drink together, men’s drinking may also contribute to sexual assault; however, evidence has been limited. The current study examined the independent role of college men’s drinking on sexual assault perpetration over the first five semesters of college. We considered whether men’s HED predicts sexual assault perpetration (between-person effect) independent of whether time-varying HED predicts when risk of perpetration is elevated (within-person effect). Similarly, we considered the between- and within-person effects of drinking contexts (parties, bars) on sexual assault.

Effects of alcohol on sexual assault perpetration

Men’s HED is thought to contribute to sexual assault perpetration via alcohol’s acute, pharmacological effect on physical and sexual aggression (see Crane et al., 2015; Ito et al., 1996, for reviews of this experimental literature). This acute effect is consistent with the Alcohol Myopia Model (see Giancola et al., 2010), which posits that intoxication narrows attention to more salient, typically instigatory cues (e.g., sexual arousal, Simons et al., 2016), while impairing the ability to attend to less salient inhibitory cues (e.g., woman’s reluctance). In natural drinking settings, pharmacological effects may be enhanced by alcohol expectancy effects (Abbey, 2011), i.e., the belief that alcohol enhances sex (George et al., 2000) and that drinking women are more interested in sex (George et al., 1995).

Consistent with these presumed mechanisms, cross-sectional studies reveal a positive association between college men’s drinking and sexual assault perpetration (Abbey, 2011; Abbey et al., 2014). Compared with nonperpetrators, perpetrators drink more heavily (Koss & Gaines, 1993; Locke & Mahalik, 2005; Tuliao & McChargue, 2014) and more frequently within dating and sexual situations (Abbey et al., 2001; Schwartz et al., 2001). Although the positive association between drinking and sexual assault is consistent with a pharmacological effect, heavy drinking men also possess characteristics associated with sexual assault perpetration (e.g., impulsivity, delinquency; Abbey et al., 2006; Thompson et al., 2011). Within the influential Confluence Model (Malamuth et al., 1991; 1995), sexual assault perpetrators are characterized by impersonal sexuality (promiscuity) and hostile masculinity, robust predictors of sexual assault both independently (Abbey & McAuslan, 2004; DeGue & DiLillo, 2004; Murnen et al., 2002) and synergistically (Jacques-Tiura et al., 2007). Men’s alcohol use adds modest predictive power to the Confluence Model (Abbey et al., 2006; Parkhill & Abbey, 2008). For example, Abbey et al. (2011) found an indirect effect of HED on sexual assault via increased impersonal sex and sexual misperception.

Identifying men at risk for sexual assault is important for targeting sexual assault prevention. However, few studies have used longitudinal designs to determine whether college men’s drinking increases the risk of later perpetration. Gidycz and colleagues (2007) found a relationship between baseline HED and later perpetration that became nonsignificant after accounting for prior sexual assault. Abbey and McAuslan (2004) found that college students who perpetrated at more than one point in time were heavier drinkers than those who perpetrated only once or not at all. A third longitudinal study failed to find either a prospective or a cross-sectional relationship (Loh et al., 2005).

In a prospective study of college men, Thompson et al. (2011) found an indirect effect of Time 1 (T1) HED on T2 sexual assault mediated via T1 perceived peer norms supporting sexual aggression. A subsequent path model revealed that T1 HED predicted T3 sexual assault via T2 fraternity membership, T2 peer norms supportive of sexual aggression, and T2 HED (Kingree & Thompson, 2013). Taken together, analyses suggest indirect effects of HED on sexual assault via drinking contexts and social networks that facilitate perpetration. Person-centered analyses were used to predict high, increasing, and decreasing trajectories of perpetration relative to no sexual assault over all 4 years. Changes in sexual assault over time were associated with changes in impulsivity, sexual compulsivity, peer norms, and hostile masculinity but not with changes in drinking (Thompson et al., 2013; 2015). However, men who never perpetrated drank less than men with increasing, decreasing, or high perpetration trajectories (i.e., there was a between-person effect of HED on sexual assault) (Thompson et al., 2015).

Effect of drinking settings on sexual assault perpetration

The indirect effects of HED on sexual assault via fraternity membership and peer norms (Kingree & Thompson, 2013) and via sexual misperception and impersonal sex (Abbey et al., 2011) suggest the potential importance of drinking settings. HED cannot lead to sexual assault unless it occurs in a context in which a potential victim is present. College students typically drink at parties and bars (Clapp et al., 2006; Harford et al., 2002), settings that facilitate sexual activity and attract people seeking to “hook up” (Norris et al., 1996; Owen et al., 2011). These settings include women who are sexually vulnerable because of their intoxication (Graham et al., 2014; Testa & Livingston, 2009) and may offer peer support for sexual aggression (Swartout, 2013). Not surprisingly, college women’s party attendance is associated with sexual victimization (Cranney, 2015; Franklin et al., 2012). Similarly, frequency of attending bars increases women’s risk of sexual victimization independent of drinking (Pino & Johnson-Johns, 2009), and men frequently make sexual advances toward women in bars (Thompson & Cracco, 2008). Men’s attendance at parties and bars may increase the risk of sexual assault. However, to our knowledge, the effects of drinking setting on sexual assault perpetration previously had not been examined.

Present study

The current study examined the role of men’s alcohol use on sexual assault perpetration over five semesters of college. Most previous studies have used a between-participant approach to examine whether heavier drinking men are more likely to perpetrate sexual assault. However, multilevel models with repeated measures permit disaggregating within-person from between-person effects (Curran & Bauer, 2011), allowing testing of the hypothesis that sexual assault will be more likely during semesters in which men drink more than their average amount relative to semesters of less-than-average drinking (i.e., a within-person effect). A positive within-person effect of HED on sexual assault perpetration indicates that changes in HED coincide with the odds of sexual assault, independent of the association of sexual assault with being a heavier drinker (i.e., the between-person effect). Because the settings in which alcohol is consumed are also associated with sexual assault, we considered the effects of party and bar attendance as independent between- and within-person predictors, hypothesizing positive associations for both effects.

