The Theory of Planned Behavior & Bystander Intention: Differences Based on Gender and Heavy Episodic Drinking

Abstract

Research indicates that people who engage in heavy episodic drinking (HED) report less intention to intervene and intervention behavior to prevent sexual violence. Researchers have also found gender differences across bystander intention, bystander confidence, and intervention behavior. However, research in this area could benefit from use of an evidence-based health behavior theory. The theory of planned behavior (TPB) posits that personal attitudes, subjective norms, and perceived behavioral control (PBC) predict behavioral intention. Substantial evidence supports the utility of the TPB for predicting behavioral intention in a variety of health-related behaviors, yet few researchers have applied this theory when predicting bystander intention to prevent sexual violence. Undergraduate students (N = 395) from a southern university (77% female; 70% White, Non-Hispanic) completed a modified Sexual Assault Bystander Behavior Questionnaire and the Daily Drinking Questionnaire. Our findings did not reveal significant differences in attitudes, subjective norms, PBC, nor intention based on HED; however, there were significant differences based on gender, with women indicating more positive attitudes and supportive subjective norms regarding bystander intervention. Multiple regression analysis indicated that theoretical antecedents of intention positively predicted bystander intention, however, the relations were not moderated by prior engagement in HED. These findings support the utility of the TPB for predicting bystander intention to intervene; however, they do not support previous research examining how HED influences bystanders’ intention to engage in prosocial actions.

Keywords

sexual assault prevention situational factors bystander intention drinking behavior

Sexual violence persists as a major concern among institutions of higher education, and bystander-based prevention models continue to be relied-upon initiatives to address this issue (DeGue et al., 2014; Orchowski et al., 2020). Considering that many sexual assaults occur in environments where alcohol is present (e.g., parties and bars) and over 60% of sexual assaults involve alcohol use (Abbey, 2002; Lawyer et al., 2010), there is potential for students’ alcohol consumption to interact with their experiences as witnesses to these acts (Fleming & Wiersma-Mosley, 2015). Recent evidence demonstrates a relationship between drinking behavior and diminished bystander intentions (Orchowski et al., 2016), and in some cases, observed intervention behavior (Leone & Parrott, 2019). Previous research also demonstrates that bystander intention to intervene as well as confidence to intervene can vary by gender (e.g., Banyard, 2008; Brown et al., 2014), with female participants indicating greater intention and confidence than male participants (cf., Katz et al., 2015). Some of the evidence-based theoretical frameworks used when studying bystander intervention include the situational model of bystander intervention (Latané & Darley, 1970), the transtheoretical model of change (Banyard et al., 2014), social norms theory (Berkowitz, 2016), and the theory of normative social behavior (Reynolds-Tylus et al., 2019). Collectively, these frameworks demonstrate the process of bystander intervention (Latané & Darley, 1970), the salience of students’ readiness to help prevent sexual violence (Banyard et al., 2014), perceptions of peers’ use of sexual aggression and intervention behavior (Berkowitz, 2016), and benefit of taking action (Reynolds-Tylus et al., 2019) in heightening students’ intent to intervene as bystanders. These studies make a valuable contribution to the field’s understanding of potentially at-risk groups for intervention engagement, as well as influences of prosocial actions as bystanders.

Although health-behavior theories are not widely used in the bystander literature, substantial evidence supports the utility of the theory of planned behavior (TPB; Ajzen, 1991) for predicting intention (for a review, see Armitage & Conner, 2001); using evidence-based theories to examine potential influences of prosocial behavior may uncover salient antecedents of helping. Researchers could also benefit from examining how theoretical antecedents of intention and bystander intention may vary by gender and drinking behavior. The TPB posits that personal attitudes, subjective norms, and perceived behavioral control (PBC) predict behavioral intention (Ajzen, 1991). “At its core, the TPB is concerned with the prediction of intentions” (Ajzen, 2011, p. 1115); given that much of the bystander research focuses on behavioral intentions as a primary outcome (Katz & Moore, 2013; Labhardt et al., 2017), and that intentions have been shown to correlate with intervention behavior (Banyard, 2008), understanding antecedents of students’ bystander intention may help guide programmatic initiatives.

Research using the TPB to predict health-related behaviors tend to find that attitudes, subjective norms, and PBC account for a relatively large portion of the variance (40–49%) in behavioral intentions (McEachan et al., 2011). Although all three components are strong predictors, attitudes toward the behavior tend to be the strongest predictor of intentions, with subjective norms being the weakest predictor (Armitage & Conner, 2001; McEachan et al., 2011). However, given that intervention against sexual violence is a complex behavior—which may be more strongly influenced by social concerns in college students (Flay et al., 2009)—it remains to be seen whether attitudes, subjective norms, and PBC are similarly predictive of intentions to intervene in the context of sexual violence as compared to predicting intentions to engage in other health behaviors.

Recently, the TPB has been applied to bystander intervention to prevent sexual violence. Specifically, Hoxmeier et al. (2018) found that students who self-reported prosocial intervention in several potential sexual assault situations reported greater PBC, more supportive subjective norms, and more positive attitudes, relative to students who did not intervene when presented with the opportunity to do so; however, the authors did not examine the explanatory power of the TPB variables for predicting intention.

Although only a few studies have applied the TPB specifically to the prediction of bystander intentions, there are many examples of scholars using theoretical constructs similar to those found in the TPB. For example, although they are operationalized differently, several studies have examined perceived social norms and found that people are more willing to intervene against sexual violence when they perceive positive peer norms regarding intervention (Brown et al., 2014; Murphy Austin et al., 2016). Similarly, researchers have examined self-efficacy and bystander confidence, constructs that are conceptually similar to PBC, and found these factors to predict bystander intentions (Banyard, 2008; Banyard & Moynihan, 2011; McMahon et al., 2015). There are fewer examples of studies measuring personal attitudes in similar ways to how the construct is operationalized in the TPB (i.e., the perceived helpfulness of a behavior) as a predictor of intentions; research examining bystander attitudes have often conceptualized bystander intentions as a measure of bystander attitudes (e.g., Banyard, 2008; McMahon et al., 2011) or have looked at attitudes like rape myth acceptance (RMA) (e.g., Kania & Cale, 2018; McMahon, 2010). Thus, although there is good reason to believe that attitudes, subjective norms, and PBC, as operationalized according to TPB, will predict intentions to intervene, more research is needed.

According to the TPB, there is room for other factors (e.g., individual demographics or social factors) to contribute to the prediction of intentions or behaviors (Ajzen, 1991, 2019). One commonly examined demographic variable in the bystander literature is gender (e.g., Brown et al., 2014); however, findings on its impact on bystander intentions are mixed. Alcohol consumption, while less explored in the bystander literature, may also impact bystander intentions and, like gender, there are mixed findings regarding the relation between alcohol consumption and bystander intention.

Gender Differences

When examining bystanders’ intention to intervene, researchers have found that women have higher intention to intervene than men (Banyard & Moynihan, 2011; Brown et al., 2014; Hoxmeier, Acock et al., 2020). Further, women report more positive attitudes about the helpfulness of bystander intervention (Hoxmeier, Acock et al., 2020), tend to have greater bystander efficacy (Banyard, 2008; Labhardt et al., 2017), and more bystander behaviors (Banyard & Moynihan, 2011) than men. Men have also been found to report more missed opportunities to intervene (Brown et al., 2014; Hoxmeier, Acock et al., 2020). However, it should be noted that not all studies have found gender differences (Katz et al., 2015; Melkonian et al., 2020). It is important to understand gender differences in variables related to bystander intervention; if researchers can consistently identify discrepancies among these variables, this can aid programming initiatives aimed at increasing bystander intervention.

Drinking Behavior

Another factor that may impact behavior or potentially moderate the influence of attitudes, subjective norms, or PBC on intentions is alcohol consumption. While some research has examined how alcohol consumption influences bystander intervention, there is a lack of consensus regarding how acute alcohol intoxication impacts bystanders’ intentions and behaviors. It has been suggested that men’s intervention behavior may reflect “liquid courage” (p. 1207), or heightened willingness to help in sexual assault situations as a result of their intoxication (Oesterle et al., 2018). This could be due to alcohol myopia (Steele & Josephs, 1990), in which acute intoxication narrows attention to only salient cues; if a bystander attends to a salient event, they may be less likely to suffer from the bystander effect (Latané & Darley, 1970). Supporting this hypothesis, van Bommel et al. (2016) found that intoxicated participants were quicker to intervene than sober participants when in the presence of others; however, this study did not pertain to bystander intervention within the context of sexual violence prevention. When applied to sexual violence, this hypothesis is contradicted by both field and lab findings: Leone and Parrott’s (2019) lab findings showed that intoxication negatively impacted bystander intervention among men who, when sober, expressed intent to intervene. Further, a field study by Melkonian et al. (2020) indicated that intoxication level did not influence bystanders’ intention to intervene in a hypothetical sexual assault vignette.

In addition to acute intoxication, researchers have also examined participants’ typical drinking patterns in relation to their bystander intention and intervention behaviors. But again, there are conflicting results, with some studies finding that people who engage in heavy episodic drinking (HED) report lower bystander intention than their non-heavy drinking peers (Orchowski et al., 2016) and others finding no differences (Leone & Parrott, 2019). When looking at engagement in bystander intervention, Leone and Parrott (2019) found that participants who reported prior HED actually had a higher probability of intervening than their non-HED counterparts within their laboratory analog of sexual violence.

Considering the paucity of research examining how intoxication can influence bystander intervention and the conflicting findings within the few studies that have examined these relations, further research is needed to understand how drinking behavior may influence bystanders. Although acute alcohol intoxication is likely to have the most immediate impact on bystander behavior, much of the data that bystander intervention researchers are interested in (e.g., program evaluations, campus climate surveys) cannot feasibly be collected with intoxicated students. Because much of the research on bystander intervention in this context is necessarily cross-sectional, it is important to better establish how a typical pattern of heavy drinking may influence bystander intention and behavior outside of qualitative and experimental settings.

Although typical drinking behavior might have direct associations with behavioral intentions to intervene as a prosocial bystander, it might also be related to attitudes, norms, or PBC and/or moderate the associations between these factors and behavioral intentions. Alcohol intoxication has been shown to be associated with greater acceptance of sexual violence and lower victim empathy among men (Johnson et al., 2000; Norris et al., 1999) and lower awareness of and discomfort with cues signaling sexual assault risk among women (Davis et al., 2009). Furthermore, both men and women who drink at fraternity and athletic parties have been found to hold more stereotypical views of rape and to see sexual assault as less common than do students who don’t drink (Boyle & Walker, 2016). These findings suggest that people who engage in HED might believe bystander intervention to be less necessary or helpful than those who do not engage in HED (lower attitudes) and/or that their bystander attitudes may be less strongly related to their willingness to intervene. Although we are not aware of research looking specifically at the role of alcohol on perceptions of norms or behavioral control specific to bystander intervention to prevent sexual violence, Davis and her colleagues (2014, 2016) have looked at intoxication in relation to predictors of intentions to use condoms. They found that intoxicated participants had more favorable condom use resistance attitudes, normative perceptions, and greater self-efficacy than sober participants (Davis et al., 2016). However, while Davis et al. (2014) found that the relation between condom use self-efficacy and condom negation intentions was stronger among intoxicated women than sober women, Davis et al. (2016) found that intoxication did not moderate the prediction of intentions from attitudes, norms, or self-efficacy. Together these findings could suggest that moderating role of drinking behavior in the prediction of intentions from attitudes, subjective norms, and PBC may be contextually dependent. However, researchers have yet to examine if drinking behavior may be a moderator when predicting intentions using the TPB in the context of bystander intervention to prevent sexual violence.

Current Study

There is limited use of health-behavior theory in the bystander literature, but the TPB seems to hold the most promise. However, few studies have tested the predictive utility of TPB antecedents in predicting bystander’s behavioral intention to prevent sexual violence. Furthermore, the intertwined and pervasive occurrence of sexual violence and alcohol consumption found on college campuses, and the mixed findings regarding the relationship between drinking behavior and bystander intervention underscore the necessity for further research in this area. Thus, the current study had three primary aims. The first aim was to examine how much of the variance in bystanders’ intention to intervene can be accounted for based on attitudes, subjective norms, and PBC. The second aim was to examine if prior engagement in HED moderated the prediction of intentions from attitudes, subjective norms, and PBC. The third and final aim of the current study was to examine differences in attitudes, subjective norms, PBC, and intention based on (a) gender and (b) prior engagement in HED.

Methods

Procedure

Participants were recruited at a southern university using the psychology department’s research participant pool in the Spring and Fall semesters of 2020. The research participation pool is open to students taking a variety of psychology classes, but most participants come from introductory classes that include students from all majors across campus. The study was listed on the department’s SONA website where students could volunteer to participate. Once participants signed up for the study, they were provided a link to the survey on Qualtrics. The first page of the survey provided participants with information about the study and asked them to indicate whether they consented to proceed with the study. All participants consented. Upon completion, participants were given additional information about the study, resources available in the community, and contact information for researchers and the IRB. Participants were rewarded course credit for completing the survey. All study procedures were approved by the first author’s institutional review board.