We hypothesized that men who reported more frequent HED, on average, would have increased odds of sexual assault perpetration over time (a between-person effect). We included as between-person covariates individual difference variables associated with sexual assault perpetration: impersonal sex orientation, hostility toward women, antisocial behavior (Abbey & Jacques-Tiura, 2011; Abbey & McAuslan, 2004), and self-control, which is negatively associated with aggression in a variety of contexts (DeWall et al., 2011). These covariates allowed us to determine whether there is a between-person effect of drinking or drinking settings independent of personality factors.

Method

Participants and procedures

All procedures were approved by the university’s institutional review board. Participants included 994 freshman men who entered a large Northeastern public university in fall 2011. Sample composition was 72.2% White; 3.9% African American; 5.2% Hispanic; 14.6% Asian American; and 4.1% mixed, other, or not reported, slightly overrepresenting White students from the class of 2011 overall (65.5% White). Most lived on campus (67.0%), slightly less than the proportion for all freshmen (74.0%).

Participants were recruited by email to participate in a study of college men’s behaviors and attitudes over five semesters of college. All first-semester freshman men who resided in the United States, allowed their university email address to be included in the student directory (about 85% of the class), and were 18 or 19 on November 1, 2011, were invited. Nonresponders were sent up to five email reminders and a letter to their permanent address at Thanksgiving. Email invitations included a link to a secure site, which required that participants provide a student ID number to enter. After providing online informed consent, respondents were directed to the 30-minute survey. On completion, they were asked to provide contact information (preferred email address, phone number). The response rate for the initial recruitment was 68.9% (994 /1,442).

For the next four semesters, in November or April, participants were sent a similar email invitation containing a link to the survey, followed by up to five email reminders and phone calls, if necessary. Men were able to continue participation if they left the university; however, such men accounted for no more than 8% of the participants (at T5).

The participants were compensated $25 in Campus Cash (or check) for completing Waves 1, 3, and 5 and $10 for completing the briefer spring semester surveys at Waves 2 and 4. To encourage completion, each semester there was a lottery in which one student was selected to win $400.

Measures

Time-varying indicators of HED, sexual assault perpetration, bar and party attendance, and hookups were assessed each semester (T1–T5). Between-person indicators (self-control, antisocial behavior, impersonal sex orientation, hostility toward women) were assessed at T1.

Sexual aggression perpetration.

This variable was assessed using a 16-item version of the Sexual Experiences Survey (SES) that measures unwanted contact, attempted intercourse, and intercourse using verbal, physical force, and incapacitation tactics (Abbey et al., 2007; Testa et al., 2015a). Men were asked how many times they had done each behavior over the past semester (never, once, or 2 or more times). They were classified as perpetrators if they reported one or more items.

Heavy episodic drinking.

HED was assessed with the item, “During the past semester, how often did you drink 5 or more drinks in a row in a single occasion (e.g., in the same evening)?” Seven response categories ranged from 0 (never) to 6 (3 or more days per week). Students who indicated in response to an earlier question that they never drank skipped the questions and were assigned a 0.

Bar and party attendance.

Men were asked, “During the past semester, how often did you attend a party?” and “… how often did you go to a bar or club?” Frequency was assessed using the same 7-point scale used to assess drinking.

Hookups.

Respondents were provided the definition of a hookup (“a romantic or sexual encounter between two people who are strangers, friends, or acquaintances. Some physical interaction is typical but it may or may not involve sexual intercourse”) and asked how many hookups they had had in the past semester. At Waves 1 and 3, hookups were assessed with an open-ended scale and responses were Winsorized to the 95th percentile (Reifman & Keyton, 2010). At others waves, hookups were assessed with a 6-point scale ranging from 0 to 5 or more.

Self-control (T1).

This variable was assessed using the 13-item Brief Self-Control Scale (Tangney et al., 2004). Items (e.g., “Sometimes I can’t stop myself from doing something even if I know it is wrong”) were assessed on 5-point scales ranging from 1 (not at all) to 5 (very much) and summed (α = .82).

Antisocial behavior (T1).

Antisocial behavior was assessed using the 18-item Antisocial Behavior Checklist, adolescent version (Zucker, 2005), which includes items such as “cursed at a teacher,” “skipped school,” or “beat someone up.” Four response options included never, rarely, sometimes, and often. Responses were assigned scores from 0 to 3 and summed (α = .82).

Hostility toward women (T1).

This scale consisted of 10 items such as, “I am easily angered by women” and “I feel that many times women flirt with men just to tease them or hurt them” (Lonsway & Fitzgerald, 1995). These were rated on 7-point scales ranging from 1 (strongly disagree) to 7 (strongly agree) and summed (α = .84).

Impersonal sex (T1).

This composite variable was based on the Sociosexuality Index (Simpson & Gangestad, 1991). Three items assessed attitudes toward impersonal sex (e.g., “sex without love is okay”), using 9-point scales ranging from 1 (strongly disagree) to 9 (strongly agree, α = .79). The number of lifetime sex partners and the number of partners desired in the next 5 years were assessed using open-ended responses and then were Winsorized (to 95th percentile, Reifman & Keyton, 2010) to reduce outliers. Men were asked, “How often do you fantasize about having sex with someone (other than your current dating partner if you have one)?” using a 7-point scale (0 = never, 6 = at least once a day). The four components (lifetime and 5-year sex partners, sexual fantasies, impersonal sex attitudes) were standardized and combined (α = .71).

Analytic strategy

Because the outcome variable was a dichotomous measure (sexual assault perpetration each semester), a series of hierarchical generalized linear models were estimated to examine the effects of HED on repeated measures of sexual assault perpetration across the five semesters. Following established recommendations (Raudenbush & Bryk, 2002), the between-person predictors (measured at T1) were centered on the sample mean (i.e., grand-mean centered) to provide between-person effects of these covariates. The time-varying predictors were centered on each individual’s overall mean (i.e., person-mean centered) to provide a within-person effect (Level 1). Each individual’s overall mean value was also centered on the grand mean to provide a between-person effect (Level 2) for each time-varying predictor.