Participants

A total of 434 participants completed the study; however, 39 participants (9%) were removed due to the following reasons: (a) the participant was under 18 years old or did not report their age (n = 8) or (b) for having duplicate responses (n = 31). Of participants included in analyses (N = 395), most identified as White, Non-Hispanic (70%), female (77%), heterosexual (84%), first-year students (62%) with a mean age of 19.7 years old (SD = 3.5). Two participants reported their gender as neither male nor female (one “trans-male” and one “other”) and were excluded from analyses that used gender as a variable. Full sociodemographic information for participants are presented in Table 1. A one-way analysis of variance (ANOVA) and chi-square analyses were used to test for significant differences between participants who were excluded and participants who were included. There were no significant differences between the two groups of participants regarding their age, gender, sexual orientation, academic classification, nor race/ethnicity.

Table 1.

Participant Sociodemographic Characteristics.

Sociodemographic	n	%
Race/Ethnicity
White, Non-Hispanic	275	70
Black or African American	75	19
Hispanic	14	4
Asian/Pacific Islander	7	2
American/Alaskan Native	3	< 1
Other/Multiple	20	5
Missing Data	1	—
Gender
Female	303	77
Male	89	23
Other	2	< 1
Missing Data	1	—
Sexual Orientation
Heterosexual	327	84
Non-Heterosexual	64	16
Missing Data	4	—
Academic Year
First Year	242	62
Second Year	80	20
Third Year	32	8
Fourth Year or Later	38	10
Missing Data	3	—

Measures

Bystander variables

A modified version of the Sexual Assault Bystander Behavior Questionnaire (SABB-Q; Hoxmeier et al., 2018) assessed participants’ attitudes, subjective norms, PBC, and intention to engage in bystander intervention. In the original SABB-Q, items addressed pre-assault, mid-assault, and post-assault bystander intervention. The current study modified the measure by removing post-assault bystander actions to reduce the likelihood of participant fatigue. Thus, four items pertaining to pre-assault bystander actions (e.g., “Confront your friend who says he plans to get a girl drunk to have sex”) and four items pertaining to mid-assault bystander actions (e.g., “Interrupt the situation when you walk in on a guy who appears to be forcing your friend to have sex with him”) were used in this study. Consistent with the original wording of the measure (Hoxmeier et al., 2018), the (potential) perpetrator was described as a man in all items. The vast majority of SV perpetrators are men (Federal Bureau of Investigation, 2020), and research suggests that people think violence perpetrated by women is less problematic than violence perpetrated by men (White & Dutton, 2013). For these reasons, we kept the perpetrator description as male. However, for most items (the example item mentioning a “girl” being the one exception) the gender of the (potential) victim was unspecified. This is a modification from the original, which used more gender-specific descriptions.

Attitudes were assessed by asking participants to indicate how unhelpful or helpful they believed each action to be. Subjective norms were assessed by asking participants to indicate how much they believed their good friends would disapprove or approve if they engaged in each action. PBC was assessed by asking participants to indicate how difficult or easy performing each action would be. Intention was assessed by asking participants to indicate how unlikely or likely they are to take each action. For each section, response options were presented as a 7-point rating scale with higher responses indicating more positive perceptions of bystander intervention. Variables were scored by calculating the mean response of the eight items for each section. This measure has previously shown high internal reliability (Cronbach’s a < .90; Hoxmeier et al., 2018). In the current sample, each of these variables had high internal reliability (see Table 2).

Table 2.

Correlations for Attitudes, Subjective Norms, Perceived Behavioral Control, Intention, and Heavy Episodic Drinking.

Variable	Cronbach’s α	1	2	3	4
1. Attitude	.93	—
2. Subjective Norms	.93	.42***	—
3. Perceived Behavioral Control	.94	.34***	.23***	—
4. Intention	.93	.42***	.51***	44***	—
5. Heavy Episodic Drinking^a		.00	−.07	−.00	−.02

^a 0 = No HED, 1 = HED.

* p < .05, ** p < .01, *** p < .001

Drinking behavior

Drinking behavior was assessed using the Daily Drinking Questionnaire (DDQ; Collins et al., 1985). This measure provided participants with information about standard drink sizes (i.e., 1.5oz of liquor, 5oz wine, or 12oz of beer; National Institute on Alcohol Abuse and Alcoholism [NIAAA], 2020a) and asked participants to indicate the average number of hours and the average number of drinks consumed each day of the week during a typical week within the past month. For the current study, male participants who consumed five or more drinks in a single occasion and female participants who consumed four or more drinks in a single occasion were categorized as having engaged in HED (NIAAA, 2020b); participants who reported drinking behavior but did not meet the criteria for HED were categorized as no HED. A total of 23 participants did not report drinking behavior and were not included in analyses involving drinking behavior.

Analytic Strategy

Bivariate correlations were used to examine the relations among attitudes, subjective norms, PBC, and intention and biserial correlations were used to examine how these variables related to engagement in HED. To examine the first and second aims of the study we used a multiple regression to examine (a) how much of the variance in bystander intention is accounted for based on participants’ attitudes, subjective norms, and PBC and (b) examine if the relations between bystander intention and attitudes, subjective norms, and PBC were moderated by HED. Multiple regression analysis controlled for participant gender and sexual orientation. To examine the third aim of the study, we conducted a multivariate analysis of variance (MANOVA) testing for differences in attitudes, subjective norms, PBC, and intention, based on participant gender and prior engagement in HED. Given that much of the literature examining bystander intentions and drinking behavior exclusively examines male participants (e.g., Leone & Parrott, 2019; Oesterle et al., 2018; Orchowski al., 2016), we additionally included the interaction between gender and prior engagement in HED for exploratory purposes and controlled for participant sexual orientation. Significant multivariate effects were further examined using univariate analyses.

Results

Analyses were conducted using JMP Pro v. 15. None of the variables were normally distributed and all were positively skewed. Data transformations were attempted to improve the normality of the distribution. However, no transformations significantly improved normality; thus, we proceeded with the untransformed data. Internal reliability for constructs and correlations among TPB constructs as well as HED are presented in Table 2. Approximately one-third (32%) of the current sample reported prior HED. While attitudes, subjective norms, PBC, and intention were all significantly and positively correlated with each other, none were associated with HED.

First, we used a multiple regression analysis to (a) examine how well attitudes, subjective norms, and PBC predict bystanders’ intention and (b) to examine if the relations between attitude, subjective norms, and PBC are moderated by HED when predicting intention. Attitudes, subjective norms, PBC, HED, and the interactions between HED and other variables were entered as predictors for bystander intention, while controlling for gender. The regression model was significant, F (9, 352) = 25.22, p < .001, R² = .38. Variance inflation factor scores ranged from 1.01 to 1.47, suggesting minor—but acceptable—levels of multicollinearity (Sheather, 2009). As shown in Table 3, attitudes, subjective norms, and PBC were all significant positive predictors of bystander intention. However, neither the main effect for HED nor any of the interactions between HED and the TPB constructs were significant. Thus, HED did not moderate the relation between attitudes, subjective norms, and PBC when predicting intention.

Table 3.

Multiple Regression Analysis on Bystander Intention.

Variable	B	95% CI		SE	t	β
Variable	B	LL	UL	SE	t	β
Intercept	2.23	1.57	2.90	.34	6.61***
Attitudes	.12	.02	.23	.05	2.32*	.12
Subjective Norms	.31	023	.39	.04	7.46***	.37
PBC	.24	.17	.31	.03	7.08***	.34
HED	−.01	−.10	.07	.04	−.28	−.01
Attitudes*HED	.01	−.09	.11	.05	.19	.01
Subjective Norms*HED	.05	−.03	.13	.04	1.19	.06
PBC*HED	−.05	−.12	.01	.03	−1.64	−.08
R ²	.38

* p < .05, ** p < .01, *** p < .001

The multivariate model testing for differences in attitudes, subjective norms, PBC, and intention based on gender, prior engagement in HED, and the interaction between gender and prior engagement in HED was significant, Wilks’s Λ = .93, F (16, 1082.1) = 1.94, p = .034. As shown in Table 4, the main effect for gender was significant, suggesting that one or more of the variables differed by gender. However, neither the main effect for HED nor the interaction between gender and HED were significant, indicating that the variables examined did not differ based on engagement in HED.

Table 4.

MANOVA Testing for Group Differences in Attitudes, Norms, PBC, and Intention.

Effect	F Value	p	Effect Size
Whole Model	F (16, 1082.1) = 1.94	.034	Wilks’s Λ = .93
Gender	F (4, 357) = 4.88	< .001	η² = .01
HED	F (4, 357) = .51	.736	η² = .00
Gender*HED	F (4,357) = .12	.975	η² = .00

To further examine which variables differed by participants’ gender, we conducted four follow-up analyses of covariance (ANCOVAs), each testing for gender differences for a separate construct. The ANCOVAs for PBC and intention were not significant. However, the ANCOVAs for attitudes, F (2, 381) = 6.31, p = .002, η² = .03; and subjective norms, F (2, 382) = 8.39, p < .001, η² = .04, were significant.¹ Specifically, female participants had more positive attitudes and more supportive subjective norms toward bystander intervention than did male participants. Mean differences by gender are presented in Table 5.

Table 5.

ANCOVA Examining Gender Differences.

Variable	Male		Female		t Value	Cohen’s d
Variable	M	SD	M	SD	t Value	Cohen’s d
Attitudes	5.92	1.13	6.30	0.85	−3.53***	.38
Subjective Norms	5.76	1.43	6.32	1.05	−3.92***	.45
PBC	5.57	1.33	5.53	1.44	.16	.03
Intention	6.15	1.00	6.36	0.97	−1.94	.21

* p < .05, ** p < .01, *** p < .001

Discussion

The purpose of the current study was to investigate the utility of the TPB within the context of bystander intervention to prevent sexual violence. In addition, we sought to examine the potential roles gender and HED may play.

Utility of the TPB for Predicting Bystander Intention

The first aim of the current study was to examine how much of the variance in bystanders’ intention to intervene can be accounted for based on attitudes, subjective norms, and PBC. Consistent with previous research testing the utility of the TPB (Armitage & Conner, 2001; McEachan et al., 2011), we found that all three antecedents of behavioral intention (i.e., personal attitudes, subjective norms, and PBC) positively associated with behavioral intention. However, contrary to the same reviews, the current study found that subjective norms (i.e., participants’ perception of their friends’ approval of intervention) were a stronger predictor of intention compared to attitudes (i.e., participants' perception of how helpful intervening would be). There is potential that the current findings may have resulted from the age of participants; there is support that the relative salience of these theoretical constructs may vary across the life course (Flay et al., 2009). Additionally, our examination of proximal subjective norms (i.e., perceptions of “good friends’” support for intervening) may have resulted in the stronger relationship than found elsewhere, given how the theory of reasoned action—the predecessor of the TPB—articulates how the importance of subjective norms stems from individuals’ motivation to comply with referent others (Ajzen & Fishbein, 1980). That is, students are likely more motivated to comply with their good friends as compared to other people more generally. The current findings underscore the importance of further research studying this construct (Armitage & Conner, 2001).

Although operationalized differently, the findings here are consistent with other studies demonstrating the relationship between norms and intentions. For example, Murphy Austin et al. (2016) found that men’s perception of their peers’ prosocial bystander intervention intentions predicted their own intention to intervene. Similarly, Brown et al. (2014), who assessed male and female students’ perceptions of peer approval for intervention, found that perceptions of peer approval positively predicted intention. Given the limited research examining attitudes as operationalized here, it is not possible to compare how well these attitudes predict intention relative to other studies adopting the TPB in the context of bystander intervention to prevent sexual violence. However, other sexual violence-related attitudes, such as RMA have been used to predict bystander intention. For example, McMahon (2010) found a significant negative relation between RMA and bystander intervention attitudes; however, it is difficult to assess whether RMA is a more predictive attitude for intervention intentions than the perception of helpfulness for preventing sexual assault. Given the variety of bystander intervention actions examined across different studies, the strength of the relation between attitude and intention may vary.