The resulting models specified a binomial distribution and a logit link function to estimate the odds of sexual assault perpetration each semester. We tested hypotheses by estimating three separate generalized linear mixed models. Model A included only the between-person effects of HED. Model B also included between-person effects of self-control, antisocial behavior, impersonal sex, and hostility toward women. Model C added the within-person effects of HED. Thus, the hierarchical logistic model estimated in Model C can be expressed as follows:

Level 1 (within-person) model:

graphic file with name jsad.2017.78.5equ1.jpg

Level 2 (between-person) model:

graphic file with name jsad.2017.78.5equ2.jpg

To test hypotheses regarding effects of drinking settings, the three modeling steps described above were repeated with the between-person and within-person effects of party attendance and bar attendance replacing HED effects. All models were estimated with SAS Proc Glimmix (SAS Institute Inc., Cary, NC) using the adaptive quadrature approach, the recommended estimation procedure for hierarchical generalized linear models (Hedeker, 2015). To take advantage of full information maximum likelihood (FIML) for missing data (Enders & Bandalos, 2001; Larsen, 2011), means, variances, and covariances of predictors with missing data were estimated. FIML addresses missing data by computing parameter estimates and standard errors using all available information from partially missing cases and produces less biased and more efficient results than listwise deletion (Graham, 2009).

Results

Retention

Of 994 men who completed the T1 survey, 2 had missing data on all 16 SES items and were dropped from subsequent analyses. Of the remaining 992, 786 completed T2, 738 completed T3, 625 completed T4, and 658 completed T5. Nearly 90% completed at least two waves of data and 77.6% completed three or more waves. Subsequent analyses revealed that the data were not missing completely at random, χ2(229) = 424.60, p < .001 (Little, 1988). However, univariate t test comparisons revealed that the likelihood of missing data on the sexual assault perpetration measures was significantly associated with three of the four T1 covariates (self-control, antisocial behavior, and impersonal sex) but not with T1 sexual assault perpetration or hostility toward women. Inclusion of these variables in all subsequent analyses allowed us to meet the missing-at-random assumptions that underlie FIML estimation techniques (Enders, 2010).

Descriptives

The proportion of men who reported any sexual assault (positive response to at least one SES item) was 5.9% at T1, 7.0% atT2, 9.5% atT3, 6.6% atT4, and 9.3% atT5. Of 992 men, 175 (17.6%) reported sexual assault in at least one semester (106 perpetrated it in one semester, 44 in two semesters, and 25 in three or more). As shown in Table 1, men who perpetrated sexual assault differed significantly from those with no sexual assault on all key T1 variables. All variables in the table were positively and linearly associated with whether perpetration occurred in zero, one, two, or three or more semesters, all ps < .001. T1 correlations among predictors are presented in Table 2. The frequencies of HED, party attendance, and bar attendance were highly correlated at this and other waves (rs range: .43–.82), supporting our decision to examine the effects of the three alcohol variables in separate models.

Table 1.

Comparison of sexual assault perpetrators and nonperpetrators on Time 1 (T1) variables

graphic file with name jsad.2017.78.5tbl1.jpg

Variable Nonperpetrators (n = 817) M (SD) Perpetrators (n = 175) M (SD)
T1 HED Frequency 1.73 (1.89) 2.54 (2.08)
T1 Party Frequency 2.37 (1.88) 3.16 (1.86)
T1 Bar Frequency 0.78 (1.25) 1.38 (1.70)
T1 Number of Hookups 1.21 (2.04) 2.01 (2.35)
T1 Sex Partners, Litetime 1.45 (1.89) 2.16 (2.14)
T1 Sex Partners Desired 4.84 (5.32) 6.86 (6.41)
T1 Antisocial Behavior 6.16 (4.64) 9.11 (7.81)
T1 Self-Control 43.55 (8.00) 39.54 (7.59)
T1 Hostility Toward Women 33.06 (10.21) 35.91 (9.89)
Open in a new tab

Notes: Perpetrators reported sexual assault in one or more semesters. HED = heavy episodic drinking. All bivariate group comparisons were significant at p < .001 or greater.

Table 2.

Correlations among Time 1 (T1) variables

graphic file with name jsad.2017.78.5tbl2.jpg

Variable Heavy episodic drinking Hostility toward women Self-control Antisocial behavior Impersonal sex, composite Frequency parties Frequency bars Frequency hookups
Heavy episodic drinking 1
Hostility toward women .184** 1
Self-control -.288** -.257** 1
Antisocial behavior .317** .179** -.414** 1
Impersonal sex, composite .485** .283** -.285** .360** 1
Frequency of parties .780** .170** -.248** .304** .514** 1
Frequency of bars .426** .130** -.146** .218** .358** .520** 1
Frequency of hookups .407** .135** -.190** .204** .508** .460** .402** 1
Any sexual assault perpetration (yes/no) .144** .086* -.168** .210** .174** .144** .121** .140**
Open in a new tab
*

p < .01;

**

p < .001.

Twenty-eight men reported on at least one survey that they perpetrated all 16 SES items. Although this seemed implausible and suggested frivolous responding, we believed it likely that they had perpetrated some sexual assault. Men with this unusual response pattern did not differ from other perpetrators on key variables, and 15 of 28 (54%) reported perpetrating in at least one other semester. We retained these men; however, removing them from analyses did not alter results.

Preliminary model

Preliminary unconditional models revealed a small, linear increase in sexual assault over time (b = 0.18, p < .001) but no evidence for a quadratic effect. Thus, a random intercept and a fixed linear slope term were included in all models. Participants were more likely to report sexual assault on long-form fall surveys compared with short-form spring surveys; hence, a dummy-coded variable (0 = fall, 1 = spring) was included as a main effect in all models. All models also included an indicator of the participants’ relationship status (0 = no, 1 = yes) reported each semester.

Model building to test hypotheses

Results of the series of hierarchical generalized linear models are presented in Tables 3–5. In each table the estimated binary logistic regression coefficients are presented along with the corresponding standard errors; these represent the log odds of reporting sexual assault perpetration for a 1-unit increase in the predictor variable. For ease of interpretation, the associated odds ratios (ORs) for each predictor are also provided. Table 3 displays the results for the within- and between-person effects of HED on sexual assault perpetration. Model A revealed, as hypothesized, that sexual assault was more likely among men who reported more frequent HED than the sample as a whole (between-person effect, OR = 1.51). However, HED was not significant after adding the T1 covariates in Model B, which indicated between-person effects of self-control (OR = 0.94), antisocial behavior (OR = 1.07), and impersonal sex orientation (OR = 1.70) in the expected directions. The within-person effect of HED, added in Model C, was not significant.

Table 3.

Results of hierarchical generalized linear models with heavy episodic drinking as a predictor of sexual assault perpetration across the five semesters

graphic file with name jsad.2017.78.5tbl3.jpg

Variable Model A
 Model B
 Model C
b (SE) OR b (SE) OR b (SE) OR
Level 2 (BP effects)
 Heavy alcohol use 0.41** (0.08) 1.51 0.13 (0.08) 1.13 0.13 (0.08) 1.13
 Hostility toward women 0.01 (0.01) 1.01 0.01 (0.01) 1.01
 Self-control -0.06*** (0.02) 0.94 -0.06*** (0.02) 0.94
 Antisocial behavior 0.07** (0.02) 1.07 0.07** (0.02) 1.07
 Impersonal sex 0.53** (0.19) 1.70 0.53** (0.19) 1.70
Level 1 (WP effects)
 Heavy alcohol use 0.07 (0.07) 1.07
Goodness-of-fit indices
 AIC 1,732.28 1,684.70 1,685.78
 BIC 1,761.68 1,733.70 1,739.68
Open in a new tab

Notes: b = logistic regression coefficient; OR = odds ratio; BP = between-person; WP = within-person; AIC = Akaike information criterion; BIC = Bayesian information criterion. All models include five semesters, nested within 992 participants.

**

p < .01;

***

p < .001.

Table 5.

Results of hierarchical linear modeling with bar attendance as a predictor of sexual assault perpetration over five semesters

graphic file with name jsad.2017.78.5tbl5.jpg

Variable Model A
 Model B
 Model C
 Model D
b (SE) OR b (SE) OR b (SE) OR b (SE) OR
Level 2 (BP effects)
 Frequency of going to bars 0.81*** (0.11) 2.25 0.56*** (0.11) 1.75 0.56*** (0.11) 1.75 0.48*** (0.11) 1.61
 Frequency of hookups 0.15 (0.09) 1.16
 Hostility toward women 0.01 (0.01) 1.01 0.01 (0.01) 1.01 0.01 (0.01) 1.01
 Self-control -0.06*** (0.02) 0.94 -0.06*** (0.02) 0.94 -0.06*** (0.02) 0.94
 Antisocial behavior 0.06** (0.02) 1.06 0.06** (0.02) 1.06 0.06** (0.02) 1.06
 Impersonal sex 0.28 (0.18) 1.32 0.29 (0.18) 1.33 0.17 (0.20) 1.19
Level 1 (WP effects)
 Frequency of going to bars 0.16* (0.07) 1.18 0.13 (0.07) 1.13
 Frequency of hookups 0.11* (0.05) 1.12
Goodness-of-fit indices
 AIC 1,696.39 1,660.21 1,656.70 1,641.05
 BIC 1,725.79 1,709.21 1,710.59 1,704.74
Open in a new tab

Notes: b = logistic regression coefficient; OR = odds ratio; BP = between-person, WP = within-person; AIC = Akaike information criterion; BIC = Bayesian information criterion. All models include five semesters, nested within 992 participants.

*

p < .05;

**

p < .01;

***

p < .001.

Table 4 displays results substituting frequency of party attendance for the within- and between-person effects of HED. The positive between-person effect of party attendance remained significant in Model B after controlling for T1 covariates (OR = 1.30). The results of Model C indicated that the within-person effect of frequency of party attendance was also significant (OR = 1.15). Thus, sexual assault perpetration was more likely to occur in semesters in which frequency of party attendance was higher than one’s own average and among students who, on average, reported more frequent party attendance than the sample as a whole. The results for frequency of bar attendance (Table 5) were nearly identical to those for party attendance.

Table 4.

Results of hierarchical linear modeling with party attendance as a predictor of sexual assault perpetration over five semesters

graphic file with name jsad.2017.78.5tbl4.jpg

Variable Model A
 Model B
 Model C
 Model D
b (SE) OR b (SE) OR b (SE) OR b (SE) OR
Level 2 (BP effects)
 Frequency of going to parties 0.51*** (0.08) 1.67 0.26** (0.09) 1.30 0.26** (0.09) 1.30 0.15 (0.09) 1.17
 Frequency of hookups 0.24* (0.10) 1.27
 Hostility toward women 0.01 (0.01) 1.01 0.01 (0.01) 1.01 0.01 (0.02) 1.01
 Self-control 0.06*** (0.02) 0.94 -0.06*** (0.02) 0.94 -0.06*** (0.02) 0.94
 Antisocial behavior 0.06** (0.02) 1.06 0.06** (0.02) 1.06 0.06** (0.02) 1.06
 Impersonal sex 0.40* (0.19) 1.49 0.41* (0.19) 1.50 0.22 (0.20) 1.25
Level 1 (WP effects)
 Frequency going to parties 0.14* (0.07) 1.15 0.13 (0.07) 1.14
 Frequency of hookups 0.11* (0.05) 1.12
Goodness-of-fit indices
 AIC 1,719.45 1,679.53 1,677.39 1,656.12
 BIC 1,748.85 1,728.52 1,731.29 1,719.81
Open in a new tab

Notes: b = logistic regression coefficient; OR = odds ratio; BP = between-person; WP = within-person; AIC = Akaike information criterion; BIC = Bayesian information criterion. All models include five semesters, nested within 992 participants.

*

p < .05;

**

p < .01;

***

p < .001.

Supplemental analyses

To better understand how drinking settings contribute to sexual assault perpetration, we considered the role of hookup frequency in the party and bar models, reasoning that these casual sexual encounters may explain the link (e.g., Flack et al., 2007). Thus, we added Model D, which included the between- and within-person effects of frequency of hookups, to Tables 4 and 5. When added to either the party or the bar frequency model, the within-person effect of hookups was significant (OR = 1.12 in both models), whereas the within-person effect of party and bar attendance became nonsignificant. The between-person effect of hookups was significant in the party model (OR = 1.27) but not significant in the bar model. After controlling for hookups, the between-person effect of bar frequency remained significant (OR = 1.61), whereas the effect of party frequency was not.