Prior research has shown correlations among the TPB constructs range from .40 to .57 and demonstrate utility in predicting behavioral intentions, explaining 40–49% of the variance in intentions for a range of health-related behaviors (McEachan et al., 2011). While the portion of the variance in intention explained by the current model (i.e., .38) is similar to that reported in McEachan et al. (2011), the correlations among the TPB constructs here ranged from .23–.51. Our findings of the salience, and overall explanatory power, of the TPB variables for predicting intention differed from Lukacena et al. (2019). The model in the current study accounted for 38% of the variance in intention while the model from Lukacena et al. (2019) accounted for 63% of the variance in intention; however, they had expanded the constructs of attitudes, norms, and PBC and included six predictors in their model, which may be why their model accounted for a greater portion of the variance in intentions. Also importantly, Lukacena et al. (2019) operationalized prosocial bystander action as “intervene to prevent sexual assault” (p. 50) and used that language consistently within measures for intention and theoretical antecedents. While their operational definition of prosocial bystander action is consistent with a general behavior description (see Fishbein & Ajzen, 2010), the measure used within the current study was in reference to distinct bystander actions. Because sexual assault exists on a continuum (McMahon et al., 2015) and students have shown variation in their intentions based on the type of intervention assessed (Hoxmeier et al., 2015), health-behavior theories such as the TPB may differ in their utility for predicting bystander intervention intentions. Also, important to note is that while this study was focused specifically on TPB variables, other variables can add to the prediction of bystander intervention intention, including the perceived cost of intervening (Dovidio, 1984), RMA (McMahon, 2010), and empathy (Hayashi & Tahmasbi, 2021).

The second aim was to examine if prior engagement in HED moderated the prediction of intentions from attitudes, subjective norms, and PBC. Our regression analyses indicated that prior engagement in HED did not predict bystander intention, nor did it moderate the relation between other theoretical antecedents and bystander intention to intervene.

There are mixed findings regarding the influence of drinking behavior, whether self-reported or in a lab setting, and this remains a critical area of study given the ubiquity of alcohol on campus, that sexual violence often occurs in social settings where alcohol is present, and scholars’ continued pursuit to understand students’ barriers to bystander intervention in sexual assault risk situations. Laboratory studies (e.g., Leone & Parrott, 2019) are crucial for studying the impact of alcohol intoxication, as dosage can be controlled and self-report bias can be reduced. Furthermore, experimental designs that allow researchers to observe bystander intervention are vital for studying behavior (Jouriles et al., 2016). However, these types of designs may be infeasible on larger scales. Indeed, as the use of bystander intervention programming is commonly used among institutions (Orchowski et al., 2020) and campus climate surveys increasingly use measures to assess bystander intervention experiences (Wood et al., 2017), it is crucial to find effective methods for capturing the potential impact alcohol consumption has on bystander intentions and behaviors for program evaluation or on-going assessment.

Group Differences in TPB Constructs

The third and final aim of the current study was to examine differences in attitudes, subjective norms, PBC, and intention based on gender and prior engagement in HED. Our findings indicated that female participants had more supportive subjective norms regarding bystander intervention when compared to their male peers. Few prior studies have examined how subjective norms, as conceptualized by the TPB (Fishbein & Ajzen, 2010), differ by gender when applied to bystander intervention in the context of sexual violence. However, our findings are consistent with those reported by Hoxmeier, Acock, and Flay (2020) in that women had more supportive subjective norms toward bystander intervention than men. One potential explanation for the gender differences in attitudes and subjective norms could be participants’ perception of gender norms. Previous researchers have found that men may not want to intervene due to not wanting to be a “cock blocker” (Koelsch et al., 2012). Other research has also suggested that men generally perceive that other men are more accepting of violence than they themselves are (Brown & Messman-Moore, 2010), and that those perceptions may relate to men believing their friends would disapprove of their intervention. The current findings underscore the importance of continued efforts to engage men with SV prevention programming.

The current findings regarding gender differences are inconsistent with prior research demonstrating that when compared to men, women have greater bystander intention (Banyard & Moynihan, 2011; Brown et al., 2014; Hoxmeier, Acock et al., 2020) and greater bystander confidence (Banyard, 2008). However, this is not the only study to fail to find gender differences in bystander intention and bystander confidence to intervene: Katz et al. (2015) found no differences between men and women in their intent to help an intoxicated woman at risk for sexual assault in a hypothetical party scenario; Melkonian et al. (2020) failed to find main effects or interactions (with participant intoxication or stated victim or perpetrator intoxication) involving participant gender on reported confidence to intervene in a different hypothetical party scenario.

When testing for differences in the TPB constructs based on prior engagement in HED, no differences were detected. In their experimental study, Leone and Parrott (2019) found that alcohol served as a barrier to prosocial intervention among men who otherwise expressed willingness to intervene, suggesting that prosocial helping diminishes as men consume alcohol. However, our findings demonstrate that outside of experimental settings, a typical pattern of heavy drinking does not necessarily relate to evidence-based antecedents of intervention, including behavioral intentions.

Our findings are different from those of Orchowski et al. (2016), who found that, among heavy drinking men, intention to intervene as bystanders was lower relative to their non-heavy drinking counterparts in many situations examined. It should be noted that the measure of bystander intentions used by Orchowski et al. (2016) differed from the one used in the current study, as they assessed a broader range of prosocial helping behaviors. In their study, heavy drinking men reported lower intention to intervene compared to their non-heavy-drinking counterparts in situations of individual party safety (i.e., “Make sure I leave the party with the same people I came with”), where intervention is non-confrontational to perpetrators (i.e., “Call 911 if I hear someone yelling and fighting”), providing post-assault support (i.e., “Call a rape crisis center if an acquaintance were sexually assaulted”), and those not specific to sexual violence (i.e., “Speak up against racist jokes”). Our measure, which focused on high-risk situations before and during an assault, did not include similar situations. However, Orchowski et al. (2016) found no differences in intentions between heavy drinking men and their counterparts for some intervention behaviors that were represented in the current study (e.g., “If I saw a friend taking a very intoxicated person up the stairs to my friends’ room, I would say something”). These findings suggest that typical patterns of heavy drinking (i.e., engaging in HED), may relate to bystander’s intention in some situations, but not in others.

Limitations & Future Directions

One limitation of the current study is the period in which data were collected. Data collection occurred over two separate periods, from March 1 - April 20, 2020, and again from August 17 – September 7, 2020. During this time, social distancing guidelines were set in place to avoid the rapid spread of COVID-19. Specifically, a stay-at-home order was implemented in the state where data were collected on March 22, 2020, and this order remained in place through the end of April. Although some restrictions had been lifted by August, social distancing guidelines were still in place within the state and were enforced by the university. Thus, although some of the data in the current study were collected prior to when social distancing guidelines were implemented, the majority were collected afterward. It is likely that these guidelines greatly reduced the frequency of social gatherings and other opportunities for participants to engage in HED. Thus, the current reported drinking behavior may not be representative of typical college student drinking behavior.

The current study is also limited by other measurement issues. To start, the items used for the SABB-Q all described male perpetrators, and while most items described gender-neutral victims (i.e., “your friend” or “someone”), one item described a female victim and a male perpetrator (i.e., “confront your friend who says he plans to get a girl drunk to have sex”). Due to the gendered nature of the measure, it is possible that we missed some situations where bystanders could intervene (e.g., instances with female or non-binary perpetrators). Because the behaviors assessed by the SABB-Q are considered high risk, participants may have reported higher intention to intervene than would be expected in more ambiguous situations (McMahon & Banyard, 2012).

While previous research on the TPB has supported the use of intention as a proxy for behavior in relation to behaviors related to cessation of smoking, exercising, and eating healthy (Fishbein & Ajzen, 2010), there is reason to believe that the relation between intentions and behaviors may be less robust when considering sexual violence intervention. Research examining bystander intervention in relation to sexual violence has found that while norms significantly predicted intention to intervene, they did not significantly predict engagement in bystander behavior (Brown et al., 2014). Further, Banyard and Moynihan (2011) found that peer norms regarding the acceptance of coercive behaviors in intimate relationships negatively predicted bystander intention; however, they positively predicted bystander behavior. In addition, some researchers have found that bystander intention is correlated with bystander behavior (Banyard, 2008), but others show that intention is not related to bystander behavior (Murphy Austin et al., 2016). Future research studying predictors of bystander intention should additionally examine whether antecedents to intention also predict engagement in bystander behaviors. Further, while Fishbein and Ajzen (2010) discuss various studies demonstrating the link between behavioral intention with past and future behaviors, researchers adopting the TPB in this context should seek to employ longitudinal designs that collect information on previous behavior, future intention, and behaviors at a later timepoint.

Finally, we are limited in our ability to generalize the current findings to all college students. While our sample demographics were similar to students enrolled at the university where the study was conducted, our sample included predominantly heterosexual and White, Non-Hispanic participants. Some previous research has suggested that antecedents of intention, such as perceptions of social norms, may differ by race/ethnicity (Brown et al., 2014). While not within the scope of the current study, it is necessary for future researchers to explore how theoretical antecedents of intention may vary across race and ethnicity. Considering the plethora of research on gender differences in the bystander literature, it is also crucial that future research in this area include samples that include all genders, not only men and women. Likewise, there is little research examining how sexual orientation may influence bystander attitudes and intentions. Results of one recent study showed variation in bystander intervention across gender and sexual minority identifying participants and that gender- and sexual-minority participants reported more opportunities to intervene (Hoxmeier, Mennicke, et al., 2020) compared to cisgender and heterosexual participants. Another recent study found that sexual minority girls detected more intervention opportunities than did boys or sexual majority girls (Waterman et al., 2020). Future research in these populations is necessary to replicate these findings and additionally examine potential differences in attitudes, subjective norms, PBC, and behavioral intention regarding bystander intervention.

Conclusion

There are many opportunities for bystander intervention and a range of ways that people can intervene. These contexts—and the range of measures that researchers use—likely contributes to the mixed findings reported in the literature regarding whether, and how, drinking behavior interacts with antecedents of intention predicted by the TPB and other antecedents to bystander intention and intervention. Considering that our findings demonstrated gender differences in attitudes and subjective norms, with men being lower than women in both, it is crucial that preventionist continue efforts to engage men with sexual violence prevention programming. While this study provides initial support for the utility of the TPB for predicting bystander intention to intervene to prevent sexual violence, additional research is needed to test the full theory, including behaviors. Despite our finding that HED did not moderate the relations between attitudes, subjective norms, or PBC and bystander intention, previous literature supports that HED is problematic for sexual violence at large (e.g., Bellis et al., 2020). Thus, initiatives to encourage bystander intervention could benefit from addressing HED because of its relationship to sexual violence perpetration.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Dylan A. John

Jill C. Hoxmeier

Notes

Author Biographies

Dylan A. John, MS, is a recent graduate from the University of Louisiana at Lafayette. His research broadly examines sexual violence prevention through bystander intervention. He is interested in examining the utility of health-behavior theory for predicting bystander intentions and behavior and examining how typical drinking behavior may influence prosocial behavior.

Jill C. Hoxmeier is an Associate Professor in the Department of Health Sciences at Central Washington University. Her research focuses on understanding the role of prosocial bystanders in the prevention and intervention of sexual violence.

Amy L. Brown, PhD, is an Associate Professor of Psychology at the University of Louisiana at Lafayette. She researches perceptions of sexual violence, bystander intervention against sexual violence, and the role of alcohol in impacting these processes.

References

Abbey

(2002). Alcohol-related sexual assault: A common problem among college students. Journal of Studies on Alcohol, Supplement(14), 118–128. https://doi.org/10.15288/jsas.2002.s14.118.

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T.

Ajzen

(2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9), 1113–1127. https://doi.org/10.1080/08870446.2011.613995.

Ajzen

(2019). TPB with background factors. Icek Ajzen.https://people.umass.edu/aizen/tpb.background.html.

Ajzen

Fishbein

(1980). Understanding attitudes and predicting social behavior. Prentice-Hall.

Armitage

C. J.

Conner

(2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471–499. https://doi.org/10.1348/014466601164939.

Banyard

V. L.

(2008). Measurement and correlates of prosocial bystander behavior: The case of interpersonal violence. Violence and Victims, 23(1), 83–97. https://doi.org/10.1891/0886-6708.23.1.83.

Banyard

V. L.

Moynihan

M. M.

(2011). Variation in bystander behavior related to sexual and intimate partner violence prevention: Correlates in a sample of college students. Psychology of Violence, 1(4), 287–301. https://doi.org/10.1037/a0023544.

Banyard

V. L.

Moynihan

M. M.

Cares

A. C.

Warner

(2014). How do we know if it works? Measuring outcomes in bystander-focused abuse prevention on campuses. Psychology of Violence, 4(1), 101–115. https://doi.org/10.1037/a0033470.

10.

Bellis

A. L.

Swartout

K. M.

Salazar

L. F.

(2020). College-level perceptions of drinking, binge drinking, and sexual violence perpetration: A multilevel mediation model. Journal of American College Health, 70(6), 1688–1695. https://doi.org/10.1080/07448481.2020.1818756.

11.

Berkowitz

A. D.

(2016). Integrating bystander intervention and norms correction as a prevention strategy. [White paper] https://doi.org/10.13140/RG.2.2.22099.17448.

12.

Boyle

K. M.

Walker

L. S.

(2016). The neutralization and denial of sexual violence in college party subcultures. Deviant Behavior, 37(12), 1392–1410. http://doi.org/10.1080/01639625.2016.1185862.