Discussion

College men with higher frequency HED were more likely to perpetrate sexual assault over the first five semesters of college. However, this between-person effect was completely explained by characteristics shared by heavier drinkers and perpetrators—impersonal sex orientation, antisocial behavior, and low self-control. The absence of an independent between-person effect of HED is consistent with the results of previous studies that have found weak or nonsignificant effects of men’s alcohol use on perpetration relative to strong effects of personality variables (Parkhill & Abbey, 2008; Thompson et al., 2013; 2015). In contrast, when we substituted frequency of party or bar attendance for HED, we found independent, between-person effects, indicating that men who frequent these settings more than average are at a higher risk for perpetrating sexual assault over time.

We also failed to support the hypothesis that sexual assault would be more likely to occur in semesters in which HED was more frequent than one’s own average. However, we found within-person effects for party and bar frequency on sexual assault, with a higher risk in semesters in which attendance exceeded one’s own average frequency. College students, aware of the strong link between drinking, drinking settings, and sexual assault, attend parties and bars to drink and find sex partners (Lindgren et al., 2009; Norris et al., 1996). Predatory men may seek out these settings to target vulnerable, intoxicated women for sexual advances (Graham et al., 2014; Mumford et al., 2011). Accordingly, when we entered time-varying hookup behavior, the within-person effects of party and bar attendance became nonsignificant, as hookups emerged as a significant predictor of sexual assault. Consistent with research linking hookups with sexual aggression (Flack et al., 2007; Testa et al., 2015b), casual sexual behavior may be an even more proximal driver of sexual assault than drinking settings.

Limitations

Because these are not event-level data, we do not know whether within-person effects reflect perpetration occurring within drinking settings (or hookups) or on separate occasions within the same semester. There are always limitations associated with self-report data; these may be relatively more significant for assessment of sexual assault perpetration, a socially proscribed behavior. Although the SES is a standard, widely used measure, there has been little psychometric evaluation of its validity (Kolivas & Gross, 2007). Because personality variables were not assessed at all waves, we were unable to model their time-varying effects on sexual assault (see Thompson et al., 2015). Finally, although we had good retention in this large sample, it is possible that missing data influenced the results in unknown ways.

Implications for preventing college sexual assault

Consistent with Thompson et al. (2015), sexual assault perpetration did not decline over the course of college. Yet, the risk of sexual assault victimization among women declines from a peak in the first year (Humphrey & White, 2000; Parks et al., 2014). Because this pattern suggests that upperclassmen may be targeting vulnerable freshman women, sexual assault prevention efforts need to include these older men.

Although we did not find an independent between- or within-person effect of HED on sexual assault, heavier drinkers were more likely to perpetrate over the next several semesters than lighter drinkers. Because heavy drinkers are potentially identifiable (e.g., mandated for intervention), it may be feasible to provide them with sexual assault prevention programming (see Orchowski et al., 2016), although our results do not imply that reducing men’s HED in itself will prevent sexual assault perpetration. The between- and within-person effects of party and bar attendance are consistent with a growing body of research pointing toward drinking settings as “hot spots” for sexual victimization (e.g., Bersamin et al., 2012; Graham et al., 2014). The choice to frequent these settings reflects not only the desire to drink but also personality characteristics, expectancies about alcohol within these settings, and desire to have sex or possibly to use sexually aggressive tactics to obtain sex (Mumford et al., 2011). Although eliminating college parties and bars seems unrealistic, it may be possible to make these social settings safer (e.g., Glindemann et al., 2007)—for example, through server or bystander intervention training (e.g., Salazar et al., 2014).

Acknowledgments

The authors thank Antonia Abbey for her assistance in developing measures for this study and Joseph Lucke for statistical consultation.

Footnotes

Research reported in this article was supported by National Institute on Alcohol Abuse and Alcoholism award number R01AA019478. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Portions of this research were presented at the annual meeting of the Research Society on Alcoholism, June 2016, New Orleans, LA.

References

  1. Abbey A. Alcohol-related sexual assault: A common problem among college students. Journal of Studies on Alcohol. 2002;Supplement 14:118–128. doi: 10.15288/jsas.2002.s14.118. doi:10.15288/jsas.2002.s14.118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Abbey A. Alcohol’s role in sexual violence perpetration: Theoretical explanations, existing evidence and future directions. Drug and Alcohol Review. 2011;30:481–489. doi: 10.1111/j.1465-3362.2011.00296.x. doi:10.1111/j.1465-3362.2011.00296.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Abbey A., Jacques-Tiura A. J. Sexual assault perpetrators’ tactics: Associations with their personal characteristics and aspects of the incident. Journal of Interpersonal Violence. 2011;26:2866–2889. doi: 10.1177/0886260510390955. doi:10.1177/0886260510390955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Abbey A., Jacques-Tiura A. J., LeBreton J. M. Risk factors for sexual aggression in young men: An expansion of the confluence model. Aggressive Behavior. 2011;37:450–464. doi: 10.1002/ab.20399. doi:10.1002/ab.20399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Abbey A., McAuslan P. A longitudinal examination of male college students’ perpetration of sexual assault. Journal of Consulting and Clinical Psychology. 2004;72:747–756. doi: 10.1037/0022-006X.72.5.747. doi:10.1037/0022-006X.72.5.747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Abbey A., McAuslan P., Zawacki T., Clinton A. M., Buck P. O. Attitudinal, experiential, and situational predictors of sexual assault perpetration. Journal of Interpersonal Violence. 2001;16:784–807. doi: 10.1177/088626001016008004. doi:10.1177/088626001016008004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Abbey A., Parkhill M. R., BeShears R., Clinton-Sherrod A. M., Zawacki T. Cross-sectional predictors of sexual assault perpetration in a community sample of single African American and Caucasian men. Aggressive Behavior. 2006;32:54–67. doi: 10.1002/ab.20107. doi:10.1002/ab.20107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Abbey A., Parkhill M. R., Clinton-Sherrod A. M., Zawacki T. A comparison of men who committed different types of sexual assault in a community sample. Journal of Interpersonal Violence. 2007;22:1567–1580. doi: 10.1177/0886260507306489. doi:10.1177/0886260507306489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Abbey A., Wegner R., Woerner J., Pegram S. E., Pierce J. Review of survey and experimental research that examines the relationship between alcohol consumption and men’s sexual aggression perpetration. Trauma, Violence & Abuse. 2014;15:265–282. doi: 10.1177/1524838014521031. doi:10.1177/1524838014521031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bersamin M. M., Paschall M. J., Saltz R. F., Zamboanga B. L. Young adults and casual sex: The relevance of college drinking settings. Journal of Sex Research. 2012;49:274–281. doi: 10.1080/00224499.2010.548012. doi:10.1080/00224499.2010.548012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Clapp J. D., Reed M. B., Holmes M. R., Lange J. E., Voas R. B. Drunk in public, drunk in private: The relationship between college students, drinking environments and alcohol consumption. American Journal of Drug and Alcohol Abuse. 2006;32:275–285. doi: 10.1080/00952990500481205. doi:10.1080/00952990500481205. [DOI] [PubMed] [Google Scholar]
  12. Crane C. A., Godleski S. A., Przybyla S. M., Schlauch R. C., Testa M. The proximal effects of acute alcohol consumption on male-to-female aggression: A meta-analytic review of the experimental literature. Trauma, Violence & Abuse. 2015 doi: 10.1177/1524838015584374. Advance online publication. doi:10.1177/1524838015584374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cranney S. The relationship between sexual victimization and year in school in U.S. colleges: Investigating the parameters of the “Red Zone.”. Journal of Interpersonal Violence. 2015;30:3133–3145. doi: 10.1177/0886260514554425. doi:10.1177/0886260514554425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Curran P. J., Bauer D. J. The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology. 2011;62:583–619. doi: 10.1146/annurev.psych.093008.100356. doi:10.1146/annurev.psych.093008.100356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. DeGue S., DiLillo D. Understanding perpetrators of nonphysical sexual coercion: Characteristics of those who cross the line. Violence and Victims. 2004;19:673–688. doi: 10.1891/vivi.19.6.673.66345. doi:10.1891/vivi.19.6.673.66345. [DOI] [PubMed] [Google Scholar]
  16. DeWall C. N., Finkel E. J., Denson T. F. Self-control inhibits aggression. Social and Personality Psychology Compass. 2011;5:458–472. doi:10.1111/j.1751-9004.2011.00363.x. [Google Scholar]
  17. Enders C. K. Applied missing data analysis. New York, NY: Guilford Press; 2010. [Google Scholar]
  18. Enders C. K., Bandalos D. L. The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal. 2001;8:430–457. doi:10.1207/S15328007SEM0803_5. [Google Scholar]
  19. Flack W. F., Jr., Daubman K. A., Caron M. L., Asadorian J. A., D’Aureli N. R., Gigliotti S. N., Stine E. R. Risk factors and consequences of unwanted sex among university students: Hooking up, alcohol, and stress response. Journal of Interpersonal Violence. 2007;22:139–157. doi: 10.1177/0886260506295354. doi:10.1177/0886260506295354. [DOI] [PubMed] [Google Scholar]
  20. Franklin C. A., Franklin T. W., Nobles M. R., Kercher G. A. Assessing the effect of routine activity theory and self-control on property, personal, and sexual assault victimization. Criminal Justice and Behavior. 2012;39:1296–1315. doi:10.1177/0093854812453673. [Google Scholar]
  21. George W, H., Cue K. L., Lopez P, A., Crowe L. C., Norris J. Self-reported alcohol expectancies and postdrinking sexual inferences about women. Journal of Applied Social Psychology. 1995;25:164–186. doi:10.1111/j.1559-1816.1995.tb01589.x. [Google Scholar]
  22. George W. H., Stoner S. A., Norris J., Lopez P. A., Lehman G. L. Alcohol expectancies and sexuality: A self-fulfilling prophecy analysis of dyadic perceptions and behavior. Journal of Studies on Alcohol. 2000;61:168–176. doi: 10.15288/jsa.2000.61.168. doi:10.15288/jsa.2000.61.168. [DOI] [PubMed] [Google Scholar]
  23. Giancola P. R., Josephs R. A., Parrott D. J., Duke A. A. Alcohol myopia revisited: Clarifying aggression and other acts of disinhibition through a distorted lens. Perspectives on Psychological Science. 2010;5:265–278. doi: 10.1177/1745691610369467. doi:10.1177/1745691610369467. [DOI] [PubMed] [Google Scholar]