13.

Brown

A. L.

Banyard

V. L.

Moynihan

M. M.

(2014). College students as helpful bystanders against sexual violence: Gender, race, and year in college moderate the impact of perceived peer norms. Psychology of Women Quarterly, 38(3), 350–362. https://doi.org/10.1177/0361684314526855.

14.

Brown

A. L.

Messman-Moore

T. L.

(2010). Personal and perceived peer attitudes supporting sexual aggression as predictors of male college students’ willingness to intervene against sexual aggression. Journal of Interpersonal Violence, 25(3), 503–517. https://doi.org/10.1177/0886260509334400.

15.

Collins

R. L.

Parks

G. A.

Marlatt

G. A.

(1985). Social determinants of alcohol consumption: The effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting and Clinical Psychology, 53(2), 189–200. https://doi.org/10.1037/0022-006X.53.2.189.

16.

Davis

K. C.

Jacques-Tiura

A. J.

Stappenbeck

C. A.

Danube

C. L.

Morrison

D. M.

Norris

George

W. H.

(2016). Men’s condom use resistance: Alcohol effects on theory of planned behavior constructs. Health Psychology, 35(2), 178–186. https://doi.org/10.1037/hea0000269.

17.

Davis

K. C.

Masters

N. T.

Eakins

Danube

C. L.

George

W. H.

Norris

Heiman

J. R.

(2014). Alcohol intoxication and condom use self-efficacy effects on women’s condom use intentions. Addictive Behaviors, 39(1), 153–158. https://doi.org/10.1016/j.addbeh.2013.09.019.

18.

Davis

K. C.

Stoner

S. A.

Norris

George

W.H.

Masters

N.T.

(2009). Women’s awareness of and discomfort with sexual assault cues: Effects of alcohol consumption and relationship type. Violence Against Women, 15(9), 1106–1125. https://doi.org/10.1177/1077801209340759.

19.

DeGue

Valle

L. A.

Holt

M. K.

Massetti

G. M.

Matjasko

J. L.

Tharp

A. T.

(2014). A systematic review of primary prevention strategies for sexual violence perpetration. Aggressive & Violent Behavior, 19(4), 346–362. https://doi.org/10.1016/j.avb.2014.05.004.

20.

Dovidio

J. F.

(1984). Helping behavior and altruism: An empirical and conceptual overview. In Berkowitz

(Ed.), Advances in experimental social psychology (17, pp. 361-427). Academic Press, Inc. https://doi.org/10.1016/S0065-2601(08)60123-9.

21.

Federal Bureau of Investigation (2020). Crime data explorer: Rape offender vs victim demographics. https://crime-data-explorer.app.cloud.gov/pages/explorer/crime/crime-trend.

22.

Fishbein

Ajzen

(2010). Predicting and changing behavior: The reasoned action approach. Psychology Press.

23.

Flay

B. R.

Snyder

F. J.

Petraitis

(2009). The theory of triadic influence. In DiClemente

R. J.

Crosby

R. A.

Kegler

M. C.

(Eds.), Emerging Theories in Health Promotion Practice and Research (pp. 451–510). Jossey-Bass/Wiley.

24.

Fleming

W. M.

Wiersma-Mosley

J. D.

(2015). The role of alcohol consumption patterns and pro-social bystander interventions in contexts of gender violence. Violence Against Women, 21(10), 1259–1283. https://doi.org/10.1177/1077801215592721.

25.

Hayashi

Tahmasbi

(2021). Psychological predictors of bystanders’ intention to help cyberbullying victims among college students: An application of the theory of planned behavior. Journal of Interpersonal Violence, 37(13–14), NP11333–NP11357 https://doi.org/10.1177/0886260521992158.

26.

Hoxmeier

J. C.

Acock

A. C.

Flay

B. R.

(2020). Students as prosocial bystanders to sexual assault: Demographic correlates of intervention norms, intentions, and missed opportunities. Journal of Interpersonal Violence, 35(3–4), 731–754. https://doi.org/10.1177/0886260517689888.

27.

Hoxmeier

J. C.

Flay

B. R.

Acock

A. C.

(2015). When will students intervene? Differences in students’ intent to intervene in a spectrum of sexual assault situations. Violence and Gender, 2(3), 179–184. https://doi.org/10.1089/vio.2015/0015.

28.

Hoxmeier

J. C.

Flay

B. R.

Acock

A. C.

(2018). Control, norms, and attitudes: Differences between students who do and do not intervene as bystanders to sexual assault. Journal of Interpersonal Violence, 33(15), 2379–2401. https://doi.org/10.1177/0886260515625503.

29.

Hoxmeier

J. C.

Mennicke

McMahon

(2022). Bystander intervention opportunities and prosocial behaviors among gender and sexual minority college students. Journal of Interpersonal Violence, 37(9–10), NP6439–NP6465. https://doi.org/10.1177/0886260520967131.

30.

Johnson

J. D.

Noel

N. E.

Sutter-Hernandez

(2000). Alcohol and male acceptance of sexual behavior. Journal of Applied Social Psychology, 30(6), 1186–1200. https://doi.org/10.1111/j.1559-1816.2000.tb02516.x.

31.

Jouriles

E. N.

Kleinsasser

Rosenfield

McDonald

(2016). Measuring bystander behavior to prevent sexual violence: Moving beyond self reports. Psychology of Violence, 6(1), 73–81. https://doi.org/10.1037/a0038230.

32.

Kania

Cale

(2021). Preventing sexual violence through bystander intervention: Attitudes, behaviors, missed opportunities, and barriers to intervention among Australian university students. Journal of Interpersonal Violence, 36(5–6), 2816–2840. https://doi.org/10.1177/0886260518764395.

33.

Katz

Moore

(2013). Bystander education training for campus sexual assault prevention: An initial meta-analysis. Violence and Victims, 28(6), 1054–1067. https://doi.org/10.1891/0886-6708.VV-D-12-00113.

34.

Katz

Pazienza

Olin

Rich

(2015). That’s what friends are for: Bystander responses to friends or strangers at risk for party rape victimization. Journal of Interpersonal Violence, 30(16), 2775–2792. https://doi.org/10.1177/0886260514554290.

35.

Koelsch

L. E.

Brown

A. L.

Boisen

(2012). Bystander perceptions: Implications for university sexual assault prevention programs. Violence and Victims, 27(4), 563–579. https://doi.org/10.1891/0886-6708.274.563.

36.