  24. Gidycz C. A., Warkentin J. B., Orchowski L. M. Predictors of perpetration of verbal, physical, and sexual violence: A prospective analysis of college men. Psychology of Men & Masculinity. 2007;8:79–94. doi:10.1037/1524-9220.8.2.79. [Google Scholar]
  25. Glindemann K. E., Ehrhart I. J., Drake E. A., Geller E. S. Reducing excessive alcohol consumption at university fraternity parties: A cost-effective incentive/reward intervention. Addictive Behaviors. 2007;32:39–48. doi: 10.1016/j.addbeh.2006.03.019. doi:10.1016/j.addbeh.2006.03.019. [DOI] [PubMed] [Google Scholar]
  26. Graham J. W. Missing data analysis: Making it work in the real world. Annual Review of Psychology. 2009;60:549–576. doi: 10.1146/annurev.psych.58.110405.085530. doi:10.1146/annurev.psych.58.110405.085530. [DOI] [PubMed] [Google Scholar]
  27. Graham K., Bernards S., Osgood D. W., Abbey A., Parks M., Flynn A., Wells S. “Blurred lines?” Sexual aggression and barroom culture. Alcoholism: Clinical and Experimental Research. 2014;38:1416–1424. doi: 10.1111/acer.12356. doi:10.1111/acer.12356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Harford T. C., Wechsler H., Seibring M. Attendance and alcohol use at parties and bars in college: A national survey of current drinkers. Journal of Studies on Alcohol. 2002;63:726–733. doi: 10.15288/jsa.2002.63.726. doi:10.15288/jsa.2002.63.726. [DOI] [PubMed] [Google Scholar]
  29. Hedeker D. Methods for multilevel ordinal data in prevention research. Prevention Science. 2015;16:997–1006. doi: 10.1007/s11121-014-0495-x. doi:10.1007/s11121-014-0495-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Humphrey J. A., White J. W. Women’s vulnerability to sexual assault from adolescence to young adulthood. Journal of Adolescent Health. 2000;27:419–424. doi: 10.1016/s1054-139x(00)00168-3. doi:10.1016/S1054-139X(00)00168-3. [DOI] [PubMed] [Google Scholar]
  31. Ito T. A., Miller N., Pollock V. E. Alcohol and aggression: A meta-analysis on the moderating effects of inhibitory cues, triggering events, and self-focused attention. Psychological Bulletin. 1996;120:60–82. doi: 10.1037/0033-2909.120.1.60. doi:10.1037/0033-2909.120.1.60. [DOI] [PubMed] [Google Scholar]
  32. Jacques-Tiura A. J., Abbey A., Parkhill M. R., Zawacki T. Why do some men misperceive women’s sexual intentions more frequently than others do? An application of the confluence model. Personality & Social Psychology Bulletin. 2007;33:1467–1480. doi: 10.1177/0146167207306281. doi:10.1177/0146167207306281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kingree J. B., Thompson M. P. Fraternity membership and sexual aggression: An examination of mediators of the association. Journal of American College Health. 2013;61:213–221. doi: 10.1080/07448481.2013.781026. doi:10.1080/07448481.2013.781026. [DOI] [PubMed] [Google Scholar]
  34. Kolivas E. D., Gross A. M. Assessing sexual aggression: Addressing the gap between rape victimization and perpetration prevalence rates. Aggression and Violent Behavior. 2007;12:315–328. doi:10.1016/j.avb.2006.10.002. [Google Scholar]
  35. Koss M. P., Gaines J. A. The prediction of sexual aggression by alcohol use, athletic participation, and fraternity affiliation. Journal of Interpersonal Violence. 1993;8:94–108. doi:10.1177/088626093008001007. [Google Scholar]
  36. Larsen R. Missing data imputation versus full information maximum likelihood with second-level dependencies. Structural Equation Modeling: A Multidisciplinary Journal. 2011;18:649–662. doi:10.1080/10705511.2011.607721. [Google Scholar]
  37. Lindgren K. P., Pantalone D. W., Lewis M. A., George W. H. College students’ perceptions about alcohol and consensual sexual behavior: Alcohol leads to sex. Journal of Drug Education. 2009;39:1–21. doi: 10.2190/DE.39.1.a. doi:10.2190/DE.39.1.a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Little R. J. A. A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association. 1988;83:1198–1202. doi:10.1080/01621459.1988.10478722. [Google Scholar]
  39. Locke B. D., Mahalik J. R. Examining masculinity norms, problem drinking, and athletic involvement as predictors of sexual aggression in college men. Journal of Counseling Psychology. 2005;52:279–283. doi:10.1037/0022-0167.52.3.279. [Google Scholar]
  40. Loh C., Gidycz C. A., Lobo T. R., Luthra R. A prospective analysis of sexual assault perpetration: Risk factors related to perpetrator characteristics. Journal of Interpersonal Violence. 2005;20:1325–1348. doi: 10.1177/0886260505278528. doi:10.1177/0886260505278528. [DOI] [PubMed] [Google Scholar]
  41. Lonsway K. A., Fitzgerald L. F. Attitudinal antecedents of rape myth acceptance: A theoretical and empirical reexamination. Journal of Personality and Social Psychology. 1995;68:704–711. doi:10.1037/0022-3514.68.4.704. [Google Scholar]
  42. Malamuth N. M., Linz D., Heavey C. L., Barnes G., Acker M. Using the confluence model of sexual aggression to predict men’s conflict with women: A 10-year follow-up study. Journal of Personality and Social Psychology. 1995;69:353–369. doi: 10.1037//0022-3514.69.2.353. doi:10.1037/0022-3514.69.2.353. [DOI] [PubMed] [Google Scholar]
  43. Malamuth N. M., Sockloskie R. J., Koss M. P., Tanaka J. S. Characteristics of aggressors against women: Testing a model using a national sample of college students. Journal of Consulting and Clinical Psychology. 1991;59:670–681. doi: 10.1037//0022-006x.59.5.670. doi:10.1037/0022-006X.59.5.670. [DOI] [PubMed] [Google Scholar]
  44. Mohler-Kuo M., Dowdall G. W., Koss M. P, Wechsler H. Correlates of rape while intoxicated in a national sample of college women. Journal of Studies on Alcohol. 2004;65:37–45. doi: 10.15288/jsa.2004.65.37. doi:10.15288/jsa.2004.65.37. [DOI] [PubMed] [Google Scholar]
  45. Mumford E. A., Kelley-Baker T., Romano E. Sexual assault histories and evening drinking among young American men in a high-risk drinking environment. Journal of Sex Research. 2011;48:53–61. doi: 10.1080/00224490903487588. doi:10.1080/00224490903487588. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Murnen S. K., Wright C., Kaluzny G. If “boys will be boys,” then girls will be victims? A meta-analytic review of the research that relates masculine ideology to sexual aggression. Sex Roles. 2002;46:359–375. doi:10.1023/A:1020488928736. [Google Scholar]
  47. Norris J., Nurius P. S., Dimeff L. A. Through her eyes: Factors affecting women’s perception of and resistance to acquaintance sexual aggression threat. Psychology of Women Quarterly. 1996;20:123–145. doi: 10.1111/j.1471-6402.1996.tb00668.x. doi:10.1111/j.1471-6402.1996.tb00668.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Orchowski L. M., Barnett N., Borsari B. Zlotnick, Oesterle D., Merrill, Wood M. Randomized pilot trial of an integrated alcohol and sexual assault prevention program for heavy drinking college men: Six month findings. Paper presented at the Annual Conference of the Research Society on Alcoholism; New Orleans, LA: 2016, June. [Google Scholar]
  49. Owen J., Fincham F. D., Moore J. Short-term prospective study of hooking up among college students. Archives of Sexual Behavior. 2011;40:331–341. doi: 10.1007/s10508-010-9697-x. doi:10.1007/s10508-010-9697-x. [DOI] [PubMed] [Google Scholar]
  50. Parkhill M. R., Abbey A. Does alcohol contribute to the confluence model of sexual assault perpetration? Journal of Social and Clinical Psychology. 2008;27:529–554. doi: 10.1521/jscp.2008.27.6.529. doi:10.1521/jscp.2008.27.6.529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Parks K. A., Hsieh Y.-P., Bradizza C. M., Romosz A. M. Factors influencing the temporal relationship between alcohol consumption and experiences with aggression among college women. Psychology of Addictive Behaviors. 2008;22:210–218. doi: 10.1037/0893-164X.22.2.210. doi:10.1037/0893-164X.22.2.210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Parks K. A., Hsieh Y.-P., Taggart C., Bradizza C. M. A longitudinal analysis of drinking and victimization in college women: Is there a reciprocal relationship? Psychology of Addictive Behaviors. 2014;28:943–951. doi: 10.1037/a0036283. doi:10.1037/a0036283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Pino N. W., Johnson-Johns A. M. College women and the occurrence of unwanted sexual advances in public drinking settings. The Social Science Journal. 2009;46:252–267. doi:10.1016/j.soscij.2009.04.005. [Google Scholar]
  54. Raudenbush S. W., Bryk A. S. Hierarchical linear models: Applications and data analysis methods. Vol. 2. Thousand Oaks, CA: Sage; 2002. [Google Scholar]
  55. Reifman A., Keyton K. Winsorize. In: Salkind N. J., editor. Encyclopedia of research design. Vol. 3. Thousand Oaks, CA: Sage; 2010. pp. 1636–1638. [Google Scholar]
  56. Salazar L. F., Vivolo-Kantor A., Hardin J., Berkowitz A. A web-based sexual violence bystander intervention for male college students: Randomized controlled trial. Journal of Medical Internet Research. 2014;16:e203. doi: 10.2196/jmir.3426. doi:10.2196/jmir.3426. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Schwartz M. D., DeKeseredy W. S., Tait D., Alvi S. Male peer support and feminist routine activities theory: Understanding sexual assault on the college campus. Justice Quarterly. 2001;18:623–649. doi:10.1080/07418820100095041. [Google Scholar]
  58. Simons J. S., Maisto S. A., Wray T. B., Emery N. N. Acute effects of intoxication and arousal on approach/avoidance biases toward sexual risk stimuli in heterosexual men. Archives of Sexual Behavior. 2016;45:43–51. doi: 10.1007/s10508-014-0477-x. doi:10.1007/s10508-014-0477-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Simpson J. A., Gangestad S. W. Individual differences in sociosexuality: Evidence for convergent and discriminant validity. Journal of Personality and Social Psychology. 1991;60:870–883. doi: 10.1037//0022-3514.60.6.870. doi:10.1037/0022-3514.60.6.870. [DOI] [PubMed] [Google Scholar]
  60. Swartout K. M. The company they keep: How peer networks influence male sexual aggression. Psychology of Violence. 2013;3:157–171. doi:10.1037/a0029997. [Google Scholar]
  61. Tangney J. P., Baumeister R. F., Boone A. L. High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality. 2004;72:271–324. doi: 10.1111/j.0022-3506.2004.00263.x. doi:10.1111/j.0022-3506.2004.00263.x. [DOI] [PubMed] [Google Scholar]
  62. Testa M., Hoffman J. H., Lucke J. F., Pagnan C. E. Measuring sexual aggression perpetration in college men: A comparison of two measures. Psychology of Violence. 2015a;5:285–293. doi: 10.1037/a0037584. doi:10.1037/a0037584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Testa M., Livingston J. A. Alcohol consumption and women’s vulnerability to sexual victimization: Can reducing women’s drinking prevent rape? Substance Use & Misuse. 2009;44:1349–1376. doi: 10.1080/10826080902961468. doi:10.1080/10826080902961468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Testa M., Parks K. A., Hoffman J. H., Crane C. A., Leonard K. E., Shyhalla K. Do drinking episodes contribute to sexual aggression perpetration in college men? Journal of Studies on Alcohol and Drugs. 2015b;76:507–515. doi: 10.15288/jsad.2015.76.507. doi:10.15288/jsad.2015.76.507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Thompson E. H., Jr., Cracco E. J. Sexual aggression in bars: What college men can normalize. Journal of Men’s Studies. 2008;16:82–96. doi:10.3149/jms.1601.82. [Google Scholar]
  66. Thompson M. P., Kingree J. B., Zinzow H., Swartout K. Time-varying risk factors and sexual aggression perpetration among male college students. Journal of Adolescent Health. 2015;57:637–642. doi: 10.1016/j.jadohealth.2015.08.015. doi:10.1016/j.jadohealth.2015.08.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Thompson M. P., Koss M. P., Kingree J. B., Goree J., Rice J. A prospective mediational model of sexual aggression among college men. Journal of Interpersonal Violence. 2011;26:2716–2734. doi: 10.1177/0886260510388285. doi:10.1177/0886260510388285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Thompson M. P., Swartout K. M., Koss M. P. Trajectories and predictors of sexually aggressive behaviors during emerging adulthood. Psychology of Violence. 2013;3:247–259. doi: 10.1037/a0030624. doi:10.1037/a0030624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Tuliao A. P., McChargue D. Problematic alcohol use and sexual assault among male college students: The moderating and mediating roles of alcohol outcome expectancies. American Journal on Addictions. 2014;23:321–328. doi: 10.1111/j.1521-0391.2014.12119.x. doi:10.1111/j.1521-0391.2014.12119.x. [DOI] [PubMed] [Google Scholar]
  70. Zucker R. A. Manual for the antisocial behavior checklist. University of Michigan; Ann Arbor, Michigan: 2005. [Google Scholar]

Articles from Journal of Studies on Alcohol and Drugs are provided here courtesy of Rutgers University. Center of Alcohol Studies

RESOURCES