Labhardt

Holdsworth

Brown

Howat

(2017). You see but you do not observe: A review of bystander intervention and sexual assault on university campuses. Aggression and Violent Behavior, 35, 13-25. https://doi.org/10.1016/j.avb.2017.05.005.

37.

Latané

Darley

J. M.

(1970). The unresponsive bystander: Why doesn’t he help. Prentice-Hall.

38.

Lawyer

Resnick

Bakanic

Burkett

Kilpatrick

(2010). Forcible, drug-facilitated, and incapacitated rape and sexual assault among undergraduate women. Journal of American College Health, 58(5), 453–460. https://doi.org/10.1080/07448480903540515.

39.

Leone

R. M.

Parrott

D. J.

(2019). Acute alcohol intoxication inhibits bystander intervention behavior for sexual aggression among men with high intent to help. Alcoholism: Clinical and Experimental Research, 43(1), 170–179. https://doi.org/10.1111/acer.13920.

40.

Lukacena

K. M.

Reynolds-Tylus

Quick

B. L.

(2019). An application of the reasoned action approach to bystander intervention for sexual assault. Health Communication, 34(1), 46–53. https://doi.org/10.1080/10410236.2017.1384356.

41.

McEachan

R. R. C.

Conner

Taylor

N. J.

Lawton

R. J.

(2011). Prospective prediction of health-related behaviours with the theory of planned behaviour: A meta-analysis. Health Psychology Review, 5(2), 97–144. https://doi.org/10.1080/17437199.2010.521684.

42.

McMahon

(2010). Rape myth beliefs and bystander attitudes among incoming college students. Journal of American College Health, 59(1), 3–11. https://doi.org/10.1080/07448481.2010.483715.

43.

McMahon

Banyard

V. L.

(2012). When can I help? A conceptual framework for the prevention of sexual violence through bystander intervention. Trauma, Violence, & Abuse, 13(1), 3–14. https://doi.org/10.1177/1524838011426015.

44.

McMahon

Peterson

A. N.

Winter

S. C.

Palmer

J. E.

Postmus

J. L.

Koenick

R. A.

(2015). Predicting bystander behavior to prevent sexual assault on college campuses: The role of self-efficacy and intent. American Journal of Community Psychology, 56(1–2), 46–56. https://doi.org/10.1007/s10464-015-9740-0.

45.

McMahon

Postmus

J. L.

Koenick

R. A.

(2011). Conceptualizing the engaging bystander approach to sexual violence prevention on college campuses. Journal of College Student Development, 52(1), 115–130. https://doi.org/10.1353/csd.2011.0002.

46.

Melkonian

A. J.

Ham

L. S.

Wiersma-Mosley

J. D.

Jackson

K. K.

Mobley

A. M.

Jozkowski

K. N.

Willis

Bridges

A. J.

(2020). Alcohol intoxication impairs the bystander intervention process in a hypothetical sexual assault: A field investigation. Psychology of Violence, 10(6), 657–666. https://doi.org/10.1037/vio0000283.

47.

Murphy Austin

M. J.

Dardis

C. M.

Wilson

M. S.

Gidycz

C. A.

Berkowitz

A. D.

(2016). Predictors of sexual assault-specific prosocial bystander behavior and intentions: A prospective analysis. Violence Against Women, 22(1), 90–111. https://doi.org/10.1177/1077801215597790.

48.

National Institute on Alcohol Abuse and Alcoholism . (2020a). What is a standard drink? https://www.niaaa.nih.gov/what-standard-drink

49.

National Institute on Alcohol Abuse and Alcoholism . (2020b). Drinking levels defined. https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking

50.

Norris

George

W.H.

Davis

K.C.

Martell

Leonesio

(1999). Alcohol and hypermasculinity as determinants of men’s empathic responses to violent pornography. Journal of Interpersonal Violence, 14(7), 683–700. http://doi.org/10.1177/088626099014007001

51.

Oesterle

D. W.

Orchowski

L. M.

Moreno

Berkowitz

(2018). A qualitative analysis of bystander intervention among heavy-drinking college men. Violence Against Women, 24(10), 1207–1231. https://doi.org/10.1177/1077801218781931.

52.

Orchowski

L. M.

Berkowitz

Boggis

Oesterle

(2016). Bystander intervention among college men: The role of alcohol and correlates of sexual aggression. Journal of Interpersonal Violence, 31(17), 2824–2846. https://doi.org/10.1177/0886260515581904.

53.

Orchowski

L. M.

Edwards

K. M.

Hollander

J. A.

Banyard

V. L.

Senn

C. Y.

Gidycz

C. A.

(2020). Integrating sexual assault resistance, bystander, and men’s social norms strategies to prevent sexual violence on college campuses: A call to action. Trauma, Violence, & Abuse, 21(4), 811–827. https://doi.org/10.1177/1524838018789153.

54.

Reynolds-Tylus

Lukacena

K. M.

Quick

B. L.

(2019). An application of the theory of normative social behavior to bystander intervention for sexual assault. Journal of American College Health, 67(6), 551–559. https://doi.org/10.1080/07448481.2018.1499648.

55.

Sheather

(2009). A modern approach to regression with R. Springer.

56.

Steele

C. M.

Josephs

R. A.

(1990). Alcohol myopia: Its prized and dangerous effects. American Psychologist, 45(8), 921–933. https://doi.org/10.1037/0003-066X.45.8.921.

57.

van Bommel

van Prooijen

J.-W.

Elffers

Van Lange

P. A. M.

(2016). Booze, bars, and bystander behavior: People who consumed alcohol help faster in the presence of others. Frontiers of Psychology, 7, Article 128. https://doi.org/10.3389/fpsyg.2016.00128.

58.

Waterman

E. A.

Edwards

K. M.

Beaulieu

A. E.

Banyard

V. L.

(2020). Who sees opportunity to help? A prospective study on adolescents’ detection of intervention opportunities in situations of sexual and dating violence. Journal of Social and Personal Relationships, 37(10–11), 2843–2862. https://doi.org/10.1177/0265407520940415.

59.

White

K. R.

Dutton

D. G.

(2013). Perceptions of female perpetrators. In Russell

B. L.

(Ed.), Perceptions of female offenders: How stereotypes and social norms affect criminal justice responses (pp. 101–116). Springer Science + Business Media. https://doi.org/10.1007/978-1-4614-5871-5_7.

60.

Wood

Sulley

Kammer-Kerwick

Follingstad

Busch-Armendariz

(2017). Climate surveys: An inventory of understanding sexual assault and other crimes of interpersonal violence at institutions of higher education. Violence Against Women, 23(10), 1249–1267. https://doi.org/10.1177/1077901216657897.